diff --git a/.dockerignore b/.dockerignore deleted file mode 100644 index cebba29e..00000000 --- a/.dockerignore +++ /dev/null @@ -1,8 +0,0 @@ -.git -/weights -*.pyc -*._ -*.png -__pycache__ -venv -.idea diff --git a/.gitignore b/.gitignore index 72669209..836db63f 100644 --- a/.gitignore +++ b/.gitignore @@ -1,11 +1,4 @@ -/weights -*.pyc -*._ -__pycache__ -venv -.idea -.DS_Store -._.DS_Store -*.hdr -google_scanned_models/ -scripts/nvisii_data_gen/output/dataset/ +test/ +training_script/ + + diff --git a/CMakeLists.txt b/CMakeLists.txt deleted file mode 100644 index 8d58b363..00000000 --- a/CMakeLists.txt +++ /dev/null @@ -1,210 +0,0 @@ -cmake_minimum_required(VERSION 3.5.2) -project(dope) - -## Compile as C++11, supported in ROS Kinetic and newer -# add_compile_options(-std=c++11) - -## Find catkin macros and libraries -## if COMPONENTS list like find_package(catkin REQUIRED COMPONENTS xyz) -## is used, also find other catkin packages -find_package(catkin REQUIRED COMPONENTS - cv_bridge - geometry_msgs - message_filters - resource_retriever - rospy - sensor_msgs - std_msgs - tf - vision_msgs - visualization_msgs -) - -## System dependencies are found with CMake's conventions -# find_package(Boost REQUIRED COMPONENTS system) - - -## Uncomment this if the package has a setup.py. This macro ensures -## modules and global scripts declared therein get installed -## See http://ros.org/doc/api/catkin/html/user_guide/setup_dot_py.html -catkin_python_setup() - -################################################ -## Declare ROS messages, services and actions ## -################################################ - -## To declare and build messages, services or actions from within this -## package, follow these steps: -## * Let MSG_DEP_SET be the set of packages whose message types you use in -## your messages/services/actions (e.g. std_msgs, actionlib_msgs, ...). -## * In the file package.xml: -## * add a build_depend tag for "message_generation" -## * add a build_depend and a exec_depend tag for each package in MSG_DEP_SET -## * If MSG_DEP_SET isn't empty the following dependency has been pulled in -## but can be declared for certainty nonetheless: -## * add a exec_depend tag for "message_runtime" -## * In this file (CMakeLists.txt): -## * add "message_generation" and every package in MSG_DEP_SET to -## find_package(catkin REQUIRED COMPONENTS ...) -## * add "message_runtime" and every package in MSG_DEP_SET to -## catkin_package(CATKIN_DEPENDS ...) -## * uncomment the add_*_files sections below as needed -## and list every .msg/.srv/.action file to be processed -## * uncomment the generate_messages entry below -## * add every package in MSG_DEP_SET to generate_messages(DEPENDENCIES ...) - -## Generate messages in the 'msg' folder -# add_message_files( -# FILES -# Message1.msg -# Message2.msg -# ) - -## Generate services in the 'srv' folder -# add_service_files( -# FILES -# Service1.srv -# Service2.srv -# ) - -## Generate actions in the 'action' folder -# add_action_files( -# FILES -# Action1.action -# Action2.action -# ) - -## Generate added messages and services with any dependencies listed here -# generate_messages( -# DEPENDENCIES -# std_msgs -# ) - -################################################ -## Declare ROS dynamic reconfigure parameters ## -################################################ - -## To declare and build dynamic reconfigure parameters within this -## package, follow these steps: -## * In the file package.xml: -## * add a build_depend and a exec_depend tag for "dynamic_reconfigure" -## * In this file (CMakeLists.txt): -## * add "dynamic_reconfigure" to -## find_package(catkin REQUIRED COMPONENTS ...) -## * uncomment the "generate_dynamic_reconfigure_options" section below -## and list every .cfg file to be processed - -## Generate dynamic reconfigure parameters in the 'cfg' folder -# generate_dynamic_reconfigure_options( -# cfg/DynReconf1.cfg -# cfg/DynReconf2.cfg -# ) - -################################### -## catkin specific configuration ## -################################### -## The catkin_package macro generates cmake config files for your package -## Declare things to be passed to dependent projects -## INCLUDE_DIRS: uncomment this if your package contains header files -## LIBRARIES: libraries you create in this project that dependent projects also need -## CATKIN_DEPENDS: catkin_packages dependent projects also need -## DEPENDS: system dependencies of this project that dependent projects also need -catkin_package( - CATKIN_DEPENDS geometry_msgs sensor_msgs std_msgs vision_msgs visualization_msgs -# DEPENDS system_lib -) - -########### -## Build ## -########### - -## Specify additional locations of header files -## Your package locations should be listed before other locations -include_directories( -# include - ${catkin_INCLUDE_DIRS} -) - -## Declare a C++ library -# add_library(${PROJECT_NAME} -# src/${PROJECT_NAME}/dope/dope.cpp -# ) - -## Add cmake target dependencies of the library -## as an example, code may need to be generated before libraries -## either from message generation or dynamic reconfigure -# add_dependencies(${PROJECT_NAME} ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS}) - -## Declare a C++ executable -## With catkin_make all packages are built within a single CMake context -## The recommended prefix ensures that target names across packages don't collide -# add_executable(${PROJECT_NAME}_node src/dope_vis_node.cpp) - -## Rename C++ executable without prefix -## The above recommended prefix causes long target names, the following renames the -## target back to the shorter version for ease of user use -## e.g. "rosrun someones_pkg node" instead of "rosrun someones_pkg someones_pkg_node" -# set_target_properties(${PROJECT_NAME}_node PROPERTIES OUTPUT_NAME node PREFIX "") - -## Add cmake target dependencies of the executable -## same as for the library above -# add_dependencies(${PROJECT_NAME}_node ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS}) - -## Specify libraries to link a library or executable target against -# target_link_libraries(${PROJECT_NAME}_node -# ${catkin_LIBRARIES} -# ) - -############# -## Install ## -############# - -# all install targets should use catkin DESTINATION variables -# See http://ros.org/doc/api/catkin/html/adv_user_guide/variables.html - -## Mark executable scripts (Python etc.) for installation -## in contrast to setup.py, you can choose the destination -# install(PROGRAMS -# scripts/my_python_script -# DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION} -# ) - -catkin_install_python(PROGRAMS - nodes/camera - nodes/dope - scripts/train.py - DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION}) - -## Mark executables and/or libraries for installation -# install(TARGETS ${PROJECT_NAME} ${PROJECT_NAME}_node -# ARCHIVE DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION} -# LIBRARY DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION} -# RUNTIME DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION} -# ) - -## Mark cpp header files for installation -# install(DIRECTORY include/${PROJECT_NAME}/ -# DESTINATION ${CATKIN_PACKAGE_INCLUDE_DESTINATION} -# FILES_MATCHING PATTERN "*.h" -# PATTERN ".svn" EXCLUDE -# ) - -## Mark other files for installation (e.g. launch and bag files, etc.) -# install(FILES -# # myfile1 -# # myfile2 -# DESTINATION ${CATKIN_PACKAGE_SHARE_DESTINATION} -# ) - -############# -## Testing ## -############# - -## Add gtest based cpp test target and link libraries -# catkin_add_gtest(${PROJECT_NAME}-test test/test_dope_vis.cpp) -# if(TARGET ${PROJECT_NAME}-test) -# target_link_libraries(${PROJECT_NAME}-test ${PROJECT_NAME}) -# endif() - -## Add folders to be run by python nosetests -# catkin_add_nosetests(test) diff --git a/README.md b/README.md new file mode 100644 index 00000000..18451a44 --- /dev/null +++ b/README.md @@ -0,0 +1,41 @@ +# Deep Object Pose Estimation (DOPE) - ROS2 Inference + +This is a ROS2 package to make inference with DOPE ([original ROS1 repo](https://github.com/NVlabs/Deep_Object_Pose/tree/master)). The official code is adapted to work on the ROS2 framework with Python3.10 and the updated requirements. See the official repository for detailed info about DOPE. + +## Installing + +The code has been tested on Ubuntu 22.04 with ROS2 Humble and Python 3.10. + +1. **Clone the repository into your ros2 workspace** + ``` + mkdir -p ~/ros2_ws/src + cd ~/ros2_ws/src + git clone https://github.com/Vanvitelli-Robotics/DOPE.git dope -b ros2_humble + ``` + +2. **Install python dependencies** + ``` + cd ~/ros2_ws/src/dope + python3 -m pip install -r requirements.txt + ``` + +3. **Build** + ``` + cd ~/ros2_ws + colcon build --packages-select dope + ``` + +4. **Run the node** + + First, you need to configure your setup in */config/config_dope.yaml*, specifying the topics of your camera and the parameters of the target objects. Note that you have to also specifity the path of the weights resulting from the training for each object you want to inferred (also several objects simultaneously). You can load the weights in ```weights/``` folder (we don't provide any .pth due to the high dimension of the file). + + After configuration, you can start the inference running the node as follows: + ``` + ros2 run dope_ros2 dope_node --ros-args --params-file ~/ros2_ws/src/dope/config/config_dope.yaml + ``` + or, if you prefer, you can use the available launch file: + ``` + ros2 launch dope_ros2 dope.launch.py + ``` + + diff --git a/config/config_dope.yaml b/config/config_dope.yaml new file mode 100644 index 00000000..b4d3e6a3 --- /dev/null +++ b/config/config_dope.yaml @@ -0,0 +1,78 @@ +/dope_node: + ros__parameters: + topic_camera: /camera/color/image_raw + topic_camera_info: /camera/color/camera_info # "/dope/webcam/camera_info" + topic_publishing: dope + input_is_rectified: true # Whether the input image is rectified (strongly suggested!) + downscale_height: 400 # if the input image is larger than this, scale it down to this pixel height + + # Comment any of these lines to prevent detection / pose estimation of that object + weights: + # "apple": "package://dope/weights/apple_120.pth" + # "santal_ace": "package://dope_ros2/weights/santal_67.pth" + "lime": "package://dope_ros2/weights/lime_215.pth" + "bowl": "package://dope_ros2/weights/bowl_114.pth" + "fork": "package://dope_ros2/weights/fork_190.pth" + "glass": "package://dope_ros2/weights/red_glass_120.pth" + "cracker": "package://dope_ros2/weights/cracker_60.pth" + + # Cuboid dimension in cm x, y, z + dimensions: + "apple": [9.756118059158325, 8.538994193077087, 9.590171277523041] + "santal_ace": [8.69415, 6.90914, 4.38487] + "lime": [4.0052998811006546,3.9955999702215195,5.266399681568146] + "bowl": [20.5,20.5,9.15] + "fork": [2.82,2.86,20.64] + "glass": [7.4,7.4,9.5] + "cracker": [16.403600692749023,21.343700408935547,7.179999828338623] + + class_ids: + "apple": 1 + "santal_ace": 4 + "lime": 5 + "bowl": 6 + "fork": 7 + "glass": 8 + "cracker": 9 + + draw_colors: + "apple": [255, 0, 0] # red + "santal_ace": [255, 127, 80] # orange + "lime": [153, 255, 51] # lime green + "bowl": [1, 172, 142] + "fork": [120, 120, 120] # silver + "glass": [255, 2, 1] # red + "cracker": [255, 255, 0] # yellow + + # optional: provide a transform that is applied to the pose returned by DOPE + # model_transforms: + # "test": [[0, 0, 1, 0], [0, -1, 0, 0], [1, 0, 0, 0], [0, 0, 0, 1]] + + # optional: if you provide a mesh of the object here, a mesh marker will be + # published for visualization in RViz + meshes: + #"apple": "/home/workstation/dope_ros_ws/src/grasp_dope/scripts/models/Apple/Apple_4K/food_apple_01_4k_simplify.obj" + #"santal_ace": "/home/sfederico/Documents/cad_models/santal_ace/santal_centered.obj" + "lime" : "package://dope_ros2/meshes/Lime.obj" + "bowl" : "package://dope_ros2/meshes/bowl.obj" + "glass" : "package://dope_ros2/meshes/simple_glass.obj" + "fork" : "package://dope_ros2/meshes/fork.obj" + "cracker": "package://dope_ros2/meshes/cracker_box.obj" + + + mesh_scales: + "apple": 1.0 + "santal_ace": 0.001 + "lime" : 1.0 + "bowl": 0.001 + "fork": 1.0 + "glass": 1.0 + "cracker": 1.0 + + overlay_belief_images: true # Whether to overlay the input image on the belief images published on /dope/belief_[obj_name] + + # Config params for DOPE + thresh_angle: 0.5 + thresh_map: 0.0001 + sigma: 3 + thresh_points: 0.1 diff --git a/config/config_pose.yaml b/config/config_pose.yaml deleted file mode 100644 index 97d545a2..00000000 --- a/config/config_pose.yaml +++ /dev/null @@ -1,176 +0,0 @@ -topic_camera: "/dope/webcam/image_raw" -topic_camera_info: "/dope/webcam/camera_info" -topic_publishing: "dope" -input_is_rectified: True # Whether the input image is rectified (strongly suggested!) -downscale_height: 400 # if the input image is larger than this, scale it down to this pixel height - -# Comment any of these lines to prevent detection / pose estimation of that object -weights: { - # "cracker":"package://dope/weights/cracker_60.pth", - # "gelatin":"package://dope/weights/gelatin_60.pth", - # "meat":"package://dope/weights/meat_20.pth", - # "mustard":"package://dope/weights/mustard_60.pth", - "soup":"package://dope/weights/soup_60.pth", - #"sugar":"package://dope/weights/sugar_60.pth", - # "bleach":"package://dope/weights/bleach_28_dr.pth" - - # NEW OBJECTS - HOPE - # "AlphabetSoup":"package://dope/weights/AlphabetSoup.pth", - # "BBQSauce":"package://dope/weights/BBQSauce.pth", - # "Butter":"package://dope/weights/Butter.pth", - # "Cherries":"package://dope/weights/Cherries.pth", - # "ChocolatePudding":"package://dope/weights/ChocolatePudding.pth", - # "Cookies":"package://dope/weights/Cookies.pth", - # "Corn":"package://dope/weights/Corn.pth", - # "CreamCheese":"package://dope/weights/CreamCheese.pth", - # "GreenBeans":"package://dope/weights/GreenBeans.pth", - # "GranolaBars":"package://dope/weights/GranolaBars.pth", - # "Ketchup":"package://dope/weights/Ketchup.pth", - # "MacaroniAndCheese":"package://dope/weights/MacaroniAndCheese.pth", - # "Mayo":"package://dope/weights/Mayo.pth", - # "Milk":"package://dope/weights/Milk.pth", - # "Mushrooms":"package://dope/weights/Mushrooms.pth", - # "Mustard":"package://dope/weights/Mustard.pth", - # "Parmesan":"package://dope/weights/Parmesan.pth", - # "PeasAndCarrots":"package://dope/weights/PeasAndCarrots.pth", - # "Peaches":"package://dope/weights/Peaches.pth", - # "Pineapple":"package://dope/weights/Pineapple.pth", - # "Popcorn":"package://dope/weights/Popcorn.pth", - # "OrangeJuice":"package://dope/weights/OrangeJuice.pth", - # "Raisins":"package://dope/weights/Raisins.pth", - # "SaladDressing":"package://dope/weights/SaladDressing.pth", - # "Spaghetti":"package://dope/weights/Spaghetti.pth", - # "TomatoSauce":"package://dope/weights/TomatoSauce.pth", - # "Tuna":"package://dope/weights/Tuna.pth", - # "Yogurt":"package://dope/weights/Yogurt.pth", - -} - -# Cuboid dimension in cm x,y,z -dimensions: { - "cracker": [16.403600692749023,21.343700408935547,7.179999828338623], - "gelatin": [8.918299674987793, 7.311500072479248, 2.9983000755310059], - "meat": [10.164673805236816,8.3542995452880859,5.7600898742675781], - "mustard": [9.6024150848388672,19.130100250244141,5.824894905090332], - "soup": [6.7659378051757813,10.185500144958496,6.771425724029541], - "sugar": [9.267730712890625,17.625339508056641,4.5134143829345703], - "bleach": [10.267730712890625,26.625339508056641,7.5134143829345703], - - # new objects - "AlphabetSoup" : [ 8.3555002212524414, 7.1121001243591309, 6.6055998802185059 ], - "Butter" : [ 5.282599925994873, 2.3935999870300293, 10.330100059509277 ], - "Ketchup" : [ 14.860799789428711, 4.3368000984191895, 6.4513998031616211 ], - "Pineapple" : [ 5.7623000144958496, 6.95989990234375, 6.567500114440918 ], - "BBQSauce" : [ 14.832900047302246, 4.3478999137878418, 6.4632000923156738 ], - "MacaroniAndCheese" : [ 16.625600814819336, 4.0180997848510742, 12.350899696350098 ], - "Popcorn" : [ 8.4976997375488281, 3.825200080871582, 12.649200439453125 ], - "Mayo" : [ 14.790200233459473, 4.1030998229980469, 6.4541001319885254 ], - "Raisins" : [ 12.317500114440918, 3.9751999378204346, 8.5874996185302734 ], - "Cherries" : [ 5.8038997650146484, 7.0907998085021973, 6.6101999282836914 ], - "Milk" : [ 19.035800933837891, 7.326200008392334, 7.2154998779296875 ], - "SaladDressing" : [ 14.744099617004395, 4.3695998191833496, 6.403900146484375 ], - "ChocolatePudding" : [ 4.947199821472168, 2.9923000335693359, 8.3498001098632812 ], - "Mushrooms" : [ 3.3322000503540039, 7.079899787902832, 6.5869998931884766 ], - "Spaghetti" : [ 4.9836997985839844, 2.8492999076843262, 24.988100051879883 ], - "Cookies" : [ 16.724300384521484, 4.015200138092041, 12.274600028991699 ], - "Mustard" : [ 16.004999160766602, 4.8573999404907227, 6.5132999420166016 ], - "TomatoSauce" : [ 8.2847003936767578, 7.0198001861572266, 6.6469998359680176 ], - "Corn" : [ 5.8038997650146484, 7.0907998085021973, 6.6101999282836914 ], - "OrangeJuice" : [ 19.248300552368164, 7.2781000137329102, 7.1582999229431152 ], - "Tuna" : [ 3.2571001052856445, 7.0805997848510742, 6.5837001800537109 ], - "CreamCheese" : [ 5.3206000328063965, 2.4230999946594238, 10.359000205993652 ], - "Parmesan" : [ 10.286199569702148, 6.6093001365661621, 7.1117000579833984 ], - "Yogurt" : [ 5.3677000999450684, 6.7961997985839844, 6.7915000915527344 ], - "GranolaBars" : [ 12.400600433349609, 3.8738000392913818, 16.53380012512207 ], - "Peaches" : [ 5.7781000137329102, 7.0961999893188477, 6.5925998687744141 ], - "GreenBeans" : [ 5.758699893951416, 7.0608000755310059, 6.5732002258300781 ], - "PeasAndCarrots" : [ 5.8512001037597656, 7.0636000633239746, 6.5918002128601074 ] -} - -class_ids: { - "cracker": 1, - "gelatin": 2, - "meat": 3, - "mustard": 4, - "soup": 5, - "sugar": 6, - "bleach": 7, - "AlphabetSoup" : 9, - "Ketchup" : 10, - "Pineapple" : 11, - "BBQSauce" : 12, - "MacaroniAndCheese" : 13, - "Popcorn" : 14, - "Butter" : 15, - "Mayo" : 16, - "Raisins" : 17, - "Cherries" : 18, - "Milk" : 19, - "SaladDressing" : 20, - "ChocolatePudding" : 21, - "Mushrooms" : 22, - "Spaghetti" : 23, - "Cookies" : 24, - "Mustard" : 25, - "TomatoSauce" : 26, - "Corn" : 27, - "OrangeJuice" : 28, - "Tuna" : 29, - "CreamCheese" : 20, - "Parmesan" : 31, - "Yogurt" : 32, - "GranolaBars" : 33, - "Peaches" : 34, - "GreenBeans" : 35, - "PeasAndCarrots" : 36 -} - -draw_colors: { - "cracker": [13, 255, 128], # green - "gelatin": [255, 255, 255], # while - "meat": [0, 104, 255], # blue - "mustard": [217,12, 232], # magenta - "soup": [255, 101, 0], # orange - "sugar": [232, 222, 12], # yellow - "bleach": [232, 222, 12], # yellow -} - -# optional: provide a transform that is applied to the pose returned by DOPE -model_transforms: { -# "cracker": [[ 0, 0, 1, 0], -# [ 0, -1, 0, 0], -# [ 1, 0, 0, 0], -# [ 0, 0, 0, 1]] -} - -# optional: if you provide a mesh of the object here, a mesh marker will be -# published for visualization in RViz -# You can use the nvdu_ycb tool to download the meshes: https://github.com/NVIDIA/Dataset_Utilities#nvdu_ycb -meshes: { -# "cracker": "file://path/to/Dataset_Utilities/nvdu/data/ycb/aligned_cm/003_cracker_box/google_16k/textured.obj", -# "gelatin": "file://path/to/Dataset_Utilities/nvdu/data/ycb/aligned_cm/009_gelatin_box/google_16k/textured.obj", -# "meat": "file://path/to/Dataset_Utilities/nvdu/data/ycb/aligned_cm/010_potted_meat_can/google_16k/textured.obj", -# "mustard": "file://path/to/Dataset_Utilities/nvdu/data/ycb/aligned_cm/006_mustard_bottle/google_16k/textured.obj", -# "soup": "file://path/to/Dataset_Utilities/nvdu/data/ycb/aligned_cm/005_tomato_soup_can/google_16k/textured.obj", -# "sugar": "file://path/to/Dataset_Utilities/nvdu/data/ycb/aligned_cm/004_sugar_box/google_16k/textured.obj", -# "bleach": "file://path/to/Dataset_Utilities/nvdu/data/ycb/aligned_cm/021_bleach_cleanser/google_16k/textured.obj", -} - -# optional: If the specified meshes are not in meters, provide a scale here (e.g. if the mesh is in centimeters, scale should be 0.01). default scale: 1.0. -mesh_scales: { - "cracker": 0.01, - "gelatin": 0.01, - "meat": 0.01, - "mustard": 0.01, - "soup": 0.01, - "sugar": 0.01, - "bleach": 0.01, -} - -overlay_belief_images: True # Whether to overlay the input image on the belief images published on /dope/belief_[obj_name] - -# Config params for DOPE -thresh_angle: 0.5 -thresh_map: 0.01 -sigma: 3 -thresh_points: 0.1 diff --git a/doc/camera_tutorial.md b/doc/camera_tutorial.md deleted file mode 100644 index 3263dbac..00000000 --- a/doc/camera_tutorial.md +++ /dev/null @@ -1,194 +0,0 @@ -## Running DOPE with a webcam - -This tutorial explains how to: - -1. start a ROS driver for a regular USB webcam -2. calibrate the camera **or** enter the camera intrinsics manually -3. rectify the images and publish them on a topic - -Since DOPE relies solely on RGB images and the associated `camera_info` topic, -it is essential that the camera is properly calibrated to give good results. -Also, unless you are using a very low-distortion lens, the images should be -rectified before feeding them to DOPE. - -### A. Starting a ROS driver for a USB webcam - -In this tutorial, we're using the [usb_cam](http://wiki.ros.org/usb_cam) -ROS package. If this package is not working with your camera, simply google -around - nowadays there is a ROS driver for almost every camera. - -1. Install the driver: - - ```bash - sudo apt install ros-kinetic-usb-cam - ``` - -2. Run the camera driver (enter each command in a separate terminal) - - ```bash - roscore - rosrun usb_cam usb_cam_node _camera_name:='usb_cam' _camera_frame_id:='usb_cam' - ``` - - See the [usb_cam wiki page](http://wiki.ros.org/usb_cam) for a list of all - parameters. - -3. Check that the camera is running: - - ``` - $ rostopic list - [...] - /usb_cam/camera_info - /usb_cam/image_raw - [...] - $ rostopic hz /usb_cam/image_raw - subscribed to [/usb_cam/image_raw] - average rate: 30.001 - min: 0.029s max: 0.038s std dev: 0.00280s window: 28 - ``` - -4. If you want, you can also run `rviz` to visualize the camera topic. - -Since the camera is still uncalibrated, you should have seen the following -warning when starting the `usb_cam` node in step 2: - -``` -[ WARN] [1561548002.895791819]: Camera calibration file /home/******/.ros/camera_info/usb_cam.yaml not found. -``` - -Also, the camera_info topic is all zeros: - -```bash -$ rostopic echo -n1 /usb_cam/camera_info -header: - seq: 87 - stamp: - secs: 1561548114 - nsecs: 388301085 - frame_id: "usb_cam" -height: 480 -width: 640 -distortion_model: '' -D: [] -K: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] -R: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] -P: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] -binning_x: 0 -binning_y: 0 -roi: - x_offset: 0 - y_offset: 0 - height: 0 - width: 0 - do_rectify: False -``` - -To fix this, we need to generate a file called -`~/.ros/camera_info/usb_cam.yaml` which holds the camera intrinsics. Either -follow step **B** or **C** to do this. - -### B. Manually entering camera intrinsics - -If you know the camera intrinsics of your webcam, you can simply generate a new -file `~/.ros/camera_info/usb_cam.yaml` which looks like this (the example is -for a Logitech C920 webcam with the following intrinsics: fx = 641.5, -fy = 641.5, cx = 320.0, cy = 240.0): - - -``` -image_width: 640 -image_height: 480 -camera_name: usb_cam -camera_matrix: - rows: 3 - cols: 3 - data: [641.5, 0, 320.0, 0, 641.5, 240.0, 0, 0, 1] -distortion_model: plumb_bob -distortion_coefficients: - rows: 1 - cols: 5 - data: [0, 0, 0, 0, 0] -rectification_matrix: - rows: 3 - cols: 3 - data: [1, 0, 0, 0, 1, 0, 0, 0, 1] -projection_matrix: - rows: 3 - cols: 4 - data: [641.5, 0, 320.0, 0, 0, 641.5, 240.0, 0, 0, 0, 1, 0] -``` - -After creating this file, restart the `usb_cam` driver for the changes to take -effect. The warning "Camera calibration file not found" should have -disappeared, and the `/usb_cam/camera_info` topic should reflect the values -entered above. - -Since the camera intrinsics we supplied above do not specify distortion -coefficients, the image does not need to be rectified, so you can skip the -remaining steps and use the `/usb_cam/image_raw` topic as input for DOPE. - -If you want to do proper calibration and rectification instead, skip step **B** -and continue with **C**. - -### C. Calibrating the webcam - -Follow the steps in [this tutorial](http://wiki.ros.org/camera_calibration/Tutorials/MonocularCalibration). - -In short, run these commands: - -```bash -sudo apt install ros-kinetic-camera-calibration -rosrun camera_calibration cameracalibrator.py --size 6x7 --square 0.0495 image:=/usb_cam/image_raw camera:=/usb_cam # adjust these values to your checkerboard -``` - -* Move your checkerboard around and make sure that you cover a good range of - distance from the camera, all parts of the image, and horizontal and vertical - skew of the checkerboard. -* When done, press "calibrate" and **wait** until the calibration is complete. - This can take a long time (minutes or hours), depending on how many - calibration samples you took. As long as the image window is frozen and - `camera_calibration` hogs a CPU, it's still computing. -* Once the calibration has finished, the window will unfreeze. Press "save", - then press "commit". - -After this, the calibration info should have been saved to -`~/.ros/camera_info/usb_cam.yaml`. Restart the `usb_cam` driver for the changes -to take effect. - - -### D. Rectifying the images - -1. Install `image_proc`: - - ```bash - sudo apt install ros-kinetic-image-proc - ``` - -2. Create a file called `usb_cam_image_proc.launch` with the following contents: - - ```xml - - - - - - - - - - - - - - - - ``` - -3. Launch it: - - ```bash - roslaunch usb_cam_image_proc.launch - ``` - -This should publish the topic `/usb_cam/image_rect_color` (among others). You -can now use this topic as the input for DOPE. diff --git a/docker/Dockerfile.noetic b/docker/Dokerfile.humble similarity index 50% rename from docker/Dockerfile.noetic rename to docker/Dokerfile.humble index e652deb6..694e02fa 100644 --- a/docker/Dockerfile.noetic +++ b/docker/Dokerfile.humble @@ -1,12 +1,9 @@ -FROM ros:noetic-robot +FROM osrf/ros:humble-desktop -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. -# Full license terms provided in LICENSE.md file. # To build: -# docker build -t nvidia-dope:noetic-v1 -f Dockerfile.noetic .. +# docker build -t dope-ros2:humble-v1 -f Dokerfile.humble .. ENV HOME /root -ENV DEBIAN_FRONTEND=noninteractive # Install system and development components RUN apt-get update && apt-get -y --no-install-recommends install \ @@ -28,34 +25,34 @@ RUN apt-get update && apt-get -y --no-install-recommends install \ # Install required ROS components RUN apt-get update && apt-get -y --no-install-recommends install \ - ros-noetic-cv-bridge \ - ros-noetic-geometry-msgs \ - ros-noetic-message-filters \ - ros-noetic-resource-retriever \ - ros-noetic-rospy \ - ros-noetic-sensor-msgs \ - ros-noetic-std-msgs \ - ros-noetic-tf \ - ros-noetic-vision-msgs \ - ros-noetic-visualization-msgs \ - ros-noetic-rviz \ + ros-humble-cv-bridge \ + ros-humble-geometry-msgs \ + ros-humble-message-filters \ + ros-humble-resource-retriever \ + ros-humble-rclpy \ + ros-humble-sensor-msgs \ + ros-humble-std-msgs \ + ros-humble-tf2 \ + ros-humble-vision-msgs \ + ros-humble-visualization-msgs \ + ros-humble-rviz2 \ && apt-get -y autoremove \ && apt-get clean # pip install required Python packages COPY requirements.txt ${HOME} -RUN python3 -m pip install --no-cache-dir -r ${HOME}/requirements.txt +RUN python3 -m pip install -r ${HOME}/requirements.txt -# Setup catkin workspace -ENV CATKIN_WS ${HOME}/catkin_ws -COPY . ${CATKIN_WS}/src/dope +# Setup colcon workspace +ENV ROS2WS ${HOME}/ros2ws +COPY . ${ROS2WS}/src/dope COPY docker/init_workspace.sh ${HOME} -RUN ${CATKIN_WS}/src/dope/docker/init_workspace.sh -RUN echo "source ${CATKIN_WS}/devel/setup.bash" >> ${HOME}/.bashrc +RUN ${ROS2WS}/src/dope/docker/init_workspace.sh +RUN echo "source ${ROS2WS}/install/setup.bash" >> ${HOME}/.bashrc ENV DISPLAY :0 ENV NVIDIA_VISIBLE_DEVICES all ENV NVIDIA_DRIVER_CAPABILITIES graphics,utility,compute ENV TERM=xterm # Some QT-Apps don't show controls without this -ENV QT_X11_NO_MITSHM 1 \ No newline at end of file +ENV QT_X11_NO_MITSHM 1 diff --git a/docker/README.md b/docker/README.md new file mode 100644 index 00000000..96c2de47 --- /dev/null +++ b/docker/README.md @@ -0,0 +1,38 @@ +## DOPE ROS2 in a Docker Container + +You can run DOPE directly in a docker container with Ubuntu 22.04 with ROS2 Humble and Python 3.10. + +### Steps + +1. **Download the DOPE code** + ``` + git clone https://github.com/Vanvitelli-Robotics/DOPE.git dope -b ros2_humble + ``` + +2. **Build the docker image** + ``` + cd dope/docker + docker build -t dope-ros2:humble-v1 -f Dokerfile.humble .. + ``` + This will take several minutes and requires an internet connection. If the build fails check if the dope/docker/init_workspace.sh has the execution rights. + +3. **Plug in your camera** + Docker will not recognize a USB device that is plugged in after the container is started. + +4. **Run the container** + ``` + ./run_docker.sh [name] [host dir] [container dir] + ``` + Parameters: + - `name` is an optional field that specifies the name of this image. By default, it is `dope-ros2-v2`. By using different names, you can create multiple containers from the same image. + - `host dir` and `container dir` are a pair of optional fields that allow you to specify a mapping between a directory on your host machine and a location inside the container. This is useful for sharing code and data between the two systems. By default, it maps the directory containing dope to `/root/ros2ws/src/dope` in the container. + + Only the first invocation of this script with a given name will create a container. Subsequent executions will attach to the running container allowing you -- in effect -- to have multiple terminal sessions into a single container. + +5. **Build DOPE** + Return to step 2 of the [installation instructions](../readme.md). + + *Note:* Since the Docker container binds directly to the host's network, it will see the `ros2 network` even if running outside the docker container. + + + diff --git a/docker/init_workspace.sh b/docker/init_workspace.sh index a66e579e..7c748142 100755 --- a/docker/init_workspace.sh +++ b/docker/init_workspace.sh @@ -7,11 +7,9 @@ # Stop in case of any error. set -e -source /opt/ros/noetic/setup.bash +source /opt/ros/humble/setup.bash -# Create catkin workspace. -mkdir -p ${CATKIN_WS}/src -cd ${CATKIN_WS}/src -catkin_init_workspace -cd .. -catkin_make +# Create colcon workspace. +mkdir -p ${ROS2WS}/src +cd ${ROS2WS}/ +colcon build diff --git a/docker/readme.md b/docker/readme.md deleted file mode 100644 index 79097df8..00000000 --- a/docker/readme.md +++ /dev/null @@ -1,44 +0,0 @@ -## DOPE in a Docker Container - -Running ROS inside of [Docker](https://www.docker.com/) is an excellent way to -experiment with DOPE, as it allows the user to completely isolate all software and configuration -changes from the host system. This document describes how to create and run a -Docker image that contains a complete ROS environment that supports DOPE, -including all required components, such as ROS Noetic, rviz, CUDA with cuDNN, -and other packages. - -The current configuration assumes all components are installed on an x86 host -platform running Ubuntu 18.04 or later. Further, use of the DOPE Docker container requires an NVIDIA GPU to be present, and the use of Docker version 19.03.0 or later. - - -### Steps - -1. **Download the DOPE code** - ``` - $ git clone https://github.com/NVlabs/Deep_Object_Pose.git dope - ``` - -2. **Build the docker image** - ``` - $ cd dope/docker - $ docker build -t nvidia-dope:noetic-v1 -f Dockerfile.noetic .. - ``` - This will take several minutes and requires an internet connection. - -3. **Plug in your camera** - Docker will not recognize a USB device that is plugged in after the container is started. - -4. **Run the container** - ``` - $ ./run_dope_docker.sh [name] [host dir] [container dir] - ``` - Parameters: - - `name` is an optional field that specifies the name of this image. By default, it is `nvidia-dope-v2`. By using different names, you can create multiple containers from the same image. - - `host dir` and `container dir` are a pair of optional fields that allow you to specify a mapping between a directory on your host machine and a location inside the container. This is useful for sharing code and data between the two systems. By default, it maps the directory containing dope to `/root/catkin_ws/src/dope` in the container. - - Only the first invocation of this script with a given name will create a container. Subsequent executions will attach to the running container allowing you -- in effect -- to have multiple terminal sessions into a single container. - -5. **Build DOPE** - Return to step 7 of the [installation instructions](../readme.md) (downloading the weights). - - *Note:* Since the Docker container binds directly to the host's network, it will see `roscore` even if running outside the docker container. diff --git a/docker/run_dope_docker.sh b/docker/run_docker.sh similarity index 80% rename from docker/run_dope_docker.sh rename to docker/run_docker.sh index 009ff690..cc1c4297 100755 --- a/docker/run_dope_docker.sh +++ b/docker/run_docker.sh @@ -5,7 +5,7 @@ CONTAINER_NAME=$1 if [[ -z "${CONTAINER_NAME}" ]]; then - CONTAINER_NAME=nvidia-dope-v2 + CONTAINER_NAME=dope-ros2-v2 fi # This specifies a mapping between a host directory and a directory in the @@ -18,7 +18,7 @@ fi CONTAINER_DIR=$3 if [[ -z "${CONTAINER_DIR}" ]]; then - CONTAINER_DIR=/root/catkin_ws/src/dope + CONTAINER_DIR=/root/ros2ws/src/dope fi echo "Container name : ${CONTAINER_NAME}" @@ -28,7 +28,8 @@ DOPE_ID=`docker ps -aqf "name=^/${CONTAINER_NAME}$"` if [ -z "${DOPE_ID}" ]; then echo "Creating new DOPE docker container." xhost +local:root - docker run --gpus all -it --privileged --network=host -v ${HOST_DIR}:${CONTAINER_DIR}:rw -v /tmp/.X11-unix:/tmp/.X11-unix:rw --env="DISPLAY" --name=${CONTAINER_NAME} nvidia-dope:noetic-v1 bash + docker run --gpus all -it --privileged -v ${HOST_DIR}:${CONTAINER_DIR}:rw -v /tmp/.X11-unix:/tmp/.X11-unix:rw --env="DISPLAY" --name=${CONTAINER_NAME} dope-ros2:humble-v1 bash + # --net=host with this option the ros2 network not work else echo "Found DOPE docker container: ${DOPE_ID}." # Check if the container is already running and start if necessary. diff --git a/dope_objects.png b/dope_objects.png deleted file mode 100644 index 4b9b38dc..00000000 Binary files a/dope_objects.png and /dev/null differ diff --git a/src/dope/__init__.py b/dope_ros2/__init__.py similarity index 100% rename from src/dope/__init__.py rename to dope_ros2/__init__.py diff --git a/dope_ros2/__pycache__/__init__.cpython-310.pyc b/dope_ros2/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 00000000..23ad1da4 Binary files /dev/null and b/dope_ros2/__pycache__/__init__.cpython-310.pyc differ diff --git a/dope_ros2/__pycache__/dope_node.cpython-310.pyc b/dope_ros2/__pycache__/dope_node.cpython-310.pyc new file mode 100644 index 00000000..3f64c256 Binary files /dev/null and b/dope_ros2/__pycache__/dope_node.cpython-310.pyc differ diff --git a/nodes/dope b/dope_ros2/dope_node.py old mode 100755 new mode 100644 similarity index 58% rename from nodes/dope rename to dope_ros2/dope_node.py index c457f7b7..1a3c3d1e --- a/nodes/dope +++ b/dope_ros2/dope_node.py @@ -1,28 +1,28 @@ -#!/usr/bin/env python3 - -# Copyright (c) 2018 NVIDIA Corporation. All rights reserved. -# This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. -# https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode - """ -This file starts a ROS node to run DOPE, +This file starts a ROS2 node to run DOPE, listening to an image topic and publishing poses. """ -from __future__ import print_function - +# Python packages import cv2 import message_filters import numpy as np import resource_retriever -import rospy -import tf.transformations from PIL import Image from PIL import ImageDraw from cv_bridge import CvBridge -from dope.inference.cuboid import Cuboid3d -from dope.inference.cuboid_pnp_solver import CuboidPNPSolver -from dope.inference.detector import ModelData, ObjectDetector +# from nptyping import NDArray + +import transformations + +# Custom library +from inference_script.cuboid import Cuboid3d +from inference_script.cuboid_pnp_solver import CuboidPNPSolver +from inference_script.detector import ModelData, ObjectDetector +import simple_colors +# ROS2 packages +import rclpy +from rclpy.node import Node from geometry_msgs.msg import PoseStamped from sensor_msgs.msg import CameraInfo, Image as ImageSensor_msg from std_msgs.msg import String @@ -31,6 +31,7 @@ class Draw(object): + """Drawing helper class to visualize the neural network output""" def __init__(self, im): @@ -91,9 +92,15 @@ def draw_cube(self, points, color=(255, 0, 0)): self.draw_line(points[1], points[4], color) -class DopeNode(object): - """ROS node that listens to image topic, runs DOPE, and publishes DOPE results""" +class DopeNode(Node): def __init__(self): + super().__init__('dope_node', + automatically_declare_parameters_from_overrides=True, allow_undeclared_parameters=True) + + print(simple_colors.cyan('Setting parameters for DOPE...',['bold'])) + + self.obj_detector = ObjectDetector() + self.pubs = {} self.models = {} self.pnp_solvers = {} @@ -108,9 +115,11 @@ def __init__(self): self.cv_bridge = CvBridge() self.prev_num_detections = 0 - self.input_is_rectified = rospy.get_param('~input_is_rectified', True) - self.downscale_height = rospy.get_param('~downscale_height', 500) - self.overlay_belief_images = rospy.get_param('~overlay_belief_images', True) + self.input_is_rectified = self.get_parameter_or( + 'input_is_rectified', True).get_parameter_value().bool_value + self.downscale_height = self.get_parameter_or('downscale_height', 500).get_parameter_value().integer_value + self.overlay_belief_images = self.get_parameter_or( + 'overlay_belief_images', True).get_parameter_value().bool_value self.config_detect = lambda: None self.config_detect.mask_edges = 1 @@ -118,138 +127,170 @@ def __init__(self): self.config_detect.vertex = 1 self.config_detect.threshold = 0.5 self.config_detect.softmax = 1000 - self.config_detect.thresh_angle = rospy.get_param('~thresh_angle', 0.5) - self.config_detect.thresh_map = rospy.get_param('~thresh_map', 0.01) - self.config_detect.sigma = rospy.get_param('~sigma', 3) - self.config_detect.thresh_points = rospy.get_param("~thresh_points", 0.1) + self.config_detect.thresh_angle = self.get_parameter_or( + 'thresh_angle', 0.5).get_parameter_value().double_value + self.config_detect.thresh_map = self.get_parameter_or( + 'thresh_map', 0.01).get_parameter_value().double_value + self.config_detect.sigma = self.get_parameter_or('sigma', 3).get_parameter_value().integer_value + self.config_detect.thresh_points = self.get_parameter_or( + "thresh_points", 0.1).get_parameter_value().double_value + # For each object to detect, load network model, create PNP solver, and start ROS publishers - for model, weights_url in rospy.get_param('~weights').items(): + for model, weights_url in self.get_parameters_by_prefix('weights').items(): + # print(model, weights_url.get_parameter_value().string_value) + self.models[model] = \ ModelData( model, - resource_retriever.get_filename(weights_url, use_protocol=False) - ) + resource_retriever.get_filename( + weights_url.get_parameter_value().string_value, use_protocol=False) + ) self.models[model].load_net_model() try: - M = np.array(rospy.get_param('~model_transforms')[model], dtype='float64') - self.model_transforms[model] = tf.transformations.quaternion_from_matrix(M) + M = self.get_parameters_by_prefix('model_transforms')[model].get_parameter_value().double_array_value + # self.model_transforms[model] = tf.transformations.quaternion_from_matrix(M) + self.model_transforms[model] = transformations.quaternion_from_matrix(M) #utils_py.quaternion_from_matrix(M) except KeyError: - self.model_transforms[model] = np.array([0.0, 0.0, 0.0, 1.0], dtype='float64') + self.model_transforms[model] = np.array([1.0, 0.0, 0.0, 0.0], dtype='float64') try: - self.meshes[model] = rospy.get_param('~meshes')[model] + self.meshes[model] = self.get_parameters_by_prefix('meshes')[model].get_parameter_value().string_value except KeyError: pass + try: - self.mesh_scales[model] = rospy.get_param('~mesh_scales')[model] + self.mesh_scales[model] = self.get_parameters_by_prefix('mesh_scales')[model].get_parameter_value().double_value except KeyError: self.mesh_scales[model] = 1.0 + + try: - self.draw_colors[model] = tuple(rospy.get_param("~draw_colors")[model]) + self.draw_colors[model] = tuple(self.get_parameters_by_prefix('draw_colors')[model].get_parameter_value().integer_array_value) except: - self.draw_colors[model] = (np.random.randint(0,255),np.random.randint(0,255),np.random.randint(0,255)) + self.draw_colors[model] = (np.random.randint(0, 255), np.random.randint(0, 255), np.random.randint(0, 255)) - self.dimensions[model] = tuple(rospy.get_param("~dimensions")[model]) - self.class_ids[model] = rospy.get_param("~class_ids")[model] + self.dimensions[model] = tuple( + self.get_parameters_by_prefix('dimensions')[model].get_parameter_value().double_array_value) + self.class_ids[model] = self.get_parameters_by_prefix('class_ids')[model].get_parameter_value().integer_value self.pnp_solvers[model] = \ CuboidPNPSolver( model, - cuboid3d=Cuboid3d(rospy.get_param('~dimensions')[model]) - ) + cuboid3d=Cuboid3d(self.get_parameters_by_prefix('dimensions')[model].get_parameter_value().double_array_value) + ) + self.pubs[model] = \ - rospy.Publisher( - '{}/pose_{}'.format(rospy.get_param('~topic_publishing'), model), + self.create_publisher( PoseStamped, - queue_size=10 - ) + '{}/pose_{}'.format(self.get_parameter('topic_publishing').get_parameter_value().string_value, model), + 10 + ) + self.pub_dimension[model] = \ - rospy.Publisher( - '{}/dimension_{}'.format(rospy.get_param('~topic_publishing'), model), + self.create_publisher( String, - queue_size=10 - ) + '{}/dimension_{}'.format( + self.get_parameter('topic_publishing').get_parameter_value().string_value, model), + 10 + ) + self.pub_belief[model] = \ - rospy.Publisher( - '{}/belief_{}'.format(rospy.get_param('~topic_publishing'), model), + self.create_publisher( ImageSensor_msg, - queue_size=10 - ) + '{}/belief_{}'.format(self.get_parameter('topic_publishing').get_parameter_value().string_value, model), + 10 + ) + + print(simple_colors.cyan("Publishers successfully created for " + model + " model.", ['bold'])) - # Start ROS publishers + # # Start ROS publishers self.pub_rgb_dope_points = \ - rospy.Publisher( - rospy.get_param('~topic_publishing') + "/rgb_points", + self.create_publisher( ImageSensor_msg, - queue_size=10 + self.get_parameter('topic_publishing').get_parameter_value().string_value + "/rgb_points", + 10 ) + self.pub_camera_info = \ - rospy.Publisher( - rospy.get_param('~topic_publishing') + "/camera_info", + self.create_publisher( CameraInfo, - queue_size=10 + self.get_parameter('topic_publishing').get_parameter_value().string_value + "/camera_info", + 10 ) + self.pub_detections = \ - rospy.Publisher( - '~detected_objects', + self.create_publisher( Detection3DArray, - queue_size=10 + '~/detected_objects', + 10 ) + self.pub_markers = \ - rospy.Publisher( - '~markers', + self.create_publisher( MarkerArray, - queue_size=10 + '~/markers', + 10 ) - # Start ROS subscriber - image_sub = message_filters.Subscriber( - rospy.get_param('~topic_camera'), - ImageSensor_msg + # # # # Start ROS subscriber + image_sub = message_filters.Subscriber(self, ImageSensor_msg, + self.get_parameter('topic_camera').get_parameter_value().string_value, + qos_profile=rclpy.qos.qos_profile_sensor_data ) - info_sub = message_filters.Subscriber( - rospy.get_param('~topic_camera_info'), - CameraInfo + + info_sub = message_filters.Subscriber(self, CameraInfo, + self.get_parameter('topic_camera_info').get_parameter_value().string_value, + qos_profile=rclpy.qos.qos_profile_sensor_data ) + ts = message_filters.TimeSynchronizer([image_sub, info_sub], 1) ts.registerCallback(self.image_callback) + print(simple_colors.cyan("Subscriber successfully created.", ['bold'])) + + print(simple_colors.cyan("Running DOPE...", [ + 'bold']) + "\nListening to camera topic: '{}'".format(self.get_parameter('topic_camera').get_parameter_value().string_value)) + print("\nCtrl-C to stop") - print("Running DOPE... (Listening to camera topic: '{}')".format(rospy.get_param('~topic_camera'))) - print("Ctrl-C to stop") def image_callback(self, image_msg, camera_info): + """Image callback""" - + msg_dim = String() img = self.cv_bridge.imgmsg_to_cv2(image_msg, "rgb8") # cv2.imwrite('img.png', cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) # for debugging # Update camera matrix and distortion coefficients if self.input_is_rectified: - P = np.matrix(camera_info.P, dtype='float64') + # P: NDArray[(3, 4), float] = np.matrix(camera_info.p) + P = np.matrix(camera_info.p, dtype='float64') P.resize((3, 4)) camera_matrix = P[:, :3] + dist_coeffs = np.zeros((4, 1)) else: - camera_matrix = np.matrix(camera_info.K, dtype='float64') + camera_matrix = np.matrix(camera_info.k, dtype='float64') camera_matrix.resize((3, 3)) - dist_coeffs = np.matrix(camera_info.D, dtype='float64') - dist_coeffs.resize((len(camera_info.D), 1)) + + dist_coeffs = np.matrix(camera_info.d, dtype='float64') + dist_coeffs.resize((len(camera_info.d), 1)) # Downscale image if necessary height, width, _ = img.shape scaling_factor = float(self.downscale_height) / height if scaling_factor < 1.0: camera_matrix[:2] *= scaling_factor - img = cv2.resize(img, (int(scaling_factor * width), int(scaling_factor * height))) + img = cv2.resize(img, (int(scaling_factor * width), + int(scaling_factor * height))) for m in self.models: self.pnp_solvers[m].set_camera_intrinsic_matrix(camera_matrix) self.pnp_solvers[m].set_dist_coeffs(dist_coeffs) # Copy and draw image + img_copy = img.copy() im = Image.fromarray(img_copy) draw = Draw(im) @@ -257,11 +298,12 @@ def image_callback(self, image_msg, camera_info): detection_array = Detection3DArray() detection_array.header = image_msg.header + for m in self.models: - publish_belief_img = (self.pub_belief[m].get_num_connections() > 0) + publish_belief_img = (self.pub_belief[m].get_subscription_count() > 0) # Detect object - results, im_belief = ObjectDetector.detect_object_in_image( + results, im_belief = self.obj_detector.detect_object_in_image( self.models[m].net, self.pnp_solvers[m], img, @@ -270,19 +312,23 @@ def image_callback(self, image_msg, camera_info): overlay_image=self.overlay_belief_images ) + # Publish pose and overlay cube on image for i_r, result in enumerate(results): + if result["location"] is None: continue loc = result["location"] - ori = result["quaternion"] + ori_ = result["quaternion"] + ori = np.array([ori_[3], ori_[0], ori_[1], ori_[2]], dtype='float64') - # transform orientation - transformed_ori = tf.transformations.quaternion_multiply(ori, self.model_transforms[m]) + transformed_ori = transformations.quaternion_multiply(ori, self.model_transforms[m]) + # rotate bbox dimensions if necessary # (this only works properly if model_transform is in 90 degree angles) - dims = rotate_vector(vector=self.dimensions[m], quaternion=self.model_transforms[m]) + dims = rotate_vector( + vector=self.dimensions[m], quaternion=self.model_transforms[m]) dims = np.absolute(dims) dims = tuple(dims) @@ -292,22 +338,30 @@ def image_callback(self, image_msg, camera_info): pose_msg.pose.position.x = loc[0] / CONVERT_SCALE_CM_TO_METERS pose_msg.pose.position.y = loc[1] / CONVERT_SCALE_CM_TO_METERS pose_msg.pose.position.z = loc[2] / CONVERT_SCALE_CM_TO_METERS - pose_msg.pose.orientation.x = transformed_ori[0] - pose_msg.pose.orientation.y = transformed_ori[1] - pose_msg.pose.orientation.z = transformed_ori[2] - pose_msg.pose.orientation.w = transformed_ori[3] + pose_msg.pose.orientation.x = transformed_ori[1] + pose_msg.pose.orientation.y = transformed_ori[2] + pose_msg.pose.orientation.z = transformed_ori[3] + pose_msg.pose.orientation.w = transformed_ori[0] # Publish self.pubs[m].publish(pose_msg) - self.pub_dimension[m].publish(str(dims)) + + msg_dim.data = str(dims) + self.pub_dimension[m].publish(msg_dim) + # self.pub_dimension[m].publish(String(dims)) # Add to Detection3DArray detection = Detection3D() hypothesis = ObjectHypothesisWithPose() - hypothesis.id = self.class_ids[result["name"]] - hypothesis.score = result["score"] + + + hypothesis.hypothesis.class_id = str(self.class_ids[result["name"]]) + hypothesis.hypothesis.score = float(result["score"]) hypothesis.pose.pose = pose_msg.pose + detection.results.append(hypothesis) + + detection.bbox.center = pose_msg.pose detection.bbox.size.x = dims[0] / CONVERT_SCALE_CM_TO_METERS detection.bbox.size.y = dims[1] / CONVERT_SCALE_CM_TO_METERS @@ -321,26 +375,35 @@ def image_callback(self, image_msg, camera_info): points2d.append(tuple(pair)) draw.draw_cube(points2d, self.draw_colors[m]) + + # Publish the belief image if publish_belief_img: - belief_img = self.cv_bridge.cv2_to_imgmsg(np.array(im_belief)[..., ::-1], "bgr8") + belief_img = self.cv_bridge.cv2_to_imgmsg( + np.array(im_belief)[..., ::-1], "bgr8") belief_img.header = camera_info.header self.pub_belief[m].publish(belief_img) + # Publish the image with results overlaid - rgb_points_img = CvBridge().cv2_to_imgmsg(np.array(im)[..., ::-1], "bgr8") + + rgb_points_img = CvBridge().cv2_to_imgmsg( + np.array(im)[..., ::-1], "bgr8") rgb_points_img.header = camera_info.header self.pub_rgb_dope_points.publish(rgb_points_img) self.pub_camera_info.publish(camera_info) self.pub_detections.publish(detection_array) self.publish_markers(detection_array) + def publish_markers(self, detection_array): - # Object markers - class_id_to_name = {class_id: name for name, class_id in self.class_ids.items()} + # # Object markers + class_id_to_name = {class_id: name for name, + class_id in self.class_ids.items()} markers = MarkerArray() for i, det in enumerate(detection_array.detections): - name = class_id_to_name[det.results[0].id] + class_id = int(det.results[0].hypothesis.class_id) + name = class_id_to_name.get(class_id) color = self.draw_colors[name] # cube marker @@ -373,7 +436,7 @@ def publish_markers(self, detection_array): marker.scale.x = 0.05 marker.scale.y = 0.05 marker.scale.z = 0.05 - marker.text = '{} ({:.2f})'.format(name, det.results[0].score) + marker.text = '{} ({:.2f})'.format(name, det.results[0].hypothesis.score) markers.markers.append(marker) # mesh marker @@ -406,31 +469,39 @@ def publish_markers(self, detection_array): marker.id = i markers.markers.append(marker) self.prev_num_detections = len(detection_array.detections) - self.pub_markers.publish(markers) def rotate_vector(vector, quaternion): - q_conj = tf.transformations.quaternion_conjugate(quaternion) + + # import tf_transformations + q_conj = transformations.quaternion_conjugate(quaternion) vector = np.array(vector, dtype='float64') vector = np.append(vector, [0.0]) - vector = tf.transformations.quaternion_multiply(q_conj, vector) - vector = tf.transformations.quaternion_multiply(vector, quaternion) + vector = transformations.quaternion_multiply(q_conj, vector) + vector = transformations.quaternion_multiply(vector, quaternion) + return vector[:3] - def main(): """Main routine to run DOPE""" - + # Initialize ROS node - rospy.init_node('dope') - DopeNode() + rclpy.init() + + dope_node = DopeNode() try: - rospy.spin() - except rospy.ROSInterruptException: + rclpy.spin(dope_node) pass + except KeyboardInterrupt: + print("Keyboard interrupt received. Shutting down...") + + finally: + dope_node.destroy_node() + rclpy.shutdown() + -if __name__ == "__main__": +if __name__ == '__main__': main() diff --git a/src/dope/inference/__init__.py b/inference_script/__init__.py similarity index 100% rename from src/dope/inference/__init__.py rename to inference_script/__init__.py diff --git a/inference_script/__pycache__/__init__.cpython-310.pyc b/inference_script/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 00000000..118d1b76 Binary files /dev/null and b/inference_script/__pycache__/__init__.cpython-310.pyc differ diff --git a/inference_script/__pycache__/cuboid.cpython-310.pyc b/inference_script/__pycache__/cuboid.cpython-310.pyc new file mode 100644 index 00000000..49db8a61 Binary files /dev/null and b/inference_script/__pycache__/cuboid.cpython-310.pyc differ diff --git a/inference_script/__pycache__/cuboid_pnp_solver.cpython-310.pyc b/inference_script/__pycache__/cuboid_pnp_solver.cpython-310.pyc new file mode 100644 index 00000000..0dd8b2be Binary files /dev/null and b/inference_script/__pycache__/cuboid_pnp_solver.cpython-310.pyc differ diff --git a/inference_script/__pycache__/detector.cpython-310.pyc b/inference_script/__pycache__/detector.cpython-310.pyc new file mode 100644 index 00000000..d2269c68 Binary files /dev/null and b/inference_script/__pycache__/detector.cpython-310.pyc differ diff --git a/inference_script/__pycache__/utils.cpython-310.pyc b/inference_script/__pycache__/utils.cpython-310.pyc new file mode 100644 index 00000000..edb0a90e Binary files /dev/null and b/inference_script/__pycache__/utils.cpython-310.pyc differ diff --git a/src/dope/inference/cuboid.py b/inference_script/cuboid.py similarity index 89% rename from src/dope/inference/cuboid.py rename to inference_script/cuboid.py index 58c0b292..2efe9b9b 100644 --- a/src/dope/inference/cuboid.py +++ b/inference_script/cuboid.py @@ -6,6 +6,7 @@ import cv2 import numpy as np +from nptyping import NDArray # Related to the object's local coordinate system @@ -105,10 +106,19 @@ def generate_vertexes(self): ] else: sx, sy, sz = self.size3d - forward = np.array(self.coord_system.forward, dtype=float) * sy * 0.5 - up = np.array(self.coord_system.up, dtype=float) * sz * 0.5 - right = np.array(self.coord_system.right, dtype=float) * sx * 0.5 - center = np.array(self.center_location, dtype=float) + + forward : NDArray[float] = np.array(self.coord_system.forward) * sy * 0.5 + # forward = np.array(self.coord_system.forward, dtype=float) * sy * 0.5 + + up : NDArray[float] = np.array(self.coord_system.up) * sz * 0.5 + # up = np.array(self.coord_system.up, dtype=float) * sz * 0.5 + + right : NDArray[float] = np.array(self.coord_system.right) * sx * 0.5 + # right = np.array(self.coord_system.right, dtype=float) * sx * 0.5 + + center : NDArray[float] = np.array(self.center_location) + # center = np.array(self.center_location, dtype=float) + self._vertices = [ center + forward + up + right, # Front Top Right center + forward + up - right, # Front Top Left diff --git a/src/dope/inference/cuboid_pnp_solver.py b/inference_script/cuboid_pnp_solver.py similarity index 94% rename from src/dope/inference/cuboid_pnp_solver.py rename to inference_script/cuboid_pnp_solver.py index e6382574..753a0508 100644 --- a/src/dope/inference/cuboid_pnp_solver.py +++ b/inference_script/cuboid_pnp_solver.py @@ -6,6 +6,7 @@ import numpy as np from .cuboid import CuboidVertexType from pyrr import Quaternion +from nptyping import NDArray class CuboidPNPSolver(object): @@ -52,6 +53,7 @@ def solve_pnp(self, cuboid2d_points, pnp_algorithm = None): location = None quaternion = None projected_points = cuboid2d_points + # print(projected_points) cuboid3d_points = np.array(self._cuboid3d.get_vertices()) obj_2d_points = [] @@ -65,8 +67,11 @@ def solve_pnp(self, cuboid2d_points, pnp_algorithm = None): obj_2d_points.append(check_point_2d) obj_3d_points.append(cuboid3d_points[i]) - obj_2d_points = np.array(obj_2d_points, dtype=float) - obj_3d_points = np.array(obj_3d_points, dtype=float) + obj_2d_points : NDArray[float] = np.array(obj_2d_points) + # obj_2d_points = np.array(obj_2d_points, dtype=float) + + obj_3d_points : NDArray[float] = np.array(obj_3d_points) + # obj_3d_points = np.array(obj_3d_points, dtype=float) valid_point_count = len(obj_2d_points) print(valid_point_count, "valid points found" ) diff --git a/src/dope/inference/detector.py b/inference_script/detector.py similarity index 97% rename from src/dope/inference/detector.py rename to inference_script/detector.py index a05cfe99..48284e90 100644 --- a/src/dope/inference/detector.py +++ b/inference_script/detector.py @@ -19,7 +19,7 @@ from scipy.ndimage.filters import gaussian_filter from torch.autograd import Variable -from dope.utils import get_image_grid +from inference_script.utils import get_image_grid # Import the definition of the neural network model and cuboids @@ -46,7 +46,7 @@ def __init__( self.stop_at_stage = stop_at_stage - vgg_full = models.vgg19(pretrained=False).features + vgg_full = models.vgg19(weights=None).features self.vgg = nn.Sequential() for i_layer in range(24): self.vgg.add_module(str(i_layer), vgg_full[i_layer]) @@ -85,6 +85,10 @@ def __init__( numAffinity, False) self.m6_1 = DopeNetwork.create_stage(128 + numBeliefMap + numAffinity, numAffinity, False) + + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = True + torch.backends.cudnn.enabled = True def forward(self, x): @@ -311,9 +315,11 @@ def find_object_poses(vertex2, aff, pnp_solver, config): for obj in objects: # Run PNP - points = obj[1] + [(obj[0][0]*8, obj[0][1]*8)] - cuboid2d = np.copy(points) - location, quaternion, projected_points = pnp_solver.solve_pnp(points) + points = np.array(obj[1] + [(obj[0][0]*8, obj[0][1]*8)],dtype=object) + points_list = obj[1] + [(obj[0][0]*8, obj[0][1]*8)] + cuboid2d_ = np.copy(points) + cuboid2d = cuboid2d_.tolist() + location, quaternion, projected_points = pnp_solver.solve_pnp(points.tolist()) # Save results detected_objects.append({ @@ -491,4 +497,4 @@ def find_objects(vertex2, aff, config, numvertex=8): objects[i_best][1][i_lists] = ((candidate[0])*8, (candidate[1])*8) objects[i_best][2][i_lists] = (best_angle, best_dist) - return objects, all_peaks + return objects, all_peaks \ No newline at end of file diff --git a/scripts/train2/inference/detector.py b/inference_script/detector_debug.py old mode 100755 new mode 100644 similarity index 54% rename from scripts/train2/inference/detector.py rename to inference_script/detector_debug.py index b4f47fa3..7731cb25 --- a/scripts/train2/inference/detector.py +++ b/inference_script/detector_debug.py @@ -9,47 +9,26 @@ ''' import time -import json -import os, shutil -import sys -import traceback from os import path -import threading -from threading import Thread import numpy as np -import cv2 - import torch import torch.nn as nn -import torchvision.transforms as transforms -from torch.autograd import Variable import torchvision.models as models - -from scipy import ndimage -import scipy -import scipy.ndimage as ndimage -import scipy.ndimage.filters as filters +import torchvision.transforms as transforms from scipy.ndimage.filters import gaussian_filter -from scipy import optimize +from torch.autograd import Variable -import sys -sys.path.append("../") -from models import * +from inference_script.utils import get_image_grid # Import the definition of the neural network model and cuboids -from cuboid_pnp_solver import * #global transform for image input transform = transforms.Compose([ # transforms.Scale(IMAGE_SIZE), # transforms.CenterCrop((imagesize,imagesize)), transforms.ToTensor(), - # transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), - transforms.Normalize( - (0.485, 0.456, 0.406), - (0.229, 0.224, 0.225) - ) + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ]) @@ -67,7 +46,7 @@ def __init__( self.stop_at_stage = stop_at_stage - vgg_full = models.vgg19(pretrained=False).features + vgg_full = models.vgg19(weights=None).features self.vgg = nn.Sequential() for i_layer in range(24): self.vgg.add_module(str(i_layer), vgg_full[i_layer]) @@ -158,7 +137,7 @@ def forward(self, x): return [out1_2, out2_2, out3_2, out4_2, out5_2, out6_2],\ [out1_1, out2_1, out3_1, out4_1, out5_1, out6_1] - + @staticmethod def create_stage(in_channels, out_channels, first=False): '''Create the neural network layers for a single stage.''' @@ -219,12 +198,11 @@ def create_stage(in_channels, out_channels, first=False): class ModelData(object): '''This class contains methods for loading the neural network''' - def __init__(self, name="", net_path="", gpu_id=0,architecture='dope'): + def __init__(self, name="", net_path="", gpu_id=0): self.name = name self.net_path = net_path # Path to trained network model self.net = None # Trained network self.gpu_id = gpu_id - self.architecture = architecture def get_net(self): '''Returns network''' @@ -245,10 +223,7 @@ def load_net_model_path(self, path): '''Loads network model from disk with given path''' model_loading_start_time = time.time() print("Loading DOPE model '{}'...".format(path)) - if self.architecture == 'dope': - net = DopeNetwork() - else: - net = ResnetSimple() + net = DopeNetwork() net = torch.nn.DataParallel(net, [0]).cuda() net.load_state_dict(torch.load(path)) net.eval() @@ -265,143 +240,11 @@ def __str__(self): class ObjectDetector(object): '''This class contains methods for object detection''' - @staticmethod - def gaussian(height, center_x, center_y, width_x, width_y): - """Returns a gaussian function with the given parameters""" - width_x = float(width_x) - width_y = float(width_y) - return lambda x,y: height*np.exp( - -(((center_x-x)/width_x)**2+((center_y-y)/width_y)**2)/2) - - @staticmethod - def moments(data): - """Returns (height, x, y, width_x, width_y) - the gaussian parameters of a 2D distribution by calculating its - moments """ - total = data.sum() - X, Y = np.indices(data.shape) - x = (X*data).sum()/total - y = (Y*data).sum()/total - col = data[:, int(y)] - width_x = np.sqrt(np.abs((np.arange(col.size)-y)**2*col).sum()/col.sum()) - row = data[int(x), :] - width_y = np.sqrt(np.abs((np.arange(row.size)-x)**2*row).sum()/row.sum()) - height = data.max() - return height, x, y, width_x, width_y - - @staticmethod - def fitgaussian(data): - """Returns (height, x, y, width_x, width_y) - the gaussian parameters of a 2D distribution found by a fit""" - params = ObjectDetector.moments(data) - errorfunction = lambda p: np.ravel(ObjectDetector.gaussian(*p)(*np.indices(data.shape)) - - data) - p, success = optimize.leastsq(errorfunction, params) - return p - @staticmethod - def make_grid(tensor, nrow=8, padding=2, - normalize=False, range_=None, scale_each=False, pad_value=0): - """Make a grid of images. - Args: - tensor (Tensor or list): 4D mini-batch Tensor of shape (B x C x H x W) - or a list of images all of the same size. - nrow (int, optional): Number of images displayed in each row of the grid. - The Final grid size is (B / nrow, nrow). Default is 8. - padding (int, optional): amount of padding. Default is 2. - normalize (bool, optional): If True, shift the image to the range (0, 1), - by subtracting the minimum and dividing by the maximum pixel value. - range (tuple, optional): tuple (min, max) where min and max are numbers, - then these numbers are used to normalize the image. By default, min and max - are computed from the tensor. - scale_each (bool, optional): If True, scale each image in the batch of - images separately rather than the (min, max) over all images. - pad_value (float, optional): Value for the padded pixels. - Example: - See this notebook `here `_ - """ - import math - - if not (torch.is_tensor(tensor) or - (isinstance(tensor, list) and all(torch.is_tensor(t) for t in tensor))): - raise TypeError('tensor or list of tensors expected, got {}'.format(type(tensor))) - - # if list of tensors, convert to a 4D mini-batch Tensor - if isinstance(tensor, list): - tensor = torch.stack(tensor, dim=0) - - if tensor.dim() == 2: # single image H x W - tensor = tensor.view(1, tensor.size(0), tensor.size(1)) - if tensor.dim() == 3: # single image - if tensor.size(0) == 1: # if single-channel, convert to 3-channel - tensor = torch.cat((tensor, tensor, tensor), 0) - tensor = tensor.view(1, tensor.size(0), tensor.size(1), tensor.size(2)) - - if tensor.dim() == 4 and tensor.size(1) == 1: # single-channel images - tensor = torch.cat((tensor, tensor, tensor), 1) - - if normalize is True: - tensor = tensor.clone() # avoid modifying tensor in-place - if range_ is not None: - assert isinstance(range_, tuple), \ - "range has to be a tuple (min, max) if specified. min and max are numbers" - - def norm_ip(img, min, max): - img.clamp_(min=min, max=max) - img.add_(-min).div_(max - min + 1e-5) - - def norm_range(t, range_): - if range_ is not None: - norm_ip(t, range_[0], range_[1]) - else: - norm_ip(t, float(t.min()), float(t.max())) - - if scale_each is True: - for t in tensor: # loop over mini-batch dimension - norm_range(t, range) - else: - norm_range(tensor, range) - - if tensor.size(0) == 1: - return tensor.squeeze() - - # make the mini-batch of images into a grid - nmaps = tensor.size(0) - xmaps = min(nrow, nmaps) - ymaps = int(math.ceil(float(nmaps) / xmaps)) - height, width = int(tensor.size(2) + padding), int(tensor.size(3) + padding) - grid = tensor.new(3, height * ymaps + padding, width * xmaps + padding).fill_(pad_value) - k = 0 - for y in range(ymaps): - for x in range(xmaps): - if k >= nmaps: - break - grid.narrow(1, y * height + padding, height - padding)\ - .narrow(2, x * width + padding, width - padding)\ - .copy_(tensor[k]) - k = k + 1 - return grid - - @staticmethod - def get_image_grid(tensor, filename, nrow=3, padding=2,mean=None, std=None): - """ - Saves a given Tensor into an image file. - If given a mini-batch tensor, will save the tensor as a grid of images. - """ - from PIL import Image - - # tensor = tensor.cpu() - grid = ObjectDetector.make_grid(tensor, nrow=nrow, padding=10,pad_value=1) - if not mean is None: - # ndarr = grid.mul(std).add(mean).mul(255).byte().transpose(0,2).transpose(0,1).numpy() - ndarr = grid.mul(std).add(mean).mul(255).byte().transpose(0,2).transpose(0,1).numpy() - else: - ndarr = grid.mul(0.5).add(0.5).mul(255).byte().transpose(0,2).transpose(0,1).numpy() - im = Image.fromarray(ndarr) - # im.save(filename) - return im + def __init__(self) -> None: + self.image_tensor = None - @staticmethod - def detect_object_in_image(net_model, pnp_solver, in_img, config, + #@staticmethod + def detect_object_in_image(self,net_model, pnp_solver, in_img, config, make_belief_debug_img=False, norm_belief=True, overlay_image=True): """ Detect objects in a image using a specific trained network model @@ -409,20 +252,43 @@ def detect_object_in_image(net_model, pnp_solver, in_img, config, """ if in_img is None: return [] + + start_time = time.time() + print(time.time()-start_time) # Run network inference - image_tensor = transform(in_img) - image_torch = Variable(image_tensor).cuda().unsqueeze(0) + if self.image_tensor is None: + self.image_tensor = transform(in_img).clone() + else: + self.image_tensor.copy_(transform(in_img),non_blocking=True) + self.image_tensor.pin_memory() + print(time.time()-start_time) + image_torch = Variable(self.image_tensor.to('cuda',non_blocking=True)).unsqueeze(0) + print(time.time()-start_time) out, seg = net_model(image_torch) - vertex2 = out[-1][0] - aff = seg[-1][0] + print(time.time()-start_time) + # vertex2 = out[-1][0] + # aff = seg[-1][0] + print(type(out[-1][0])) + print("############################################") + + print(time.time()-start_time) + vertex2 = out[-1][0].to('cpu',non_blocking=True) + # [vertex2.append(x.to('cpu',non_blocking=True)) for x in out[-1][0]] + aff = (seg[-1][0]).to('cpu',non_blocking=True) + #[aff.append(x.to('cpu',non_blocking=True)) for x in seg[-1][0]] # Find objects from network output + print(time.time()-start_time) detected_objects = ObjectDetector.find_object_poses(vertex2, aff, pnp_solver, config) + print(time.time()-start_time) if not make_belief_debug_img: + print("if not make") + print(time.time()-start_time) return detected_objects, None else: + print(time.time()-start_time) # Run the belief maps debug display on the belief maps tensor = vertex2 belief_imgs = [] @@ -430,14 +296,14 @@ def detect_object_in_image(net_model, pnp_solver, in_img, config, upsampling = nn.UpsamplingNearest2d(size=in_img.shape[:2]) in_img = (torch.tensor(in_img).float() / 255.0) in_img *= 0.5 - + print(time.time()-start_time) for j in range(tensor.size()[0]): belief = tensor[j].clone() if norm_belief: belief -= float(torch.min(belief).item()) belief /= float(torch.max(belief).item()) - belief = torch.clamp(belief, 0, 1).cpu() + belief = torch.clamp(belief, 0, 1).to('cpu',non_blocking=True) if overlay_image: belief = upsampling(belief.unsqueeze(0).unsqueeze(0)).squeeze().squeeze().data belief = torch.cat([ @@ -453,112 +319,65 @@ def detect_object_in_image(net_model, pnp_solver, in_img, config, belief.unsqueeze(0) ]).unsqueeze(0) belief_imgs.append(belief.data.squeeze().numpy()) - + print(time.time()-start_time) # Create the image grid belief_imgs = torch.tensor(np.array(belief_imgs)) - - im_belief = ObjectDetector.get_image_grid(belief_imgs, None, - mean=0, std=1) - + im_belief = get_image_grid(belief_imgs, mean=0, std=1) + print(time.time()-start_time) return detected_objects, im_belief - - @staticmethod - def find_object_poses(vertex2, aff, pnp_solver, config, run_sampling=False, num_sample=100,scale_factor=8): + def find_object_poses(vertex2, aff, pnp_solver, config): '''Detect objects given network output''' - # run_sampling = True - # Detect objects from belief maps and affinities - objects, all_peaks = ObjectDetector.find_objects(vertex2, aff, config, - run_sampling=run_sampling, num_sample=num_sample,scale_factor=scale_factor) + objects, all_peaks = ObjectDetector.find_objects(vertex2, aff, config) detected_objects = [] obj_name = pnp_solver.object_name - - # print(all_peaks) - #print("find_object_poses: found {} objects ================".format(len(objects))) + for obj in objects: # Run PNP - points = obj[1] + [(obj[0][0]*scale_factor, obj[0][1]*scale_factor)] - cuboid2d = np.copy(points) - location, quaternion, projected_points = pnp_solver.solve_pnp(points) - - # run multiple sample - if run_sampling: - lx,ly,lz = [],[],[] - qx,qy,qz,qw = [],[],[],[] - - for i_sample in range(num_sample): - sample = [] - for i_point in range(len(obj[-1])): - if not obj[-1][i_point][i_sample] is None: - sample.append( (obj[-1][i_point][i_sample][0]*scale_factor, - obj[-1][i_point][i_sample][1]*scale_factor)) - else: - sample.append( None) - # final_cuboids.append(sample) - pnp_sample = pnp_solver.solve_pnp(sample) - - try: - lx.append(pnp_sample[0][0]) - ly.append(pnp_sample[0][1]) - lz.append(pnp_sample[0][2]) - - qx.append(pnp_sample[1][0]) - qy.append(pnp_sample[1][1]) - qz.append(pnp_sample[1][2]) - qw.append(pnp_sample[1][3]) - except: - pass - # TODO - # RUN quaternion as well for the std and avg. - - try: - print ("----") - print ("location:") - print (location[0],location[1],location[2]) - print (np.mean(lx),np.mean(ly),np.mean(lz)) - print (np.std(lx),np.std(ly),np.std(lz)) - print ("quaternion:") - print (quaternion[0],quaternion[1],quaternion[2],quaternion[3]) - print (np.mean(qx),np.mean(qy),np.mean(qz),np.mean(qw)) - print (np.std(qx),np.std(qy),np.std(qz),np.std(qw)) - - - except: - pass - if not location is None: - detected_objects.append({ - 'name': obj_name, - 'location': location, - 'quaternion': quaternion, - 'cuboid2d': cuboid2d, - 'projected_points': projected_points, - 'confidence': obj[-1], - 'raw_points': points - }) - - #print("find_object_poses: points = ", type(points), points) - #print("find_object_poses: locn = ", location, "quat =", quaternion) - #print("find_object_poses: projected_points = ", type(projected_points), projected_points) + points = np.array(obj[1] + [(obj[0][0]*8, obj[0][1]*8)],dtype=object) + points_list = obj[1] + [(obj[0][0]*8, obj[0][1]*8)] + cuboid2d_ = np.copy(points) + cuboid2d = cuboid2d_.tolist() + location, quaternion, projected_points = pnp_solver.solve_pnp(points.tolist()) + + # Save results + detected_objects.append({ + 'name': obj_name, + 'location': location, + 'quaternion': quaternion, + 'cuboid2d': cuboid2d, + 'projected_points': projected_points, + 'score': obj[-1], + }) + #print(detected_objects) return detected_objects @staticmethod - def find_objects(vertex2, aff, config, numvertex=8, run_sampling=False, num_sample=100,scale_factor=8): + def find_objects(vertex2, aff, config, numvertex=8): '''Detects objects given network belief maps and affinities, using heuristic method''' all_peaks = [] - all_samples = [] - peak_counter = 0 + + print("synctime:") + start_time = time.time() + #torch.cuda.synchronize() + #print(time.time()-start_time) + for j in range(vertex2.size()[0]): - belief = vertex2[j].clone() - map_ori = belief.cpu().data.numpy() - - map = gaussian_filter(belief.cpu().data.numpy(), sigma=config.sigma) + #belief = vertex2[j].clone() + #map_ori = belief.cpu().data.numpy() + #start_time = time.time() + map_ori = vertex2[j].to('cpu',non_blocking=True).numpy(force=True) + #print(time.time()-start_time) + + + map = gaussian_filter(map_ori, sigma=config.sigma) p = 1 map_left = np.zeros(map.shape) map_left[p:,:] = map[:-p,:] @@ -571,20 +390,19 @@ def find_objects(vertex2, aff, config, numvertex=8, run_sampling=False, num_samp peaks_binary = np.logical_and.reduce( ( - map >= map_left, - map >= map_right, - map >= map_up, - map >= map_down, + map >= map_left, + map >= map_right, + map >= map_up, + map >= map_down, map > config.thresh_map) ) - peaks = zip(np.nonzero(peaks_binary)[1], np.nonzero(peaks_binary)[0]) - + peaks = zip(np.nonzero(peaks_binary)[1], np.nonzero(peaks_binary)[0]) + # Computing the weigthed average for localizing the peaks peaks = list(peaks) - win = 11 - ran = win//2 + win = 5 + ran = win // 2 peaks_avg = [] - point_sample_list = [] for p_value in range(len(peaks)): p = peaks[p_value] weights = np.zeros((win,win)) @@ -596,38 +414,22 @@ def find_objects(vertex2, aff, config, numvertex=8, run_sampling=False, num_samp or p[1]+i >= map_ori.shape[0] \ or p[0]+j < 0 \ or p[0]+j >= map_ori.shape[1]: - continue + continue i_values[j+ran, i+ran] = p[1] + i j_values[j+ran, i+ran] = p[0] + j weights[j+ran, i+ran] = (map_ori[p[1]+i, p[0]+j]) + # if the weights are all zeros # then add the none continuous points OFFSET_DUE_TO_UPSAMPLING = 0.4395 - - # Sample the points using the gaussian - if run_sampling: - data = weights - params = ObjectDetector.fitgaussian(data) - fit = ObjectDetector.gaussian(*params) - _, mu_x,mu_y,std_x,std_y = params - points_sample = np.random.multivariate_normal( - np.array([p[1] + mu_x + OFFSET_DUE_TO_UPSAMPLING, - p[0] - mu_y + OFFSET_DUE_TO_UPSAMPLING]), - # np.array([[std_x*std_x,0],[0,std_y*std_y]]), size=num_sample) - np.array([[std_x,0],[0,std_y]]), size=num_sample) - point_sample_list.append(points_sample) - - try: peaks_avg.append( (np.average(j_values, weights=weights) + OFFSET_DUE_TO_UPSAMPLING, \ np.average(i_values, weights=weights) + OFFSET_DUE_TO_UPSAMPLING)) except: peaks_avg.append((p[0] + OFFSET_DUE_TO_UPSAMPLING, p[1] + OFFSET_DUE_TO_UPSAMPLING)) - - # Note: Python3 doesn't support len for zip object peaks_len = min(len(np.nonzero(peaks_binary)[1]), len(np.nonzero(peaks_binary)[0])) @@ -638,22 +440,10 @@ def find_objects(vertex2, aff, config, numvertex=8, run_sampling=False, num_samp peaks_with_score_and_id = [peaks_with_score[i] + (id[i],) for i in range(len(id))] all_peaks.append(peaks_with_score_and_id) - all_samples.append(point_sample_list) peak_counter += peaks_len objects = [] - - if aff is None: - # Assume there is only one object - points = [None for i in range(numvertex)] - for i_peak, peaks in enumerate(all_peaks): - # print (peaks) - for peak in peaks: - if peak[2] > config.threshold: - points[i_peak] = (peak[0],peak[1]) - - return points - + print(time.time()-start_time) # Check object centroid and build the objects if the centroid is found for nb_object in range(len(all_peaks[-1])): @@ -662,52 +452,44 @@ def find_objects(vertex2, aff, config, numvertex=8, run_sampling=False, num_samp [all_peaks[-1][nb_object][:2][0],all_peaks[-1][nb_object][:2][1]], [None for i in range(numvertex)], [None for i in range(numvertex)], - all_peaks[-1][nb_object][2], - [[None for j in range(num_sample)] for i in range(numvertex+1)] + all_peaks[-1][nb_object][2] ]) - - # Check if the object was added before - if run_sampling and nb_object < len(objects): - # add the samples to the object centroids - objects[nb_object][4][-1] = all_samples[-1][nb_object] - - + print(time.time()-start_time) # Working with an output that only has belief maps if aff is None: + print("if true") if len (objects) > 0 and len(all_peaks)>0 and len(all_peaks[0])>0: for i_points in range(8): if len(all_peaks[i_points])>0 and all_peaks[i_points][0][2] > config.threshold: objects[0][1][i_points] = (all_peaks[i_points][0][0], all_peaks[i_points][0][1]) else: # For all points found + aff_cpu = aff.to('cpu',non_blocking=True) for i_lists in range(len(all_peaks[:-1])): lists = all_peaks[i_lists] - # Candidate refers to point that needs to be match with a centroid object - for i_candidate, candidate in enumerate(lists): + for candidate in lists: if candidate[2] < config.thresh_points: continue i_best = -1 - best_dist = 10000 + best_dist = 10000 best_angle = 100 - - # Find the points that links to that centroid. for i_obj in range(len(objects)): center = [objects[i_obj][0][0], objects[i_obj][0][1]] - # integer is used to look into the affinity map, - # but the float version is used to run + # integer is used to look into the affinity map, + # but the float version is used to run point_int = [int(candidate[0]), int(candidate[1])] point = [candidate[0], candidate[1]] # look at the distance to the vector field. v_aff = np.array([ - aff[i_lists*2, + aff_cpu[i_lists*2, point_int[1], point_int[0]].data.item(), - aff[i_lists*2+1, - point_int[1], + aff_cpu[i_lists*2+1, + point_int[1], point_int[0]].data.item()]) * 10 # normalize the vector @@ -718,7 +500,7 @@ def find_objects(vertex2, aff, config, numvertex=8, run_sampling=False, num_samp xvec/=norms yvec/=norms - + v_aff = np.concatenate([[xvec],[yvec]]) v_center = np.array(center) - np.array(point) @@ -726,41 +508,30 @@ def find_objects(vertex2, aff, config, numvertex=8, run_sampling=False, num_samp yvec = v_center[1] norms = np.sqrt(xvec * xvec + yvec * yvec) - + xvec /= norms yvec /= norms v_center = np.concatenate([[xvec],[yvec]]) - + # vector affinity dist_angle = np.linalg.norm(v_center - v_aff) # distance between vertexes dist_point = np.linalg.norm(np.array(point) - np.array(center)) - - if dist_angle < config.thresh_angle \ - and best_dist > 1000 \ - or dist_angle < config.thresh_angle \ - and best_dist > dist_point: + + if dist_angle < config.thresh_angle and (best_dist > 1000 or best_dist > dist_point): i_best = i_obj best_angle = dist_angle best_dist = dist_point if i_best == -1: continue - + if objects[i_best][1][i_lists] is None \ or best_angle < config.thresh_angle \ and best_dist < objects[i_best][2][i_lists][1]: - # set the points - objects[i_best][1][i_lists] = ((candidate[0])*scale_factor, (candidate[1])*scale_factor) - # set information about the points: angle and distance + objects[i_best][1][i_lists] = ((candidate[0])*8, (candidate[1])*8) objects[i_best][2][i_lists] = (best_angle, best_dist) - # add the sample points - if run_sampling: - # print ("---") - # print(len(all_samples[i_lists])) - # print (len(all_peaks[i_lists])) - # print(i_obj) - objects[i_best][4][i_lists] = all_samples[i_lists][i_candidate] + print(time.time()-start_time) return objects, all_peaks diff --git a/src/dope/utils.py b/inference_script/utils.py similarity index 100% rename from src/dope/utils.py rename to inference_script/utils.py diff --git a/launch/camera.launch b/launch/camera.launch deleted file mode 100644 index 21716077..00000000 --- a/launch/camera.launch +++ /dev/null @@ -1,5 +0,0 @@ - - - - - diff --git a/launch/dope.launch b/launch/dope.launch deleted file mode 100644 index 59609dd9..00000000 --- a/launch/dope.launch +++ /dev/null @@ -1,7 +0,0 @@ - - - - - - - diff --git a/launch/dope.launch.py b/launch/dope.launch.py new file mode 100644 index 00000000..ae274cfd --- /dev/null +++ b/launch/dope.launch.py @@ -0,0 +1,54 @@ +import os +from ament_index_python.packages import get_package_share_directory +from launch import LaunchDescription +from launch_ros.actions import Node +from launch.actions import DeclareLaunchArgument +from launch.substitutions import LaunchConfiguration +from launch.conditions import IfCondition + +def generate_launch_description(): + # Get the path to the config_dope.yaml file + config_file = os.path.join(get_package_share_directory('dope_ros2'), 'config', 'config_dope.yaml') + + # Declare the log_level argument + log_level_arg = DeclareLaunchArgument( + 'log_level', + default_value='info', + description='Logging level' + ) + + rviz_absolute_path = os.path.join(get_package_share_directory('dope_ros2'),'rviz','rviz.rviz') + rviz_ = LaunchConfiguration("rviz_") + + rviz_launch_arg = DeclareLaunchArgument( + name='rviz_', + default_value= "true", + description='Set to true if you want to visualize on rviz, false otherwise.' + ) + + # Launch the dope_node node + dope_node = Node( + package='dope_ros2', + executable='dope_node', + name='dope_node', + output='screen', + emulate_tty=True, + parameters=[config_file], + arguments=['--ros-args', '--log-level', LaunchConfiguration('log_level')] + ) + + node_rviz = Node( + package='rviz2', + executable='rviz2', + name='rviz2', + arguments=['-d', rviz_absolute_path], + condition=IfCondition(rviz_) + ) + + + return LaunchDescription([ + log_level_arg, + rviz_launch_arg, + node_rviz, + dope_node + ]) diff --git a/nodes/camera b/nodes/camera deleted file mode 100755 index 873c6990..00000000 --- a/nodes/camera +++ /dev/null @@ -1,75 +0,0 @@ -#!/usr/bin/env python3 -# Copyright (c) 2018 NVIDIA Corporation. All rights reserved. -# This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. -# https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode - -""" -This file opens an RGB camera and publishes images via ROS. -It uses OpenCV to capture from camera 0. -""" - -import cv2 -import rospy -from camera_info_manager import CameraInfoManager -from cv_bridge import CvBridge -from sensor_msgs.msg import Image, CameraInfo -import sys - - -def publish_images(freq=100): - cam_index = 0 # index of camera to capture - - ### initialize ROS publishers etc. - rospy.init_node('dope_webcam') - camera_ns = rospy.get_param('camera', 'dope/webcam') - img_topic = '{}/image_raw'.format(camera_ns) - info_topic = '{}/camera_info'.format(camera_ns) - image_pub = rospy.Publisher(img_topic, Image, queue_size=10) - info_pub = rospy.Publisher(info_topic, CameraInfo, queue_size=10) - info_manager = CameraInfoManager(cname='dope_webcam_{}'.format(cam_index), - namespace=camera_ns) - try: - camera_info_url = rospy.get_param('~camera_info_url') - if not info_manager.setURL(camera_info_url): - rospy.logwarn('Camera info URL invalid: %s', camera_info_url) - except KeyError: - # we don't have a camera_info_url, so we'll keep the - # default ('file://${ROS_HOME}/camera_info/${NAME}.yaml') - pass - - info_manager.loadCameraInfo() - if not info_manager.isCalibrated(): - rospy.logwarn('Camera is not calibrated, please supply a valid camera_info_url parameter!') - - ### open camera - cap = cv2.VideoCapture(cam_index) - if not cap.isOpened(): - rospy.logfatal("ERROR: Unable to open camera for capture. Is camera plugged in?") - sys.exit(1) - - rospy.loginfo("Publishing images from camera %s to topic '%s'...", cam_index, img_topic) - rospy.loginfo("Ctrl-C to stop") - - ### publish images - rate = rospy.Rate(freq) - while not rospy.is_shutdown(): - ret, frame = cap.read() - - if ret: - image = CvBridge().cv2_to_imgmsg(frame, "bgr8") - image.header.frame_id = 'dope_webcam' - image.header.stamp = rospy.Time.now() - image_pub.publish(image) - # we need to call getCameraInfo() every time in case it was updated - camera_info = info_manager.getCameraInfo() - camera_info.header = image.header - info_pub.publish(camera_info) - - rate.sleep() - - -if __name__ == "__main__": - try: - publish_images() - except rospy.ROSInterruptException: - pass diff --git a/package.xml b/package.xml index 45c00a5f..3766e846 100644 --- a/package.xml +++ b/package.xml @@ -1,77 +1,33 @@ - - dope + + + dope_ros2 0.0.0 - The DOPE package for deep object pose estimation + TODO: Package description + sfederico + TODO: License declaration - - - - jtremblay + rclpy + + ament_cmake + rclpy_components + ros2launch + geometry_msgs + sensor_msgs + std_msgs_msgs + vision_msgs_msgs + visualization_msgs - - - - CC BY-NC-SA 4.0 + ament_copyright + ament_flake8 + ament_pep257 + python3-pytest + uclv_utilities - - - - https://research.nvidia.com/publication/2018-09_Deep-Object-Pose - - - - - - - - - - - - - - - - - - - - - - - - - - - - - catkin - camera_info_manager_py - cv_bridge - geometry_msgs - message_filters - python-argparse - resource_retriever - rospy - sensor_msgs - std_msgs - tf - vision_msgs - visualization_msgs - python3-pyrr-pip - python-pytorch-pip - python3-numpy - python3-scipy - python3-opencv - python3-pil - python-configparser - - - - + ament_python + diff --git a/readme.md b/readme.md deleted file mode 100644 index dfe9556e..00000000 --- a/readme.md +++ /dev/null @@ -1,198 +0,0 @@ -[![License CC BY-NC-SA 4.0](https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-blue.svg)](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode) -![Python 3.8](https://img.shields.io/badge/python-3.8-blue.svg) -# Deep Object Pose Estimation - ROS Inference - -This is the official DOPE ROS package for detection and 6-DoF pose estimation of **known objects** from an RGB camera. The network has been trained on the following YCB objects: cracker box, sugar box, tomato soup can, mustard bottle, potted meat can, and gelatin box. For more details, see our [CoRL 2018 paper](https://arxiv.org/abs/1809.10790) and [video](https://youtu.be/yVGViBqWtBI). - -*Note:* The instructions below refer to inference only. Training code is also provided but not supported. Thank you to [@Blaine141](https://github.com/blaine141) You can check out how to train DOPE on a single [GPU and using NVISII](https://github.com/NVlabs/Deep_Object_Pose/issues/155#issuecomment-791148200). - -![DOPE Objects](dope_objects.png) - -## Updates - -2022/07/13 - Added a script with a simple example for computing the ADD and ADD-S metric on data. Please refer to [script/metrics/](https://github.com/NVlabs/Deep_Object_Pose/tree/master/scripts/metrics). - -2022/03/30 - Update on the NViSII script to handle [symmetrical objects](https://github.com/NVlabs/Deep_Object_Pose/tree/master/scripts/nvisii_data_gen#handling-objects-with-symmetries). Also the NViSII script is compatible with the original training script. Thanks to Martin Günther. - -2021/12/13 - Added a NViSII script to generate synthetic data for training DOPE. See this [readme](https://github.com/NVlabs/Deep_Object_Pose/tree/master/scripts/nvisii_data_gen) for more details. We also added the update training and inference (without ROS) scripts for the NViSII paper [here](https://github.com/NVlabs/Deep_Object_Pose/tree/master/scripts/train2). - -2021/10/20 - Added ROS2 Foxy inference support through [Isaac ROS DOPE package](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_pose_estimation) for Jetson and x86+CUDA-capable GPU. - -2021/08/07 - Added publishing belief maps. Thank you to Martin Günther. - -2020/03/09 - Added HOPE [weights to google drive](https://drive.google.com/open?id=1DfoA3m_Bm0fW8tOWXGVxi4ETlLEAgmcg), [the 3d models](https://drive.google.com/drive/folders/1jiJS9KgcYAkfb8KJPp5MRlB0P11BStft), and the objects dimensions to config. [Tremblay et al., IROS 2020](https://arxiv.org/abs/2008.11822). The HOPE dataset can be found [here](https://github.com/swtyree/hope-dataset/) and is also part of the [BOP challenge](https://bop.felk.cvut.cz/datasets/#HOPE) - - - - - - - - - -## Installing - -We have tested on Ubuntu 20.04 with ROS Noetic with an NVIDIA Titan X and RTX 2080ti with Python 3.8. The code may work on other systems. - ---- -***NOTE*** - -For hardware-accelerated ROS2 inference support, please visit [Isaac ROS DOPE](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_pose_estimation/tree/main/isaac_ros_dope) which has been tested with ROS2 Foxy on Jetson AGX Xavier/JetPack 4.6 and on x86/Ubuntu 20.04 with RTX3060i. - ---- - -The following steps describe the native installation. Alternatively, use the provided [Docker image](docker/readme.md) and skip to Step #7. - -1. **Install ROS** - - Follow these [instructions](http://wiki.ros.org/noetic/Installation/Ubuntu). - You can select any of the default configurations in step 1.4; even the - ROS-Base (Bare Bones) package (`ros-noetic-ros-base`) is enough. - -2. **Create a catkin workspace** (if you do not already have one). To create a catkin workspace, follow these [instructions](http://wiki.ros.org/catkin/Tutorials/create_a_workspace): - ``` - $ mkdir -p ~/catkin_ws/src # Replace `catkin_ws` with the name of your workspace - $ cd ~/catkin_ws/ - $ catkin_make - ``` - -3. **Download the DOPE code** - ``` - $ cd ~/catkin_ws/src - $ git clone https://github.com/NVlabs/Deep_Object_Pose.git dope - ``` - -4. **Install python dependencies** - ``` - $ cd ~/catkin_ws/src/dope - $ python3 -m pip install -r requirements.txt - ``` - -5. **Install ROS dependencies** - ``` - $ cd ~/catkin_ws - $ rosdep install --from-paths src -i --rosdistro noetic - $ sudo apt-get install ros-noetic-rosbash ros-noetic-ros-comm - ``` - -6. **Build** - ``` - $ cd ~/catkin_ws - $ catkin_make - ``` - -7. **Download [the weights](https://drive.google.com/open?id=1DfoA3m_Bm0fW8tOWXGVxi4ETlLEAgmcg)** and save them to the `weights` folder, *i.e.*, `~/catkin_ws/src/dope/weights/`. - - -## Running - -1. **Start ROS master** - ``` - $ cd ~/catkin_ws - $ source devel/setup.bash - $ roscore - ``` - -2. **Start camera node** (or start your own camera node) - ``` - $ roslaunch dope camera.launch # Publishes RGB images to `/dope/webcam_rgb_raw` - ``` - - The camera must publish a correct `camera_info` topic to enable DOPE to compute the correct poses. Basically all ROS drivers have a `camera_info_url` parameter where you can set the calibration info (but most ROS drivers include a reasonable default). - - For details on calibration and rectification of your camera see the [camera tutorial](doc/camera_tutorial.md). - -3. **Edit config info** (if desired) in `~/catkin_ws/src/dope/config/config_pose.yaml` - * `topic_camera`: RGB topic to listen to - * `topic_camera_info`: camera info topic to listen to - * `topic_publishing`: topic namespace for publishing - * `input_is_rectified`: Whether the input images are rectified. It is strongly suggested to use a rectified input topic. - * `downscale_height`: If the input image is larger than this, scale it down to this pixel height. Very large input images eat up all the GPU memory and slow down inference. Also, DOPE works best when the object size (in pixels) has appeared in the training data (which is downscaled to 400 px). For these reasons, downscaling large input images to something reasonable (e.g., 400-500 px) improves memory consumption, inference speed *and* recognition results. - * `weights`: dictionary of object names and there weights path name, **comment out any line to disable detection/estimation of that object** - * `dimensions`: dictionary of dimensions for the objects (key values must match the `weights` names) - * `class_ids`: dictionary of class ids to be used in the messages published on the `/dope/detected_objects` topic (key values must match the `weights` names) - * `draw_colors`: dictionary of object colors (key values must match the `weights` names) - * `model_transforms`: dictionary of transforms that are applied to the pose before publishing (key values must match the `weights` names) - * `meshes`: dictionary of mesh filenames for visualization (key values must match the `weights` names) - * `mesh_scales`: dictionary of scaling factors for the visualization meshes (key values must match the `weights` names) - * `overlay_belief_images`: whether to overlay the input image on the belief images published on /dope/belief_[obj_name] - * `thresh_angle`: undocumented - * `thresh_map`: undocumented - * `sigma`: undocumented - * `thresh_points`: Thresholding the confidence for object detection; increase this value if you see too many false positives, reduce it if objects are not detected. - -4. **Start DOPE node** - ``` - $ roslaunch dope dope.launch [config:=/path/to/my_config.yaml] # Config file is optional; default is `config_pose.yaml` - ``` - - -## Debugging - -* The following ROS topics are published (assuming `topic_publishing == 'dope'`): - ``` - /dope/belief_[obj_name] # belief maps of object - /dope/dimension_[obj_name] # dimensions of object - /dope/pose_[obj_name] # timestamped pose of object - /dope/rgb_points # RGB images with detected cuboids overlaid - /dope/detected_objects # vision_msgs/Detection3DArray of all detected objects - /dope/markers # RViz visualization markers for all objects - ``` - *Note:* `[obj_name]` is in {cracker, gelatin, meat, mustard, soup, sugar} - -* To debug in RViz, run `rviz`, then add one or more of the following displays: - * `Add > Image` to view the raw RGB image or the image with cuboids overlaid - * `Add > Pose` to view the object coordinate frame in 3D. - * `Add > MarkerArray` to view the cuboids, meshes etc. in 3D. - * `Add > Camera` to view the RGB Image with the poses and markers from above. - - If you do not have a coordinate frame set up, you can run this static transformation: `rosrun tf2_ros static_transform_publisher 0 0 0 0.7071 0 0 -0.7071 world `, where `` is the `frame_id` of your input camera messages. Make sure that in RViz's `Global Options`, the `Fixed Frame` is set to `world`. Alternatively, you can skip the `static_transform_publisher` step and directly set the `Fixed Frame` to your ``. - -* If `rosrun` does not find the package (`[rospack] Error: package 'dope' not found`), be sure that you called `source devel/setup.bash` as mentioned above. To find the package, run `rospack find dope`. - - -## YCB 3D Models - -DOPE returns the poses of the objects in the camera coordinate frame. DOPE uses the aligned YCB models, which can be obtained using [NVDU](https://github.com/NVIDIA/Dataset_Utilities) (see the `nvdu_ycb` command). - -## HOPE 3D Models - -![HOPE 3D models rendered in UE4](https://i.imgur.com/V6wX64p.png) - -We introduce new toy 3d models that you download [here](https://drive.google.com/drive/folders/1jiJS9KgcYAkfb8KJPp5MRlB0P11BStft). -The folders are arranged like the YCB 3d models organization. -You can buy the real objects using the following links -[set 1](https://www.amazon.com/gp/product/B071ZMT9S2), -[set 2](https://www.amazon.com/gp/product/B007EA6PKS), -[set 3](https://www.amazon.com/gp/product/B00H4SKSPS), -and -[set 4](https://www.amazon.com/gp/product/B072M2PGX9). - -The HOPE dataset can be found [here](https://github.com/swtyree/hope-dataset/) and is also part of the [BOP challenge](https://bop.felk.cvut.cz/datasets/#HOPE). - -## How to cite DOPE - -If you use this tool in a research project, please cite as follows: -``` -@inproceedings{tremblay2018corl:dope, - author = {Jonathan Tremblay and Thang To and Balakumar Sundaralingam and Yu Xiang and Dieter Fox and Stan Birchfield}, - title = {Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects}, - booktitle = {Conference on Robot Learning (CoRL)}, - url = "https://arxiv.org/abs/1809.10790", - year = 2018 -} -``` - -## License - -Copyright (C) 2018 NVIDIA Corporation. All rights reserved. Licensed under the [CC BY-NC-SA 4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode). - - -## Acknowledgment - -Thanks to Jeffrey Smith (jeffreys@nvidia.com) for creating the Docker image. - - -## Contact - -Jonathan Tremblay (jtremblay@nvidia.com), Stan Birchfield (sbirchfield@nvidia.com) diff --git a/requirements.txt b/requirements.txt index 984c65ba..9844c539 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,8 +1,11 @@ pyrr==0.10.3 -torch==1.6.0 -torchvision==0.7.0 -numpy==1.17.4 -scipy==1.5.2 -opencv_python==4.4.0.44 -Pillow==8.1.1 -configparser==5.0.0 +torch==2.1.0 +torchvision==0.16.0 +numpy==1.26.0 +scipy==1.8.0 +opencv_python==4.8.1.78 +Pillow==9.0.1 +configparser==6.0.0 +transformations +nptyping +simple_colors diff --git a/resource/dope_ros2 b/resource/dope_ros2 new file mode 100644 index 00000000..e69de29b diff --git a/rviz/rviz.rviz b/rviz/rviz.rviz new file mode 100644 index 00000000..42e57341 --- /dev/null +++ b/rviz/rviz.rviz @@ -0,0 +1,263 @@ +Panels: + - Class: rviz_common/Displays + Help Height: 78 + Name: Displays + Property Tree Widget: + Expanded: + - /Status1 + - /MarkerArray1/Status1 + - /MarkerArray1/Topic1 + - /DepthCloud1/Auto Size1 + Splitter Ratio: 0.5 + Tree Height: 137 + - Class: rviz_common/Selection + Name: Selection + - Class: rviz_common/Tool Properties + Expanded: + - /2D Goal Pose1 + - /Publish Point1 + Name: Tool Properties + Splitter Ratio: 0.5886790156364441 + - Class: rviz_common/Views + Expanded: + - /Current View1 + Name: Views + Splitter Ratio: 0.5 + - Class: rviz_common/Time + Experimental: false + Name: Time + SyncMode: 0 + SyncSource: DepthCloud +Visualization Manager: + Class: "" + Displays: + - Alpha: 0.5 + Cell Size: 1 + Class: rviz_default_plugins/Grid + Color: 160; 160; 164 + Enabled: true + Line Style: + Line Width: 0.029999999329447746 + Value: Lines + Name: Grid + Normal Cell Count: 0 + Offset: + X: 0 + Y: 0 + Z: 0 + Plane: XY + Plane Cell Count: 10 + Reference Frame: + Value: true + - Class: rviz_default_plugins/Camera + Enabled: true + Far Plane Distance: 100 + Image Rendering: background and overlay + Name: Camera + Overlay Alpha: 0.5 + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /dope/rgb_points + Value: true + Visibility: + Camera: true + DepthCloud: true + Grid: true + MarkerArray: true + Pose: true + Value: true + Zoom Factor: 1 + - Alpha: 1 + Axes Length: 0.10000000149011612 + Axes Radius: 0.009999999776482582 + Class: rviz_default_plugins/Pose + Color: 255; 25; 0 + Enabled: false + Head Length: 0.30000001192092896 + Head Radius: 0.10000000149011612 + Name: Pose + Shaft Length: 1 + Shaft Radius: 0.05000000074505806 + Shape: Axes + Topic: + Depth: 5 + Durability Policy: Volatile + Filter size: 10 + History Policy: Keep Last + Reliability Policy: Reliable + Value: /dope/pose_lime + Value: false + - Class: rviz_default_plugins/MarkerArray + Enabled: false + Name: MarkerArray + Namespaces: + {} + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /dope_node/markers + Value: false + - Alpha: 1 + Auto Size: + Auto Size Factor: 1 + Value: true + Autocompute Intensity Bounds: true + Autocompute Value Bounds: + Max Value: 10 + Min Value: -10 + Value: true + Axis: Z + Channel Name: intensity + Class: rviz_default_plugins/DepthCloud + Color: 255; 255; 255 + Color Image Topic: /camera/color/image_raw + Color Transformer: RGB8 + Color Transport Hint: raw + Decay Time: 0 + Depth Map Topic: /camera/aligned_depth_to_color/image_raw + Depth Map Transport Hint: raw + Enabled: false + Invert Rainbow: false + Max Color: 255; 255; 255 + Max Intensity: 4096 + Min Color: 0; 0; 0 + Min Intensity: 0 + Name: DepthCloud + Occlusion Compensation: + Occlusion Time-Out: 30 + Value: false + Position Transformer: XYZ + Queue Size: 5 + Selectable: true + Size (Pixels): 3 + Style: Flat Squares + Topic Filter: true + Use Fixed Frame: true + Use rainbow: true + Value: false + - Class: rviz_default_plugins/MarkerArray + Enabled: true + Name: MarkerArray + Namespaces: + bboxes: true + meshes: true + texts: true + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /dope_node/markers + Value: true + - Class: rviz_default_plugins/Camera + Enabled: true + Far Plane Distance: 100 + Image Rendering: background and overlay + Name: Camera + Overlay Alpha: 0.5 + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /dope/belief_lime + Value: true + Visibility: + Camera: true + DepthCloud: true + Grid: true + MarkerArray: true + Pose: true + Value: true + Zoom Factor: 1 + Enabled: true + Global Options: + Background Color: 48; 48; 48 + Fixed Frame: camera_color_optical_frame + Frame Rate: 30 + Name: root + Tools: + - Class: rviz_default_plugins/Interact + Hide Inactive Objects: true + - Class: rviz_default_plugins/MoveCamera + - Class: rviz_default_plugins/Select + - Class: rviz_default_plugins/FocusCamera + - Class: rviz_default_plugins/Measure + Line color: 128; 128; 0 + - Class: rviz_default_plugins/SetInitialPose + Covariance x: 0.25 + Covariance y: 0.25 + Covariance yaw: 0.06853891909122467 + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /initialpose + - Class: rviz_default_plugins/SetGoal + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /goal_pose + - Class: rviz_default_plugins/PublishPoint + Single click: true + Topic: + Depth: 5 + Durability Policy: Volatile + History Policy: Keep Last + Reliability Policy: Reliable + Value: /clicked_point + Transformation: + Current: + Class: rviz_default_plugins/TF + Value: true + Views: + Current: + Class: rviz_default_plugins/Orbit + Distance: 0.5507269501686096 + Enable Stereo Rendering: + Stereo Eye Separation: 0.05999999865889549 + Stereo Focal Distance: 1 + Swap Stereo Eyes: false + Value: false + Focal Point: + X: -0.015215063467621803 + Y: 0.10234975069761276 + Z: 0.5954170823097229 + Focal Shape Fixed Size: true + Focal Shape Size: 0.05000000074505806 + Invert Z Axis: false + Name: Current View + Near Clip Distance: 0.009999999776482582 + Pitch: -1.5697963237762451 + Target Frame: + Value: Orbit (rviz) + Yaw: 6.031761646270752 + Saved: ~ +Window Geometry: + Camera: + collapsed: false + Displays: + collapsed: false + Height: 846 + Hide Left Dock: false + Hide Right Dock: true + QMainWindow State: 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 + Selection: + collapsed: false + Time: + collapsed: false + Tool Properties: + collapsed: false + Views: + collapsed: true + Width: 1200 + X: 172 + Y: 107 diff --git a/scripts/metrics/.gitignore b/scripts/metrics/.gitignore deleted file mode 100644 index 7a17db0c..00000000 --- a/scripts/metrics/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -content/ -data/ -results/ diff --git a/scripts/metrics/add_compute.py b/scripts/metrics/add_compute.py deleted file mode 100644 index 9d9bb526..00000000 --- a/scripts/metrics/add_compute.py +++ /dev/null @@ -1,564 +0,0 @@ -""" -things needed - -predictions for image -ground thruth for that image -3d model loaded - -compare the poses. - -""" - - - -import argparse -import os -import numpy as np -import glob -import math - -# from pymesh import obj -# from pymesh import ply -# import pywavefront -# import pymesh -from scipy import spatial - -import simplejson as json -import copy -from pyquaternion import Quaternion -import pickle -import nvisii as visii -import subprocess - - - -parser = argparse.ArgumentParser() - -parser.add_argument('--data_prediction', - default = "data/table_dope_results/", - help='path to prediction data') -parser.add_argument('--data', - default="data/table_ground_truth/", - help='path to data ground truth') -parser.add_argument('--models', - # /home/jtremblay/code/nvdu/nvdu/data/ycb/aligned_cm/AlphabetSoup/google_16k - # default="/home/jtremblay/code/nvdu/nvdu/data/ycb/original/", - default="content/", - help='path to the 3D grocery models') -parser.add_argument("--outf", - default="results/", - help="where to put the data" - ) -parser.add_argument('--adds', - action='store_true', - help="run ADDS, this might take a while" - ) -parser.add_argument("--cuboid", - action='store_true', - help="use cuboid to compute the ADD" - ) -parser.add_argument("--show", - action='store_true', - help="show the graph at the end. " - ) - -opt = parser.parse_args() - - - -if opt.outf is None: - opt.outf = opt.data_prediction - -if not os.path.isdir(opt.outf): - print(f'creating the folder: {opt.outf}') - os.mkdir(opt.outf) - -if os.path.isdir(opt.outf + "/tmp"): - print(f'folder {opt.outf + "/tmp"}/ exists') -else: - os.mkdir(opt.outf + "/tmp") - print(f'created folder {opt.outf + "/tmp"}/') - - - -def get_all_entries(path_to_explore, what='*.json'): - - imgs = [] - - def add_images(path): - # print(path) - # print(glob.glob(path+"/*json")) - # print(glob.glob(path+"/"+what)) - for j in sorted(glob.glob(path+"/"+what)): - # print(j) - imgs.append(j) - # imgsname.append(j.replace(path,"").replace("/","")) - - - def explore(path): - if not os.path.isdir(path): - return - folders = [os.path.join(path, o) for o in os.listdir(path) - if os.path.isdir(os.path.join(path,o))] - # if len(folders)>0: - for path_entry in folders: - explore(path_entry) - - - add_images(path) - - explore(path_to_explore) - return imgs - - - - - - -def create_obj( - name = 'name', - path_obj = "", - path_tex = None, - scale = 1, - rot_base = None, #visii quat - pos_base = (-10,-10,-10), # visii vec3 - ): - - - # This is for YCB like dataset - if path_obj in create_obj.meshes: - obj_mesh = create_obj.meshes[path_obj] - else: - obj_mesh = visii.mesh.create_from_obj(name, path_obj) - create_obj.meshes[path_obj] = obj_mesh - - - obj_entity = visii.entity.create( - name = name, - # mesh = visii.mesh.create_sphere("mesh1", 1, 128, 128), - mesh = obj_mesh, - transform = visii.transform.create(name), - material = visii.material.create(name) - ) - - # should randomize - obj_entity.get_material().set_metallic(0) # should 0 or 1 - obj_entity.get_material().set_transmission(0) # should 0 or 1 - obj_entity.get_material().set_roughness(1) # default is 1 - - if not path_tex is None: - - if path_tex in create_obj.textures: - obj_texture = create_obj.textures[path_tex] - else: - obj_texture = visii.texture.create_from_image(name,path_tex) - create_obj.textures[path_tex] = obj_texture - - - obj_entity.get_material().set_base_color_texture(obj_texture) - - obj_entity.get_transform().set_scale(visii.vec3(scale)) - - if not rot_base is None: - obj_entity.get_transform().set_rotation(rot_base) - if not pos_base is None: - obj_entity.get_transform().set_position(pos_base) - print(f' created: {obj_entity.get_name()}') - return obj_entity - -create_obj.meshes = {} -create_obj.textures = {} - - -def add_cuboid(name, debug=False): - obj = visii.entity.get(name) - - min_obj = obj.get_mesh().get_min_aabb_corner() - max_obj = obj.get_mesh().get_max_aabb_corner() - centroid_obj = obj.get_mesh().get_aabb_center() - - - cuboid = [ - visii.vec3(max_obj[0], max_obj[1], max_obj[2]), - visii.vec3(min_obj[0], max_obj[1], max_obj[2]), - visii.vec3(max_obj[0], min_obj[1], max_obj[2]), - visii.vec3(max_obj[0], max_obj[1], min_obj[2]), - visii.vec3(min_obj[0], min_obj[1], max_obj[2]), - visii.vec3(max_obj[0], min_obj[1], min_obj[2]), - visii.vec3(min_obj[0], max_obj[1], min_obj[2]), - visii.vec3(min_obj[0], min_obj[1], min_obj[2]), - visii.vec3(centroid_obj[0], centroid_obj[1], centroid_obj[2]), - ] - - # change the ids to be like ndds / DOPE - cuboid = [ cuboid[2],cuboid[0],cuboid[3], - cuboid[5],cuboid[4],cuboid[1], - cuboid[6],cuboid[7],cuboid[-1]] - - cuboid.append(visii.vec3(centroid_obj[0], centroid_obj[1], centroid_obj[2])) - - for i_p, p in enumerate(cuboid): - child_transform = visii.transform.create(f"{name}_cuboid_{i_p}") - child_transform.set_position(p) - child_transform.set_scale(visii.vec3(0.1)) - child_transform.set_parent(obj.get_transform()) - if debug: - visii.entity.create( - name = f"{name}_cuboid_{i_p}", - mesh = visii.mesh.create_sphere(f"{name}_cuboid_{i_p}"), - transform = child_transform, - material = visii.material.create(f"{name}_cuboid_{i_p}") - ) - - for i_v, v in enumerate(cuboid): - cuboid[i_v]=[v[0], v[1], v[2]] - - - - return cuboid - - -def get_models(path,suffix=""): - models = {} - for folder in glob.glob(path+"/*/"): - - model_name = folder.replace(path,"").replace('/',"") - print('loading',model_name + suffix) - models[model_name] = create_obj( - name = model_name + suffix, - path_obj = folder + "/google_16k/textured.obj", - path_tex = folder + "/google_16k/texture_map_flat.png", - scale = 0.01 - ) - if opt.cuboid: - add_cuboid(model_name + suffix) - if 'gu' in suffix: - models[model_name].get_material().set_metallic(1) - models[model_name].get_material().set_roughness(0.05) - - return models - - - -# START OF THE PROGRAM HERE -visii.initialize_headless() - - -# data_thruth = get_all_entries(opt.data,'scene_all_realsense.json') -# if len(data_thruth) == 0: -# data_thruth = get_all_entries(opt.data,'scene_realsense.json') -data_thruth = get_all_entries(opt.data,"*.json") -data_prediction = get_all_entries(opt.data_prediction,"*.json") - - -print('number of ground thruths found',len(data_thruth)) -print("number of predictions found",len(data_prediction)) - -meshes_gt = get_models(opt.models,'_gt') -meshes_gu = get_models(opt.models,'_gu') - -adds_objects = {} - -adds_all = [] -all_gts = [] -count_all_annotations = 0 -count_by_object = {} - -count_all_guesses = 0 -count_by_object_guesses = {} - - -for gt_file in data_thruth: - scene_gt = gt_file.replace(opt.data,"").replace('.json','') - pred_scene = None - - - for d in data_prediction: - scene_d = d.replace(opt.data_prediction,'').replace('json','').replace('.','') - - # if scene in d: - # print(scene_d,scene_gt) - if scene_d.split('/')[-1] == scene_gt.split('/')[-1]: - pred_scene = d - break - - if pred_scene is None: - continue - # print(gt_file) - gt_json = None - with open(gt_file) as json_file: - gt_json = json.load(json_file) - - gu_json = None - with open(pred_scene) as json_file: - gu_json = json.load(json_file) - - - objects_gt = [] #name obj, pose - - for obj in gt_json['objects']: - - name_gt = obj['class'] - - # little hack from bug in the data - if name_gt == '003': - name_gt = "003_cracker_box_16k" - objects_gt.append( - [ - name_gt, - { - "rotation":visii.quat( - obj['quaternion_xyzw'][3], - obj['quaternion_xyzw'][0], - obj['quaternion_xyzw'][1], - obj['quaternion_xyzw'][2], - ), - "position":visii.vec3( - obj['location'][0], - obj['location'][1], - obj['location'][2], - ) - } - ] - ) - - count_all_annotations += 1 - - if name_gt in count_by_object: - count_by_object[name_gt] +=1 - else: - count_by_object[name_gt] = 1 - - for obj_guess in gu_json['objects']: - - name_guess = obj_guess['class'] - # name_look_up = obj_guess['class'].split("_")[0] - name_look_up = obj_guess['class'] - - # need to add rotation for DOPE prediction, if your frames are aligned - try: - pose_mesh = { - "rotation":visii.quat( - float(obj_guess['quaternion_xyzw'][3]), - float(obj_guess['quaternion_xyzw'][0]), - float(obj_guess['quaternion_xyzw'][1]), - float(obj_guess['quaternion_xyzw'][2]), - ) - # * visii.angleAxis(1.57, visii.vec3(1,0,0)) * visii.angleAxis(1.57, visii.vec3(0,0,1)) - # * visii.angleAxis(1.57*2, visii.vec3(0,0,1)) - # * visii.angleAxis(1.57, visii.vec3(0,1,0)) - , - "position":visii.vec3( - float(str(obj_guess['location'][0]))/100.0, - float(str(obj_guess['location'][1]))/100.0, - float(str(obj_guess['location'][2]))/100.0, - ) - } - except: - # in case there is an inf or null in the location prediction/gt - pose_mesh = { - "rotation":visii.quat( - float(obj_guess['quaternion_xyzw'][3]), - float(obj_guess['quaternion_xyzw'][0]), - float(obj_guess['quaternion_xyzw'][1]), - float(obj_guess['quaternion_xyzw'][2]), - ) * visii.angleAxis(1.57, visii.vec3(1,0,0)) * visii.angleAxis(1.57, visii.vec3(0,0,1)) - , - "position":visii.vec3( - 1000000, - 1000000, - 1000000, - ) - } - count_all_guesses += 1 - - if name_guess in count_by_object_guesses: - count_by_object_guesses[name_guess] +=1 - else: - count_by_object_guesses[name_guess] = 1 - - - # print (name, pose_mesh) - candidates = [] - for i_obj_gt, obj_gt in enumerate(objects_gt): - name_gt, pose_mesh_gt = obj_gt - - # print(name_look_up,name_gt) - - if name_look_up == name_gt: - candidates.append([i_obj_gt, pose_mesh_gt, name_gt]) - - best_dist = 10000000000 - best_index = -1 - - for candi_gt in candidates: - # compute the add - i_gt, pose_gt, name_gt = candi_gt - # if i_gt in used_index: - # continue - # print(meshes_gt.keys()) - visii_gt = meshes_gt[name_gt] - - visii_gt.get_transform().set_position(pose_gt['position']) - visii_gt.get_transform().set_rotation(pose_gt['rotation']) - - # visii_gt.get_transform().set_position(visii.vec3(-10,-10,-10)) - # visii_gt.get_transform().set_rotation(pose_gt['rotation']) - - - visii_gu = meshes_gu[name_look_up] - - visii_gu.get_transform().set_position(pose_mesh['position']) - visii_gu.get_transform().set_rotation(pose_mesh['rotation']) - - # dope is in the opencv frame, need to be put in the opengl frame - visii_gu.get_transform().rotate_around(visii.vec3(0,0,0),visii.angleAxis(visii.pi(), visii.vec3(1,0,0))) - - - if opt.adds: - if opt.cuboid: - dist = 0 - for i_p in range(9): - corner_gt = visii.transform.get(f"{name_gt + '_gt'}_cuboid_{i_p}") - dist_s = [] - for i_ps in range(9): - corner_gu = visii.transform.get(f"{name_look_up+ '_gu'}_cuboid_{i_ps}") - gt_trans = corner_gt.get_local_to_world_matrix() - gu_trans = corner_gu.get_local_to_world_matrix() - - # print(corner_pos,cuboid_gt[i_p]) - dist_now =\ - math.sqrt( - (gt_trans[3][0]-gu_trans[3][0])**2+\ - (gt_trans[3][1]-gu_trans[3][1])**2+\ - (gt_trans[3][2]-gu_trans[3][2])**2 - ) - dist_s.append(dist_now) - dist += min(dist_s) - - dist /= 9 - print(dist) - else: - dist = [] - dist2 = [] - vertices = visii_gt.get_mesh().get_vertices() - points_gt = [] - points_gu = [] - - for i in range(len(vertices)): - v = visii.vec4(vertices[i][0],vertices[i][1],vertices[i][2],1) - p0 = visii_gt.get_transform().get_local_to_world_matrix() * v - p1 = visii_gu.get_transform().get_local_to_world_matrix() * v - points_gt.append([p0[0],p0[1],p0[2]]) - points_gu.append([p1[0],p1[1],p1[2]]) - - dist = np.mean(spatial.distance_matrix( - np.array(points_gt), - np.array(points_gu),p=2).min(axis=1)) - - - else: - if opt.cuboid: - dist = 0 - for i_p in range(9): - corner_gt = visii.transform.get(f"{name_gt + '_gt'}_cuboid_{i_p}") - corner_gu = visii.transform.get(f"{name_look_up+ '_gu'}_cuboid_{i_p}") - gt_trans = corner_gt.get_local_to_world_matrix() - gu_trans = corner_gu.get_local_to_world_matrix() - - # print(corner_pos,cuboid_gt[i_p]) - dist +=\ - math.sqrt( - (gt_trans[3][0]-gu_trans[3][0])**2+\ - (gt_trans[3][1]-gu_trans[3][1])**2+\ - (gt_trans[3][2]-gu_trans[3][2])**2 - ) - - dist /= 9 - else: - dist = [] - vertices = visii_gt.get_mesh().get_vertices() - for i in range(len(vertices)): - v = visii.vec4(vertices[i][0],vertices[i][1],vertices[i][2],1) - p0 = visii_gt.get_transform().get_local_to_world_matrix() * v - p1 = visii_gu.get_transform().get_local_to_world_matrix() * v - dist.append(visii.distance(p0, p1)) - - - dist = np.mean(dist) - - if dist < best_dist: - best_dist = dist - best_index = i_gt - - if best_index != -1: - if not name_guess in adds_objects.keys(): - adds_objects[name_guess] = [] - adds_all.append(best_dist) - adds_objects[name_guess].append(best_dist) - -# save the data -if len(opt.outf.split("/"))>1: - path = None - for folder in opt.outf.split("/"): - if path is None: - path = folder - else: - path = path + "/" + folder - try: - os.mkdir(path) - except: - pass -else: - try: - os.mkdir(opt.outf) - except: - pass -print(adds_objects.keys()) -count_by_object["all"] = count_all_annotations -pickle.dump(count_by_object,open(f'{opt.outf}/count_all_annotations.p','wb')) -pickle.dump(adds_all,open(f'{opt.outf}/adds_all.p','wb')) - -count_by_object_guesses["all"] = count_all_guesses -pickle.dump(count_by_object,open(f'{opt.outf}/count_all_guesses.p','wb')) - - -labels = [] -data = [] -for key in adds_objects.keys(): - pickle.dump(adds_objects[key],open(f'{opt.outf}/adds_{key}.p','wb')) - labels.append(key) - data.append(f'{opt.outf}/adds_{key}.p') - - -array_to_call = ["python", "make_graphs.py","--outf", opt.outf,'--labels'] - -for label in labels: - array_to_call.append(label) - -array_to_call.append('--data') -for d_p in data: - array_to_call.append(d_p) - -array_to_call.append('--colours') -for i in range(len(data)): - array_to_call.append(str(i)) -if opt.show: - array_to_call.append('--show') - -print(array_to_call) -subprocess.call(array_to_call) - -# subprocess.call( -# [ -# "python", "make_graphs.py", -# "--data", f'{opt.outf}/adds_{key}.p', -# "--labels", key, -# "--outf", opt.outf, -# '--colours', "0", -# ] -# ) - - -visii.deinitialize() - diff --git a/scripts/metrics/download_content.sh b/scripts/metrics/download_content.sh deleted file mode 100644 index 73e27943..00000000 --- a/scripts/metrics/download_content.sh +++ /dev/null @@ -1,28 +0,0 @@ -mkdir data - -cd data - -wget https://www.dropbox.com/s/qeljw3vjnc416bs/table_003_cracker_box_dope_results.zip -wget https://www.dropbox.com/s/mn2yqflc6fcqaic/table_003_cracker_box.zip - -unzip table_003_cracker_box_dope_results.zip -rm table_003_cracker_box_dope_results.zip -mkdir table_dope_results/ -mv table_003_cracker_box table_dope_results/scene1/ - -unzip table_003_cracker_box.zip -rm table_003_cracker_box.zip -mkdir table_ground_truth/ -mv table_003_cracker_box table_ground_truth/scene1/ - -cd ../ - -mkdir content -cd content - -wget https://www.dropbox.com/s/b61es9q5nhwtooi/003_cracker_box.zip -unzip 003_cracker_box.zip -rm 003_cracker_box.zip -mv 003_cracker_box 003_cracker_box_16k - -cd ../ \ No newline at end of file diff --git a/scripts/metrics/kpd_compute.py b/scripts/metrics/kpd_compute.py deleted file mode 100644 index 58f4167f..00000000 --- a/scripts/metrics/kpd_compute.py +++ /dev/null @@ -1,298 +0,0 @@ -""" -This script computes the average distance metric at the keypoint level -from GT to GU. -""" - - - -import argparse -import os -import numpy as np -import glob -import math - -# from pymesh import obj -# from pymesh import ply -# import pywavefront -# import pymesh -from scipy import spatial - -import simplejson as json -import copy -from pyquaternion import Quaternion -import pickle -import nvisii as visii -import subprocess - - - -parser = argparse.ArgumentParser() - -parser.add_argument('--data_prediction', - default = "data/table_dope_results/", - help='path to prediction data') -parser.add_argument('--data', - default="data/table_ground_truth/", - help='path to data ground truth') -parser.add_argument("--outf", - default="results_kpd/", - help="where to put the data" - ) -parser.add_argument("--show", - action='store_true', - help="show the graph at the end. " - ) - -opt = parser.parse_args() - - - -if opt.outf is None: - opt.outf = opt.data_prediction - -if not os.path.isdir(opt.outf): - print(f'creating the folder: {opt.outf}') - os.mkdir(opt.outf) - -if os.path.isdir(opt.outf + "/tmp"): - print(f'folder {opt.outf + "/tmp"}/ exists') -else: - os.mkdir(opt.outf + "/tmp") - print(f'created folder {opt.outf + "/tmp"}/') - -def get_all_entries(path_to_explore, what='*.json'): - - imgs = [] - - def add_images(path): - # print(path) - # print(glob.glob(path+"/*json")) - # print(glob.glob(path+"/"+what)) - for j in sorted(glob.glob(path+"/"+what)): - # print(j) - imgs.append(j) - # imgsname.append(j.replace(path,"").replace("/","")) - - - def explore(path): - if not os.path.isdir(path): - return - folders = [os.path.join(path, o) for o in os.listdir(path) - if os.path.isdir(os.path.join(path,o))] - # if len(folders)>0: - for path_entry in folders: - explore(path_entry) - - - add_images(path) - - explore(path_to_explore) - return imgs - - - - - -###### START ####### - -data_thruth = get_all_entries(opt.data,"*.json") -data_prediction = get_all_entries(opt.data_prediction,"*.json") - - -print('number of ground thruths found',len(data_thruth)) -print("number of predictions found",len(data_prediction)) - -adds_objects = {} - -adds_all = [] -all_gts = [] -count_all_annotations = 0 -count_by_object = {} - -count_all_guesses = 0 -count_by_object_guesses = {} - - -for gt_file in data_thruth: - scene_gt = gt_file.replace(opt.data,"").replace('.json','') - pred_scene = None - - - for d in data_prediction: - scene_d = d.replace(opt.data_prediction,'').replace('json','').replace('.','') - - # if scene in d: - # print(scene_d,scene_gt) - if scene_d.split('/')[-1] == scene_gt.split('/')[-1]: - pred_scene = d - break - - if pred_scene is None: - continue - # print(gt_file) - gt_json = None - with open(gt_file) as json_file: - gt_json = json.load(json_file) - - gu_json = None - with open(pred_scene) as json_file: - gu_json = json.load(json_file) - - - objects_gt = [] #name obj, keypoints - - for obj in gt_json['objects']: - if 'class' not in obj: - name_gt = obj['name'] - else: - name_gt = obj['class'] - # little hack from bug in the data - if name_gt == '003': - name_gt = "003_cracker_box_16k" - - objects_gt.append( - [ - name_gt, - obj["projected_cuboid"] - ] - ) - - count_all_annotations += 1 - - if name_gt in count_by_object: - count_by_object[name_gt] +=1 - else: - count_by_object[name_gt] = 1 - - for obj_guess in gu_json['objects']: - - if 'class' not in obj: - name_guess = obj_guess['name'] - name_look_up = obj_guess['name'] - else: - name_guess = obj_guess['class'] - name_look_up = obj_guess['class'] - - - keypoints_gu = obj_guess["projected_cuboid"] - - count_all_guesses += 1 - - if name_guess in count_by_object_guesses: - count_by_object_guesses[name_guess] +=1 - else: - count_by_object_guesses[name_guess] = 1 - - - # print (name, pose_mesh) - candidates = [] - for i_obj_gt, obj_gt in enumerate(objects_gt): - name_gt, pose_mesh_gt = obj_gt - - # print(name_look_up,name_gt) - - if name_look_up == name_gt: - candidates.append([i_obj_gt, pose_mesh_gt, name_gt]) - - best_dist = 10000000000 - best_index = -1 - - for candi_gt in candidates: - # compute the add - i_gt, keypoint_gt, name_gt = candi_gt - dist = [] - - for i in range(len(keypoints_gu)): - dist_key = 100000 - for j in range(len(keypoints_gu)): - d = np.sqrt((keypoint_gt[i][0]-keypoints_gu[j][0])**2+(keypoint_gt[i][1]-keypoints_gu[j][1])**2) - # print(keypoint_gt[i],keypoints_gu[i],i,d) - if d < dist_key: - dist_key = d - dist.append(dist_key) - - - dist = np.mean(dist) - - if dist < best_dist: - best_dist = dist - best_index = i_gt - - if best_index != -1: - if not name_guess in adds_objects.keys(): - adds_objects[name_guess] = [] - adds_all.append(best_dist) - adds_objects[name_guess].append(best_dist) - -# save the data -if len(opt.outf.split("/"))>1: - path = None - for folder in opt.outf.split("/"): - if path is None: - path = folder - else: - path = path + "/" + folder - try: - os.mkdir(path) - except: - pass -else: - try: - os.mkdir(opt.outf) - except: - pass -print(adds_objects.keys()) -count_by_object["all"] = count_all_annotations -pickle.dump(count_by_object,open(f'{opt.outf}/count_all_annotations.p','wb')) -pickle.dump(adds_all,open(f'{opt.outf}/adds_all.p','wb')) - -count_by_object_guesses["all"] = count_all_guesses -pickle.dump(count_by_object,open(f'{opt.outf}/count_all_guesses.p','wb')) - - -labels = [] -data = [] -for key in adds_objects.keys(): - pickle.dump(adds_objects[key],open(f'{opt.outf}/adds_{key}.p','wb')) - labels.append(key) - data.append(f'{opt.outf}/adds_{key}.p') - - -array_to_call = ["python", - "make_graphs.py", - '--pixels', - '--threshold',"50.0", - "--outf", - opt.outf, - '--labels', - ] - -for label in labels: - array_to_call.append(label) - -array_to_call.append('--data') -for d_p in data: - array_to_call.append(d_p) - -array_to_call.append('--colours') -for i in range(len(data)): - array_to_call.append(str(i)) -if opt.show: - array_to_call.append('--show') - -print(array_to_call) -subprocess.call(array_to_call) - -# subprocess.call( -# [ -# "python", "make_graphs.py", -# "--data", f'{opt.outf}/adds_{key}.p', -# "--labels", key, -# "--outf", opt.outf, -# '--colours', "0", -# ] -# ) - - -visii.deinitialize() - diff --git a/scripts/metrics/make_graphs.py b/scripts/metrics/make_graphs.py deleted file mode 100644 index c5c602b1..00000000 --- a/scripts/metrics/make_graphs.py +++ /dev/null @@ -1,212 +0,0 @@ -import matplotlib -import pickle -import argparse -import seaborn as sns -import matplotlib.pyplot as plt -import numpy as np -import os - -import glob - -# load the data -# might be multiple datasets - -# make the plots -os.environ["CUDA_VISIBLE_DEVICES"]="1" - -parser = argparse.ArgumentParser() - -parser.add_argument('--data_folder', - default = None, - help='path to data output') -parser.add_argument('--data', - nargs='+', - default=None, - help='list of csv files') -parser.add_argument('--labels', - nargs='+', - default=None, - help='labels to put') -parser.add_argument('--colours', - nargs='+', - default=None, - help = '') - -parser.add_argument("--outf", - default=None, - help="where to put the data") - -parser.add_argument('--threshold', - default = 0.1, - type = float - ) -parser.add_argument('--title', - default = 'AUC') -parser.add_argument('--filename', - default = 'output') -parser.add_argument('--styles', - nargs='+', - default=None, - help = '') -parser.add_argument("--show", - action='store_true', - help="show the graph at the end. " - ) -parser.add_argument("--pixels", - action='store_true', - help="Using keypoint distance as metric" - ) -opt = parser.parse_args() -sns.set_style("white") -sns.set_style("ticks") -sns.set_context("paper") -# sns.set_context("notebook") -# sns.set_context("talk") -sns.despine() -# load the data - -# if folder load all the files and create a graph -# if a list put all of them in the same graph - -plt.tight_layout() -# sns.set(font_scale=1.1) - -if opt.data_folder is not None: - # load the data from the file - adds_to_load = glob.glob(f"{opt.data_folder}/adds*") - counts_dict = pickle.load(open(f"{opt.data_folder}/count_all_annotations.p",'rb')) - -else: - # load the files in the list - adds_to_load = opt.data - counts_dict = None - - fig = plt.figure() - ax = plt.axes() - - -for i_file, file in enumerate(adds_to_load): - print(file) - label = file.split("/")[-1] - label = label.replace('adds_','').replace(".p",'') - filename = label - - if not counts_dict is None: - fig = plt.figure() - ax = plt.axes() - - n_pnp_possible_frames = counts_dict[filename] - - else: - # check labels - try: - label = opt.labels[i_file] - except: - label = filename - - # get n possible solutions - path = "/".join(file.split("/")[0:-1]) + '/' - n_pnp_possible_frames = pickle.load(open(f"{path}/count_all_annotations.p",'rb'))[filename] - - adds_objects = pickle.load(open(file,'rb')) - - # add_pnp_found = np.array(adds_objects)/100 - add_pnp_found = np.array(adds_objects) - print('mean',add_pnp_found.mean(),'std',add_pnp_found.std(), - 'ratio',f'{len(add_pnp_found)}/{n_pnp_possible_frames}') - n_pnp_found = len(add_pnp_found) - - delta_threshold = opt.threshold/300 - add_threshold_values = np.arange(0., opt.threshold, delta_threshold) - - counts = [] - for value in add_threshold_values: - under_threshold = len(np.where(add_pnp_found <= value)[0])/n_pnp_possible_frames - counts.append(under_threshold) - - for value in [0.02,0.04,0.06]: - under_threshold = len(np.where(add_pnp_found <= value)[0])/n_pnp_possible_frames - print('auc at ',value,':', under_threshold) - auc = np.trapz(counts, dx = delta_threshold)/opt.threshold - - # divide might screw this up .... to check! - print('auc',auc) - # print('found', n_pnp_found/n_pnp_possible_frames) - # print('mean', np.mean(add[np.where(add > pnp_sol_found_magic_number)])) - # print('median',np.median(add[np.where(add > pnp_sol_found_magic_number)])) - # print('std',np.std(add[np.where(add > pnp_sol_found_magic_number)])) - - cycle = plt.rcParams['axes.prop_cycle'].by_key()['color'] - if counts_dict is None: - colour = cycle[int(opt.colours[i_file])] - # colour = cycle[int(i_file)] - else: - colour = cycle[0] - - try: - style = args.styles[i_csv] - if style == '0': - style = '-' - elif style == '1': - style = '--' - elif style == '2': - style = ':' - - else: - style = '-' - except: - style = '-' - - label = f'{label} ({auc:.3f})' - ax.plot(add_threshold_values, counts,style,color=colour,label=label) - - if not counts_dict is None: - if opt.pixels: - plt.xlabel('L2 threshold distance (pixels)') - else: - plt.xlabel('ADD threshold distance (m)') - plt.ylabel('Accuracy') - plt.title(f'{filename} auc: {auc:.3f}') - - ax.set_ylim(0,1) - ax.set_xlim(0, float(opt.threshold)) - - # ax.set_xticklabels([0,20,40,60,80,100]) - plt.tight_layout() - plt.savefig(f'{opt.data_folder}/{filename}.png') - plt.close() - -if counts_dict is None: - if opt.pixels: - plt.xlabel('L2 threshold distance (pixels)') - else: - plt.xlabel('ADD threshold distance (m)') - - plt.ylabel('Accuracy') - plt.title(opt.title) - ax.legend(loc='lower right',frameon = True, fancybox=True, framealpha=0.8) - - - legend = ax.get_legend() - for i, t in enumerate(legend.get_texts()): - if opt.data[i] == '666': - t.set_ha('left') # ha is alias for horizontalalignment - t.set_position((-30,0)) - - ax.set_ylim(0,1) - ax.set_xlim(0, float(opt.threshold)) - # ax.set_xticklabels([0,20,40,60,80,100]) - plt.tight_layout() - try: - os.mkdir(opt.outf) - except: - pass - if opt.outf is None: - plt.savefig(f'{opt.filename}.png') - else: - plt.savefig(f'{opt.outf}/{opt.filename}.png') - if opt.show: - plt.show() - plt.close() - - diff --git a/scripts/metrics/overlay.png b/scripts/metrics/overlay.png deleted file mode 100644 index 2b0f891a..00000000 Binary files a/scripts/metrics/overlay.png and /dev/null differ diff --git a/scripts/metrics/readme.md b/scripts/metrics/readme.md deleted file mode 100644 index 689e0088..00000000 --- a/scripts/metrics/readme.md +++ /dev/null @@ -1,72 +0,0 @@ -# ADD metrics computation and figure - -## Requirements - -Run the download content file: `./download_content.sh`, this downloads a simple scene with annotation rendered by NViSII and with DOPE predictions. - -## How to run - -If you downloaded the previous content you can execute the following: - -``` -python add_compute.py -``` -and you should get the following displayed: -``` -mean 0.0208515107260977 std 0.016006083915162977 ratio 17/22 -auc at 0.02 : 0.5 -auc at 0.04 : 0.6818181818181818 -auc at 0.06 : 0.7272727272727273 -auc 0.6115249999999999 -``` -This means the area under the curve, *auc* from 0 cm to 10 cm is 0.61. This script also produces graphs such as: - -![example of graph](results/output.png) - -These are the metrics we reported in the original DOPE paper. I will refer to the paper for explaining the graph. - -## Assumptions -We make a few assumptions in this script. -1. We assume the folders structures are the same and there are only scenes in the folder. See `data/` folder example from downloading the content. -2. We assume the notation folder is in the opengl format and that it is using the nvisii outputs from the data generation pipeline. If you use a diffirent file format please update the script or your data. -3. We assume the inferences are from DOPE inference, _e.g._, the poses are in the opengl format. These conventions are easy to change, _e.g._, look for the line `visii_gu.get_transform().rotate_around` in `add_compute.py` to change the pose convention. - -If the script takes to long to run, please run with `--cuboid`, instead of using the 3d models vertices to compare the metric, it uses the 3d cuboid of the 3d model to compute the metric. - -## 2D image-based metric - -If you do not have a 3d model of your model and you would prefer to just measure the quality of your detections with a simple euclidean distance for the predicted keypoints. You can use `python kpd_compute.py`, this is very similar to `add_compute.py` and it behaves very similarly. -The metric used here is the euclidean (L2) distance from predicted keypoint and the ground truth keypoint. Then we propose to use a threshold plot to evaluate the data, similar to the ADD metric. - -# Rendering 3d predictions using NViSII - -![example of overlay](overlay.png) - -We added a script for you to add render of the 3d model to your predictions. It uses a version of NViSII that is not released yet. Please manually install this [wheel](https://www.dropbox.com/s/m85v7ts981xs090/nvisii-1.2.dev47%2Bgf122b5b.72-cp36-cp36m-manylinux2014_x86_64.whl?dl=0). -``` -# for scenes with DOPE inference -python render_json.py --path_json data/table_dope_results/scene1/00300.json --scale 0.01 --opencv --contour --gray -# for scenes generated by nvisii -python render_json.py --path_json data/table_ground_truth/scene1/00100.json --scale 0.01 --contour --gray -``` - -`--gray` render the 3d model as a gray image and `--contour` adds the 3d model contour in green. - -## Rendering BOP format on images - -Using the same argument, you can use this script on the BOP annotation with 3d models. The script simply rebuilds the data structure that is needed to load the scene. - -``` -python render_json.py --path_json /PATH/TO/hope_bop/hope_val/val/000001/scene_gt.json --bop --objs_folder /PATH/TO/hope_bop/hope_models/models/ --gray --contour --bop_scene 0 -``` - -Only `--bop` is needed to be passed to load a bop scene. You can pass which scene you want to load with `--bop_scene`. The rest is the same behaviour. This was only tested on the HOPE data. - -## Assumptions - -We assume that you have the intrinsics stored in the camera data. If you do not have them, the script uses 512 x 512 with a fov of 0.78. If the camera data is complete, like with NViSII data, it will use the camera intrinsics. - -# TODO -- Make a `requirement.txt` file. -- Possibly subsamble vertices so computation is faster - diff --git a/scripts/metrics/render_json.py b/scripts/metrics/render_json.py deleted file mode 100644 index c6dddb19..00000000 --- a/scripts/metrics/render_json.py +++ /dev/null @@ -1,457 +0,0 @@ - -import nvisii as visii -import argparse -import numpy as np - -import simplejson as json -import pyrender -import cv2 -import numpy as np -import os -import glob -import pyrr - -parser = argparse.ArgumentParser() - -parser.add_argument( - '--spp', - default=100, - type=int, - help = "number of sample per pixel, higher the more costly" -) -parser.add_argument( - '--opencv', - action='store_true', - default=False, - help = "Use the opencv coordinate frame, e.g., dope inference,\ - otherwise it uses the opengl coordinate frame." -) - -parser.add_argument( - '--bop', - action='store_true', - default=False, - help = "Use the bop format for the camera poses and .ply format \ - for the 3d object." -) -parser.add_argument( - '--bop_scene', - default=0, - type=int, - help = "Which scene to load, only applied to bop format." -) - -parser.add_argument( - '--contour', - action='store_true', - default=False, - help = "Only draw the contour instead of the 3d model overlay" -) - -parser.add_argument( - '--overlay', - action='store_true', - default=False, - help = "add the overlay" -) - -parser.add_argument( - '--gray', - action='store_true', - default=False, - help = "draw the 3d model in gray" -) - -parser.add_argument( - '--path_json', - required=True, - help = "path to the json files you want loaded,\ - it assumes that there is a png accompanying." -) - -parser.add_argument( - '--objs_folder', - default='content/', - help = "object to load folder, should follow YCB structure" -) - -parser.add_argument( - '--scale', - default=1, - type=float, - help='Specify the scale of the target object(s). If the obj mesh is in ' - 'meters -> scale=1; if it is in cm -> scale=0.01.' -) - -parser.add_argument( - '--out', - default='overlay.png', - help = "output filename" -) - -opt = parser.parse_args() - -# # # # # # # # # # # # # # # # # # # # # # # # # - -def create_obj( - name = 'name', - path_obj = "", - path_tex = None, - scale = 1, - rot_base = None, #visii quat - pos_base = (-10,-10,-10), # visii vec3 - ): - - - # This is for YCB like dataset - if path_obj in create_obj.meshes: - obj_mesh = create_obj.meshes[path_obj] - else: - obj_mesh = visii.mesh.create_from_obj(name, path_obj) - create_obj.meshes[path_obj] = obj_mesh - - - obj_entity = visii.entity.create( - name = name, - # mesh = visii.mesh.create_sphere("mesh1", 1, 128, 128), - mesh = obj_mesh, - transform = visii.transform.create(name), - material = visii.material.create(name) - ) - - # should randomize - obj_entity.get_material().set_metallic(0) # should 0 or 1 - obj_entity.get_material().set_transmission(0) # should 0 or 1 - obj_entity.get_material().set_roughness(1) # default is 1 - - if not path_tex is None: - - if path_tex in create_obj.textures: - obj_texture = create_obj.textures[path_tex] - else: - obj_texture = visii.texture.create_from_image(name,path_tex) - create_obj.textures[path_tex] = obj_texture - - - obj_entity.get_material().set_base_color_texture(obj_texture) - - obj_entity.get_transform().set_scale(visii.vec3(scale)) - - if not rot_base is None: - obj_entity.get_transform().set_rotation(rot_base) - if not pos_base is None: - obj_entity.get_transform().set_position(pos_base) - print(f' created: {obj_entity.get_name()}') - return obj_entity - -create_obj.meshes = {} -create_obj.textures = {} - -# # # # # # # # # # # # # # # # # # # # # # # # # - - -visii.initialize_headless() -visii.enable_denoiser() - -camera = visii.entity.create( - name = "camera", - transform = visii.transform.create("camera"), - camera = visii.camera.create( - name = "camera", - ) -) - -camera.get_transform().look_at( - visii.vec3(0,0,-1), # look at (world coordinate) - visii.vec3(0,1,0), # up vector - visii.vec3(0,0,0), # camera_origin -) - -visii.set_camera_entity(camera) - -visii.set_dome_light_intensity(1) - -try: - visii.set_dome_light_color(visii.vec3(1, 1, 1), 0) -except TypeError: - # Support for alpha transparent backgrounds was added in nvisii ef1880aa, - # but as of 2022-11-03, the latest released version (1.1) does not include - # that change yet. - print("WARNING! Your version of NVISII does not support alpha transparent backgrounds yet; --contour will not work properly.") - visii.set_dome_light_color(visii.vec3(1, 1, 1)) - -# # # # # # # # # # # # # # # # # # # # # # # # # - -# LOAD THE SCENE - -objects_added = [] -dope_trans = [] -gt = None - -with open(opt.path_json) as f: - data_json = json.load(f) - -if opt.bop: - # opt.opencv = True - - with open(opt.path_json.replace("gt","camera")) as f: - camera_data_json = json.load(f) - # a little bit of a hack, use the first camera, would need - # print(camera_data_json[str(opt.bop_scene)]["cam_K"]) - # raise() - data_json['camera_data'] = {} - data_json['camera_data']['intrinsics'] = {} - data_json['camera_data']['intrinsics']['fx'] = camera_data_json[str(opt.bop_scene)]["cam_K"][0] - data_json['camera_data']['intrinsics']['fy'] = camera_data_json[str(opt.bop_scene)]["cam_K"][4] - data_json['camera_data']['intrinsics']['cx'] = camera_data_json[str(opt.bop_scene)]["cam_K"][2] - data_json['camera_data']['intrinsics']['cy'] = camera_data_json[str(opt.bop_scene)]["cam_K"][5] - - # load an image for resolution - path_imgs = "/".join(opt.path_json.split("/")[:-1]+ ['rgb/']) - path_imgs = sorted(glob.glob(path_imgs + "*.png")) - opt.im_path = path_imgs[0] - img = cv2.imread(path_imgs[0]) - data_json['camera_data']['height'] = img.shape[0] - data_json['camera_data']['width'] = img.shape[1] - -# set the camera -if "camera_data" in data_json.keys() and "intrinsics" in data_json['camera_data'].keys(): - intrinsics = data_json['camera_data']['intrinsics'] - im_height = data_json['camera_data']['height'] - im_width = data_json['camera_data']['width'] - - cam = pyrender.IntrinsicsCamera(intrinsics['fx'],intrinsics['fy'],intrinsics['cx'],intrinsics['cy']) - - proj_matrix = cam.get_projection_matrix(im_width, im_height) - - proj_matrix = visii.mat4( - proj_matrix.flatten()[0], - proj_matrix.flatten()[1], - proj_matrix.flatten()[2], - proj_matrix.flatten()[3], - proj_matrix.flatten()[4], - proj_matrix.flatten()[5], - proj_matrix.flatten()[6], - proj_matrix.flatten()[7], - proj_matrix.flatten()[8], - proj_matrix.flatten()[9], - proj_matrix.flatten()[10], - proj_matrix.flatten()[11], - proj_matrix.flatten()[12], - proj_matrix.flatten()[13], - proj_matrix.flatten()[14], - proj_matrix.flatten()[15], - ) - proj_matrix = visii.transpose(proj_matrix) - - camera.get_camera().set_projection(proj_matrix) -else: - im_height = 512 - im_width = 512 - intrinsics = { "cx": 964.957, - "cy": 522.586, - "fx": 1390.53, - "fy": 1386.99, - } - - cam = pyrender.IntrinsicsCamera(intrinsics['fx'],intrinsics['fy'],intrinsics['cx'],intrinsics['cy']) - - proj_matrix = cam.get_projection_matrix(im_width, im_height) - # print(proj_matrix) - proj_matrix = visii.mat4( - proj_matrix.flatten()[0], - proj_matrix.flatten()[1], - proj_matrix.flatten()[2], - proj_matrix.flatten()[3], - proj_matrix.flatten()[4], - proj_matrix.flatten()[5], - proj_matrix.flatten()[6], - proj_matrix.flatten()[7], - proj_matrix.flatten()[8], - proj_matrix.flatten()[9], - proj_matrix.flatten()[10], - proj_matrix.flatten()[11], - proj_matrix.flatten()[12], - proj_matrix.flatten()[13], - proj_matrix.flatten()[14], - proj_matrix.flatten()[15], - ) - proj_matrix = visii.transpose(proj_matrix) - # print(proj_matrix) - camera.get_camera().set_projection(proj_matrix) - -if opt.bop: - # get the objects to load. - scene_objs = data_json[str(opt.bop_scene)] - data_json['objects'] = [] - for obj in scene_objs: - # print(obj) - to_add = {} - to_add['class'] = str(obj['obj_id']) - to_add['location'] = obj['cam_t_m2c'] - - # figuring out the rotation now - rot = obj['cam_R_m2c'] - m = pyrr.Matrix33( - [ - [rot[0],rot[1],rot[2]], - [rot[3],rot[4],rot[5]], - [rot[6],rot[7],rot[8]], - # [rot[0],rot[3],rot[6]], - # [rot[1],rot[4],rot[7]], - # [rot[2],rot[5],rot[8]], - ]) - quat = m.quaternion - to_add['quaternion_xyzw'] = [quat.x,quat.y,quat.z,quat.w] - data_json['objects'].append(to_add) - - -for i_obj, obj in enumerate(data_json['objects']): - name = obj['class'] - - # hack because notation is old - if name == '003': - name = '003_cracker_box_16k' - - print('loading',name) - if opt.bop: - entity_visii = create_obj( - name = obj['class'] + "_" + str(i_obj), - path_obj = f"{opt.objs_folder}/obj_{str(name).zfill(6)}.ply", - path_tex = f"{opt.objs_folder}/obj_{str(name).zfill(6)}.png", - scale = .001, - rot_base = None - ) - - else: - entity_visii = create_obj( - name = obj['class'] + "_" + str(i_obj), - path_obj = opt.objs_folder + "/"+name + "/google_16k/textured.obj", - path_tex = opt.objs_folder + "/"+name + "/google_16k/texture_map_flat.png", - scale = opt.scale, - rot_base = None - ) - - pos = obj['location'] - rot = obj['quaternion_xyzw'] - - entity_visii.get_transform().set_rotation( - visii.quat( - rot[3], - rot[0], - rot[1], - rot[2], - ) - ) - if opt.opencv: - entity_visii.get_transform().set_position( - visii.vec3( - pos[0]/100, - pos[1]/100, - pos[2]/100, - ) - ) - elif opt.bop: - entity_visii.get_transform().set_position( - visii.vec3( - pos[0]/1000, - pos[1]/1000, - pos[2]/1000, - ) - ) - else: - entity_visii.get_transform().set_position( - visii.vec3( - pos[0], - pos[1], - pos[2], - ) - ) - if opt.opencv or opt.bop: - entity_visii.get_transform().rotate_around(visii.vec3(0,0,0),visii.angleAxis(visii.pi(), visii.vec3(1,0,0))) - # print(entity_visii.get_transform().get_position()) - # entity_visii.get_transform().set_position(visii.vec3(-0.04,-.2,-0.6)) - # entity_visii.get_transform().set_rotation(visii.quat(0,0,1,0)) - # print(entity_visii.get_transform().get_position()) - - # camera.get_transform().look_at( - # entity_visii.get_transform().get_world_position(), # look at (world coordinate) - # visii.vec3(0,1,0), # up vector - # visii.vec3(0,0,0), # camera_origin - # ) - - # break - -# # # # # # # # # # # # # # # # # # # # # # # # # - - -visii.render_to_file( - width=im_width, - height=im_height, - samples_per_pixel=400, - file_path=opt.out -) -# raise() - -# # # # # # # # # # # # # # # # # # # # # # # # # - -# # create overlay -if opt.bop: - im_path = opt.im_path -if os.path.exists(opt.path_json.replace("json",'png')): - im_path = opt.path_json.replace("json",'png') - -elif os.path.exists(opt.path_json.replace("json",'jpg')): - im_path = opt.path_json.replace("json",'jpg') - -im = cv2.imread(im_path) - -if im is not None: - im_pred = cv2.imread(opt.out,cv2.IMREAD_UNCHANGED) - - alpha = im_pred[:,:,-1]/255.0 * 0.75 - alpha = np.stack((alpha,)*3, axis=-1) - im_pred = im_pred.astype(float) - - im = im.astype(float) - - - - - # if opt.gray: - # im_pred_gray = cv2.imread(opt.out) - # im_pred_gray = cv2.cvtColor(im_pred_gray, cv2.COLOR_BGR2GRAY) - # im_pred_gray = im_pred_gray.astype(float) - # im_pred_gray = np.stack((im_pred_gray,)*3, axis=-1) - # foreground = cv2.multiply(alpha,im_pred_gray) - # else: - - foreground = cv2.multiply(alpha,im_pred[:,:,:3]) - - - if opt.gray: - im_gray = cv2.imread(im_path) - im_gray = cv2.cvtColor(im_gray, cv2.COLOR_BGR2GRAY) - im_gray = im_gray.astype(float) - im_gray = np.stack((im_gray,)*3, axis=-1) - background = cv2.multiply(1-alpha,im_gray) - elif opt.overlay: - background = cv2.multiply(1-alpha,im[:,:,:3]) - else: - background = im[:,:,:3] - foreground = background - - outrgb = cv2.add(foreground,background) - - if opt.contour: - gray = cv2.cvtColor((alpha*255).astype(np.uint8), cv2.COLOR_RGB2GRAY) - cnts = cv2.findContours(gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) - cnts = cnts[0] if len(cnts) == 2 else cnts[1] - - for c in cnts: - cv2.drawContours(outrgb, [c], -1, (36, 255, 12), thickness=3,lineType=cv2.LINE_AA) - print(f'outputing {opt.out}') - cv2.imwrite(opt.out,outrgb) - -# let's clean up the GPU -visii.deinitialize() diff --git a/scripts/metrics/results/output.png b/scripts/metrics/results/output.png deleted file mode 100644 index 66f30667..00000000 Binary files a/scripts/metrics/results/output.png and /dev/null differ diff --git a/scripts/nvisii_data_gen/.gitignore b/scripts/nvisii_data_gen/.gitignore deleted file mode 100644 index 9b1960e7..00000000 --- a/scripts/nvisii_data_gen/.gitignore +++ /dev/null @@ -1 +0,0 @@ -output/ \ No newline at end of file diff --git a/scripts/nvisii_data_gen/debug_json_ros_node.py b/scripts/nvisii_data_gen/debug_json_ros_node.py deleted file mode 100755 index 14645201..00000000 --- a/scripts/nvisii_data_gen/debug_json_ros_node.py +++ /dev/null @@ -1,72 +0,0 @@ -#!/usr/bin/env python3 -""" -This is a simple ROS node that reads the various transform data from a set of -json files that were generated by `nvisii_data_gen` and publishes them as TF -transforms over ROS, so that they can be visualized using RViz (to debug -whether the transforms are correct). It was only used while debugging the json -output of `nvisii_data_gen`, so most users should not need this file. It is -only left in here as an example on how to use the transformations from the json -fields in ROS. -""" - -import json -import time - -import numpy as np -import rospy -import tf -from tf.transformations import quaternion_from_matrix, translation_from_matrix - -rospy.init_node("debug_json") - -tf_broadcaster = tf.TransformBroadcaster() - -while True: - # This assumes that there are (at least) 50 frames (00000.json, 00001.json, ...) in the directory. - for frame_number in range(50): - if rospy.is_shutdown(): - break - path = f"{str(frame_number).zfill(5)}.json" - with open(path) as json_file: - conf = json.load(json_file) - print(path) - - stamp = rospy.Time.now() - - camera_data = conf['camera_data'] - tf_broadcaster.sendTransform(translation=camera_data['location_worldframe'], - rotation=camera_data['quaternion_xyzw_worldframe'], - time=stamp, - parent='world', - child='camera', - ) - # transpose to transform between column-major and row-major order - camera_view_matrix = np.array(camera_data['camera_view_matrix']).transpose() - tf_broadcaster.sendTransform(translation=translation_from_matrix(camera_view_matrix), - rotation=quaternion_from_matrix(camera_view_matrix), - time=stamp, - parent='camera', - child='world_from_matrix', - ) - for object_data in conf['objects']: - tf_broadcaster.sendTransform(translation=object_data['location_worldframe'], - rotation=object_data['quaternion_xyzw_worldframe'], - time=stamp, - parent='world', - child=f"{object_data['name']}_world", - ) - tf_broadcaster.sendTransform(translation=object_data['location'], - rotation=object_data['quaternion_xyzw'], - time=stamp, - parent='camera', - child=f"{object_data['name']}_cam", - ) - local_to_world_matrix = np.array(object_data['local_to_world_matrix']).transpose() - tf_broadcaster.sendTransform(translation=translation_from_matrix(local_to_world_matrix), - rotation=quaternion_from_matrix(local_to_world_matrix), - time=stamp, - parent='world', - child=f"{object_data['name']}_cam_from_matrix", - ) - - time.sleep(1 / 30) diff --git a/scripts/nvisii_data_gen/doc/videos/cylinder_nosym.mp4 b/scripts/nvisii_data_gen/doc/videos/cylinder_nosym.mp4 deleted file mode 100644 index 221727c9..00000000 Binary files a/scripts/nvisii_data_gen/doc/videos/cylinder_nosym.mp4 and /dev/null differ diff --git a/scripts/nvisii_data_gen/doc/videos/cylinder_sym.mp4 b/scripts/nvisii_data_gen/doc/videos/cylinder_sym.mp4 deleted file mode 100644 index 3c25ddf2..00000000 Binary files a/scripts/nvisii_data_gen/doc/videos/cylinder_sym.mp4 and /dev/null differ diff --git a/scripts/nvisii_data_gen/doc/videos/hex_screw.mp4 b/scripts/nvisii_data_gen/doc/videos/hex_screw.mp4 deleted file mode 100644 index a30e5a01..00000000 Binary files a/scripts/nvisii_data_gen/doc/videos/hex_screw.mp4 and /dev/null differ diff --git a/scripts/nvisii_data_gen/dome_hdri_haven/download.md b/scripts/nvisii_data_gen/dome_hdri_haven/download.md deleted file mode 100644 index 9f6438b1..00000000 --- a/scripts/nvisii_data_gen/dome_hdri_haven/download.md +++ /dev/null @@ -1 +0,0 @@ -download hdri maps from https://polyhaven.com/hdris \ No newline at end of file diff --git a/scripts/nvisii_data_gen/download_google_scanned_objects.py b/scripts/nvisii_data_gen/download_google_scanned_objects.py deleted file mode 100644 index dcef0701..00000000 --- a/scripts/nvisii_data_gen/download_google_scanned_objects.py +++ /dev/null @@ -1,66 +0,0 @@ -import sys,json,requests -import simplejson as json -import subprocess - -collection_name = 'Google%20Scanned%20Objects' -owner_name = 'GoogleResearch' -# The server URL -base_url ='https://fuel.ignitionrobotics.org' -# Path to get the models in the collection -next_url = '/1.0/models?page=2&per_page=100&q=collections:{}'.format(collection_name) -next_url = '/1.0/models?per_page=100&page={}&q=collections:Google%20Scanned%20Objects' -# Path to download a single model in the collection -download_url = 'https://fuel.ignitionrobotics.org/1.0/{}/models/'.format(owner_name) -count = 0 -total_count = 0 -# Iterate over the pages -# while next_url: -downloaded = {} - -subprocess.call(['mkdir','google_scanned_models/']) - - - -for i in range(1,1100): - print(count) - # Get the contents of the current page. - try: - r = requests.get(base_url + next_url.format(str(i))) - # print(base_url + next_url) - # print(r.headers) - # break - # Convert to JSON - # print(r.text) - models = json.loads(r.text) - except: - continue - # print(models) - # break - # Get the next page's URL - # next_url = '' - # if 'Link' in r.headers: - # links = r.headers['Link'].split(',') - # for link in links: - # parts = link.split(';') - # if 'next' in parts[1]: - # next_url = parts[0].replace('<','').replace('>','') - # Get the total number of models to download - if total_count <= 0 and 'X-Total-Count' in r.headers: - total_count = int(r.headers['X-Total-Count']) - # Download each model - for model in models: - # count+=1 - model_name = model['name'] - if model_name not in downloaded: - downloaded[model_name] = 1 - count+=1 - print ('Downloading (%d/%d) %s' % (count, total_count, model_name)) - download = requests.get(download_url+model_name+'.zip', stream=True) - with open("google_scanned_models/"+model_name+'.zip', 'wb') as fd: - for chunk in download.iter_content(chunk_size=1024*1024): - fd.write(chunk) - - subprocess.call(['unzip',"google_scanned_models/"+model_name+'.zip','-d', "google_scanned_models/"+model_name]) - subprocess.call(['rm',"google_scanned_models/"+model_name+'.zip']) - - diff --git a/scripts/nvisii_data_gen/generate_dataset.py b/scripts/nvisii_data_gen/generate_dataset.py deleted file mode 100755 index c35b2f0b..00000000 --- a/scripts/nvisii_data_gen/generate_dataset.py +++ /dev/null @@ -1,17 +0,0 @@ -#!/usr/bin/env python3 -import random -import subprocess - - -# 20 000 images - -for i in range(0, 100): - to_call = [ - "python",'single_video_pybullet.py', - '--spp','10', - '--nb_frames', '200', - '--nb_objects',str(int(random.uniform(50,75))), - '--scale', '0.01', - '--outf',f"dataset/{str(i).zfill(3)}", - ] - subprocess.call(to_call) \ No newline at end of file diff --git a/scripts/nvisii_data_gen/models/Ketchup/google_16k/texture_map.png b/scripts/nvisii_data_gen/models/Ketchup/google_16k/texture_map.png deleted file mode 100644 index be328e31..00000000 Binary files a/scripts/nvisii_data_gen/models/Ketchup/google_16k/texture_map.png and /dev/null differ diff --git a/scripts/nvisii_data_gen/models/Ketchup/google_16k/texture_map_flat.png b/scripts/nvisii_data_gen/models/Ketchup/google_16k/texture_map_flat.png deleted file mode 100644 index d894919f..00000000 Binary files a/scripts/nvisii_data_gen/models/Ketchup/google_16k/texture_map_flat.png and /dev/null differ diff --git a/scripts/nvisii_data_gen/models/Ketchup/google_16k/textured.mtl b/scripts/nvisii_data_gen/models/Ketchup/google_16k/textured.mtl deleted file mode 100644 index 2eceea94..00000000 --- a/scripts/nvisii_data_gen/models/Ketchup/google_16k/textured.mtl +++ /dev/null @@ -1,2 +0,0 @@ -newmtl textured:_texture -map_Kd texture_map.png diff --git a/scripts/nvisii_data_gen/models/Ketchup/google_16k/textured.obj b/scripts/nvisii_data_gen/models/Ketchup/google_16k/textured.obj deleted file mode 100644 index 318e805f..00000000 --- a/scripts/nvisii_data_gen/models/Ketchup/google_16k/textured.obj +++ /dev/null @@ -1,78420 +0,0 @@ -# This file uses centimeters as units for non-parametric coordinates. - -mtllib textured.mtl -v 1.971900 -7.273875 -0.550586 -v 3.070511 -6.798608 -0.512939 -v 3.141501 -4.300522 -0.682230 -v 2.408555 0.368984 -1.351691 -v -0.857248 -7.276962 -1.192920 -v 2.370729 0.588502 -1.327560 -v -2.075388 -5.070049 -1.703439 -v -0.968231 -7.314677 -1.237399 -v -1.136982 -7.353720 -1.292777 -v -1.273122 -7.353231 -1.247192 -v -1.232277 -7.356763 -1.323511 -v -1.643497 -7.354622 -1.396137 -v 1.033534 2.801146 -1.819141 -v -1.825069 -7.343524 -1.472931 -v 2.888364 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@@ -1,274 +0,0 @@ -# UPDATES - -- 11/01/2022: Added the possility to load a single object with `--path_single_obj`. Just give the direct path to the object. -This function uses [nvisii.import_scene()](https://nvisii.com/nvisii.html#nvisii.import_scene). -If the obj file is complex, it will break the object into sub components, -so you might not have the projected cuboid, and you will get each pose of the different components with the cuboid. -Be careful using this one, make sure your understand the implications. -TODO: track the cuboid of the import_scene from nvisii. - - -# Description - -These sample scripts use [NViSII](https://github.com/owl-project/NVISII) to generate synthetic data for training the [DOPE](https://github.com/NVlabs/Deep_Object_Pose) object pose estimator. -The data can also be used for training other networks. -To generate the data, you will need NVIDIA drivers 450 or above. -We also highly recommend a GPU with RTX capabilities, as ray tracing can be costly on a non-RTX GPU. - -# Installation -``` -pip install -r requirements.txt -``` - -## HDRI maps -You will need to download HDRI maps to illuminate the scene. These can be found freely on [polyhaven](https://polyhaven.com/hdris). -For testing purposes, you can download a single one here: -``` -wget https://www.dropbox.com/s/na3vo8rca7feoiq/teatro_massimo_2k.hdr -mv teatro_massimo_2k.hdr dome_hdri_haven/ -``` - - -## Distractors - -The script, as is, expects some objects to be used as distractors. It is currently using the [Google scanned objects dataset](https://app.ignitionrobotics.org/GoogleResearch/fuel/collections/Google%20Scanned%20Objects), which can be download automatically with the following: - -``` -python download_google_scanned_objects.py -``` - -If you do *not* want to use the distractors, use the following argument when running the script: `--nb_distractors 0`. - -# Running the script - -If you downloaded everything from the previous steps, _e.g._, a single HDRI map and some distractors from Google scanned objects, you can run the following command: - -``` -python single_video_pybullet.py --nb_frames 1 --scale 0.01 -``` - -This will generate a single frame example in `output/output_example/`. The image should be similar to the following: - -![example output image](output/output_example/00000.png) - -The script has a few controls that are exposed at the beginning of the file. -Please consult `single_video_pybullet.py --help` for a complete list of parameters. -The major parameters are as follows: -- `--spp` for the number of sample per pixel, the higher it is the better quality the resulting image. -- `--nb_frames` number of images to export. -- `--outf` folder to store the data. -- `--nb_objects` the number of objects to load, this can reload the same object multiple times. -- `--nb_distractors` how many objects to add as distractors, this uses 3D models from Google scanned objects. - -# Adding your own 3D models - -You can simply use `--path_single_obj` to load your own 3d model. But there are some limitations for exporting the meta data if the obj is complex. Try to have it as a single obj, e.g., not multiple textures, similar to the provided one in the repo. - -## Modifying the code to load your object - -The script loads 3d models that are expressed in the format that was introduced by YCB dataset. -But it is fairly easy to change the script to load your own 3d model, [NViSII](https://github.com/owl-project/NVISII) allows you to load different format -as well, not just `obj` files. In `single_video_pybullet.py` find the following code: - -```python -for i_obj in range(int(opt.nb_objects)): - - toy_to_load = google_content_folder[random.randint(0,len(google_content_folder)-1)] - - obj_to_load = toy_to_load + "/google_16k/textured.obj" - texture_to_load = toy_to_load + "/google_16k/texture_map_flat.png" - name = "hope_" + toy_to_load.split('/')[-2] + f"_{i_obj}" - adding_mesh_object(name,obj_to_load,texture_to_load,scale=0.01) -``` -You can change the `obj_to_load` and `texture_to_load` to match your data format. If your file format is quite different, for example you are using a `.glb` file, then in the function `adding_mesh_object()` you will need to change the following: - -```python - if obj_to_load in mesh_loaded: - toy_mesh = mesh_loaded[obj_to_load] - else: - toy_mesh = visii.mesh.create_from_file(name,obj_to_load) - mesh_loaded[obj_to_load] = toy_mesh -``` -`visii.mesh.create_from_file` is the function that is used to load the data, this can load different file format. The rest of that function also loads the right texture as well as applying a material. The function also creates a collision mesh to make the object move. - -# Handling objects with symmetries - -If your object has any rotational symmetries, they have to be handled specially. - -## Cylinder object - -Here is a video that demonstrates what happens with a rotationally symmetric object if you do not specifiy the symmetries: - -https://user-images.githubusercontent.com/320188/159683931-8e87f778-8711-4e54-9ad8-536cf5862e01.mp4 - -As you can see on the left side of that video, the cuboid corners (visualized as small colored spheres) rotate with the object. Because the object has a rotational symmetry, this results in two frames that are pixel-wise identical to have different cuboid corners. Since the cuboid corners are what DOPE is trained on, this will cause the training to fail. - -The right side of the video shows the same object with a debug texture to demonstrate the "real" pose of the object. If your real object actually has a texture like this, it **does not** have any rotational symmetries in our sense, because two images where the cuboid corners are in different places will also not be pixel-wise identical due to the texture. Also, you only need to deal with rotational symmetries, not mirror symmetries for the same reason. - -To handle symmetries, you need to add a `model_info.json` file (see the `models_with_symmetries` folder for examples). Here is the `model_info.json` file for the cylinder object: - -```json -{ - "symmetries_discrete": [[1, 0, 0, 0, - 0, -1, 0, 0, - 0, 0, -1, 0, - 0, 0, 0, 1]], - "symmetries_continuous": [{"axis": [0, 0, 1], "offset": [0, 0, 0]}], - "align_axes": [{"object": [0, 1, 0], "camera": [0, 0, 1]}, {"object": [0, 0, 1], "camera": [0, 1, 0]}] -} -``` - -As you can see, we have specified one *discrete* symmetry (rotating the object by 180° around the x axis) and one *continuous* symmetry (rotating around the z axis). Also, we have to specify how to align the axes. With the `align_axes` specified as above, the algorithm will: - -1. Discretize `symmetries_continuous` into 64 discrete rotations. -2. Combine all discrete and continuous symmetries into one set of complete symmetry transformations. -3. Find the combined symmetry transformation such that when the object is rotated by that transformation, - - the y axis of the object (`"object": [0, 1, 0]`) has the best alignment (smallest angle) with the z axis of the camera (`"camera": [0, 0, 1]`) - - if there are multiple equally good such transformations, it will choose the obje where the z axis of the object (`"object": [0, 0, 1]`) has the best alignment with the y axis of the camera (`"camera": [0, 1, 0]`). - -See below for a documentation of the object and camera coordinate systems. - -With this `model_info.json` file, the result is the following: - -https://user-images.githubusercontent.com/320188/159683953-0fe390ab-1d26-4395-ae15-352d360f3cd9.mp4 - -## Hex screw object - -As another example, here's a rather unusual object that has a 60° rotational symmetry around the z axis. The `model_info.json` file looks like this: - -```json -{ - "symmetries_discrete": [[ 0.5, -0.866, 0, 0, - 0.866, 0.5, 0, 0, - 0, 0, 1, 0, - 0, 0, 0, 1], - [-0.5, -0.866, 0, 0, - 0.866, -0.5, 0, 0, - 0, 0, 1, 0, - 0, 0, 0, 1], - [-1, 0, 0, 0, - 0, -1, 0, 0, - 0, 0, 1, 0, - 0, 0, 0, 1], - [-0.5, 0.866, 0, 0, - -0.866, -0.5, 0, 0, - 0, 0, 1, 0, - 0, 0, 0, 1], - [ 0.5, 0.866, 0, 0, - -0.866, 0.5, 0, 0, - 0, 0, 1, 0, - 0, 0, 0, 1]], - "align_axes": [{"object": [0, 1, 0], "camera": [0, 0, 1]}] -} -``` - -The transformation matrices have been computed like this: - -```python -from math import sin, cos, pi -for yaw_degree in [60, 120, 180, 240, 300]: - yaw = yaw_degree / 180 * pi - print([cos(yaw), -sin(yaw), 0, 0, sin(yaw), cos(yaw), 0, 0, 0, 0, 1, 0, 0, 0, 0, 1]) -``` - -The resulting symmetry-corrected output looks like this: - -https://user-images.githubusercontent.com/320188/159683969-33a46225-94c0-43d8-b888-5e702ae3c31a.mp4 - -## Final remarks on symmetries - -This symmetry handling scheme allows the data generation script to compute consistent cuboid corners for most rotations of the object. Note however that there are object rotations where the cuboid corners become unstable and "flip over" to a different symmetry transformation. For the cylinder object, this is when the camera looks at the top or bottom of the cylinder (not shown in the video above). For the hex screw object, this is also when the camera looks at the top or bottom or when the rotation is close to the 60° boundary between two transformations (this can be seen in the video). Rotations within a few degrees of the "flipping over" rotation will not be handled well by the trained network. Unfortunately, this cannot be easily avoided. - -Further note that specifying symmetries also improves the recognition results for "almost-symmetrical" objects, where there are only minor non-symmetrical parts, such as most of the objects from the [T-LESS dataset](https://bop.felk.cvut.cz/datasets/). - - -# Extra - -This script is close to what was used to generate the data called `dome` in our NViSII [paper](https://arxiv.org/abs/2105.13962). - -If you use this data generation script in your research, please cite as follows: - -```latex -@misc{morrical2021nvisii, - title={NViSII: A Scriptable Tool for Photorealistic Image Generation}, - author={Nathan Morrical and Jonathan Tremblay and Yunzhi Lin and Stephen Tyree and Stan Birchfield and Valerio Pascucci and Ingo Wald}, - year={2021}, - eprint={2105.13962}, - archivePrefix={arXiv}, - primaryClass={cs.CV} -} -``` - -# Training - -You can either use the main training script ([`scripts/train.py`](https://github.com/NVlabs/Deep_Object_Pose/blob/master/scripts/train.py), -at least commit [9b509fa8](https://github.com/NVlabs/Deep_Object_Pose/commit/9b509fa842dbcb3c23858378b0d1cdfbbf29f235)) -or the updated training script ([`scripts/train2`](https://github.com/NVlabs/Deep_Object_Pose/tree/master/scripts/train2)) with this data. - -# Dataset format - -This section contains a description of the generated dataset format. - -## Coordinate systems and units - -All coordinate systems (world, model, camera) are right-handed. - -The world coordinate system is X forward, Y left and Z up. - -The model coordinate system is ultimately defined by the mesh, but if the model should appear "naturally upright" in its neutral orientation in the world frame, the Z axis should point up (when the object is standing "naturally upright"), the X axis should point from the "natural backside" of the model towards the front, the Y axis should point left and the origin should coincide with the center of the 3D bounding box of the object model. - -The camera coordinate system is the same as in OpenCV with X right, Y down and Z into the image away from the viewer. - -All pixel coordinates (U, V) have the origin at the top left corner of the image, with U going right and V going down. - -All length measurements are in meters. - -## Projected cuboid corners - -The indices of the 3D bounding cuboid are in the order shown in the sketch below (0..7), with the object being in its neutral orientation (X axis pointing forward, Y left, Z up). - -The order of the indices is the same as NVidia Deep learning Dataset Synthesizer (NDDS) and nvdu_viz from NVidia Dataset Utilities. - -```text - (m) 3 +-----------------+ 0 (b) - / /| - / / | -(m) 2 +-----------------+ 1| (b) - | | | - | ^ z | | - | | | | - | y <--x | | - (y) | | + 4 (g) - | | / - | |/ -(y) 6 +-----------------+ 5 (g) -``` -Debug markers for the cuboid corners can be rendered using the `--debug` option, with (b) = blue, (m) = magenta, (g) = green, (y) = yellow and the centroid being white. - -## JSON Fields - -Each generated image is accompanied by a JSON file. This JSON file contains the following fields: - -* `camera_data` - - `camera_look_at`: an alternative representation of the `camera_view_matrix` - - `camera_view_matrix`: 4×4 transformation matrix from the world to the camera coordinate system. This is the inverse of `location_worldframe` + `quaternion_xyzw_worldframe`. - - `height` and `width`: dimensions of the image in pixels - - `intrinsics`: the camera intrinsics - - `location_worldframe` and `quaternion_xyzw_worldframe`: see below - -* `objects`: one entry for each object instance, with: - - `bounding_box_minx_maxx_miny_maxy`: 2D bounding box of the object in the image: left, right, top, bottom (in pixels) - - `class`: class name - - `local_cuboid`: 3D coordinates of the vertices of the 3D bounding cuboid (in meters); currently always `null` - - `local_to_world_matrix`: 4×4 transformation matrix from the object to the world coordinate system - - `location` and `quaternion_xyzw`: position and orientation of the object in the *camera* coordinate system - - `location_worldframe` and `quaternion_xyzw_worldframe`: position and orientation of the object (or camera) in the *world* coordinate system - - `name`: unique string that identifies the object instance internally - - `projected_cuboid`: 2D coordinates of the projection of the the vertices of the 3D bounding cuboid (in pixels) plus the centroid. See section "Projected cuboid corners". - - `provenance`: always `nvisii` - - `segmentation_id`: segmentation instance ID; unique integer value that is used for this object instance in the `.seg.exr` file - - `px_count_all`: number of pixels in the object silhouette without occlusions - - `px_count_visib`: number of pixels in the visible part of the object silhouette, with occlusions - - `visibility`: The visible fraction of the object silhouette (= `px_count_visib`/`px_count_all`). - Note that the object may still not be fully visible when `visib_fract == 1.0` because it may extend beyond the borders of the image. - If run with `--no-visibility-fraction`, this field will always be set to `1`. diff --git a/scripts/nvisii_data_gen/requirements.txt b/scripts/nvisii_data_gen/requirements.txt deleted file mode 100644 index 2463fd07..00000000 --- a/scripts/nvisii_data_gen/requirements.txt +++ /dev/null @@ -1,9 +0,0 @@ -nvisii -numpy -opencv-python -pybullet -randomcolor -requests -simplejson -Pillow -pyquaternion diff --git a/scripts/nvisii_data_gen/single_video_pybullet.py b/scripts/nvisii_data_gen/single_video_pybullet.py deleted file mode 100755 index 59470112..00000000 --- a/scripts/nvisii_data_gen/single_video_pybullet.py +++ /dev/null @@ -1,630 +0,0 @@ -#!/usr/bin/env python3 - -import os -import cv2 -import glob -import math -import time -import nvisii as visii -import numpy as np -import pybullet as p -import random -import argparse -import colorsys -import subprocess -from math import acos -from math import sqrt -from math import pi - -from utils import * - -parser = argparse.ArgumentParser() - -parser.add_argument( - '--spp', - default=800, - type=int, - help = "number of sample per pixel, higher the more costly" -) -parser.add_argument( - '--width', - default=500, - type=int, - help = 'image output width' -) -parser.add_argument( - '--height', - default=500, - type=int, - help = 'image output height' -) -# TODO: change for an array -parser.add_argument( - '--objs_folder_distrators', - default='google_scanned_models/', - help = "object to load folder" -) -parser.add_argument( - '--objs_folder', - default='models/', - help = "object to load folder" -) -parser.add_argument( - '--path_single_obj', - default=None, - help='If you have a single obj file, path to the obj directly.' -) -parser.add_argument( - '--scale', - default=1, - type=float, - help='Specify the scale of the target object(s). If the obj mesh is in ' - 'meters -> scale=1; if it is in cm -> scale=0.01.' -) - -parser.add_argument( - '--skyboxes_folder', - default='dome_hdri_haven/', - help = "dome light hdr" -) -parser.add_argument( - '--nb_objects', - default=1, - type = int, - help = "how many objects" -) -parser.add_argument( - '--nb_distractors', - default=1, - help = "how many objects" -) -parser.add_argument( - '--nb_frames', - default=2000, - help = "how many frames to save" -) -parser.add_argument( - '--skip_frame', - default=100, - type=int, - help = "how many frames to skip" -) -parser.add_argument( - '--noise', - action='store_true', - default=False, - help = "if added the output of the ray tracing is not sent to optix's denoiser" -) -parser.add_argument( - '--outf', - default='output_example/', - help = "output filename inside output/" -) -parser.add_argument('--seed', - default = None, - help = 'seed for random selection' -) - -parser.add_argument( - '--interactive', - action='store_true', - default=False, - help = "make the renderer in its window" -) - -parser.add_argument( - '--motionblur', - action='store_true', - default=False, - help = "use motion blur to generate images" -) - -parser.add_argument( - '--box_size', - default=0.5, - type=float, - help = "make the object movement easier" -) - -parser.add_argument( - '--focal-length', - default=None, - type=float, - help = "focal length of the camera" -) - -parser.add_argument( - '--visibility-fraction', - action='store_true', - default=False, - help = "Compute the fraction of visible pixels and store it in the " - "`visibility` field of the json output. Without this argument, " - "`visibility` is always set to 1. Slows down rendering by about " - "50 %%, depending on the number of visible objects." -) - -parser.add_argument( - '--debug', - action='store_true', - default=False, - help="Render the cuboid corners as small spheres. Only for debugging purposes, do not use for training!" -) - -opt = parser.parse_args() - -# # # # # # # # # # # # # # # # # # # # # # # # # -if os.path.isdir(f'output/{opt.outf}'): - print(f'folder output/{opt.outf}/ exists') -else: - os.makedirs(f'output/{opt.outf}') - print(f'created folder output/{opt.outf}/') - -opt.outf = f'output/{opt.outf}' - -if not opt.seed is None: - random.seed(int(opt.seed)) - -# # # # # # # # # # # # # # # # # # # # # # # # # - - -# # # # # # # # # # # # # # # # # # # # # # # # # - - -visii.initialize(headless = not opt.interactive) - -if not opt.motionblur: - visii.sample_time_interval((1,1)) - -visii.sample_pixel_area( - x_sample_interval = (.5,.5), - y_sample_interval = (.5, .5)) - -# visii.set_max_bounce_depth(1) - -if not opt.noise: - visii.enable_denoiser() - -if opt.focal_length: - camera = visii.entity.create( - name = "camera", - transform = visii.transform.create("camera"), - camera = visii.camera.create_from_intrinsics( - name = "camera", - fx=opt.focal_length, - fy=opt.focal_length, - cx=(opt.width / 2), - cy=(opt.height / 2), - width=opt.width, - height=opt.height - ) - ) -else: - camera = visii.entity.create( - name = "camera", - transform = visii.transform.create("camera"), - camera = visii.camera.create_perspective_from_fov( - name = "camera", - field_of_view = 0.785398, - aspect = float(opt.width)/float(opt.height) - ) - ) - -# data structure -random_camera_movement = { - 'at':visii.vec3(1,0,0), - 'up':visii.vec3(0,0,1), - 'eye':visii.vec3(0,0,0) -} - -camera.get_transform().look_at( - at = random_camera_movement['at'], # look at (world coordinate) - up = random_camera_movement['up'], # up vector - eye = random_camera_movement['eye'], -) - -visii.set_camera_entity(camera) - -# # # # # # # # # # # # # # # # # # # # # # # # # - -# lets turn off the ambiant lights -# load a random skybox -skyboxes = glob.glob(f'{opt.skyboxes_folder}/*.hdr') -skybox_random_selection = skyboxes[random.randint(0,len(skyboxes)-1)] - -dome_tex = visii.texture.create_from_file('dome_tex',skybox_random_selection) -visii.set_dome_light_texture(dome_tex) -visii.set_dome_light_intensity(random.uniform(1.1,2)) -# visii.set_dome_light_intensity(1.15) -visii.set_dome_light_rotation( - # visii.angleAxis(visii.pi()/2,visii.vec3(1,0,0)) \ - # * visii.angleAxis(visii.pi()/2,visii.vec3(0,0,1))\ - visii.angleAxis(random.uniform(-visii.pi(),visii.pi()),visii.vec3(0,0,1))\ - # * visii.angleAxis(visii.pi()/2,visii.vec3(0,1,0))\ - # * visii.angleAxis(random.uniform(-visii.pi()/8,visii.pi()/8),visii.vec3(0,0,1))\ -) - -# # # # # # # # # # # # # # # # # # # # # # # # # -# Lets set some objects in the scene - -mesh_loaded = {} -objects_to_move = [] - -sample_space= [ - [-20,20], - [-20,20], - [-30,-2] - ] - -if opt.interactive: - physicsClient = p.connect(p.GUI) # non-graphical version -else: - physicsClient = p.connect(p.DIRECT) # non-graphical version - - -visii_pybullet = [] -names_to_export = [] - - -def adding_mesh_object( - name, - obj_to_load, - texture_to_load, - model_info_path=None, - scale=1, - debug=False - ): - global mesh_loaded, visii_pybullet, names_to_export - # obj_to_load = toy_to_load + "/meshes/model.obj" - # texture_to_load = toy_to_load + "/materials/textures/texture.png" - - if texture_to_load is None: - toys = load_obj_scene(obj_to_load) - if len(toys) > 1: - print("more than one model in the object, \ - materials might be wrong!") - toy_transform = visii.entity.get(toys[0]).get_transform() - toy_material = visii.entity.get(toys[0]).get_material() - toy_mesh = visii.entity.get(toys[0]).get_mesh() - - obj_export = visii.entity.create( - name = name, - transform = visii.transform.create( - name = name, - position = toy_transform.get_position(), - rotation = toy_transform.get_rotation(), - scale = toy_transform.get_scale(), - ), - material = toy_material, - mesh = visii.mesh.create_from_file(name,obj_to_load), - ) - - toy_transform = obj_export.get_transform() - obj_export.get_material().set_roughness(random.uniform(0.1, 0.5)) - - for toy in toys: - visii.entity.remove(toy) - - toys = [name] - else: - toys = [name] - - if obj_to_load in mesh_loaded: - toy_mesh = mesh_loaded[obj_to_load] - else: - toy_mesh = visii.mesh.create_from_file(name, obj_to_load) - mesh_loaded[obj_to_load] = toy_mesh - - toy = visii.entity.create( - name=name, - transform=visii.transform.create(name), - mesh=toy_mesh, - material=visii.material.create(name) - ) - - toy_rgb_tex = visii.texture.create_from_file(name, texture_to_load) - toy.get_material().set_base_color_texture(toy_rgb_tex) - toy.get_material().set_roughness(random.uniform(0.1, 0.5)) - - toy_transform = toy.get_transform() - - ########################### - - toy_transform.set_scale(visii.vec3(scale)) - toy_transform.set_position( - visii.vec3( - random.uniform(0.1, 2), - random.uniform(-1, 1), - random.uniform(-1, 1), - ) - ) - toy_transform.set_rotation( - visii.quat( - random.uniform(0, 1), - random.uniform(0, 1), - random.uniform(0, 1), - random.uniform(0, 1), - ) - ) - - # add symmetry_corrected transform - child_transform = visii.transform.create(f"{toy_transform.get_name()}_symmetry_corrected") - # print(toy_transform.get_name()) - # print(child_transform) - child_transform.set_parent(toy_transform) - - # store symmetry transforms for later use. - symmetry_transforms = get_symmetry_transformations(model_info_path) - - # create physics for object - id_pybullet = create_physics(name, mass=(np.random.rand() * 5)) - - if model_info_path is not None: - try: - with open(model_info_path) as json_file: - model_info = json.load(json_file) - except FileNotFoundError: - model_info = {} - else: - model_info = {} - - visii_pybullet.append( - { - 'visii_id': name, - 'bullet_id': id_pybullet, - 'base_rot': None, - 'model_info': model_info, - 'symmetry_transforms': symmetry_transforms - } - ) - gemPos, gemOrn = p.getBasePositionAndOrientation(id_pybullet) - force_rand = 10 - object_position = 0.01 - p.applyExternalForce( - id_pybullet, - -1, - [random.uniform(-force_rand, force_rand), - random.uniform(-force_rand, force_rand), - random.uniform(-force_rand, force_rand)], - [random.uniform(-object_position, object_position), - random.uniform(-object_position, object_position), - random.uniform(-object_position, object_position)], - flags=p.WORLD_FRAME - ) - - for entity_name in toys: - names_to_export.append(entity_name) - add_cuboid(entity_name, scale=scale, debug=debug) - -google_content_folder = glob.glob(opt.objs_folder_distrators + "*/") - -for i_obj in range(int(opt.nb_distractors)): - - toy_to_load = google_content_folder[random.randint(0,len(google_content_folder)-1)] - - obj_to_load = toy_to_load + "/meshes/model.obj" - texture_to_load = toy_to_load + "/materials/textures/texture.png" - name = "google_"+toy_to_load.split('/')[-2] + f"_{i_obj}" - - adding_mesh_object(name, obj_to_load, texture_to_load, debug=opt.debug) - -if opt.path_single_obj is not None: - for i_object in range(opt.nb_objects): - model_info_path = os.path.dirname(opt.path_single_obj) + '/model_info.json' - - if not os.path.exists(model_info_path): - model_info_path = None - - adding_mesh_object(f"single_obj_{i_object}", - opt.path_single_obj, - None, - model_info_path, - scale=opt.scale, - debug=opt.debug) -else: - google_content_folder = glob.glob(opt.objs_folder + "*/") - - for i_obj in range(int(opt.nb_objects)): - toy_to_load = google_content_folder[random.randint(0, len(google_content_folder) - 1)] - - obj_to_load = toy_to_load + "/google_16k/textured.obj" - texture_to_load = toy_to_load + "/google_16k/texture_map_flat.png" - model_info_path = toy_to_load + "/google_16k/model_info.json" - name = "hope_" + toy_to_load.split('/')[-2] + f"_{i_obj}" - - adding_mesh_object(name, obj_to_load, texture_to_load, model_info_path, scale=opt.scale, debug=opt.debug) - - # p.applyExternalTorque(id_pybullet,-1, - # [ random.uniform(-force_rand,force_rand), - # random.uniform(-force_rand,force_rand), - # random.uniform(-force_rand,force_rand)], - # [0,0,0], - # flags=p.WORLD_FRAME - # ) - - -camera_pybullet_col = p.createCollisionShape(p.GEOM_SPHERE,0.05) -camera_pybullet = p.createMultiBody( - baseCollisionShapeIndex = camera_pybullet_col, - basePosition = camera.get_transform().get_position(), - # baseOrientation= rot, -) - -# print('simulate') -# for i in range (10000): -# p.stepSimulation() -# # time.sleep(0.1) -# time.sleep(1./240.) - -box_position = opt.box_size - -plane1 = p.createCollisionShape(p.GEOM_PLANE,planeNormal = [0,0,-1]) -rot1 = visii.angleAxis(-visii.pi()/16,visii.vec3(0,1,0)) -plane1_body = p.createMultiBody( - baseCollisionShapeIndex = plane1, - basePosition = [0,0,-box_position], - baseOrientation= [rot1[3],rot1[0],rot1[1],rot1[2]], -) - -plane1 = p.createCollisionShape(p.GEOM_PLANE,planeNormal = [0,0,1]) -rot1 = visii.angleAxis(visii.pi()/16,visii.vec3(0,1,0)) -plane2_body = p.createMultiBody( - baseCollisionShapeIndex = plane1, - basePosition = [0,0,box_position], - baseOrientation= [rot1[3],rot1[0],rot1[1],rot1[2]], -) - - -plane1 = p.createCollisionShape(p.GEOM_PLANE,planeNormal = [0,1,0]) -rot1 = visii.angleAxis(visii.pi()/16,visii.vec3(1,0,0)) -plane3_body = p.createMultiBody( - baseCollisionShapeIndex = plane1, - basePosition = [0,+box_position,0], - baseOrientation= [rot1[3],rot1[0],rot1[1],rot1[2]], -) - -plane1 = p.createCollisionShape(p.GEOM_PLANE,planeNormal = [0,-1,0]) -rot1 = visii.angleAxis(-visii.pi()/16,visii.vec3(1,0,0)) -plane4_body = p.createMultiBody( - baseCollisionShapeIndex = plane1, - basePosition = [0,-box_position,0], - baseOrientation= [rot1[3],rot1[0],rot1[1],rot1[2]], -) - - -plane1 = p.createCollisionShape(p.GEOM_PLANE,planeNormal = [-1,0,0]) -plane5_body = p.createMultiBody( - baseCollisionShapeIndex = plane1, - basePosition = [2,0,0], - # baseOrientation= [rot1[3],rot1[0],rot1[1],rot1[2]], -) - -plane1 = p.createCollisionShape(p.GEOM_PLANE,planeNormal = [1,0,0]) -plane6_body = p.createMultiBody( - baseCollisionShapeIndex = plane1, - basePosition = [0.1,0,0], - # baseOrientation= [rot1[3],rot1[0],rot1[1],rot1[2]], -) - - -# # # # # # # # # # # # # # # # # # # # # # # # # - - -export_to_ndds_folder_settings_files( - opt.outf, - obj_names=names_to_export, - width=opt.width, - height=opt.height, - camera_name='camera', -) - -i_frame = -1 -i_render = 0 - -while True: - p.stepSimulation() - - i_frame += 1 - - if i_render >= int(opt.nb_frames) and not opt.interactive: - break - - # update the position from pybullet - for i_entry, entry in enumerate(visii_pybullet): - # print('update',entry) - visii.entity.get(entry['visii_id']).get_transform().set_position( - visii.entity.get(entry['visii_id']).get_transform().get_position(), - previous = True - ) - visii.entity.get(entry['visii_id']).get_transform().set_rotation( - visii.entity.get(entry['visii_id']).get_transform().get_rotation(), - previous = True - ) - - update_pose(entry) - if random.random() > 0.99: - force_rand = 10 - object_position = 0.01 - p.applyExternalForce( - entry['bullet_id'], - -1, - [ random.uniform(-force_rand,force_rand), - random.uniform(-force_rand,force_rand), - random.uniform(-force_rand,force_rand)], - [ random.uniform(-object_position,object_position), - random.uniform(-object_position,object_position), - random.uniform(-object_position,object_position)], - flags=p.WORLD_FRAME - ) - # break - if i_frame == 0: - continue - - if not opt.interactive: - if not i_frame % int(opt.skip_frame) == 0: - continue - - print(f"{str(i_render).zfill(5)}/{str(opt.nb_frames).zfill(5)}") - - visii.sample_pixel_area( - x_sample_interval = (0,1), - y_sample_interval = (0,1)) - - visii.render_to_file( - width=int(opt.width), - height=int(opt.height), - samples_per_pixel=int(opt.spp), - file_path=f"{opt.outf}/{str(i_render).zfill(5)}.png" - ) - visii.sample_pixel_area( - x_sample_interval = (.5,.5), - y_sample_interval = (.5,.5)) - - visii.sample_time_interval((1,1)) - - visii.render_data_to_file( - width=opt.width, - height=opt.height, - start_frame=0, - frame_count=1, - bounce=int(0), - options="entity_id", - file_path = f"{opt.outf}/{str(i_render).zfill(5)}.seg.exr" - ) - segmentation_mask = visii.render_data( - width=int(opt.width), - height=int(opt.height), - start_frame=0, - frame_count=1, - bounce=int(0), - options="entity_id", - ) - segmentation_mask = np.array(segmentation_mask).reshape((opt.height, opt.width, 4))[:, :, 0] - export_to_ndds_file( - f"{opt.outf}/{str(i_render).zfill(5)}.json", - obj_names = names_to_export, - width = opt.width, - height = opt.height, - camera_name = 'camera', - # cuboids = cuboids, - camera_struct = random_camera_movement, - segmentation_mask=segmentation_mask, - compute_visibility_fraction=opt.visibility_fraction, - ) - visii.render_data_to_file( - width=opt.width, - height=opt.height, - start_frame=0, - frame_count=1, - bounce=int(0), - options="depth", - file_path = f"{opt.outf}/{str(i_render).zfill(5)}.depth.exr" - ) - - i_render +=1 - -# subprocess.call(['ffmpeg', '-y',\ -# '-framerate', '30', "-hide_banner", "-loglevel", \ -# "panic",'-pattern_type', 'glob', '-i',\ -# f"{opt.outf}/*.png", f"{opt.outf}/video.mp4"]) - -visii.deinitialize() diff --git a/scripts/nvisii_data_gen/utils.py b/scripts/nvisii_data_gen/utils.py deleted file mode 100644 index f67c43bc..00000000 --- a/scripts/nvisii_data_gen/utils.py +++ /dev/null @@ -1,1850 +0,0 @@ -import colorsys -import glob -import math -import os -import random -import subprocess -import warnings - -import cv2 -import numpy as np -import nvisii as visii -import pybullet as p -import randomcolor -import simplejson as json -from PIL import Image -from pyquaternion import Quaternion - - -def add_random_obj(name = "name", - x_lim = [-1,1], - y_lim = [-1,1], - z_lim = [-1,1], - scale_lim = [0.01,1], - obj_id = None, - ): - - # obj = visii.entity.get(name) - # if obj is None: - obj= visii.entity.create( - name = name, - transform = visii.transform.create(name), - material = visii.material.create(name) - ) - if obj_id is None: - obj_id = random.randint(0,15) - - mesh = None - if obj_id == 0: - if add_random_obj.create_sphere is None: - add_random_obj.create_sphere = visii.mesh.create_sphere(name) - mesh = add_random_obj.create_sphere - if obj_id == 1: - if add_random_obj.create_torus_knot is None: - add_random_obj.create_torus_knot = visii.mesh.create_torus_knot(name, - random.randint(2,6), - random.randint(4,10)) - mesh = add_random_obj.create_torus_knot - if obj_id == 2: - if add_random_obj.create_teapotahedron is None: - add_random_obj.create_teapotahedron = visii.mesh.create_teapotahedron(name) - mesh = add_random_obj.create_teapotahedron - if obj_id == 3: - if add_random_obj.create_box is None: - add_random_obj.create_box = visii.mesh.create_box(name) - - mesh = add_random_obj.create_box - if obj_id == 4: - if add_random_obj.create_capped_cone is None: - add_random_obj.create_capped_cone = visii.mesh.create_capped_cone(name) - mesh = add_random_obj.create_capped_cone - if obj_id == 5: - if add_random_obj.create_capped_cylinder is None: - add_random_obj.create_capped_cylinder = visii.mesh.create_capped_cylinder(name) - mesh = add_random_obj.create_capped_cylinder - if obj_id == 6: - if add_random_obj.create_capsule is None: - add_random_obj.create_capsule = visii.mesh.create_capsule(name) - mesh = add_random_obj.create_capsule - if obj_id == 7: - if add_random_obj.create_cylinder is None: - add_random_obj.create_cylinder = visii.mesh.create_cylinder(name) - mesh = add_random_obj.create_cylinder - if obj_id == 8: - if add_random_obj.create_disk is None: - add_random_obj.create_disk = visii.mesh.create_disk(name) - mesh = add_random_obj.create_disk - if obj_id == 9: - if add_random_obj.create_dodecahedron is None: - add_random_obj.create_dodecahedron = visii.mesh.create_dodecahedron(name) - mesh = add_random_obj.create_dodecahedron - if obj_id == 10: - if add_random_obj.create_icosahedron is None: - add_random_obj.create_icosahedron = visii.mesh.create_icosahedron(name) - mesh = add_random_obj.create_icosahedron - if obj_id == 11: - if add_random_obj.create_icosphere is None: - add_random_obj.create_icosphere = visii.mesh.create_icosphere(name) - mesh = add_random_obj.create_icosphere - if obj_id == 12: - if add_random_obj.create_rounded_box is None: - add_random_obj.create_rounded_box = visii.mesh.create_rounded_box(name) - mesh = add_random_obj.create_rounded_box - if obj_id == 13: - if add_random_obj.create_spring is None: - add_random_obj.create_spring = visii.mesh.create_spring(name) - mesh = add_random_obj.create_spring - if obj_id == 14: - if add_random_obj.create_torus is None: - add_random_obj.create_torus = visii.mesh.create_torus(name) - mesh = add_random_obj.create_torus - if obj_id == 15: - if add_random_obj.create_tube is None: - add_random_obj.create_tube = visii.mesh.create_tube(name) - mesh = add_random_obj.create_tube - if obj_id == 16: - if add_random_obj.create_surface is None: - add_random_obj.create_surface = visii.mesh.create_plane(name) - mesh = add_random_obj.create_tube - - obj.set_mesh(mesh) - - obj.get_transform().set_position( - visii.vec3( - random.uniform(x_lim[0],x_lim[1]), - random.uniform(y_lim[0],y_lim[1]), - random.uniform(z_lim[0],z_lim[1]) - ) - ) - - obj.get_transform().set_rotation( - visii.quat(1.0 ,random.random(), random.random(), random.random()) - ) - - obj.get_transform().set_scale( - visii.vec3( - random.uniform(scale_lim[0],scale_lim[1]) - ) - ) - return obj -add_random_obj.rcolor = randomcolor.RandomColor() -add_random_obj.create_sphere = None -add_random_obj.create_torus_knot = None -add_random_obj.create_teapotahedron = None -add_random_obj.create_box = None -add_random_obj.create_capped_cone = None -add_random_obj.create_capped_cylinder = None -add_random_obj.create_capsule = None -add_random_obj.create_cylinder = None -add_random_obj.create_disk = None -add_random_obj.create_dodecahedron = None -add_random_obj.create_icosahedron = None -add_random_obj.create_icosphere = None -add_random_obj.create_rounded_box = None -add_random_obj.create_spring = None -add_random_obj.create_torus = None -add_random_obj.create_tube = None - - - -def random_material( - obj_id, - color = None, # list of 3 numbers between [0..1] - just_simple = False, - ): - - if random_material.textures is None: - textures = [] - - path = 'content/materials_omniverse/' - for folder in glob.glob(path + "/*/"): - for folder_in in glob.glob(folder+"/*/"): - name = folder_in.replace(folder,'').replace('/','').replace('\\', '') - # print (folder_in) - # print (name) - # print (folder_in + "/" + name + "_BaseColor.png") - if os.path.exists(folder_in + "/" + name + "_BaseColor.png"): - if os.path.exists(folder_in + "/" + name + "_Normal.png"): - normal = folder_in + "/" + name + "_Normal.png" - else: - normal = folder_in + "/" + name + "_N.png" - - textures.append({ - 'base':folder_in + "/" + name + "_BaseColor.png", - 'normal':folder_in + "/" + name + "_Normal.png", - 'orm':folder_in + "/" + name + "_ORM.png", - }) - - if 'glass' in folder.lower(): - #read the mtl - print("---") - for mdl in glob.glob(folder + "/*.mdl"): - - with open(mdl,'r') as f: - data = {'glass':1} - for line in f.readlines(): - if "transmission_color" in line and not 'transmission_color_texture' in line: - text = line.replace("transmission_color","")\ - .replace(" ","").replace('color','').replace(":","")\ - .replace("f",'') - text = eval(text)[0] - # print(text) - data['color'] = text - if 'roughness' in line and not 'roughness_texture_influence' in line\ - and not "frosting_roughness" in line and not 'roughness_texture' in line\ - and not "reflection_roughness_constant" in line: - # print(line) - text = line.replace("roughness","")\ - .replace(" ","").replace('color','').replace(":","")\ - .replace("f",'').replace(',','') - # print(text) - text = eval(text) - data['roughness'] = text - if 'frosting_roughness' in line and not 'roughness_texture_influence' in line\ - and not 'roughness_texture' in line\ - and not "reflection_roughness_constant" in line: - # print(line) - text = line.replace("frosting_roughness","")\ - .replace(" ","").replace('color','').replace(":","")\ - .replace("f",'').replace(',','') - # print(text) - text = eval(text) - - data['transmisive_roughness'] = text - if 'ior' in line and not 'stop' in line\ - and not 'roughness_texture' in line\ - and not "reflection_roughness_constant" in line: - text = line.replace("ior","")\ - .replace(" ","").replace('glass_ior','').replace(":","")\ - .replace("f",'').replace(',','').replace('glass','')\ - .replace("_",'') - text = eval(text) - - data['ior'] = text - if 'specular_level' in line and not 'stop' in line\ - and not 'roughness_texture' in line\ - and not "reflection_roughness_constant" in line: - # print(text) - text = line.replace("specular_level","")\ - .replace(" ","").replace('glass_ior','').replace(":","")\ - .replace("f",'').replace(',','').replace('glass','')\ - .replace("_",'') - text = eval(text) - # print(text) - data['specular'] = text - - if 'metallic_constant' in line and not 'stop' in line\ - and not 'roughness_texture' in line\ - and not "reflection_roughness_constant" in line: - text = line.replace("metallic_constant","")\ - .replace(" ","").replace('glass_ior','').replace(":","")\ - .replace("f",'').replace(',','').replace('glass','')\ - .replace("_",'') - text = eval(text) - data['metallic'] = text - - textures.append(data) - random_material.textures = textures - - obj_mat = visii.material.get(str(obj_id)) - - if color is None: - rgb = colorsys.hsv_to_rgb( - random.uniform(0,1), - random.uniform(0.1,1), - random.uniform(0.1,1) - ) - - obj_mat.set_base_color( - visii.vec3( - rgb[0], - rgb[1], - rgb[2], - ) - ) - else: - obj_mat.set_base_color( - visii.vec3( - color[0],color[1],color[2] - ) - ) - - - texture = random_material.textures[random.randint(0,len(random_material.textures)-1)] - - if random.uniform(0,1) < 0.25 or just_simple is True: - r = random.randint(0,2) - - if r == 0: - # Plastic / mat - obj_mat.set_metallic(0) # should 0 or 1 - obj_mat.set_transmission(0) # should 0 or 1 - obj_mat.set_roughness(random.uniform(0,1)) # default is 1 - if r == 1: - # metallic - obj_mat.set_metallic(random.uniform(0.9,1)) # should 0 or 1 - obj_mat.set_transmission(0) # should 0 or 1 - if r == 2: - # glass - obj_mat.set_metallic(0) # should 0 or 1 - obj_mat.set_transmission(random.uniform(0.9,1)) # should 0 or 1 - - if r > 0: # for metallic and glass - r2 = random.randint(0,1) - if r2 == 1: - obj_mat.set_roughness(random.uniform(0,.1)) # default is 1 - else: - obj_mat.set_roughness(random.uniform(0.9,1)) # default is 1 - - obj_mat.set_sheen(random.uniform(0,1)) # degault is 0 - obj_mat.set_clearcoat(random.uniform(0,1)) # degault is 0 - obj_mat.set_specular(random.uniform(0,1)) # degault is 0 - - r = random.randint(0,1) - if r == 0: - obj_mat.set_anisotropic(random.uniform(0,0.1)) # degault is 0 - else: - obj_mat.set_anisotropic(random.uniform(0.9,1)) # degault is 0 - - - elif 'glass' in texture: - obj_mat.set_transmission(1) - if 'metallic' in texture.keys(): - obj_mat.set_transmission(0) - obj_mat.set_metallic(texture['metallic']) - if 'specular' in texture.keys(): - obj_mat.set_specular(texture['specular']) - if 'transmisive_roughness' in texture.keys(): - obj_mat.set_transmission_roughness(texture['transmisive_roughness']) - if 'roughness' in texture.keys(): - obj_mat.set_roughness(texture['roughness']) - if 'color' in texture.keys(): - obj_mat.set_base_color(visii.vec3( - texture['color'][0], - texture['color'][1], - texture['color'][2], - )) - - else: - # print(texture['base']) - tex = visii.texture.get(texture['base']) - tex_r = visii.texture.get(texture['base']+"_r") - tex_m = visii.texture.get(texture['base']+"_m") - tex_n = visii.texture.get(texture['base']+"_n") - # tex_n = None - - if tex is None: - tex = visii.texture.create_from_image(texture['base'],texture['base']) - - # load metallic and roughness - im = np.array(Image.open(texture['orm'])) - - im_r = np.concatenate( - [ - im[:,:,1].reshape(im.shape[0],im.shape[1],1), - im[:,:,1].reshape(im.shape[0],im.shape[1],1), - im[:,:,1].reshape(im.shape[0],im.shape[1],1), - im[:,:,1].reshape(im.shape[0],im.shape[1],1) - ], - 2 - ) - # im_r = Image.fromarray(im_r) - im_r = (im_r/255.0) - - tex_r = visii.texture.create_from_data(texture['base']+'_r', - im_r.shape[0], - im_r.shape[1], - im_r.reshape(im_r.shape[0]*im_r.shape[1],4).astype(np.float32).flatten().tolist() - ) - - # im_r.save('tmp.png') - # tex_r = visii.texture.create_from_image(texture['base']+'_r',"tmp.png") - - im_m = np.concatenate( - [ - im[:,:,2].reshape(im.shape[0],im.shape[1],1), - im[:,:,2].reshape(im.shape[0],im.shape[1],1), - im[:,:,2].reshape(im.shape[0],im.shape[1],1) - ], - 2 - ) - im_m = Image.fromarray(im_m) - im_m.save('tmp.png') - - tex_m = visii.texture.create_from_image(texture['base']+'_m',"tmp.png") - - if os.path.exists(texture['normal']): - tex_n = visii.texture.create_from_image( - texture['base']+"_n", - texture['normal'], - linear=True - ) - else: - tex_n = None - obj_mat.set_base_color_texture(tex) - obj_mat.set_metallic_texture(tex_m) - obj_mat.set_roughness_texture(tex_r) - if not tex_n is None: - obj_mat.set_normal_map_texture(tex_n) - - -#### - - -random_material.textures = None - -######################################## -# ANIMATION RANDOMIZATION -######################################## - - - -def distance(v0,v1=[0,0,0]): - l2 = 0 - try: - for i in range(len(v0)): - l2 += (v0[i]-v1[i])**2 - except: - for i in range(3): - l2 += (v0[i]-v1[i])**2 - return math.sqrt(l2) - -def normalize(v): - l2 = distance(v) - return [v[0]/l2,v[1]/l2,v[2]/l2] - -def random_translation(obj_id, - x_lim = [-1,1], - y_lim = [-1,1], - z_lim = [-1,1], - speed_lim = [0.01,0.05], - sample_method = None, - motion_blur = False, - strenght = 1, - ): - # return - trans = visii.transform.get(str(obj_id)) - - # Position - if not str(obj_id) in random_translation.destinations.keys() : - if sample_method is None: - random_translation.destinations[str(obj_id)] = [ - random.uniform(x_lim[0],x_lim[1]), - random.uniform(y_lim[0],y_lim[1]), - random.uniform(z_lim[0],z_lim[1]) - ] - else: - random_translation.destinations[str(obj_id)] = sample_method() - random_translation.speeds[str(obj_id)] = random.uniform(speed_lim[0],speed_lim[1]) - else: - goal = random_translation.destinations[str(obj_id)] - pos = trans.get_position() - - if distance(goal,pos) < min(speed_lim)*2: - if sample_method is None: - random_translation.destinations[str(obj_id)] = [ - random.uniform(x_lim[0],x_lim[1]), - random.uniform(y_lim[0],y_lim[1]), - random.uniform(z_lim[0],z_lim[1]) - ] - else: - random_translation.destinations[str(obj_id)] = sample_method() - - random_translation.speeds[str(obj_id)] = random.uniform(speed_lim[0],speed_lim[1]) - goal = random_translation.destinations[str(obj_id)] - - dir_vec = normalize( - [ - goal[0] - pos[0], - goal[1] - pos[1], - goal[2] - pos[2] - ] - ) - - trans.add_position( - visii.vec3( - dir_vec[0] * random_translation.speeds[str(obj_id)], - dir_vec[1] * random_translation.speeds[str(obj_id)], - dir_vec[2] * random_translation.speeds[str(obj_id)] - ) - ) - if motion_blur: - trans.set_linear_velocity( - visii.vec3( - - dir_vec[0] * random_translation.speeds[str(obj_id)] * strenght, - dir_vec[1] * random_translation.speeds[str(obj_id)] * strenght, - dir_vec[2] * random_translation.speeds[str(obj_id)] * strenght - ) - ) - -random_translation.destinations = {} -random_translation.speeds = {} - - -def circle_path(camera_distance = 0.5, camera_height=0.3): - global opt - - if circle_path.locations is None: - circle_path.locations = [] - for i in range(0,210,10): - circle_path.locations.append([ - math.cos(i*0.01745) * camera_distance, - math.sin(i*0.01745) * camera_distance, - camera_height - ]) - circle_path.last = circle_path.locations[-1] - if len(circle_path.locations) == 0: - return circle_path.last - - pos = circle_path.locations[0] - circle_path.locations = circle_path.locations[1:] - return pos - -circle_path.locations = None - -def random_sample_hemispher( - max_radius = 2.5, - min_radius = 0.2 - ): - if min_radius > max_radius: - print('min bigger than max') - outside = True - while outside: - - x = random.uniform(-1, 1) - y = random.uniform(-1, 1) - z = random.uniform( 0, 0.95) - if (x**2 + y**2 + z**2) * max_radius < max_radius \ - and (x**2 + y**2 + z**2) * max_radius > min_radius: - outside = False - - return [x*max_radius,y*max_radius,z*max_radius] - - - -def random_rotation(obj_id, - speed_lim = [0.01,0.05], - motion_blur = False, - strenght = 1, - ): - # return - - trans = visii.transform.get(str(obj_id)) - - # Rotation - if not str(obj_id) in random_rotation.destinations.keys() : - random_rotation.destinations[str(obj_id)] = Quaternion.random() - random_rotation.speeds[str(obj_id)] = random.uniform(speed_lim[0],speed_lim[1]) - - else: - goal = random_rotation.destinations[str(obj_id)] - rot = trans.get_rotation() - rot = Quaternion(w=rot.w,x=rot.x,y=rot.y,z=rot.z) - if Quaternion.sym_distance(goal, rot) < 0.1: - random_rotation.destinations[str(obj_id)] = Quaternion.random() - random_rotation.speeds[str(obj_id)] = random.uniform(speed_lim[0],speed_lim[1]) - goal = random_rotation.destinations[str(obj_id)] - dir_vec = Quaternion.slerp(rot,goal,random_rotation.speeds[str(obj_id)]) - q = visii.quat() - q.w,q.x,q.y,q.z = dir_vec.w,dir_vec.x,dir_vec.y,dir_vec.z - trans.set_rotation(q) - # if motion_blur: - # dir_vec = Quaternion.slerp(rot,goal,random_rotation.speeds[str(obj_id)] * strenght) - - # q.w,q.x,q.y,q.z = dir_vec.w,dir_vec.x,dir_vec.y,dir_vec.z - # trans.set_angular_velocity(q) - -random_rotation.destinations = {} -random_rotation.speeds = {} - -def random_scale(obj_id, - scale_lim = [0.01,0.2], - speed_lim = [0.01,0.02], - x_lim = None, - y_lim = None, - z_lim = None, - motion_blur = False, - strenght = 1, - ): - # return - # This assumes only one dimensions gets scale - - trans = visii.transform.get(str(obj_id)) - - limit = min(speed_lim)*2 - - if not x_lim is None: - limit = [min(y_lim)*2,min(x_lim)*2,min(z_lim)*2] - - if not str(obj_id) in random_scale.destinations.keys() : - if not x_lim is None: - random_scale.destinations[str(obj_id)] = [ - random.uniform(x_lim[0],x_lim[1]), - random.uniform(y_lim[0],y_lim[1]), - random.uniform(z_lim[0],z_lim[1]) - ] - else: - random_scale.destinations[str(obj_id)] = random.uniform(scale_lim[0],scale_lim[1]) - - random_scale.speeds[str(obj_id)] = random.uniform(speed_lim[0],speed_lim[1]) - - else: - goal = random_scale.destinations[str(obj_id)] - - if x_lim is None: - current = trans.get_scale()[0] - else: - current = trans.get_scale() - - if x_lim is None: - - if abs(goal-current) < limit: - random_scale.destinations[str(obj_id)] = random.uniform(scale_lim[0],scale_lim[1]) - random_scale.speeds[str(obj_id)] = random.uniform(speed_lim[0],speed_lim[1]) - goal = random_scale.destinations[str(obj_id)] - if goal>current: - q = random_scale.speeds[str(obj_id)] - else: - q = -random_scale.speeds[str(obj_id)] - trans.set_scale(current + q) - - else: - limits = [x_lim,y_lim,z_lim] - q = [0,0,0] - for i in range(3): - if abs(goal[i]-current[i]) < limit[i]: - random_scale.destinations[str(obj_id)][i] = random.uniform(limits[i][0],limits[i][1]) - random_scale.speeds[str(obj_id)] = random.uniform(speed_lim[0],speed_lim[1]) - goal = random_scale.destinations[str(obj_id)] - if goal[i]>current[i]: - q[i] = random_scale.speeds[str(obj_id)] - else: - q[i] = -random_scale.speeds[str(obj_id)] - trans.set_scale(current + visii.vec3(q[0],q[1],q[2])) - # if motion_blur: - # trans.set_scalar_velocity(visii.vec3(q[0]*strenght,q[1]*strenght,q[2]*strenght)) - -random_scale.destinations = {} -random_scale.speeds = {} - - -def random_color(obj_id, - speed_lim = [0.01,0.1] - ): - - # color - if not str(obj_id) in random_color.destinations.keys() : - c = eval(str(random_color.rcolor.generate(luminosity='bright',format_='rgb')[0])[3:]) - random_color.destinations[str(obj_id)] = visii.vec3(c[0]/255.0, c[1]/255.0, c[2]/255.0) - random_color.speeds[str(obj_id)] = random.uniform(speed_lim[0],speed_lim[1]) - - else: - goal = random_color.destinations[str(obj_id)] - current = visii.material.get(str(obj_id)).get_base_color() - - if distance(goal,current) < 0.1: - random_color.speeds[str(obj_id)] = random.uniform(speed_lim[0],speed_lim[1]) - c = eval(str(random_color.rcolor.generate(luminosity='bright',format_='rgb')[0])[3:]) - random_color.destinations[str(obj_id)] = visii.vec3(c[0]/255.0, c[1]/255.0, c[2]/255.0) - goal = random_color.destinations[str(obj_id)] - - target = visii.mix(current,goal, - visii.vec3( random_color.speeds[str(obj_id)], - random_color.speeds[str(obj_id)], - random_color.speeds[str(obj_id)] - ) - ) - - visii.material.get(str(obj_id)).set_base_color(target) - -random_color.destinations = {} -random_color.speeds = {} -random_color.rcolor = randomcolor.RandomColor() - -######## RANDOM LIGHTS ############ - -def random_light(obj_id, - intensity_lim = [5000,10000], - color = None, - temperature_lim = [10,10000], - exposure_lim = [0,0], - ): - - obj = visii.entity.get(str(obj_id)) - obj.set_light(visii.light.create(str(obj_id))) - - obj.get_light().set_intensity(random.uniform(intensity_lim[0],intensity_lim[1])) - obj.get_light().set_exposure(random.uniform(exposure_lim[0],exposure_lim[1])) - # obj.get_light().set_temperature(np.random.randint(100,9000)) - - - if not color is None: - obj.get_material().set_base_color(color[0],color[1],color[2]) - # c = eval(str(rcolor.generate(luminosity='bright',format_='rgb')[0])[3:]) - # obj.get_light().set_color( - # c[0]/255.0, - # c[1]/255.0, - # c[2]/255.0) - - else: - obj.get_light().set_temperature(random.uniform(temperature_lim[0],temperature_lim[1])) - -def random_intensity(obj_id, - intensity_lim = [5000,10000], - speed_lim = [0.1,1] - ): - - obj = visii.entity.get(str(obj_id)).get_light() - - if not str(obj_id) in random_intensity.destinations.keys() : - random_intensity.destinations[str(obj_id)] = random.uniform(intensity_lim[0],intensity_lim[1]) - random_intensity.speeds[str(obj_id)] = random.uniform(speed_lim[0],speed_lim[1]) - random_intensity.current[str(obj_id)] = random_intensity.destinations[str(obj_id)] - obj.set_intensity(random_intensity.current[str(obj_id)]) - else: - goal = random_intensity.destinations[str(obj_id)] - current = random_intensity.current[str(obj_id)] - - if abs(goal-current) < min(speed_lim)*2: - random_intensity.destinations[str(obj_id)] = random.uniform(intensity_lim[0],intensity_lim[1]) - random_intensity.speeds[str(obj_id)] = random.uniform(speed_lim[0],speed_lim[1]) - goal = random_intensity.destinations[str(obj_id)] - if goal>current: - q = random_intensity.speeds[str(obj_id)] - else: - q = -random_intensity.speeds[str(obj_id)] - obj.set_intensity(random_intensity.current[str(obj_id)] + q) - random_intensity.current[str(obj_id)] = random_intensity.current[str(obj_id)] + q - -random_intensity.destinations = {} -random_intensity.current = {} -random_intensity.speeds = {} - - -def random_texture_material(entity): - # select a random texture - textures = random_texture_material.textures - if len(textures) == 0: - "load the textures" - path = 'content/materials_omniverse/' - for folder in glob.glob(path + "/*/"): - for folder_in in glob.glob(folder+"/*/"): - name = folder_in.replace(folder,'').replace('/','').replace('\\', '') - # print (folder_in) - # print (name) - # print (folder_in + "/" + name + "_BaseColor.png") - if os.path.exists(folder_in + "/" + name + "_BaseColor.png"): - if os.path.exists(folder_in + "/" + name + "_Normal.png"): - normal = folder_in + "/" + name + "_Normal.png" - else: - normal = folder_in + "/" + name + "_N.png" - - textures.append({ - 'base':folder_in + "/" + name + "_BaseColor.png", - 'normal':folder_in + "/" + name + "_Normal.png", - 'orm':folder_in + "/" + name + "_ORM.png", - }) - - if 'glass' in folder.lower(): - #read the mtl - # print("---") - for mdl in glob.glob(folder + "/*.mdl"): - - with open(mdl,'r') as f: - data = {'glass':1} - for line in f.readlines(): - if "transmission_color" in line and not 'transmission_color_texture' in line: - text = line.replace("transmission_color","")\ - .replace(" ","").replace('color','').replace(":","")\ - .replace("f",'') - text = eval(text)[0] - # print(text) - data['color'] = text - if 'roughness' in line and not 'roughness_texture_influence' in line\ - and not "frosting_roughness" in line and not 'roughness_texture' in line\ - and not "reflection_roughness_constant" in line: - # print(line) - text = line.replace("roughness","")\ - .replace(" ","").replace('color','').replace(":","")\ - .replace("f",'').replace(',','') - # print(text) - text = eval(text) - data['roughness'] = text - if 'frosting_roughness' in line and not 'roughness_texture_influence' in line\ - and not 'roughness_texture' in line\ - and not "reflection_roughness_constant" in line: - # print(line) - text = line.replace("frosting_roughness","")\ - .replace(" ","").replace('color','').replace(":","")\ - .replace("f",'').replace(',','') - # print(text) - text = eval(text) - - data['transmisive_roughness'] = text - if 'ior' in line and not 'stop' in line\ - and not 'roughness_texture' in line\ - and not "reflection_roughness_constant" in line: - text = line.replace("ior","")\ - .replace(" ","").replace('glass_ior','').replace(":","")\ - .replace("f",'').replace(',','').replace('glass','')\ - .replace("_",'') - text = eval(text) - - data['ior'] = text - if 'specular_level' in line and not 'stop' in line\ - and not 'roughness_texture' in line\ - and not "reflection_roughness_constant" in line: - # print(text) - text = line.replace("specular_level","")\ - .replace(" ","").replace('glass_ior','').replace(":","")\ - .replace("f",'').replace(',','').replace('glass','')\ - .replace("_",'') - text = eval(text) - # print(text) - data['specular'] = text - - if 'metallic_constant' in line and not 'stop' in line\ - and not 'roughness_texture' in line\ - and not "reflection_roughness_constant" in line: - # print(text) - text = line.replace("metallic_constant","")\ - .replace(" ","").replace('glass_ior','').replace(":","")\ - .replace("f",'').replace(',','').replace('glass','')\ - .replace("_",'') - text = eval(text) - - data['metallic'] = text - - textures.append(data) - - - - texture = random_texture_material.textures[random.randint(0,len(textures)-1)] - if random.uniform(0,1) < 0.25: - random_material(entity) - - elif 'glass' in texture: - - mat = visii.material.get(entity) - - mat.set_transmission(1) - if 'metallic' in texture.keys(): - mat.set_transmission(0) - mat.set_metallic(texture['metallic']) - if 'specular' in texture.keys(): - mat.set_specular(texture['specular']) - if 'transmisive_roughness' in texture.keys(): - mat.set_transmission_roughness(texture['transmisive_roughness']) - if 'roughness' in texture.keys(): - mat.set_roughness(texture['roughness']) - if 'color' in texture.keys(): - mat.set_base_color(visii.vec3( - texture['color'][0], - texture['color'][1], - texture['color'][2], - )) - - else: - print(texture['base']) - tex = visii.texture.get(texture['base']) - tex_r = visii.texture.get(texture['base']+"_r") - tex_m = visii.texture.get(texture['base']+"_m") - - if tex is None: - tex = visii.texture.create_from_image(texture['base'],texture['base']) - - # load metallic and roughness - im = np.array(Image.open(texture['orm'])) - - im_r = np.concatenate( - [ - im[:,:,1].reshape(im.shape[0],im.shape[1],1), - im[:,:,1].reshape(im.shape[0],im.shape[1],1), - im[:,:,1].reshape(im.shape[0],im.shape[1],1), - im[:,:,1].reshape(im.shape[0],im.shape[1],1) - ], - 2 - ) - # im_r = Image.fromarray(im_r) - im_r = (im_r/255.0) - - tex_r = visii.texture.create_from_data(texture['base']+'_r', - im_r.shape[0], - im_r.shape[1], - im_r.reshape(im_r.shape[0]*im_r.shape[1],4).astype(np.float32).flatten().tolist() - ) - - # im_r.save('tmp.png') - # tex_r = visii.texture.create_from_image(texture['base']+'_r',"tmp.png") - - im_m = np.concatenate( - [ - im[:,:,2].reshape(im.shape[0],im.shape[1],1), - im[:,:,2].reshape(im.shape[0],im.shape[1],1), - im[:,:,2].reshape(im.shape[0],im.shape[1],1) - ], - 2 - ) - im_m = Image.fromarray(im_m) - im_m.save('tmp.png') - tex_m = visii.texture.create_from_image(texture['base']+'_m',"tmp.png") - if os.path.exists(texture['normal']): - tex_n = visii.texture.create_from_image( - texture['normal'], - texture['normal'], - linear=True - ) - else: - tex_n = None - visii.material.get(entity).set_base_color_texture(tex) - visii.material.get(entity).set_metallic_texture(tex_m) - visii.material.get(entity).set_roughness_texture(tex_r) - if not tex_n is None: - visii.material.get(entity).set_normal_map_texture(tex_n) - - -random_texture_material.textures = [] - - - -######## NDDS ########## - -def add_cuboid(name, scale=1, debug=False): - """ - Computes the 3D bounding cuboid for object `name` and the centroid, adds the - corresponding child transforms to the object (which will later be used in - `get_cuboid_image_space`), and optionally adds small spheres as debug markers - to the scene. - - The order of the indexes is the same as NVidia Deep learning Dataset Synthesizer (NDDS), - nvdu_viz from NVidia Dataset Utilities and DOPE. - - The indexes of the 3D bounding cuboid are in the order shown in the sketch - below (0..7), with the object being in its neutral orientation (X axis pointing - forward, Y left, Z up). - - (m) 3 +-----------------+ 0 (b) - / /| - / / | - (m) 2 +-----------------+ 1| (b) - | | | - | ^ z | | - | | | | - | y <--x | | - (y) | | + 4 (g) - | | / - | |/ - (y) 6 +-----------------+ 5 (g) - - Debug markers for the cuboid corners can be rendered using the `--debug` option, - with (b) = blue, (m) = magenta, (g) = green, (y) = yellow and the centroid being - white. - """ - obj = visii.entity.get(name) - - min_obj = obj.get_mesh().get_min_aabb_corner() - max_obj = obj.get_mesh().get_max_aabb_corner() - centroid_obj = obj.get_mesh().get_aabb_center() - - cuboid = [ - visii.vec3(max_obj[0], min_obj[1], max_obj[2]), - visii.vec3(min_obj[0], min_obj[1], max_obj[2]), - visii.vec3(min_obj[0], max_obj[1], max_obj[2]), - visii.vec3(max_obj[0], max_obj[1], max_obj[2]), - visii.vec3(max_obj[0], min_obj[1], min_obj[2]), - visii.vec3(min_obj[0], min_obj[1], min_obj[2]), - visii.vec3(min_obj[0], max_obj[1], min_obj[2]), - visii.vec3(max_obj[0], max_obj[1], min_obj[2]), - visii.vec3(centroid_obj[0], centroid_obj[1], centroid_obj[2]), - ] - - # same colors as nvdu_viz - cuboid_colors = [ - visii.vec3(0.0, 0.0, 1.0), # blue - visii.vec3(0.0, 0.0, 1.0), # blue - visii.vec3(1.0, 0.0, 1.0), # magenta - visii.vec3(1.0, 0.0, 1.0), # magenta - visii.vec3(0.0, 1.0, 0.0), # green - visii.vec3(0.0, 1.0, 0.0), # green - visii.vec3(1.0, 1.0, 0.0), # yellow - visii.vec3(1.0, 1.0, 0.0), # yellow - visii.vec3(1.0, 1.0, 1.0), # centroid: white - ] - - for i_p, p in enumerate(cuboid): - child_transform = visii.transform.create(f"{name}_cuboid_{i_p}") - child_transform.set_position(p) - - # Attach the cuboids not directly to the object's transform, but to the symmetry_corrected - # transform instead. That way, when the symmetry_corrected transform gets updated to the - # "canonical" orientation (i.e., the symmetry transformation with the best alignment), - # the cuboid corners get rotated along with it. - child_transform.set_parent(visii.transform.get(f"{name}_symmetry_corrected")) - - if debug: - visii.entity.create( - name = f"{name}_cuboid_{i_p}", - mesh = visii.mesh.create_sphere(f"{name}_cuboid_{i_p}", radius=(0.006 / scale)), - transform = child_transform, - material = visii.material.create(f"{name}_cuboid_{i_p}", base_color=cuboid_colors[i_p]) - ) - - for i_v, v in enumerate(cuboid): - cuboid[i_v]=[v[0], v[1], v[2]] - - return cuboid - -def get_cuboid_image_space(obj_id, camera_name = 'my_camera'): - # return cubdoid + centroid projected to the image, values [0..1] - - cam_matrix = visii.entity.get(camera_name).get_transform().get_world_to_local_matrix() - cam_proj_matrix = visii.entity.get(camera_name).get_camera().get_projection() - - points = [] - points_cam = [] - for i_t in range(9): - trans = visii.transform.get(f"{obj_id}_cuboid_{i_t}") - if trans is None: - return None,None - mat_trans = trans.get_local_to_world_matrix() - pos_m = visii.vec4( - mat_trans[3][0], - mat_trans[3][1], - mat_trans[3][2], - 1) - - p_cam = cam_matrix * pos_m - - # The nvisii pixel coordinates have the origin in the middle of the image, - # with X going right, Y going up and range [-1..1]. - # We want to transform it so that the origin is at the top left of the image, - # with X going right, Y going down and range [0..1]. - p_image = cam_proj_matrix * (cam_matrix * pos_m) - p_image = visii.vec2(p_image) / p_image.w # normalize - p_image = p_image * visii.vec2(1, -1) # flip Y - p_image = (p_image + visii.vec2(1, 1)) * 0.5 # shift origin to top left corner and divide by 2 - - points.append([p_image[0],p_image[1]]) - points_cam.append([p_cam[0],p_cam[1],p_cam[2]]) - return points, points_cam - - -def object_to_class_name(obj_name): - try: - # strip off part before first '_' ('hope_' or 'google_') and after last '_' ('_1234') - return obj_name.split('_', 1)[1].rsplit('_', 1)[0] - except: - return obj_name - - -def export_to_ndds_folder_settings_files( - output_folder=".", - obj_names=None, - height=500, - width=500, - camera_name='my_camera', -): - if obj_names is None: - obj_names = [] - - # write _camera_settings.json - cam_intrinsics = visii.entity.get(camera_name).get_camera().get_intrinsic_matrix(width, height) - dict_out = { - "camera_settings": [ - { - "name": camera_name, - "intrinsic_settings": - { - "resX": width, - "resY": height, - "fx": cam_intrinsics[0][0], - "fy": cam_intrinsics[1][1], - "cx": cam_intrinsics[2][0], - "cy": cam_intrinsics[2][1], - "s": cam_intrinsics[1][0] - }, - "captured_image_size": - { - "width": width, - "height": height - } - } - ] - } - camera_settings_filename = output_folder + "/_camera_settings.json" - with open(camera_settings_filename, 'w') as fp: - json.dump(dict_out, fp, indent=4) - - # write _object_settings.json - exported_object_classes = [] - exported_objects = {} - for obj_name in obj_names: - class_name = object_to_class_name(obj_name) - if class_name in exported_object_classes: - continue - exported_object_classes.append(class_name) - mesh = visii.mesh.get(obj_name) - if mesh is None: - continue - bbox_size = mesh.get_max_aabb_corner() - mesh.get_min_aabb_corner() - exported_objects[class_name] = {"class": class_name, - "cuboid_dimensions": [bbox_size[0], bbox_size[1], bbox_size[2]]} - exported_object_classes.sort() - dict_out = { - "exported_object_classes": exported_object_classes, - "exported_objects": [exported_objects[class_name] for class_name in exported_object_classes] - } - object_settings_filename = output_folder + "/_object_settings.json" - with open(object_settings_filename, 'w') as fp: - json.dump(dict_out, fp, indent=4) - - -def export_to_ndds_file( - filename = "tmp.json", #this has to include path as well - obj_names = [], # this is a list of ids to load and export - height = 500, - width = 500, - camera_name = 'my_camera', - cuboids = None, - camera_struct = None, - segmentation_mask = None, - compute_visibility_fraction = True, - ): - - if segmentation_mask is None: - segmentation_mask = visii.render_data( - width=int(width), - height=int(height), - start_frame=0, - frame_count=1, - bounce=int(0), - options="entity_id", - ) - segmentation_mask = np.array(segmentation_mask).reshape((height, width, 4))[:, :, 0] - - visible_object_ids = np.unique(segmentation_mask.astype(int)) - - # assume we only use the view camera - cam_matrix = visii.entity.get(camera_name).get_transform().get_world_to_local_matrix() - - # rotate camera by 180° around x - # from: X right, Y up and Z out of the image towards the viewer - # to: X right, Y down and Z into the image away from the viewer (= OpenCV / NDDS / ROS "optical" frame) - cam_matrix = visii.mat4(1, 0, 0, 0, - 0, -1, 0, 0, - 0, 0, -1, 0, - 0, 0, 0, 1) * cam_matrix - - cam_matrix_export = [] - for row in cam_matrix: - cam_matrix_export.append([row[0],row[1],row[2],row[3]]) - - inverse_cam_matrix = visii.inverse(cam_matrix) - cam_world_location = visii.vec3(inverse_cam_matrix[3][0], inverse_cam_matrix[3][1], inverse_cam_matrix[3][2]) - cam_world_quaternion = visii.quat(inverse_cam_matrix) - - cam_intrinsics = visii.entity.get(camera_name).get_camera().get_intrinsic_matrix(width, height) - - if camera_struct is None: - camera_struct = { - 'at': [0,0,0,], - 'eye': [0,0,0,], - 'up': [0,0,0,] - } - - dict_out = { - "camera_data" : { - "width" : width, - 'height' : height, - 'camera_look_at': - { - 'at': [ - camera_struct['at'][0], - camera_struct['at'][1], - camera_struct['at'][2], - ], - 'eye': [ - camera_struct['eye'][0], - camera_struct['eye'][1], - camera_struct['eye'][2], - ], - 'up': [ - camera_struct['up'][0], - camera_struct['up'][1], - camera_struct['up'][2], - ] - }, - 'camera_view_matrix':cam_matrix_export, - 'location_worldframe': - [ - cam_world_location[0], - cam_world_location[1], - cam_world_location[2], - ], - 'quaternion_xyzw_worldframe':[ - cam_world_quaternion[0], - cam_world_quaternion[1], - cam_world_quaternion[2], - cam_world_quaternion[3], - ], - 'intrinsics':{ - 'fx':cam_intrinsics[0][0], - 'fy':cam_intrinsics[1][1], - 'cx':cam_intrinsics[2][0], - 'cy':cam_intrinsics[2][1] - } - }, - "objects" : [] - } - - # Segmentation id to export - id_keys_map = visii.entity.get_name_to_id_map() - for obj_name in obj_names: - # don't write objects without a single visible pixel in the output image to json file - if int(id_keys_map[obj_name]) not in visible_object_ids: - continue - - projected_keypoints, _ = get_cuboid_image_space(obj_name, camera_name=camera_name) - - if projected_keypoints is not None: - # put them in the image space. - for i_p, p in enumerate(projected_keypoints): - projected_keypoints[i_p] = [p[0]*width, p[1]*height] - - # Get the location and rotation of the object in the camera frame - trans = visii.transform.get(f"{obj_name}_symmetry_corrected") - if trans is None: - trans = visii.entity.get(f"{obj_name}_symmetry_corrected").get_transform() - if trans is None: - continue - - quaternion_xyzw = visii.inverse(cam_world_quaternion) * visii.quat(trans.get_local_to_world_matrix()) - - object_world = visii.vec4( - trans.get_world_position()[0], - trans.get_world_position()[1], - trans.get_world_position()[2], - 1 - ) - pos_camera_frame = cam_matrix * object_world - - - if not cuboids is None: - cuboid = cuboids[obj_name] - else: - cuboid = None - - # compute visibility fraction - visibility = 1 - px_count_visib = 0 - px_count_all = 0 - if compute_visibility_fraction: - transforms_to_keep = {} - - for name in id_keys_map.keys(): - if 'camera' in name.lower() or obj_name in name: - continue - trans_to_keep = visii.entity.get(name).get_transform() - transforms_to_keep[name]=trans_to_keep - visii.entity.get(name).clear_transform() - - # render segmentation mask of object in isolation to determine px_count_all (number of - # pixels in the object silhouette without occlusions) - segmentation_unique_mask = visii.render_data( - width=int(width), - height=int(height), - start_frame=0, - frame_count=1, - bounce=int(0), - options="entity_id", - ) - segmentation_unique_mask = np.array(segmentation_unique_mask).reshape((height, width, 4))[:, :, 0] - - px_count_visib = len(np.where(segmentation_mask == int(id_keys_map[obj_name]))[0]) - px_count_all = len(np.where(segmentation_unique_mask == int(id_keys_map[obj_name]))[0]) - visibility = px_count_visib / px_count_all - - # set back the objects from remove - for entity_name in transforms_to_keep.keys(): - visii.entity.get(entity_name).set_transform(transforms_to_keep[entity_name]) - - # bounding box calculation - y, x = np.where(segmentation_mask == int(id_keys_map[obj_name])) - bounding_box = [int(min(x)), int(max(x)), height - int(max(y)), height - int(min(y))] - - tran_matrix = trans.get_local_to_world_matrix() - - trans_matrix_export = [] - for row in tran_matrix: - trans_matrix_export.append([row[0],row[1],row[2],row[3]]) - - # Final export - class_name = object_to_class_name(obj_name) - try: - seg_id = id_keys_map[obj_name] - except : - seg_id = -1 - - dict_out['objects'].append({ - 'class':class_name, - 'name':obj_name, - 'provenance':'nvisii', - # TODO check the location - 'location': [ - pos_camera_frame[0], - pos_camera_frame[1], - pos_camera_frame[2] - ], - 'location_worldframe': [ - trans.get_world_position()[0], - trans.get_world_position()[1], - trans.get_world_position()[2] - ], - 'quaternion_xyzw':[ - quaternion_xyzw[0], - quaternion_xyzw[1], - quaternion_xyzw[2], - quaternion_xyzw[3], - ], - 'quaternion_xyzw_worldframe':[ - visii.quat(trans.get_local_to_world_matrix())[0], - visii.quat(trans.get_local_to_world_matrix())[1], - visii.quat(trans.get_local_to_world_matrix())[2], - visii.quat(trans.get_local_to_world_matrix())[3], - ], - 'local_to_world_matrix':trans_matrix_export, - 'projected_cuboid':projected_keypoints, - 'segmentation_id':seg_id, - 'local_cuboid': cuboid, - 'px_count_visib': px_count_visib, - 'px_count_all': px_count_all, - 'visibility':visibility, - 'bounding_box_minx_maxx_miny_maxy':bounding_box - }) - - with open(filename, 'w+') as fp: - json.dump(dict_out, fp, indent=4, sort_keys=True) - # return bounding_box - - -def change_image_extension(path,extension="jpg"): - im = cv2.imread(path) - cv2.imwrite(path.replace("png",extension),im) - subprocess.call(['rm',path]) - del im - - -######## SYMMETRIES ########## - -def update_symmetry_corrected_transform(obj_id, model_info, symmetry_transforms, camera_name='camera'): - """ - Updates the "object transform" -> "symmetry_corrected transform" to the "canonical" symmetry - transform. The "canonical" symmetry transform is the one with the smallest angle - between the object and camera axes specified in "align_axes" in the model_info. - - The cuboid corners are child transforms of the "symmetry_corrected" transform, so that when that - transform is updated here, the cuboid corners will be rotated along with it such that they are - identical for two poses which are symmetrical. - """ - - # convert align_axes vectors to visii.vec3 - align_axes = [] - try: - # e.g., "align_axes": [{"object": [0, 1, 0], "camera": [0, 0, 1]}, {"object": [0, 0, 1], "camera": [0, 1, 0]}] - for axis_pair in model_info["align_axes"]: - object_axis = axis_pair["object"] - object_axis = visii.vec3(object_axis[0], object_axis[1], object_axis[2]) - camera_axis = axis_pair["camera"] - camera_axis = visii.vec3(camera_axis[0], camera_axis[1], camera_axis[2]) - align_axes.append({"object": object_axis, "camera": camera_axis}) - except (KeyError, IndexError): - align_axes.append({"object": visii.vec3(1, 0, 0), "camera": visii.vec3(0, 0, 1)}) - - # find the symmetry transform with the best alignment between the first object_axis and the first camera_axis, - # using the second object_axis/camera_axis as a tie breaker - - cam_matrix = visii.entity.get(camera_name).get_transform().get_world_to_local_matrix() - - # calculate rotation error for each symmetry transform - def calc_alignment_score(object_axis, camera_axis, symmetry_trans): - """ - Transforms the `object_axis` vector into the camera frame using the transformation `trans` and calculates - the "alignment score" between the rotated vector and the `camera_axis` vector. The "alignment score" is - just the dot product between the vectors, so `math.acos(alignment_score)` is the angle between the vectors. - `alignment_score` is between -1 (180 degree angle, worst alignment) and 1 (0 degree angle, best alignment). - """ - mat_trans = visii.transform.get(f"{obj_id}").get_local_to_world_matrix() * symmetry_trans - sym_object_to_camera_rotation = visii.quat(cam_matrix * mat_trans) - sym_object_axis = sym_object_to_camera_rotation * object_axis - return visii.dot(sym_object_axis, camera_axis) - - # score according to the first element of align_axes - scored_symmetry_transforms = [] - for i, trans in enumerate(symmetry_transforms): - alignment_score = calc_alignment_score(align_axes[0]["object"], align_axes[0]["camera"], trans) - scored_symmetry_transforms.append((alignment_score, trans)) - - # Sort from best to worst. Using only first element (score) for sorting to - # avoid undefined "<" operator between second element (transforms). - scored_symmetry_transforms.sort(key=lambda x: x[0], reverse=True) - - best_symmetry_transform = scored_symmetry_transforms[0][1] - - if len(align_axes) >= 2: - # collect best transforms with identical score - if len(scored_symmetry_transforms) == 0: - return - reference_score = scored_symmetry_transforms[0][0] - top_symmetry_transforms = [] - EPSILON = 1e-3 - for (score, symmetry_transform) in scored_symmetry_transforms: - if math.fabs(score - reference_score) > EPSILON: - break - top_symmetry_transforms.append(symmetry_transform) - - # use second element of align_axes as a tie breaker - max_alignment_score = float("-inf") - for trans in top_symmetry_transforms: - alignment_score = calc_alignment_score(align_axes[1]["object"], align_axes[1]["camera"], trans) - if alignment_score > max_alignment_score: - max_alignment_score = alignment_score - best_symmetry_transform = trans - - # update the symmetry_corrected transform - symmetry_corrected_trans = visii.transform.get(f"{obj_id}_symmetry_corrected") - symmetry_corrected_trans.set_transform(best_symmetry_transform) - - -# Modified from https://github.com/thodan/bop_toolkit/ -def rotation_matrix(angle, direction, point=None): - """Return matrix to rotate about axis defined by point and direction. - - >>> R = rotation_matrix(math.pi/2, [0, 0, 1], [1, 0, 0]) - >>> np.allclose(np.dot(R, [0, 0, 0, 1]), [1, -1, 0, 1]) - True - >>> angle = (random.random() - 0.5) * (2*math.pi) - >>> direc = np.random.random(3) - 0.5 - >>> point = np.random.random(3) - 0.5 - >>> R0 = rotation_matrix(angle, direc, point) - >>> R1 = rotation_matrix(angle-2*math.pi, direc, point) - >>> is_same_transform(R0, R1) - True - >>> R0 = rotation_matrix(angle, direc, point) - >>> R1 = rotation_matrix(-angle, -direc, point) - >>> is_same_transform(R0, R1) - True - >>> I = np.identity(4, np.float64) - >>> np.allclose(I, rotation_matrix(math.pi*2, direc)) - True - >>> np.allclose(2, np.trace(rotation_matrix(math.pi/2, - ... direc, point))) - True - - """ - sina = math.sin(angle) - cosa = math.cos(angle) - direction = direction[:3] / np.linalg.norm(direction[:3]) - # rotation matrix around unit vector - R = np.diag([cosa, cosa, cosa]) - R += np.outer(direction, direction) * (1.0 - cosa) - direction *= sina - R += np.array([[0.0, -direction[2], direction[1]], - [direction[2], 0.0, -direction[0]], - [-direction[1], direction[0], 0.0]]) - M = np.identity(4) - M[:3, :3] = R - if point is not None: - # rotation not around origin - point = np.array(point[:3], dtype=np.float64, copy=False) - M[:3, 3] = point - np.dot(R, point) - return M - - -def get_symmetry_transformations(model_info_path, discrete_steps_count=64): - if model_info_path is not None: - try: - with open(model_info_path) as json_file: - model_info = json.load(json_file) - except FileNotFoundError: - model_info = {} - else: - model_info = {} - - trans = _get_symmetry_transformations(model_info, discrete_steps_count) - - # convert to visii format - trans_visii = [] - for tran in trans: - R = tran['R'] - t = tran['t'] - mat_4x4 = visii.mat4(R[0][0], R[1][0], R[2][0], t[0], - R[0][1], R[1][1], R[2][1], t[1], - R[0][2], R[1][2], R[2][2], t[2], - 0, 0, 0, 1) - trans_visii.append(mat_4x4) - - return trans_visii - - -# Modified from https://github.com/thodan/bop_toolkit/ -def _get_symmetry_transformations(model_info, discrete_steps_count=64): - """Returns a set of symmetry transformations for an object model. - - :param model_info: See files models_info.json provided with the datasets. - :param discrete_steps_count: The number of discretization steps - for continuous rotational symmetries. - :return: The set of symmetry transformations. - """ - # Discrete symmetries. - trans_disc = [{'R': np.eye(3), 't': np.array([[0.0, 0.0, 0.0]]).T}] # Identity. - if 'symmetries_discrete' in model_info: - for sym in model_info['symmetries_discrete']: - sym_4x4 = np.array(sym, dtype=np.float64).reshape((4, 4)) - R = sym_4x4[:3, :3] - t = sym_4x4[:3, 3].reshape((3, 1)) - trans_disc.append({'R': R, 't': t}) - - # Discretized continuous symmetries. - trans_cont = [] - discrete_steps_count = int(discrete_steps_count) - if 'symmetries_continuous' in model_info: - for sym in model_info['symmetries_continuous']: - axis = np.array(sym['axis'], dtype=np.float64) - offset = np.array(sym['offset'], dtype=np.float64).reshape((3, 1)) - - # Discrete step in radians. - discrete_step = 2.0 * np.pi / discrete_steps_count - - for i in range(0, discrete_steps_count): - R = rotation_matrix(i * discrete_step, axis)[:3, :3] - t = -R.dot(offset) + offset - trans_cont.append({'R': R, 't': t}) - - # Combine the discrete and the discretized continuous symmetries. - trans = [] - for tran_disc in trans_disc: - if len(trans_cont): - for tran_cont in trans_cont: - R = tran_cont['R'].dot(tran_disc['R']) - t = tran_cont['R'].dot(tran_disc['t']) + tran_cont['t'] - trans.append({'R': R, 't': t}) - else: - trans.append(tran_disc) - - return trans - - -#################### BULLET THINGS ############################## - -def load_obj_scene(path): - """ - This loads a single entity low poly from turbosquid. - - return: a list of visii entity names - """ - - obj_to_load = path - name_model = path - - # print("loading:",name_model) - name = path.split('/')[-2] - - toys = visii.import_scene(obj_to_load, - visii.vec3(0,0,0), - visii.vec3(1,1,1), # the scale - visii.angleAxis(1.57, visii.vec3(1,0,0)) - ) - for material in toys.materials: - # print(material.get_name()) - if 'Glass' in material.get_name(): - # print('changing') - # material.set_transmission(0.7) - material.set_metallic(0.7) - material.set_roughness(0) - entity_names = [] - for entity in toys.entities: - entity_names.append(entity.get_name()) - - return entity_names - - -def create_obj( - name = 'name', - path_obj = "", - path_tex = None, - scale = 1, - rot_base = None - ): - - - # This is for YCB like dataset - if path_obj in create_obj.meshes: - obj_mesh = create_obj.meshes[path_obj] - else: - # obj_mesh = visii.mesh.create_from_obj(name, path_obj) - # create_obj.meshes[path_obj] = obj_mesh - - obj_mesh = visii.mesh.create_from_file(name, path_obj) - create_obj.meshes[path_obj] = obj_mesh - - - - obj_entity = visii.entity.create( - name = name, - # mesh = visii.mesh.create_sphere("mesh1", 1, 128, 128), - mesh = obj_mesh, - transform = visii.transform.create(name), - material = visii.material.create(name) - ) - - # should randomize - obj_entity.get_material().set_metallic(0) # should 0 or 1 - obj_entity.get_material().set_transmission(0) # should 0 or 1 - obj_entity.get_material().set_roughness(random.uniform(0,1)) # default is 1 - - if not path_tex is None: - - if path_tex in create_obj.textures: - obj_texture = create_obj.textures[path_tex] - else: - obj_texture = visii.texture.create_from_file(name,path_tex) - create_obj.textures[path_tex] = obj_texture - - - obj_entity.get_material().set_base_color_texture(obj_texture) - - obj_entity.get_transform().set_scale(visii.vec3(scale)) - - return obj_entity -create_obj.meshes = {} -create_obj.textures = {} - -def create_physics( - name="", - mass = 1, # mass in kg - concave = False, - ): - - # Set the collision with the floor mesh - # first lets get the vertices - obj = visii.entity.get(name) - vertices = [] - # print(name) - # print(obj.get_mesh()) - # print(obj.to_string()) - # print(visii.mesh.get("mesh_floor")) - - for v in obj.get_mesh().get_vertices(): - vertices.append([float(v[0]),float(v[1]),float(v[2])]) - - # get the position of the object - trans = obj.get_transform().get_parent() - if trans is None: - trans = obj.get_transform() - pos = trans.get_position() - pos = [pos[0],pos[1],pos[2]] - scale = trans.get_scale() - scale = [scale[0],scale[1],scale[2]] - rot = trans.get_rotation() - rot = [rot[0],rot[1],rot[2],rot[3]] - - # create a collision shape that is a convez hull - - if concave: - indices = obj.get_mesh().get_triangle_indices() - obj_col_id = p.createCollisionShape( - p.GEOM_MESH, - vertices = vertices, - meshScale = scale, - indices = indices, - ) - - - else: - try: - obj_col_id = p.createCollisionShape( - p.GEOM_MESH, - vertices = vertices, - meshScale = scale, - ) - except: - # TODO use the size of the object to - # create the cuboid. - print(f'cannot load the collision from the mesh for {name}') - obj_col_id = p.createCollisionShape( - p.GEOM_BOX, - halfExtents = [0.1,0.1,0.1] - ) - # create a body without mass so it is static - if not mass is None : - obj_id = p.createMultiBody( - baseMass = mass, - baseCollisionShapeIndex = obj_col_id, - basePosition = pos, - baseOrientation= rot, - ) - else: - obj_id = p.createMultiBody( - baseCollisionShapeIndex = obj_col_id, - basePosition = pos, - baseOrientation= rot, - ) - - return obj_id - - -def update_pose(obj_dict,parent=False): - pos, rot = p.getBasePositionAndOrientation(obj_dict['bullet_id']) - # print(pos) - - obj_entity = visii.entity.get(obj_dict['visii_id']) - if parent: - obj_entity.get_transform().get_parent().set_position(visii.vec3( - pos[0], - pos[1], - pos[2] - ) - ) - else: - obj_entity.get_transform().set_position(visii.vec3( - pos[0], - pos[1], - pos[2] - ) - ) - - if not obj_dict['base_rot'] is None: - obj_entity.get_transform().set_rotation(visii.quat( - rot[3], - rot[0], - rot[1], - rot[2] - ) * obj_dict['base_rot'] - ) - else: - if parent: - obj_entity.get_transform().get_parent().set_rotation(visii.quat( - rot[3], - rot[0], - rot[1], - rot[2] - ) - ) - else: - obj_entity.get_transform().set_rotation(visii.quat( - rot[3], - rot[0], - rot[1], - rot[2] - ) - ) - update_symmetry_corrected_transform(obj_dict['visii_id'], obj_dict['model_info'], obj_dict['symmetry_transforms']) - -def material_from_json(path_json, randomize = False): - """Load a json file visii material definition. - - Parameters: - path_json (str): The path to the json file to load - randomize (bool): Randomize the material using the file definition (default:False) - Return: - visii.material (visii.material): Returns a material object or None if there is a problem - - """ - - if not os.path.isfile(path_json): - warnings.warn(f"{path_json} does not exist") - return None - - with open(path_json) as json_file: - data = json.load(json_file) - - mat = visii.material.create(data['name']) - - - # Load rgb image for base color - if visii.texture.get(data['color']): - mat.set_base_color_texture(visii.texture.get(data['color'])) - else: - mat.set_base_color_texture(visii.texture.create_from_file(data['color'],data['color'])) - - if 'gloss' in data: - if visii.texture.get(data['gloss']): - mat.set_roughness_texture(visii.texture.get(data['gloss'])) - else: - im = cv2.imread(data['gloss']) - # print(im.shape) - im = np.power(1-(im/255.0),2) - im_r = np.concatenate( - [ - im[:,:,1].reshape(im.shape[0],im.shape[1],1), - im[:,:,1].reshape(im.shape[0],im.shape[1],1), - im[:,:,1].reshape(im.shape[0],im.shape[1],1), - im[:,:,1].reshape(im.shape[0],im.shape[1],1) - ], - 2 - ) - roughness_tex = visii.texture.create_from_data( - data['gloss'], - im_r.shape[0], - im_r.shape[1], - im_r.reshape(im_r.shape[0]*im_r.shape[1],4).astype(np.float32).flatten() - ) - mat.set_roughness_texture(visii.texture.create_from_file(data['gloss'],roughness_tex)) - - if "normal" in data: - if visii.texture.get(data['normal']): - mat.set_normal_texture(visii.texture.get(data['normal'])) - else: - mat.set_normal_texture(visii.texture.create_from_file(data['normal'],data['normal'],linear = True)) - - return mat - - -def material_from_cco(path_folder,scale=1,name = None): - """Load a json file visii material definition. - - Parameters: - path_json (str): The path to the textures location - scale (float): by how much to scale the texture - default 1.0 - Return: - visii.material (visii.material): Returns a material object or None if there is a problem - """ - print(path_folder.split("/")[-2]) - if not name is None: - name = name + path_folder.split("/")[-2] - else: - name = path_folder.split("/")[-2] - - mat = visii.material.create(name) - - files = glob.glob(path_folder+'/*.jpg')+glob.glob(path_folder+'/*.png') - print(files) - - for file in files: - name_file_visii = name + file + str(scale) - if 'color' in file.lower(): - if visii.texture.get(name_file_visii): - mat.set_base_color_texture(visii.texture.get(name_file_visii)) - else: - mat.set_base_color_texture(visii.texture.create_from_file(name_file_visii,file)) - if 'normal' in file.lower(): - if visii.texture.get(name_file_visii): - mat.set_normal_map_texture(visii.texture.get(name_file_visii)) - else: - mat.set_normal_map_texture(visii.texture.create_from_file(name_file_visii,file,linear=True)) - if 'rough' in file.lower(): - if visii.texture.get(name_file_visii): - mat.set_roughness_texture(visii.texture.get(name_file_visii)) - else: - mat.set_roughness_texture(visii.texture.create_from_file(name_file_visii,file,linear=True)) - if 'metal' in file.lower(): - if visii.texture.get(name_file_visii): - mat.set_metallic_texture(visii.texture.get(name_file_visii)) - else: - mat.set_metallic_texture(visii.texture.create_from_file(name_file_visii,file,linear=True)) - - if visii.texture.get(name_file_visii): - visii.texture.get(name_file_visii).set_scale((scale,scale)) - - return mat diff --git a/scripts/readme.md b/scripts/readme.md deleted file mode 100644 index fbec5764..00000000 --- a/scripts/readme.md +++ /dev/null @@ -1,9 +0,0 @@ -# Notes - -Please note that the version of pytorch and cuda you use are important. - -``` -Pytorch=1.7.0 and Cuda = 11.0 -``` - -Thank you to [Arvind Pandit](https://github.com/Avi241) - see [issue 301](https://github.com/NVlabs/Deep_Object_Pose/issues/301) diff --git a/scripts/train.py b/scripts/train.py deleted file mode 100755 index 49c02284..00000000 --- a/scripts/train.py +++ /dev/null @@ -1,1315 +0,0 @@ -#!/usr/bin/env python3 - -# Copyright (c) 2018 NVIDIA Corporation. All rights reserved. -# This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. -# https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode - -from __future__ import print_function - -###################################################### -""" -REQUIREMENTS: -simplejson==3.16.0 -numpy==1.14.1 -opencv_python==3.4.3.18 -horovod==0.13.5 -photutils==0.5 -scipy==1.1.0 -torch==0.4.0 -pyquaternion==0.9.2 -tqdm==4.25.0 -pyrr==0.9.2 -Pillow==5.2.0 -torchvision==0.2.1 -PyYAML==3.13 -""" - -###################################################### -""" -HOW TO TRAIN DOPE - -This is the DOPE training code. -It is provided as a convenience for researchers, but it is otherwise unsupported. - -Please refer to `python3 train.py --help` for specific details about the -training code. - -If you download the FAT dataset -(https://research.nvidia.com/publication/2018-06_Falling-Things) -you can train a YCB object DOPE detector as follows: - -``` -python3 train.py --data path/to/FAT --object soup --outf soup ---gpuids 0 1 2 3 4 5 6 7 -``` - -This will create a folder called `train_soup` where the weights will be saved -after each epoch. It will use the 8 gpus using pytorch data parallel. -""" - - -import argparse -import configparser -import random -import numpy as np - -import torch -import torch.nn as nn -import torch.nn.parallel -import torch.optim as optim -import torch.utils.data -import torchvision.transforms as transforms -from torch.autograd import Variable -import torch.utils.data as data -import torchvision.models as models -import datetime -import json -import glob -import os - -from PIL import Image -from PIL import ImageDraw -from PIL import ImageEnhance - -from math import acos -from math import sqrt -from math import pi - -from os.path import exists - -import cv2 -import colorsys - -from dope.utils import make_grid - -import warnings -warnings.filterwarnings("ignore") -os.environ["CUDA_VISIBLE_DEVICES"]="0,1,2,3,4,5,6,7" - - -################################################## -# NEURAL NETWORK MODEL -################################################## - -class DopeNetwork(nn.Module): - def __init__( - self, - pretrained=False, - numBeliefMap=9, - numAffinity=16, - stop_at_stage=6 # number of stages to process (if less than total number of stages) - ): - super(DopeNetwork, self).__init__() - - self.stop_at_stage = stop_at_stage - - if pretrained is False: - print("Training network without imagenet weights.") - else: - print("Training network pretrained on imagenet.") - - vgg_full = models.vgg19(pretrained=pretrained).features - self.vgg = nn.Sequential() - for i_layer in range(24): - self.vgg.add_module(str(i_layer), vgg_full[i_layer]) - - # Add some layers - i_layer = 23 - self.vgg.add_module(str(i_layer), nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1)) - self.vgg.add_module(str(i_layer+1), nn.ReLU(inplace=True)) - self.vgg.add_module(str(i_layer+2), nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=1)) - self.vgg.add_module(str(i_layer+3), nn.ReLU(inplace=True)) - - # print('---Belief------------------------------------------------') - # _2 are the belief map stages - self.m1_2 = DopeNetwork.create_stage(128, numBeliefMap, True) - self.m2_2 = DopeNetwork.create_stage(128 + numBeliefMap + numAffinity, - numBeliefMap, False) - self.m3_2 = DopeNetwork.create_stage(128 + numBeliefMap + numAffinity, - numBeliefMap, False) - self.m4_2 = DopeNetwork.create_stage(128 + numBeliefMap + numAffinity, - numBeliefMap, False) - self.m5_2 = DopeNetwork.create_stage(128 + numBeliefMap + numAffinity, - numBeliefMap, False) - self.m6_2 = DopeNetwork.create_stage(128 + numBeliefMap + numAffinity, - numBeliefMap, False) - - # print('---Affinity----------------------------------------------') - # _1 are the affinity map stages - self.m1_1 = DopeNetwork.create_stage(128, numAffinity, True) - self.m2_1 = DopeNetwork.create_stage(128 + numBeliefMap + numAffinity, - numAffinity, False) - self.m3_1 = DopeNetwork.create_stage(128 + numBeliefMap + numAffinity, - numAffinity, False) - self.m4_1 = DopeNetwork.create_stage(128 + numBeliefMap + numAffinity, - numAffinity, False) - self.m5_1 = DopeNetwork.create_stage(128 + numBeliefMap + numAffinity, - numAffinity, False) - self.m6_1 = DopeNetwork.create_stage(128 + numBeliefMap + numAffinity, - numAffinity, False) - - - def forward(self, x): - '''Runs inference on the neural network''' - - out1 = self.vgg(x) - - out1_2 = self.m1_2(out1) - out1_1 = self.m1_1(out1) - - if self.stop_at_stage == 1: - return [out1_2],\ - [out1_1] - - out2 = torch.cat([out1_2, out1_1, out1], 1) - out2_2 = self.m2_2(out2) - out2_1 = self.m2_1(out2) - - if self.stop_at_stage == 2: - return [out1_2, out2_2],\ - [out1_1, out2_1] - - out3 = torch.cat([out2_2, out2_1, out1], 1) - out3_2 = self.m3_2(out3) - out3_1 = self.m3_1(out3) - - if self.stop_at_stage == 3: - return [out1_2, out2_2, out3_2],\ - [out1_1, out2_1, out3_1] - - out4 = torch.cat([out3_2, out3_1, out1], 1) - out4_2 = self.m4_2(out4) - out4_1 = self.m4_1(out4) - - if self.stop_at_stage == 4: - return [out1_2, out2_2, out3_2, out4_2],\ - [out1_1, out2_1, out3_1, out4_1] - - out5 = torch.cat([out4_2, out4_1, out1], 1) - out5_2 = self.m5_2(out5) - out5_1 = self.m5_1(out5) - - if self.stop_at_stage == 5: - return [out1_2, out2_2, out3_2, out4_2, out5_2],\ - [out1_1, out2_1, out3_1, out4_1, out5_1] - - out6 = torch.cat([out5_2, out5_1, out1], 1) - out6_2 = self.m6_2(out6) - out6_1 = self.m6_1(out6) - - return [out1_2, out2_2, out3_2, out4_2, out5_2, out6_2],\ - [out1_1, out2_1, out3_1, out4_1, out5_1, out6_1] - - @staticmethod - def create_stage(in_channels, out_channels, first=False): - '''Create the neural network layers for a single stage.''' - - model = nn.Sequential() - mid_channels = 128 - if first: - padding = 1 - kernel = 3 - count = 6 - final_channels = 512 - else: - padding = 3 - kernel = 7 - count = 10 - final_channels = mid_channels - - # First convolution - model.add_module("0", - nn.Conv2d( - in_channels, - mid_channels, - kernel_size=kernel, - stride=1, - padding=padding) - ) - - # Middle convolutions - i = 1 - while i < count - 1: - model.add_module(str(i), nn.ReLU(inplace=True)) - i += 1 - model.add_module(str(i), - nn.Conv2d( - mid_channels, - mid_channels, - kernel_size=kernel, - stride=1, - padding=padding)) - i += 1 - - # Penultimate convolution - model.add_module(str(i), nn.ReLU(inplace=True)) - i += 1 - model.add_module(str(i), nn.Conv2d(mid_channels, final_channels, kernel_size=1, stride=1)) - i += 1 - - # Last convolution - model.add_module(str(i), nn.ReLU(inplace=True)) - i += 1 - model.add_module(str(i), nn.Conv2d(final_channels, out_channels, kernel_size=1, stride=1)) - i += 1 - - return model - - - -################################################## -# UTILS CODE FOR LOADING THE DATA -################################################## - -def default_loader(path): - return Image.open(path).convert('RGB') - -def loadjson(path, objectofinterest): - """ - Loads the data from a json file. - If there are no objects of interest, then load all the objects. - """ - with open(path) as data_file: - data = json.load(data_file) - pointsBelief = [] - centroids = [] - - translations = [] - rotations = [] - points = [] - - for i_line in range(len(data['objects'])): - info = data['objects'][i_line] - if not objectofinterest is None and \ - not objectofinterest in info['class'].lower(): - continue - - # 3d bbox with belief maps - points3d = [] - - pointdata = info['projected_cuboid'] - for p in pointdata: - points3d.append((p[0], p[1])) - - if len(points3d) == 8: - # NDDS format: 8 points in 'projected_cuboid', 1 point in 'projected_cuboid_centroid' - pcenter = info['projected_cuboid_centroid'] - points3d.append((pcenter[0], pcenter[1])) - elif len(points3d) == 9: - # nvisii format: 9 points in 'projected_cuboid', no 'projected_cuboid_centroid' key - pcenter = points3d[-1] - else: - raise RuntimeError(f'projected_cuboid has to have 8 or 9 points while reading "{path}"') - - pointsBelief.append(points3d) - points.append(points3d + [(pcenter[0], pcenter[1])]) # NOTE: Adding the centroid again is probably a bug. - centroids.append((pcenter[0], pcenter[1])) - - # load translations - location = info['location'] - translations.append([location[0], location[1], location[2]]) - - # quaternion - rot = info["quaternion_xyzw"] - rotations.append(rot) - - return { - "pointsBelief": pointsBelief, - "rotations": rotations, - "translations": translations, - "centroids": centroids, - "points": points, - "keypoints_2d": [], - } - -def loadimages(root): - """ - Find all the images in the path and folders, return them in imgs. - """ - imgs = [] - - def add_json_files(path,): - for imgpath in glob.glob(path+"/*.png"): - if exists(imgpath) and exists(imgpath.replace('png',"json")): - imgs.append((imgpath,imgpath.replace(path,"").replace("/",""), - imgpath.replace('png',"json"))) - for imgpath in glob.glob(path+"/*.jpg"): - if exists(imgpath) and exists(imgpath.replace('jpg',"json")): - imgs.append((imgpath,imgpath.replace(path,"").replace("/",""), - imgpath.replace('jpg',"json"))) - - def explore(path): - if not os.path.isdir(path): - return - folders = [os.path.join(path, o) for o in os.listdir(path) - if os.path.isdir(os.path.join(path,o))] - if len(folders)>0: - for path_entry in folders: - explore(path_entry) - add_json_files(path) - - explore(root) - - return imgs - -class MultipleVertexJson(data.Dataset): - """ - Dataloader for the data generated by NDDS (https://github.com/NVIDIA/Dataset_Synthesizer). - This is the same data as the data used in FAT. - """ - def __init__(self, root,transform=None, nb_vertex = 8, - keep_orientation = True, - normal = None, test=False, - target_transform = None, - loader = default_loader, - objectofinterest = "", - img_size = 400, - save = False, - noise = 2, - data_size = None, - sigma = 16, - random_translation = (25.0,25.0), - random_rotation = 15.0, - ): - ################### - self.objectofinterest = objectofinterest - self.img_size = img_size - self.loader = loader - self.transform = transform - self.target_transform = target_transform - self.root = root - self.imgs = [] - self.test = test - self.normal = normal - self.keep_orientation = keep_orientation - self.save = save - self.noise = noise - self.data_size = data_size - self.sigma = sigma - self.random_translation = random_translation - self.random_rotation = random_rotation - - def load_data(path): - '''Recursively load the data. This is useful to load all of the FAT dataset.''' - imgs = loadimages(path) - - # Check all the folders in path - for name in os.listdir(str(path)): - imgs += loadimages(path +"/"+name) - return imgs - - - self.imgs = load_data(root) - - # Shuffle the data, this is useful when we want to use a subset. - np.random.shuffle(self.imgs) - - def __len__(self): - # When limiting the number of data - if not self.data_size is None: - return int(self.data_size) - - return len(self.imgs) - - def __getitem__(self, index): - """ - Depending on how the data loader is configured, - this will return the debug info with the cuboid drawn on it, - this happens when self.save is set to true. - Otherwise, during training this function returns the - belief maps and affinity fields and image as tensors. - """ - path, name, txt = self.imgs[index] - img = self.loader(path) - - img_size = img.size - img_size = (400,400) - - loader = loadjson - - data = loader(txt, self.objectofinterest) - - pointsBelief = data['pointsBelief'] - objects_centroid = data['centroids'] - points_all = data['points'] - points_keypoints = data['keypoints_2d'] - translations = torch.from_numpy(np.array( - data['translations'])).float() - rotations = torch.from_numpy(np.array( - data['rotations'])).float() - - if len(points_all) == 0: - points_all = torch.zeros(1, 10, 2).double() - - # self.save == true assumes there is only - # one object instance in the scene. - if translations.size()[0] > 1: - translations = translations[0].unsqueeze(0) - rotations = rotations[0].unsqueeze(0) - - # If there are no objects, still need to return similar shape array - if len(translations) == 0: - translations = torch.zeros(1,3).float() - rotations = torch.zeros(1,4).float() - - # Camera intrinsics - path_cam = path.replace(name,'_camera_settings.json') - with open(path_cam) as data_file: - data = json.load(data_file) - # Assumes one camera - cam = data['camera_settings'][0]['intrinsic_settings'] - - matrix_camera = np.zeros((3,3)) - matrix_camera[0,0] = cam['fx'] - matrix_camera[1,1] = cam['fy'] - matrix_camera[0,2] = cam['cx'] - matrix_camera[1,2] = cam['cy'] - matrix_camera[2,2] = 1 - - # Load the cuboid sizes - path_set = path.replace(name,'_object_settings.json') - with open(path_set) as data_file: - data = json.load(data_file) - - cuboid = torch.zeros(1) - - if self.objectofinterest is None: - cuboid = np.array(data['exported_objects'][0]['cuboid_dimensions']) - else: - for info in data["exported_objects"]: - if self.objectofinterest in info['class']: - cuboid = np.array(info['cuboid_dimensions']) - - img_original = img.copy() - - - def Reproject(points,tm, rm): - """ - Reprojection of points when rotating the image - """ - proj_cuboid = np.array(points) - - rmat = np.identity(3) - rmat[0:2] = rm - tmat = np.identity(3) - tmat[0:2] = tm - - new_cuboid = np.matmul( - rmat, np.vstack((proj_cuboid.T, np.ones(len(points))))) - new_cuboid = np.matmul(tmat, new_cuboid) - new_cuboid = new_cuboid[0:2].T - - return new_cuboid - - # Random image manipulation, rotation and translation with zero padding - # These create a bug, thank you to - # https://tanelp.github.io/posts/a-bug-that-plagues-thousands-of-open-source-ml-projects/ - # dx = round(np.random.normal(0, 2) * float(self.random_translation[0])) - # dy = round(np.random.normal(0, 2) * float(self.random_translation[1])) - # angle = round(np.random.normal(0, 1) * float(self.random_rotation)) - - dx = round(float(torch.normal(torch.tensor(0.0), torch.tensor(2.0)) * float(self.random_translation[0]))) - dy = round(float(torch.normal(torch.tensor(0.0), torch.tensor(2.0)) * float(self.random_translation[1]))) - angle = round(float(torch.normal(torch.tensor(0.0), torch.tensor(1.0)) * float(self.random_rotation))) - - tm = np.float32([[1, 0, dx], [0, 1, dy]]) - rm = cv2.getRotationMatrix2D( - (img.size[0]/2, img.size[1]/2), angle, 1) - - for i_objects in range(len(pointsBelief)): - points = pointsBelief[i_objects] - new_cuboid = Reproject(points, tm, rm) - pointsBelief[i_objects] = new_cuboid.tolist() - objects_centroid[i_objects] = tuple(new_cuboid.tolist()[-1]) - pointsBelief[i_objects] = list(map(tuple, pointsBelief[i_objects])) - - for i_objects in range(len(points_keypoints)): - points = points_keypoints[i_objects] - new_cuboid = Reproject(points, tm, rm) - points_keypoints[i_objects] = new_cuboid.tolist() - points_keypoints[i_objects] = list(map(tuple, points_keypoints[i_objects])) - - image_r = cv2.warpAffine(np.array(img), rm, img.size) - result = cv2.warpAffine(image_r, tm, img.size) - img = Image.fromarray(result) - - # Note: All point coordinates are in the image space, e.g., pixel value. - # This is used when we do saving --- helpful for debugging - if self.save or self.test: - # Use the save to debug the data - if self.test: - draw = ImageDraw.Draw(img_original) - else: - draw = ImageDraw.Draw(img) - - # PIL drawing functions, here for sharing draw - def DrawKeypoints(points): - for key in points: - DrawDot(key,(12, 115, 170),7) - - def DrawLine(point1, point2, lineColor, lineWidth): - if not point1 is None and not point2 is None: - draw.line([point1,point2],fill=lineColor,width=lineWidth) - - def DrawDot(point, pointColor, pointRadius): - if not point is None: - xy = [point[0]-pointRadius, point[1]-pointRadius, point[0]+pointRadius, point[1]+pointRadius] - draw.ellipse(xy, fill=pointColor, outline=pointColor) - - def DrawCube(points, which_color = 0, color = None): - '''Draw cube with a thick solid line across the front top edge.''' - lineWidthForDrawing = 2 - lineColor1 = (255, 215, 0) # yellow-ish - lineColor2 = (12, 115, 170) # blue-ish - lineColor3 = (45, 195, 35) # green-ish - if which_color == 3: - lineColor = lineColor3 - else: - lineColor = lineColor1 - - if not color is None: - lineColor = color - - # draw front - DrawLine(points[0], points[1], lineColor, 8) #lineWidthForDrawing) - DrawLine(points[1], points[2], lineColor, lineWidthForDrawing) - DrawLine(points[3], points[2], lineColor, lineWidthForDrawing) - DrawLine(points[3], points[0], lineColor, lineWidthForDrawing) - - # draw back - DrawLine(points[4], points[5], lineColor, lineWidthForDrawing) - DrawLine(points[6], points[5], lineColor, lineWidthForDrawing) - DrawLine(points[6], points[7], lineColor, lineWidthForDrawing) - DrawLine(points[4], points[7], lineColor, lineWidthForDrawing) - - # draw sides - DrawLine(points[0], points[4], lineColor, lineWidthForDrawing) - DrawLine(points[7], points[3], lineColor, lineWidthForDrawing) - DrawLine(points[5], points[1], lineColor, lineWidthForDrawing) - DrawLine(points[2], points[6], lineColor, lineWidthForDrawing) - - # draw dots - DrawDot(points[0], pointColor=(255,255,255), pointRadius = 3) - DrawDot(points[1], pointColor=(0,0,0), pointRadius = 3) - - # Draw all the found objects. - for points_belief_objects in pointsBelief: - DrawCube(points_belief_objects) - for keypoint in points_keypoints: - DrawKeypoints(keypoint) - - img = self.transform(img) - - return { - "img":img, - "translations":translations, - "rot_quaternions":rotations, - 'pointsBelief':np.array(points_all[0]), - 'matrix_camera':matrix_camera, - 'img_original': np.array(img_original), - 'cuboid': cuboid, - 'file_name':name, - } - - # Create the belief map - beliefsImg = CreateBeliefMap( - img, - pointsBelief=pointsBelief, - nbpoints = 9, - sigma = self.sigma) - - # Create the image maps for belief - transform = transforms.Compose([transforms.Resize(min(img_size))]) - totensor = transforms.Compose([transforms.ToTensor()]) - - for j in range(len(beliefsImg)): - beliefsImg[j] = self.target_transform(beliefsImg[j]) - # beliefsImg[j].save('{}.png'.format(j)) - beliefsImg[j] = totensor(beliefsImg[j]) - - beliefs = torch.zeros((len(beliefsImg),beliefsImg[0].size(1),beliefsImg[0].size(2))) - for j in range(len(beliefsImg)): - beliefs[j] = beliefsImg[j][0] - - - # Create affinity maps - scale = 8 - if min (img.size) / 8.0 != min (img_size)/8.0: - # print (scale) - scale = min (img.size)/(min (img_size)/8.0) - - affinities = GenerateMapAffinity(img,8,pointsBelief,objects_centroid,scale) - img = self.transform(img) - - # Transform the images for training input - w_crop = np.random.randint(0, img.size[0] - img_size[0]+1) - h_crop = np.random.randint(0, img.size[1] - img_size[1]+1) - transform = transforms.Compose([transforms.Resize(min(img_size))]) - totensor = transforms.Compose([transforms.ToTensor()]) - - if not self.normal is None: - normalize = transforms.Compose([transforms.Normalize - ((self.normal[0],self.normal[0],self.normal[0]), - (self.normal[1],self.normal[1],self.normal[1])), - AddNoise(self.noise)]) - else: - normalize = transforms.Compose([AddNoise(0.0001)]) - - img = crop(img,h_crop,w_crop,img_size[1],img_size[0]) - img = totensor(img) - - img = normalize(img) - - w_crop = int(w_crop/8) - h_crop = int(h_crop/8) - - affinities = affinities[:,h_crop:h_crop+int(img_size[1]/8),w_crop:w_crop+int(img_size[0]/8)] - beliefs = beliefs[:,h_crop:h_crop+int(img_size[1]/8),w_crop:w_crop+int(img_size[0]/8)] - - if affinities.size()[1] == 49 and not self.test: - affinities = torch.cat([affinities,torch.zeros(16,1,50)],dim=1) - - if affinities.size()[2] == 49 and not self.test: - affinities = torch.cat([affinities,torch.zeros(16,50,1)],dim=2) - - return { - 'img':img, - "affinities":affinities, - 'beliefs':beliefs, - } - -""" -Some simple vector math functions to find the angle -between two points, used by affinity fields. -""" -def length(v): - return sqrt(v[0]**2+v[1]**2) - -def dot_product(v,w): - return v[0]*w[0]+v[1]*w[1] - -def normalize(v): - norm=np.linalg.norm(v, ord=1) - if norm==0: - norm=np.finfo(v.dtype).eps - return v/norm - -def determinant(v,w): - return v[0]*w[1]-v[1]*w[0] - -def inner_angle(v,w): - cosx=dot_product(v,w)/(length(v)*length(w)) - rad=acos(cosx) # in radians - return rad*180/pi # returns degrees - -def py_ang(A, B=(1,0)): - inner=inner_angle(A,B) - det = determinant(A,B) - if det<0: #this is a property of the det. If the det < 0 then B is clockwise of A - return inner - else: # if the det > 0 then A is immediately clockwise of B - return 360-inner - -def GenerateMapAffinity(img,nb_vertex,pointsInterest,objects_centroid,scale): - """ - Function to create the affinity maps, - e.g., vector maps pointing toward the object center. - - Args: - img: PIL image - nb_vertex: (int) number of points - pointsInterest: list of points - objects_centroid: (x,y) centroids for the obects - scale: (float) by how much you need to scale down the image - return: - return a list of tensors for each point except centroid point - """ - - # Apply the downscale right now, so the vectors are correct. - img_affinity = Image.new(img.mode, (int(img.size[0]/scale),int(img.size[1]/scale)), "black") - # Create the empty tensors - totensor = transforms.Compose([transforms.ToTensor()]) - - affinities = [] - for i_points in range(nb_vertex): - affinities.append(torch.zeros(2,int(img.size[1]/scale),int(img.size[0]/scale))) - - for i_pointsImage in range(len(pointsInterest)): - pointsImage = pointsInterest[i_pointsImage] - center = objects_centroid[i_pointsImage] - for i_points in range(nb_vertex): - point = pointsImage[i_points] - affinity_pair, img_affinity = getAfinityCenter(int(img.size[0]/scale), - int(img.size[1]/scale), - tuple((np.array(pointsImage[i_points])/scale).tolist()), - tuple((np.array(center)/scale).tolist()), - img_affinity = img_affinity, radius=1) - - affinities[i_points] = (affinities[i_points] + affinity_pair)/2 - - - # Normalizing - v = affinities[i_points].numpy() - - xvec = v[0] - yvec = v[1] - - norms = np.sqrt(xvec * xvec + yvec * yvec) - nonzero = norms > 0 - - xvec[nonzero]/=norms[nonzero] - yvec[nonzero]/=norms[nonzero] - - affinities[i_points] = torch.from_numpy(np.concatenate([[xvec],[yvec]])) - affinities = torch.cat(affinities,0) - - return affinities - -def getAfinityCenter(width, height, point, center, radius=7, img_affinity=None): - """ - Function to create the affinity maps, - e.g., vector maps pointing toward the object center. - - Args: - width: image wight - height: image height - point: (x,y) - center: (x,y) - radius: pixel radius - img_affinity: tensor to add to - return: - return a tensor - """ - tensor = torch.zeros(2,height,width).float() - - # Create the canvas for the afinity output - imgAffinity = Image.new("RGB", (width,height), "black") - totensor = transforms.Compose([transforms.ToTensor()]) - - draw = ImageDraw.Draw(imgAffinity) - r1 = radius - p = point - draw.ellipse((p[0]-r1,p[1]-r1,p[0]+r1,p[1]+r1),(255,255,255)) - - del draw - - # Compute the array to add the afinity - array = (np.array(imgAffinity)/255)[:,:,0] - - angle_vector = np.array(center) - np.array(point) - angle_vector = normalize(angle_vector) - affinity = np.concatenate([[array*angle_vector[0]],[array*angle_vector[1]]]) - - # print (tensor) - if not img_affinity is None: - # Find the angle vector - # print (angle_vector) - if length(angle_vector) >0: - angle=py_ang(angle_vector) - else: - angle = 0 - # print(angle) - c = np.array(colorsys.hsv_to_rgb(angle/360,1,1)) * 255 - draw = ImageDraw.Draw(img_affinity) - draw.ellipse((p[0]-r1,p[1]-r1,p[0]+r1,p[1]+r1),fill=(int(c[0]),int(c[1]),int(c[2]))) - del draw - re = torch.from_numpy(affinity).float() + tensor - return re, img_affinity - - -def CreateBeliefMap(img,pointsBelief,nbpoints,sigma=16): - """ - Args: - img: image - pointsBelief: list of points in the form of - [nb object, nb points, 2 (x,y)] - nbpoints: (int) number of points, DOPE uses 8 points here - sigma: (int) size of the belief map point - return: - return an array of PIL black and white images representing the - belief maps - """ - beliefsImg = [] - sigma = int(sigma) - for numb_point in range(nbpoints): - array = np.zeros(img.size) - out = np.zeros(img.size) - - for point in pointsBelief: - p = point[numb_point] - w = int(sigma*2) - if p[0]-w>=0 and p[0]+w=0 and p[1]+w int(opt.nbupdates): - torch.save(net.state_dict(), '{}/net_{}.pth'.format(opt.outf, opt.namefile)) - break - - -for epoch in range(1, opt.epochs + 1): - - if not trainingdata is None: - _runnetwork(epoch,trainingdata) - - if not opt.datatest == "": - _runnetwork(epoch,testingdata,train = False) - if opt.data == "": - break # lets get out of this if we are only testing - try: - torch.save(net.state_dict(), '{}/net_{}_{}.pth'.format(opt.outf, opt.namefile ,epoch)) - except: - pass - - if not opt.nbupdates is None and nb_update_network > int(opt.nbupdates): - break - -print ("end:" , datetime.datetime.now().time()) diff --git a/scripts/train2/config_inference/camera_info.yaml b/scripts/train2/config_inference/camera_info.yaml deleted file mode 100644 index 2d638127..00000000 --- a/scripts/train2/config_inference/camera_info.yaml +++ /dev/null @@ -1,20 +0,0 @@ -image_width: 640 -image_height: 480 -camera_name: dope_webcam_0 -camera_matrix: - rows: 3 - cols: 3 - data: [641.5, 0, 320.0, 0, 641.5, 240.0, 0, 0, 1] -distortion_model: plumb_bob -distortion_coefficients: - rows: 1 - cols: 5 - data: [0, 0, 0, 0, 0] -rectification_matrix: - rows: 3 - cols: 3 - data: [1, 0, 0, 0, 1, 0, 0, 0, 1] -projection_matrix: - rows: 3 - cols: 4 - data: [641.5, 0, 320.0, 0, 0, 641.5, 240.0, 0, 0, 0, 1, 0] diff --git a/scripts/train2/config_inference/config_pose.yaml b/scripts/train2/config_inference/config_pose.yaml deleted file mode 100644 index deeb7090..00000000 --- a/scripts/train2/config_inference/config_pose.yaml +++ /dev/null @@ -1,109 +0,0 @@ -topic_camera: "/dope/webcam/image_raw" -topic_camera_info: "/dope/webcam/camera_info" -topic_publishing: "dope" -input_is_rectified: True # Whether the input image is rectified (strongly suggested!) -downscale_height: 400 # if the input image is larger than this, scale it down to this pixel height - -# Comment any of these lines to prevent detection / pose estimation of that object -weights: { - # "cracker":"package://dope/weights/cracker_60.pth", - # "gelatin":"package://dope/weights/gelatin_60.pth", - # "meat":"package://dope/weights/meat_20.pth", - # "mustard":"package://dope/weights/mustard_60.pth", - # "soup":"package://dope/weights/soup_60.pth", - # 'peg_hole': "package://dope/weights/peg_box_40.pth", - # 'cube_red': "package://dope/weights/red_40.pth", - #"sugar":"package://dope/weights/sugar_60.pth" - # "bleach":"package://dope/weights/bleach_28_dr.pth" - # 'pudding':"weights_dope/pudding.pth" - # 'pudding':"weights_dope/dope_network/net_epoch_60.pth" - 'alphabet_soup':"weights_dope/resnet_simple/alphabe_soup.pth" -} - -# Type of neural network architecture -architectures: { - 'pudding':"dope", - 'alphabet_soup':'resnet_simple', -} - - -# Cuboid dimension in cm x,y,z -dimensions: { - "cracker": [16.403600692749023,21.343700408935547,7.179999828338623], - "gelatin": [8.918299674987793, 7.311500072479248, 2.9983000755310059], - "meat": [10.164673805236816,8.3542995452880859,5.7600898742675781], - "mustard": [9.6024150848388672,19.130100250244141,5.824894905090332], - "soup": [6.7659378051757813,10.185500144958496,6.771425724029541], - "sugar": [9.267730712890625,17.625339508056641,4.5134143829345703], - "bleach": [10.267730712890625,26.625339508056641,7.5134143829345703], - "peg_hole": [12.6,3.9,12.6], - 'cube_red':[5,5,5], - 'pudding':[49.47199821472168, 29.923000335693359, 83.498001098632812], - 'alphabet_soup':[8.3555002212524414, 7.1121001243591309, 6.6055998802185059] - -} - -class_ids: { - "cracker": 1, - "gelatin": 2, - "meat": 3, - "mustard": 4, - "soup": 5, - "sugar": 6, - "bleach": 7, - "peg_hole": 8, - "cube_red": 9, - 'pudding': 10, - 'alphabet_soup': 12, -} - -draw_colors: { - "cracker": [13, 255, 128], # green - "gelatin": [255, 255, 255], # while - "meat": [0, 104, 255], # blue - "mustard": [217,12, 232], # magenta - "soup": [255, 101, 0], # orange - "sugar": [232, 222, 12], # yellow - "bleach": [232, 222, 12], # yellow - "peg_hole": [232, 222, 12], # yellow - "cube_red": [255,0,0], - "pudding": [255,0,0], -} - -# optional: provide a transform that is applied to the pose returned by DOPE -model_transforms: { -# "cracker": [[ 0, 0, 1, 0], -# [ 0, -1, 0, 0], -# [ 1, 0, 0, 0], -# [ 0, 0, 0, 1]] -} - -# optional: if you provide a mesh of the object here, a mesh marker will be -# published for visualization in RViz -# You can use the nvdu_ycb tool to download the meshes: https://github.com/NVIDIA/Dataset_Utilities#nvdu_ycb -meshes: { -# "cracker": "file://path/to/Dataset_Utilities/nvdu/data/ycb/aligned_cm/003_cracker_box/google_16k/textured.obj", -# "gelatin": "file://path/to/Dataset_Utilities/nvdu/data/ycb/aligned_cm/009_gelatin_box/google_16k/textured.obj", -# "meat": "file://path/to/Dataset_Utilities/nvdu/data/ycb/aligned_cm/010_potted_meat_can/google_16k/textured.obj", -# "mustard": "file://path/to/Dataset_Utilities/nvdu/data/ycb/aligned_cm/006_mustard_bottle/google_16k/textured.obj", -# "soup": "file://path/to/Dataset_Utilities/nvdu/data/ycb/aligned_cm/005_tomato_soup_can/google_16k/textured.obj", -# "sugar": "file://path/to/Dataset_Utilities/nvdu/data/ycb/aligned_cm/004_sugar_box/google_16k/textured.obj", -# "bleach": "file://path/to/Dataset_Utilities/nvdu/data/ycb/aligned_cm/021_bleach_cleanser/google_16k/textured.obj", -} - -# optional: If the specified meshes are not in meters, provide a scale here (e.g. if the mesh is in centimeters, scale should be 0.01). default scale: 1.0. -mesh_scales: { - "cracker": 0.01, - "gelatin": 0.01, - "meat": 0.01, - "mustard": 0.01, - "soup": 0.01, - "sugar": 0.01, - "bleach": 0.01, -} - -# Config params for DOPE -thresh_angle: 0.5 -thresh_map: 0.0001 -sigma: 3 -thresh_points: 0.1 diff --git a/scripts/train2/inference.py b/scripts/train2/inference.py deleted file mode 100755 index 45c86a25..00000000 --- a/scripts/train2/inference.py +++ /dev/null @@ -1,388 +0,0 @@ -#!/usr/bin/env python3 - -# Copyright (c) 2018 NVIDIA Corporation. All rights reserved. -# This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. -# https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode - -""" -This file runs DOPE without ROS, either on an image folder or from a Realsense -camera image stream. -""" - -from __future__ import print_function - -import cv2 -import numpy as np -from PIL import Image -from PIL import ImageDraw - -import sys -sys.path.append("inference") -from cuboid import Cuboid3d -from cuboid_pnp_solver import CuboidPNPSolver -from detector import ModelData, ObjectDetector - -import simplejson as json -import copy - -class Draw(object): - """Drawing helper class to visualize the neural network output""" - - def __init__(self, im): - """ - :param im: The image to draw in. - """ - self.draw = ImageDraw.Draw(im) - - def draw_line(self, point1, point2, line_color, line_width=2): - """Draws line on image""" - if point1 is not None and point2 is not None: - self.draw.line([point1, point2], fill=line_color, width=line_width) - - def draw_dot(self, point, point_color, point_radius): - """Draws dot (filled circle) on image""" - if point is not None: - xy = [ - point[0] - point_radius, - point[1] - point_radius, - point[0] + point_radius, - point[1] + point_radius - ] - self.draw.ellipse(xy, - fill=point_color, - outline=point_color - ) - - def draw_cube(self, points, color=(255, 0, 0)): - """ - Draws cube with a thick solid line across - the front top edge and an X on the top face. - """ - - # draw front - self.draw_line(points[0], points[1], color) - self.draw_line(points[1], points[2], color) - self.draw_line(points[3], points[2], color) - self.draw_line(points[3], points[0], color) - - # draw back - self.draw_line(points[4], points[5], color) - self.draw_line(points[6], points[5], color) - self.draw_line(points[6], points[7], color) - self.draw_line(points[4], points[7], color) - - # draw sides - self.draw_line(points[0], points[4], color) - self.draw_line(points[7], points[3], color) - self.draw_line(points[5], points[1], color) - self.draw_line(points[2], points[6], color) - - # draw dots - self.draw_dot(points[0], point_color=color, point_radius=4) - self.draw_dot(points[1], point_color=color, point_radius=4) - - # draw x on the top - self.draw_line(points[0], points[5], color) - self.draw_line(points[1], points[4], color) - - -class DopeNode(object): - """ROS node that listens to image topic, runs DOPE, and publishes DOPE results""" - def __init__(self, - config, # config yaml loaded eg dict - ): - self.pubs = {} - self.models = {} - self.pnp_solvers = {} - self.pub_dimension = {} - self.draw_colors = {} - self.dimensions = {} - self.class_ids = {} - self.model_transforms = {} - self.meshes = {} - self.mesh_scales = {} - - self.input_is_rectified = config['input_is_rectified'] - self.downscale_height = config['downscale_height'] - - self.config_detect = lambda: None - self.config_detect.mask_edges = 1 - self.config_detect.mask_faces = 1 - self.config_detect.vertex = 1 - self.config_detect.threshold = 0.5 - self.config_detect.softmax = 1000 - self.config_detect.thresh_angle = config['thresh_angle'] - self.config_detect.thresh_map = config['thresh_map'] - self.config_detect.sigma = config['sigma'] - self.config_detect.thresh_points = config["thresh_points"] - - # For each object to detect, load network model, create PNP solver, and start ROS publishers - print(config['weights']) - for model in config['weights']: - print(model) - self.models[model] = \ - ModelData( - model, - config['weights'][model], - architecture = config['architectures'][model] - ) - self.models[model].load_net_model() - print('loaded') - - try: - self.draw_colors[model] = tuple(config["draw_colors"][model]) - except: - self.draw_colors[model] = (0,255,0) - self.dimensions[model] = tuple(config["dimensions"][model]) - self.class_ids[model] = config["class_ids"][model] - - self.pnp_solvers[model] = \ - CuboidPNPSolver( - model, - cuboid3d=Cuboid3d(config['dimensions'][model]) - ) - - - # print("Running DOPE... (Listening to camera topic: '{}')".format(config['~topic_camera'))) - print("Ctrl-C to stop") - - def image_callback(self, - img, - camera_info, - img_name = "00000.png", # this is the name of the img file to save, it needs the .png at the end - output_folder = 'out_inference', # folder where to put the output - ): - img_name = str(img_name).zfill(5) - """Image callback""" - - # img = self.cv_bridge.imgmsg_to_cv2(image_msg, "rgb8") - - # cv2.imwrite('img.png', cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) # for debugging - - # Update camera matrix and distortion coefficients - if self.input_is_rectified: - P = np.matrix(camera_info['projection_matrix']['data'], dtype='float64').copy() - P.resize((3, 4)) - camera_matrix = P[:, :3] - dist_coeffs = np.zeros((4, 1)) - else: - # TODO - camera_matrix = np.matrix(camera_info.K, dtype='float64') - camera_matrix.resize((3, 3)) - dist_coeffs = np.matrix(camera_info.D, dtype='float64') - dist_coeffs.resize((len(camera_info.D), 1)) - - camera_matrix_for_json = copy.deepcopy(camera_matrix) - - # Downscale image if necessary - height, width, _ = img.shape - scaling_factor = float(self.downscale_height) / height - if scaling_factor < 1.0: - camera_matrix[:2] *= scaling_factor - img = cv2.resize(img, (int(scaling_factor * width), int(scaling_factor * height))) - - for m in self.models: - self.pnp_solvers[m].set_camera_intrinsic_matrix(camera_matrix) - self.pnp_solvers[m].set_dist_coeffs(dist_coeffs) - - # Copy and draw image - img_copy = img.copy() - im = Image.fromarray(img_copy) - draw = Draw(im) - - # dictionary for the final output - dict_out = { - "camera_data": { - "intrinsics": { - "cx": camera_matrix_for_json[0, 2], - "cy": camera_matrix_for_json[1, 2], - "fx": camera_matrix_for_json[0, 0], - "fy": camera_matrix_for_json[1, 1], - }, - "width": width, - "height": height, - }, - "objects": [], - } - for m in self.models: - # Detect object - results, beliefs = ObjectDetector.detect_object_in_image( - self.models[m].net, - self.pnp_solvers[m], - img, - self.config_detect, - make_belief_debug_img=True - ) - # print(results) - # print('---') - # continue - # Publish pose and overlay cube on image - for i_r, result in enumerate(results): - if result["location"] is None: - continue - # print(result) - loc = result["location"] - ori = result["quaternion"] - - CONVERT_SCALE_CM_TO_METERS = 100 - loc = [l / CONVERT_SCALE_CM_TO_METERS for l in loc] - - print(loc) - - dict_out['objects'].append({ - 'class':m, - 'location':np.array(loc).tolist(), - 'quaternion_xyzw':np.array(ori).tolist(), - 'projected_cuboid':np.array(result['projected_points']).tolist(), - }) - # print( dict_out ) - - # transform orientation - # TODO - # transformed_ori = tf.transformations.quaternion_multiply(ori, self.model_transforms[m]) - - # rotate bbox dimensions if necessary - # (this only works properly if model_transform is in 90 degree angles) - # dims = rotate_vector(vector=self.dimensions[m], quaternion=self.model_transforms[m]) - # dims = np.absolute(dims) - # dims = tuple(dims) - - # Draw the cube - if None not in result['projected_points']: - points2d = [] - for pair in result['projected_points']: - points2d.append(tuple(pair)) - draw.draw_cube(points2d, self.draw_colors[m]) - # save the output of the image. - im.save(f"{output_folder}/{img_name}") - if beliefs is not None: - beliefs.save(f"{output_folder}/{img_name[:img_name.rfind('.')]}_belief.png") - - # save the json files - with open(f"{output_folder}/{img_name.replace('png','json')}", 'w') as fp: - json.dump(dict_out, fp, indent=4) - - - -def rotate_vector(vector, quaternion): - q_conj = tf.transformations.quaternion_conjugate(quaternion) - vector = np.array(vector, dtype='float64') - vector = np.append(vector, [0.0]) - vector = tf.transformations.quaternion_multiply(q_conj, vector) - vector = tf.transformations.quaternion_multiply(vector, quaternion) - return vector[:3] - -if __name__ == "__main__": - - import argparse - import yaml - import glob - import os - - parser = argparse.ArgumentParser() - parser.add_argument("--pause", - default=0, - help='pause between images') - parser.add_argument("--showbelief", - action="store_true", - help='show the belief maps') - parser.add_argument("--dontshow", - action="store_true", - help='headless mode') - parser.add_argument("--outf", - default="out_experiment", - help='where to store the output') - parser.add_argument("--data", - default=None, - help='folder for data images to load, *.png, *.jpeg, *jpg') - parser.add_argument("--config", - default="config_inference/config_pose.yaml", - help='folder for the inference configs') - parser.add_argument("--camera", - default="config_inference/camera_info.yaml", - help='camera info file') - parser.add_argument('--realsense', - action='store_true', - help='use the realsense camera') - - - opt = parser.parse_args() - - # load the configs - with open(opt.config) as f: - config = yaml.load(f, Loader=yaml.FullLoader) - with open(opt.camera) as f: - camera_info = yaml.load(f, Loader=yaml.FullLoader) - - # setup the realsense - if opt.realsense: - import pyrealsense2 as rs - # Configure depth and color streams - pipeline = rs.pipeline() - config = rs.config() - config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30) - config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30) - - # Start streaming - pipeline.start(config) - - - # create the output folder - print (f"output is located in {opt.outf}") - - try: - os.makedirs(f"{opt.outf}") - except OSError: - pass - - - # load the images if there are some - imgs = [] - imgsname = [] - - if not opt.data is None: - videopath = opt.data - for j in sorted(glob.glob(videopath+"/*.png")): - imgs.append(j) - imgsname.append(j.replace(videopath,"").replace("/","")) - else: - if not opt.realsense: - cap = cv2.VideoCapture(0) - - # An object to run dope node - dope_node = DopeNode(config) - - - # starting the loop here - i_image = -1 - - while True: - i_image+=1 - - # Capture frame-by-frame - - if not opt.data: - if opt.realsense: - frames = pipeline.wait_for_frames() - depth_frame = frames.get_depth_frame() - color_frame = frames.get_color_frame() - else: - ret, frame = cap.read() - - img_name = i_image - else: - if i_image >= len(imgs): - break - - frame = cv2.imread(imgs[i_image]) - print(f"frame {imgsname[i_image]}") - img_name = imgsname[i_image] - - frame = frame[...,::-1].copy() - - # call the inference node - dope_node.image_callback( - frame, - camera_info, - img_name = img_name, - output_folder = opt.outf) diff --git a/scripts/train2/inference/cuboid.py b/scripts/train2/inference/cuboid.py deleted file mode 100755 index d819beaa..00000000 --- a/scripts/train2/inference/cuboid.py +++ /dev/null @@ -1,148 +0,0 @@ -# Copyright (c) 2018 NVIDIA Corporation. All rights reserved. -# This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. -# https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode - -from enum import IntEnum, unique -import numpy as np -import cv2 -from pyrr import Quaternion, Matrix44, Vector3, euler - -# Related to the object's local coordinate system -# @unique -class CuboidVertexType(IntEnum): - FrontTopRight = 0 - FrontTopLeft = 1 - FrontBottomLeft = 2 - FrontBottomRight = 3 - RearTopRight = 4 - RearTopLeft = 5 - RearBottomLeft = 6 - RearBottomRight = 7 - Center = 8 - TotalCornerVertexCount = 8 # Corner vertexes doesn't include the center point - TotalVertexCount = 9 - -# List of the vertex indexes in each line edges of the cuboid -CuboidLineIndexes = [ - # Front face - [ CuboidVertexType.FrontTopLeft, CuboidVertexType.FrontTopRight ], - [ CuboidVertexType.FrontTopRight, CuboidVertexType.FrontBottomRight ], - [ CuboidVertexType.FrontBottomRight, CuboidVertexType.FrontBottomLeft ], - [ CuboidVertexType.FrontBottomLeft, CuboidVertexType.FrontTopLeft ], - # Back face - [ CuboidVertexType.RearTopLeft, CuboidVertexType.RearTopRight ], - [ CuboidVertexType.RearTopRight, CuboidVertexType.RearBottomRight ], - [ CuboidVertexType.RearBottomRight, CuboidVertexType.RearBottomLeft ], - [ CuboidVertexType.RearBottomLeft, CuboidVertexType.RearTopLeft ], - # Left face - [ CuboidVertexType.FrontBottomLeft, CuboidVertexType.RearBottomLeft ], - [ CuboidVertexType.FrontTopLeft, CuboidVertexType.RearTopLeft ], - # Right face - [ CuboidVertexType.FrontBottomRight, CuboidVertexType.RearBottomRight ], - [ CuboidVertexType.FrontTopRight, CuboidVertexType.RearTopRight ], -] - - -# ========================= Cuboid3d ========================= -class Cuboid3d(): - '''This class contains a 3D cuboid.''' - - # Create a box with a certain size - def __init__(self, size3d = [1.0, 1.0, 1.0], center_location = [0, 0, 0], - coord_system = None, parent_object = None): - - # NOTE: This local coordinate system is similar - # to the intrinsic transform matrix of a 3d object - self.center_location = center_location - self.coord_system = coord_system - self.size3d = size3d - self._vertices = [0, 0, 0] * CuboidVertexType.TotalVertexCount - - self.generate_vertexes() - - def get_vertex(self, vertex_type): - """Returns the location of a vertex. - - Args: - vertex_type: enum of type CuboidVertexType - - Returns: - Numpy array(3) - Location of the vertex type in the cuboid - """ - return self._vertices[vertex_type] - - def get_vertices(self): - return self._vertices - - def generate_vertexes(self): - width, height, depth = self.size3d - - # By default just use the normal OpenCV coordinate system - if (self.coord_system is None): - cx, cy, cz = self.center_location - # X axis point to the right - right = cx + width / 2.0 - left = cx - width / 2.0 - # Y axis point downward - top = cy - height / 2.0 - bottom = cy + height / 2.0 - # Z axis point forward - front = cz + depth / 2.0 - rear = cz - depth / 2.0 - - # List of 8 vertices of the box - self._vertices = [ - [right, top, front], # Front Top Right - [left, top, front], # Front Top Left - [left, bottom, front], # Front Bottom Left - [right, bottom, front], # Front Bottom Right - [right, top, rear], # Rear Top Right - [left, top, rear], # Rear Top Left - [left, bottom, rear], # Rear Bottom Left - [right, bottom, rear], # Rear Bottom Right - self.center_location, # Center - ] - else: - sx, sy, sz = self.size3d - forward = np.array(self.coord_system.forward, dtype=float) * sy * 0.5 - up = np.array(self.coord_system.up, dtype=float) * sz * 0.5 - right = np.array(self.coord_system.right, dtype=float) * sx * 0.5 - center = np.array(self.center_location, dtype=float) - self._vertices = [ - center + forward + up + right, # Front Top Right - center + forward + up - right, # Front Top Left - center + forward - up - right, # Front Bottom Left - center + forward - up + right, # Front Bottom Right - center - forward + up + right, # Rear Top Right - center - forward + up - right, # Rear Top Left - center - forward - up - right, # Rear Bottom Left - center - forward - up + right, # Rear Bottom Right - self.center_location, # Center - ] - - def get_projected_cuboid2d(self, cuboid_transform, camera_intrinsic_matrix): - """ - Projects the cuboid into the image plane using camera intrinsics. - - Args: - cuboid_transform: the world transform of the cuboid - camera_intrinsic_matrix: camera intrinsic matrix - - Returns: - Cuboid2d - the projected cuboid points - """ - - world_transform_matrix = cuboid_transform - rvec = [0, 0, 0] - tvec = [0, 0, 0] - dist_coeffs = np.zeros((4, 1)) - - transformed_vertices = [0, 0, 0] * CuboidVertexType.TotalVertexCount - for vertex_index in range(CuboidVertexType.TotalVertexCount): - vertex3d = self._vertices[vertex_index] - transformed_vertices[vertex_index] = world_transform_matrix * vertex3d - - projected_vertices = cv2.projectPoints(transformed_vertices, rvec, tvec, - camera_intrinsic_matrix, dist_coeffs) - - return Cuboid2d(projected_vertices) diff --git a/scripts/train2/inference/cuboid_pnp_solver.py b/scripts/train2/inference/cuboid_pnp_solver.py deleted file mode 100755 index b19fa779..00000000 --- a/scripts/train2/inference/cuboid_pnp_solver.py +++ /dev/null @@ -1,143 +0,0 @@ -# Copyright (c) 2018 NVIDIA Corporation. All rights reserved. -# This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. -# https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode - -import cv2 -import numpy as np -from cuboid import CuboidVertexType -from pyrr import Quaternion - - -class CuboidPNPSolver(object): - """ - This class is used to find the 6-DoF pose of a cuboid given its projected vertices. - - Runs perspective-n-point (PNP) algorithm. - """ - - # Class variables - cv2version = cv2.__version__.split('.') - cv2majorversion = int(cv2version[0]) - - def __init__(self, object_name="", camera_intrinsic_matrix = None, cuboid3d = None, - dist_coeffs = np.zeros((4, 1))): - self.object_name = object_name - if (not camera_intrinsic_matrix is None): - self._camera_intrinsic_matrix = camera_intrinsic_matrix - else: - self._camera_intrinsic_matrix = np.array([ - [0, 0, 0], - [0, 0, 0], - [0, 0, 0] - ]) - self._cuboid3d = cuboid3d - - # print (cuboid3d.get_vertices()) - # raise() - self._dist_coeffs = dist_coeffs - - def set_camera_intrinsic_matrix(self, new_intrinsic_matrix): - '''Sets the camera intrinsic matrix''' - self._camera_intrinsic_matrix = new_intrinsic_matrix - - def set_dist_coeffs(self, dist_coeffs): - '''Sets the camera intrinsic matrix''' - self._dist_coeffs = dist_coeffs - - def solve_pnp(self, cuboid2d_points, pnp_algorithm = None): - """ - Detects the rotation and traslation - of a cuboid object from its vertexes' - 2D location in the image - """ - - # Fallback to default PNP algorithm base on OpenCV version - if pnp_algorithm is None: - if CuboidPNPSolver.cv2majorversion == 2: - pnp_algorithm = cv2.CV_ITERATIVE - elif CuboidPNPSolver.cv2majorversion == 3: - pnp_algorithm = cv2.SOLVEPNP_ITERATIVE - # Alternative algorithms: - # pnp_algorithm = SOLVE_PNP_P3P - # pnp_algorithm = cv2.SOLVEPNP_EPNP - if pnp_algorithm is None: - # pnp_algorithm = 1 - pnp_algorithm = cv2.SOLVEPNP_EPNP - - - location = None - quaternion = None - projected_points = cuboid2d_points - - cuboid3d_points = np.array(self._cuboid3d.get_vertices()) - obj_2d_points = [] - obj_3d_points = [] - - for i in range(CuboidVertexType.TotalVertexCount): - check_point_2d = cuboid2d_points[i] - # Ignore invalid points - if (check_point_2d is None): - continue - obj_2d_points.append(check_point_2d) - obj_3d_points.append(cuboid3d_points[i]) - - obj_2d_points = np.array(obj_2d_points, dtype=float) - obj_3d_points = np.array(obj_3d_points, dtype=float) - - valid_point_count = len(obj_2d_points) - - # Can only do PNP if we have more than 3 valid points - is_points_valid = valid_point_count >= 4 - - if is_points_valid: - - ret, rvec, tvec = cv2.solvePnP( - obj_3d_points, - obj_2d_points, - self._camera_intrinsic_matrix, - self._dist_coeffs, - flags=pnp_algorithm - ) - - if ret: - location = list(x[0] for x in tvec) - quaternion = self.convert_rvec_to_quaternion(rvec) - - projected_points, _ = cv2.projectPoints(cuboid3d_points, rvec, tvec, self._camera_intrinsic_matrix, self._dist_coeffs) - projected_points = np.squeeze(projected_points) - - # If the location.Z is negative or object is behind the camera then flip both location and rotation - x, y, z = location - if z < 0: - # Get the opposite location - location = [-x, -y, -z] - - # Change the rotation by 180 degree - rotate_angle = np.pi - rotate_quaternion = Quaternion.from_axis_rotation(location, rotate_angle) - quaternion = rotate_quaternion.cross(quaternion) - - return location, quaternion, projected_points - - def convert_rvec_to_quaternion(self, rvec): - '''Convert rvec (which is log quaternion) to quaternion''' - theta = np.sqrt(rvec[0] * rvec[0] + rvec[1] * rvec[1] + rvec[2] * rvec[2]) # in radians - raxis = [rvec[0] / theta, rvec[1] / theta, rvec[2] / theta] - - # pyrr's Quaternion (order is XYZW), https://pyrr.readthedocs.io/en/latest/oo_api_quaternion.html - return Quaternion.from_axis_rotation(raxis, theta) - - # Alternatively: pyquaternion - # return Quaternion(axis=raxis, radians=theta) # uses OpenCV's Quaternion (order is WXYZ) - - def project_points(self, rvec, tvec): - '''Project points from model onto image using rotation, translation''' - output_points, tmp = cv2.projectPoints( - self.__object_vertex_coordinates, - rvec, - tvec, - self.__camera_intrinsic_matrix, - self.__dist_coeffs) - - output_points = np.squeeze(output_points) - return output_points \ No newline at end of file diff --git a/scripts/train2/models.py b/scripts/train2/models.py deleted file mode 100755 index 3475733e..00000000 --- a/scripts/train2/models.py +++ /dev/null @@ -1,877 +0,0 @@ -''' -NVIDIA from jtremblay@gmail.com -''' - -# Networks -import numpy as np -import torch -import argparse -import os -import random -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.nn.parallel -import torch.backends.cudnn as cudnn -import torch.optim as optim -import torch.utils.data -import torchvision.datasets as dset -import torchvision.transforms as transforms -import torchvision.utils as vutils -from torch.autograd import Variable -import torchvision.models as models -import torch.utils.data as data -import time - - -# two classes taken from mobilenet -# https://github.com/pytorch/vision/blob/master/torchvision/models/mobilenet.py -class ConvBNReLU(nn.Sequential): - def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1, norm_layer=None): - padding = (kernel_size - 1) // 2 - if norm_layer is None: - norm_layer = nn.BatchNorm2d - super(ConvBNReLU, self).__init__( - nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, groups=groups, bias=False), - norm_layer(out_planes), - nn.ReLU6(inplace=True) - ) - - - -class InvertedResidual(nn.Module): - def __init__(self, inp, oup, stride, expand_ratio, norm_layer=None): - super(InvertedResidual, self).__init__() - self.stride = stride - assert stride in [1, 2] - - if norm_layer is None: - norm_layer = nn.BatchNorm2d - - hidden_dim = int(round(inp * expand_ratio)) - self.use_res_connect = self.stride == 1 and inp == oup - - layers = [] - if expand_ratio != 1: - # pw - layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1, norm_layer=norm_layer)) - layers.extend([ - # dw - ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim, norm_layer=norm_layer), - # pw-linear - nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False), - norm_layer(oup), - ]) - self.conv = nn.Sequential(*layers) - - def forward(self, x): - if self.use_res_connect: - return x + self.conv(x) - else: - return self.conv(x) - -class DopeMobileNet(nn.Module): - def __init__( - self, - pretrained=False, - numBeliefMap=9, - numAffinity=16, - stop_at_stage=6 # number of stages to process (if less than total number of stages) - ): - super(DopeMobileNet, self).__init__() - - self.mobile_feature = torch.hub.load('pytorch/vision:v0.6.0', 'mobilenet_v2', pretrained=True).features - - # upsample to 50x50 from 13x13 - self.upsample = nn.Sequential() - self.upsample.add_module('0', nn.Upsample(scale_factor=2)) - - # should this go before the upsample? - # self.upsample.add_module('4', nn.Conv2d(1280, 640, - # kernel_size=3, stride=1, padding=1)) - self.upsample.add_module('44',InvertedResidual(1280, 640, stride=1, expand_ratio=6, norm_layer=nn.BatchNorm2d)) - # self.upsample.add_module('55',InvertedResidual(1280, 640, stride=1, expand_ratio=6, norm_layer=nn.BatchNorm2d)) - - # self.upsample.add_module('5', nn.ReLU(inplace=True)) - - # self.upsample.add_module('6', nn.Conv2d(640, 320, - # kernel_size=3, stride=1, padding=1)) - - self.upsample.add_module('10', nn.Upsample(scale_factor=2)) - # self.upsample.add_module('14', nn.Conv2d(320, 160, - # kernel_size=3, stride=1, padding=1)) - # self.upsample.add_module('15', nn.ReLU(inplace=True)) - # self.upsample.add_module('16', nn.Conv2d(160, 64, - # kernel_size=3, stride=1, padding=0)) - self.upsample.add_module('55',InvertedResidual(640, 320, stride=1, expand_ratio=6, norm_layer=nn.BatchNorm2d)) - self.upsample.add_module('56',InvertedResidual(320, 64, stride=1, expand_ratio=6, norm_layer=nn.BatchNorm2d)) - - # set 50,50 - self.upsample.add_module('4', nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=0)) - - # final output - change that for mobile block - # self.heads_0 = nn.Sequential() - - def build_block(inputs, outputs, nb_layers = 2 ): - layers = [] - layers.append(InvertedResidual(inputs, 64, stride=1, expand_ratio=6, norm_layer=nn.BatchNorm2d)) - for l in range(nb_layers-1): - layers.append(InvertedResidual(64, 64, stride=1, expand_ratio=6, norm_layer=nn.BatchNorm2d)) - layers.append(nn.Conv2d(64, outputs, kernel_size=3, stride=1, padding=1)) - # layers.append('4', nn.Conv2d(64, outputs, kernel_size=3, stride=1, padding=1)) - return nn.Sequential(*layers) - - self.head_0_beliefs = build_block(64,numBeliefMap) - self.head_0_aff = build_block(64,(numBeliefMap-1)*2,3) - - self.head_1_beliefs = build_block(64+numBeliefMap+((numBeliefMap-1)*2),numBeliefMap,3) - self.head_1_aff = build_block(64+numBeliefMap+(numBeliefMap-1)*2,(numBeliefMap-1)*2,2) - - self.head_2_beliefs = build_block(64+numBeliefMap+((numBeliefMap-1)*2),numBeliefMap,3) - self.head_2_aff = build_block(64+numBeliefMap+(numBeliefMap-1)*2,(numBeliefMap-1)*2,1) - - - - def forward(self, x): - '''Runs inference on the neural network''' - # print(x.shape) - out_features = self.mobile_feature(x) - # print('out2_features',out_features.shape) - output_up = self.upsample(out_features) - # print('output_up',output_up.shape) - - # stages - belief_0 = self.head_0_beliefs(output_up) - aff_0 = self.head_0_aff(output_up) - - # print(belief_0.shape) - - out_0 = torch.cat([output_up, belief_0, aff_0], 1) - - # print(out_0.shape) - # raise() - belief_1 = self.head_1_beliefs(out_0) - aff_1 = self.head_1_aff(out_0) - - out_1 = torch.cat([output_up, belief_1, aff_1], 1) - - belief_2 = self.head_2_beliefs(out_1) - aff_2 = self.head_2_aff(out_1) - - return [belief_0,belief_1,belief_2],\ - [aff_0,aff_1,aff_2] - - - -class DopeNetwork(nn.Module): - def __init__( - self, - pretrained=False, - numBeliefMap=9, - numAffinity=16, - stop_at_stage=6 # number of stages to process (if less than total number of stages) - ): - super(DopeNetwork, self).__init__() - - self.stop_at_stage = stop_at_stage - - vgg_full = models.vgg19(pretrained=False).features - self.vgg = nn.Sequential() - for i_layer in range(24): - self.vgg.add_module(str(i_layer), vgg_full[i_layer]) - - # Add some layers - i_layer = 23 - self.vgg.add_module(str(i_layer), nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1)) - self.vgg.add_module(str(i_layer+1), nn.ReLU(inplace=True)) - self.vgg.add_module(str(i_layer+2), nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=1)) - self.vgg.add_module(str(i_layer+3), nn.ReLU(inplace=True)) - - # print('---Belief------------------------------------------------') - # _2 are the belief map stages - self.m1_2 = DopeNetwork.create_stage(128, numBeliefMap, True) - self.m2_2 = DopeNetwork.create_stage(128 + numBeliefMap + numAffinity, - numBeliefMap, False) - self.m3_2 = DopeNetwork.create_stage(128 + numBeliefMap + numAffinity, - numBeliefMap, False) - self.m4_2 = DopeNetwork.create_stage(128 + numBeliefMap + numAffinity, - numBeliefMap, False) - self.m5_2 = DopeNetwork.create_stage(128 + numBeliefMap + numAffinity, - numBeliefMap, False) - self.m6_2 = DopeNetwork.create_stage(128 + numBeliefMap + numAffinity, - numBeliefMap, False) - - # print('---Affinity----------------------------------------------') - # _1 are the affinity map stages - self.m1_1 = DopeNetwork.create_stage(128, numAffinity, True) - self.m2_1 = DopeNetwork.create_stage(128 + numBeliefMap + numAffinity, - numAffinity, False) - self.m3_1 = DopeNetwork.create_stage(128 + numBeliefMap + numAffinity, - numAffinity, False) - self.m4_1 = DopeNetwork.create_stage(128 + numBeliefMap + numAffinity, - numAffinity, False) - self.m5_1 = DopeNetwork.create_stage(128 + numBeliefMap + numAffinity, - numAffinity, False) - self.m6_1 = DopeNetwork.create_stage(128 + numBeliefMap + numAffinity, - numAffinity, False) - - - def forward(self, x): - '''Runs inference on the neural network''' - - out1 = self.vgg(x) - - out1_2 = self.m1_2(out1) - out1_1 = self.m1_1(out1) - - if self.stop_at_stage == 1: - return [out1_2],\ - [out1_1] - - out2 = torch.cat([out1_2, out1_1, out1], 1) - out2_2 = self.m2_2(out2) - out2_1 = self.m2_1(out2) - - if self.stop_at_stage == 2: - return [out1_2, out2_2],\ - [out1_1, out2_1] - - out3 = torch.cat([out2_2, out2_1, out1], 1) - out3_2 = self.m3_2(out3) - out3_1 = self.m3_1(out3) - - if self.stop_at_stage == 3: - return [out1_2, out2_2, out3_2],\ - [out1_1, out2_1, out3_1] - - out4 = torch.cat([out3_2, out3_1, out1], 1) - out4_2 = self.m4_2(out4) - out4_1 = self.m4_1(out4) - - if self.stop_at_stage == 4: - return [out1_2, out2_2, out3_2, out4_2],\ - [out1_1, out2_1, out3_1, out4_1] - - out5 = torch.cat([out4_2, out4_1, out1], 1) - out5_2 = self.m5_2(out5) - out5_1 = self.m5_1(out5) - - if self.stop_at_stage == 5: - return [out1_2, out2_2, out3_2, out4_2, out5_2],\ - [out1_1, out2_1, out3_1, out4_1, out5_1] - - out6 = torch.cat([out5_2, out5_1, out1], 1) - out6_2 = self.m6_2(out6) - out6_1 = self.m6_1(out6) - - return [out1_2, out2_2, out3_2, out4_2, out5_2, out6_2],\ - [out1_1, out2_1, out3_1, out4_1, out5_1, out6_1] - - @staticmethod - def create_stage(in_channels, out_channels, first=False): - '''Create the neural network layers for a single stage.''' - - model = nn.Sequential() - mid_channels = 128 - if first: - padding = 1 - kernel = 3 - count = 6 - final_channels = 512 - else: - padding = 3 - kernel = 7 - count = 10 - final_channels = mid_channels - - # First convolution - model.add_module("0", - nn.Conv2d( - in_channels, - mid_channels, - kernel_size=kernel, - stride=1, - padding=padding) - ) - - # Middle convolutions - i = 1 - while i < count - 1: - model.add_module(str(i), nn.ReLU(inplace=True)) - i += 1 - model.add_module(str(i), - nn.Conv2d( - mid_channels, - mid_channels, - kernel_size=kernel, - stride=1, - padding=padding)) - i += 1 - - # Penultimate convolution - model.add_module(str(i), nn.ReLU(inplace=True)) - i += 1 - model.add_module(str(i), nn.Conv2d(mid_channels, final_channels, kernel_size=1, stride=1)) - i += 1 - - # Last convolution - model.add_module(str(i), nn.ReLU(inplace=True)) - i += 1 - model.add_module(str(i), nn.Conv2d(final_channels, out_channels, kernel_size=1, stride=1)) - i += 1 - - return model - - - -class BoundaryAwareNet(nn.Module): - def __init__( - self, - pretrained_dope_path = None, # dope pretrained network - num_keypoints = 9, # number of keypoints to refress to - ): - super(BoundaryAwareNet,self).__init__() - - self.dope = DopeNetwork() - if not pretrained_dope_path is None: - # print(pretrained_dope_path) - # self.dope = torch.nn.DataParallel(self.dope) - from collections import OrderedDict - state_dict = torch.load(pretrained_dope_path) - new_state_dict = OrderedDict() - for k, v in state_dict.items(): - name = k[7:] # remove `module.` - new_state_dict[name] = v - - self.dope.load_state_dict(new_state_dict) - print("DOPE pretrained loaded") - #rest of vgg - vgg_full = models.vgg19(pretrained=True).features - - self.vgg = nn.Sequential() - for i_layer in range(0,len(vgg_full)): - self.vgg.add_module(str(i_layer), vgg_full[i_layer]) - - # input resampling - self.upsample = torch.nn.Upsample(scale_factor=8) - - self.avgpool = nn.AdaptiveAvgPool2d((7, 7)) - - self.classifier = nn.Sequential( - nn.Linear(512 * 7 * 7, 4096), - nn.ReLU(True), - nn.Dropout(), - nn.Linear(4096, 4096), - nn.ReLU(True), - nn.Dropout(), - nn.Linear(4096, num_keypoints*2), - ) - - - def forward(self,x): - - output_belief, output_affinity = self.dope(x) - - belief_summed = torch.sum(output_belief[-1],dim=1) - - # upsample - belief_summed_up = self.upsample(belief_summed.unsqueeze(1)).detach() - x_p = x * torch.cat([belief_summed_up,belief_summed_up,belief_summed_up],dim=1) + x * 0.5 - - y_vgg = self.vgg(x_p) - y = self.avgpool(y_vgg) - y_class = self.classifier(y.flatten(1)) - - - return output_belief, output_affinity, y_class - - - - -class DreamHourglassMultiStage(nn.Module): - def __init__(self, n_keypoints, - n_image_input_channels = 3, - internalize_spatial_softmax = True, - learned_beta = True, - initial_beta = 1., - n_stages = 2, - joints_input = 0, - skip_connections = False, - deconv_decoder = False, - full_output = False): - super(DreamHourglassMultiStage, self).__init__() - - self.n_keypoints = n_keypoints - self.n_image_input_channels = n_image_input_channels - self.internalize_spatial_softmax = internalize_spatial_softmax - self.skip_connections = skip_connections - self.deconv_decoder = deconv_decoder - self.full_output = full_output - - if self.internalize_spatial_softmax: - # This warning is because the forward code just ignores the second head (spatial softmax) - # Revisit later if we need multistage networks where each stage has multiple output heads that are needed - print("WARNING: Keypoint softmax output head is currently unused. Prefer training new models of this type with internalize_spatial_softmax = False.") - self.n_output_heads = 2 - self.learned_beta = learned_beta - self.initial_beta = initial_beta - else: - self.n_output_heads = 1 - self.learned_beta = False - self.joints_input = joints_input - - # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - - assert isinstance(n_stages, int), \ - "Expected \"n_stages\" to be an integer, but it is {}.".format(type(n_stages)) - assert 0 < n_stages and n_stages <= 6, \ - "DreamHourglassMultiStage can only be constructed with 1 to 6 stages at this time." - - self.num_stages = n_stages - - # Stage 1 - self.stage1 = DreamHourglass( - n_keypoints, - n_image_input_channels, - internalize_spatial_softmax, - learned_beta, - initial_beta, - joints_input = joints_input, - skip_connections = skip_connections, - deconv_decoder = deconv_decoder, - full_output = self.full_output, - ) - - # Stage 2 - if self.num_stages > 1: - self.stage2 = DreamHourglass( - n_keypoints, - n_image_input_channels + n_keypoints + (n_keypoints-1)*2, # Includes the most previous stage - internalize_spatial_softmax, - learned_beta, - initial_beta, - joints_input = joints_input, - skip_connections = skip_connections, - deconv_decoder = deconv_decoder, - full_output = self.full_output, - ) - - # Stage 3 - if self.num_stages > 2: - self.stage3 = DreamHourglass( - n_keypoints, - n_image_input_channels + n_keypoints, # Includes the most previous stage - internalize_spatial_softmax, - learned_beta, - initial_beta, - joints_input = joints_input, - skip_connections = skip_connections, - deconv_decoder = deconv_decoder, - full_output = self.full_output, - ) - - # Stage 4 - if self.num_stages > 3: - self.stage4 = DreamHourglass( - n_keypoints, - n_image_input_channels + n_keypoints, # Includes the most previous stage - internalize_spatial_softmax, - learned_beta, - initial_beta, - joints_input = joints_input, - skip_connections = skip_connections, - deconv_decoder = deconv_decoder, - full_output = self.full_output, - ) - - # Stage 5 - if self.num_stages > 4: - self.stage5 = DreamHourglass( - n_keypoints, - n_image_input_channels + n_keypoints, # Includes the most previous stage - internalize_spatial_softmax, - learned_beta, - initial_beta, - joints_input = joints_input, - skip_connections = skip_connections, - deconv_decoder = deconv_decoder, - full_output = self.full_output, - ) - - # Stage 6 - if self.num_stages > 5: - self.stage6 = DreamHourglass( - n_keypoints, - n_image_input_channels + n_keypoints, # Includes the most previous stage - internalize_spatial_softmax, - learned_beta, - initial_beta, - joints_input = joints_input, - skip_connections = skip_connections, - deconv_decoder = deconv_decoder, - full_output = self.full_output, - ) - - def forward(self, x, joints = None, verbose = False): - - y_output_stage1 = self.stage1(x, joints=joints) - y_0_1 = y_output_stage1[0] # Just keeping belief maps for now - y_1_1 = y_output_stage1[1] - - if self.num_stages == 1: - return [y_0_1],[y_1_1] - - if self.num_stages > 1: - # Upsample - y_output_stage2 = self.stage2(torch.cat([x, y_0_1,y_1_1], dim=1), joints=joints) - y2 = y_output_stage2[0] # Just keeping belief maps for now - - if self.num_stages == 2: - return [y_0_1, y2],[y_1_1,y_output_stage2[1]] - - # if self.num_stages > 2: - # # Upsample - # if self.deconv_decoder or self.full_output: - # y2_upsampled = y2 - # else: - # y2_upsampled = nn.functional.interpolate(y2, scale_factor=4) # TBD: change scale factor depending on image resolution - # y_output_stage3 = self.stage3(torch.cat([x, y2_upsampled], dim=1), joints=joints) - # y3 = y_output_stage3[0] # Just keeping belief maps for now - - # if self.num_stages == 3: - # return [y_0_1, y2, y3] - - # if self.num_stages > 3: - # # Upsample - # if self.deconv_decoder or self.full_output: - # y3_upsampled = y3 - # else: - # y3_upsampled = nn.functional.interpolate(y3, scale_factor=4) # TBD: change scale factor depending on image resolution - # y_output_stage4 = self.stage4(torch.cat([x, y3_upsampled], dim=1), joints=joints) - # y4 = y_output_stage4[0] # Just keeping belief maps for now - - # if self.num_stages == 4: - # return [y_0_1, y2, y3, y4] - - # if self.num_stages > 4: - # # Upsample - # if self.deconv_decoder or self.full_output: - # y4_upsampled = y4 - # else: - # y4_upsampled = nn.functional.interpolate(y4, scale_factor=4) # TBD: change scale factor depending on image resolution - # y_output_stage5 = self.stage5(torch.cat([x, y4_upsampled], dim=1), joints=joints) - # y5 = y_output_stage5[0] # Just keeping belief maps for now - - # if self.num_stages == 5: - # return [y_0_1, y2, y3, y4, y5] - - # if self.num_stages > 5: - # # Upsample - # if self.deconv_decoder or self.full_output: - # y5_upsampled = y5 - # else: - # y5_upsampled = nn.functional.interpolate(y5, scale_factor=4) # TBD: change scale factor depending on image resolution - # y_output_stage6 = self.stage6(torch.cat([x, y5_upsampled], dim=1), joints=joints) - # y6 = y_output_stage6[0] # Just keeping belief maps for now - - # if self.num_stages == 6: - # return [y_0_1, y2, y3, y4, y5, y6] - - -# Based on DopeHourglassBlockSmall, not using skipped connections -class DreamHourglass(nn.Module): - def __init__(self, n_keypoints, - n_image_input_channels = 3, - internalize_spatial_softmax = True, - learned_beta = True, - initial_beta = 1., - joints_input = 0, - skip_connections = False, - deconv_decoder = False, - full_output = False): - super(DreamHourglass, self).__init__() - self.n_keypoints = n_keypoints - self.n_image_input_channels = n_image_input_channels - self.internalize_spatial_softmax = internalize_spatial_softmax - self.skip_connections = skip_connections - self.deconv_decoder = deconv_decoder - self.full_output = full_output - - if self.internalize_spatial_softmax: - self.n_output_heads = 2 - self.learned_beta = learned_beta - self.initial_beta = initial_beta - else: - self.n_output_heads = 1 - self.learned_beta = False - - # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - - vgg_t = models.vgg19(pretrained=True).features - - self.down_sample = nn.MaxPool2d(2) - - self.layer_0_1_down = nn.Sequential() - self.layer_0_1_down.add_module('0', nn.Conv2d(self.n_image_input_channels, 64, - kernel_size=3, stride=1, padding=1)) - for layer in range(1,4): - self.layer_0_1_down.add_module(str(layer), vgg_t[layer]) - - self.layer_0_2_down = nn.Sequential() - for layer in range(5,9): - self.layer_0_2_down.add_module(str(layer), vgg_t[layer]) - - self.layer_0_3_down = nn.Sequential() - for layer in range(10,18): - self.layer_0_3_down.add_module(str(layer), vgg_t[layer]) - - self.layer_0_4_down = nn.Sequential() - for layer in range(19,27): - self.layer_0_4_down.add_module(str(layer), vgg_t[layer]) - - self.layer_0_5_down = nn.Sequential() - for layer in range(28,36): - self.layer_0_5_down.add_module(str(layer), vgg_t[layer]) - - #Head 1 - if self.deconv_decoder: - # Decoder primarily uses ConvTranspose2d - self.deconv_0_4 = nn.Sequential() - deconv_input = 513 if joints_input > 0 else 512 - self.deconv_0_4.add_module('0', nn.ConvTranspose2d(deconv_input, 256, - kernel_size=(3, 3), stride=(2, 2), padding=1, output_padding=1)) - self.deconv_0_4.add_module('1', nn.ReLU(inplace=True)) - self.deconv_0_4.add_module('2', nn.Conv2d(256, 256, - kernel_size=3, stride=1, padding=1)) - self.deconv_0_4.add_module('3', nn.ReLU(inplace=True)) - - self.deconv_0_3 = nn.Sequential() - self.deconv_0_3.add_module('0', nn.ConvTranspose2d(256, 128, - kernel_size=(3, 3), stride=(2, 2), padding=1, output_padding=1)) - self.deconv_0_3.add_module('1', nn.ReLU(inplace=True)) - self.deconv_0_3.add_module('2', nn.Conv2d(128, 128, - kernel_size=3,stride=1,padding=1)) - self.deconv_0_3.add_module('3', nn.ReLU(inplace=True)) - - self.deconv_0_2 = nn.Sequential() - self.deconv_0_2.add_module('0', nn.ConvTranspose2d(128, 64, - kernel_size=(3, 3), stride=(2, 2), padding=1, output_padding=1)) - self.deconv_0_2.add_module('1', nn.ReLU(inplace=True)) - self.deconv_0_2.add_module('2', nn.Conv2d(64, 64, - kernel_size=3,stride=1,padding=1)) - self.deconv_0_2.add_module('3', nn.ReLU(inplace=True)) - - self.deconv_0_1 = nn.Sequential() - self.deconv_0_1.add_module('0', nn.ConvTranspose2d(64, 64, - kernel_size=(3, 3), stride=(2, 2), padding=1, output_padding=1)) - self.deconv_0_1.add_module('1', nn.ReLU(inplace=True)) - - else: - # Decoder primarily uses Upsampling - for keypoints - self.upsample_0_4 = nn.Sequential() - self.upsample_0_4.add_module('0', nn.Upsample(scale_factor=2)) - - # should this go before the upsample? - upsample_input = 513 if joints_input > 0 else 512 - self.upsample_0_4.add_module('4', nn.Conv2d(upsample_input, 256, - kernel_size=3, stride=1, padding=1)) - self.upsample_0_4.add_module('5', nn.ReLU(inplace=True)) - self.upsample_0_4.add_module('6', nn.Conv2d(256, 256, - kernel_size=3, stride=1, padding=1)) - - self.upsample_0_3 = nn.Sequential() - self.upsample_0_3.add_module('0', nn.Upsample(scale_factor=2)) - self.upsample_0_3.add_module('4', nn.Conv2d(256, 128, - kernel_size=3, stride=1, padding=1)) - self.upsample_0_3.add_module('5', nn.ReLU(inplace=True)) - self.upsample_0_3.add_module('6', nn.Conv2d(128, 64, - kernel_size=3, stride=1, padding=1)) - - if self.full_output: - self.upsample_0_2 = nn.Sequential() - self.upsample_0_2.add_module('0', nn.Upsample(scale_factor=2)) - self.upsample_0_2.add_module('2', nn.Conv2d(64, 64, - kernel_size=3,stride=1,padding=1)) - self.upsample_0_2.add_module('3', nn.ReLU(inplace=True)) - self.upsample_0_2.add_module('4', nn.Conv2d(64, 64, - kernel_size=3,stride=1,padding=1)) - self.upsample_0_2.add_module('5', nn.ReLU(inplace=True)) - - - self.upsample_0_1 = nn.Sequential() - self.upsample_0_1.add_module('00', nn.Upsample(scale_factor=2)) - self.upsample_0_1.add_module('2', nn.Conv2d(64, 64, - kernel_size=3,stride=1,padding=1)) - self.upsample_0_1.add_module('3', nn.ReLU(inplace=True)) - self.upsample_0_1.add_module('4', nn.Conv2d(64, 64, - kernel_size=3,stride=1,padding=1)) - self.upsample_0_1.add_module('5', nn.ReLU(inplace=True)) - - # Decoder primarily uses Upsampling - for affinities - self.upsample_1_4 = nn.Sequential() - self.upsample_1_4.add_module('0', nn.Upsample(scale_factor=2)) - - # should this go before the upsample? - upsample_input = 513 if joints_input > 0 else 512 - self.upsample_1_4.add_module('4', nn.Conv2d(upsample_input, 256, - kernel_size=3, stride=1, padding=1)) - self.upsample_1_4.add_module('5', nn.ReLU(inplace=True)) - self.upsample_1_4.add_module('6', nn.Conv2d(256, 256, - kernel_size=3, stride=1, padding=1)) - - self.upsample_1_3 = nn.Sequential() - self.upsample_1_3.add_module('0', nn.Upsample(scale_factor=2)) - self.upsample_1_3.add_module('4', nn.Conv2d(256, 128, - kernel_size=3, stride=1, padding=1)) - self.upsample_1_3.add_module('5', nn.ReLU(inplace=True)) - self.upsample_1_3.add_module('6', nn.Conv2d(128, 64, - kernel_size=3, stride=1, padding=1)) - - if self.full_output: - self.upsample_1_2 = nn.Sequential() - self.upsample_1_2.add_module('0', nn.Upsample(scale_factor=2)) - self.upsample_1_2.add_module('2', nn.Conv2d(64, 64, - kernel_size=3,stride=1,padding=1)) - self.upsample_1_2.add_module('3', nn.ReLU(inplace=True)) - self.upsample_1_2.add_module('4', nn.Conv2d(64, 64, - kernel_size=3,stride=1,padding=1)) - self.upsample_1_2.add_module('5', nn.ReLU(inplace=True)) - - - self.upsample_1_1 = nn.Sequential() - self.upsample_1_1.add_module('00', nn.Upsample(scale_factor=2)) - self.upsample_1_1.add_module('2', nn.Conv2d(64, 64, - kernel_size=3,stride=1,padding=1)) - self.upsample_1_1.add_module('3', nn.ReLU(inplace=True)) - self.upsample_1_1.add_module('4', nn.Conv2d(64, 64, - kernel_size=3,stride=1,padding=1)) - self.upsample_1_1.add_module('5', nn.ReLU(inplace=True)) - - - # Output head - goes from [batch x 64 x height x width] -> [batch x n_keypoints x height x width] - self.heads_0 = nn.Sequential() - self.heads_0.add_module('0', nn.Conv2d(64, 64, - kernel_size=3, stride=1, padding=1)) - self.heads_0.add_module('1', nn.ReLU(inplace=True)) - self.heads_0.add_module('2', nn.Conv2d(64, 32, - kernel_size=3, stride=1, padding=1)) - self.heads_0.add_module('3', nn.ReLU(inplace=True)) - self.heads_0.add_module('4', nn.Conv2d(32, self.n_keypoints, - kernel_size=3, stride=1, padding=1)) - - self.heads_1 = nn.Sequential() - self.heads_1.add_module('0', nn.Conv2d(64, 64, - kernel_size=3, stride=1, padding=1)) - self.heads_1.add_module('1', nn.ReLU(inplace=True)) - self.heads_1.add_module('2', nn.Conv2d(64, 32, - kernel_size=3, stride=1, padding=1)) - self.heads_1.add_module('3', nn.ReLU(inplace=True)) - self.heads_1.add_module('4', nn.Conv2d(32, (self.n_keypoints-1)*2, - kernel_size=3, stride=1, padding=1)) - - - def forward(self, x, joints=None): - - # Encoder - x_0_1 = self.layer_0_1_down(x) - x_0_1_d = self.down_sample(x_0_1) - x_0_2 = self.layer_0_2_down(x_0_1_d) - x_0_2_d = self.down_sample(x_0_2) - x_0_3 = self.layer_0_3_down(x_0_2_d) - x_0_3_d = self.down_sample(x_0_3) - x_0_4 = self.layer_0_4_down(x_0_3_d) - x_0_4_d = self.down_sample(x_0_4) - x_0_5 = self.layer_0_5_down(x_0_4_d) - - # Append joints to latent space if provided - if joints is not None: - joint_output = self.joint_head(joints) - if self.skip_connections: - decoder_input = torch.cat([x_0_5 + x_0_4_d, joint_output.reshape(joint_output.shape[0],1,25,25)], dim=1) - else: - decoder_input = torch.cat([x_0_5, joint_output.reshape(joint_output.shape[0],1,25,25)], dim=1) - else: - if self.skip_connections: - decoder_input = x_0_5 + x_0_4_d - else: - decoder_input = x_0_5 - - # Decoder - if self.deconv_decoder: - y_0_5 = self.deconv_0_4(decoder_input) - - if self.skip_connections: - y_0_4 = self.deconv_0_3(y_0_5 + x_0_3_d) - else: - y_0_4 = self.deconv_0_3(y_0_5) - - if self.skip_connections: - y_0_3 = self.deconv_0_2(y_0_4 + x_0_2_d) - else: - y_0_3 = self.deconv_0_2(y_0_4) - - if self.skip_connections: - y_0_out = self.deconv_0_1(y_0_3 + x_0_1_d) - else: - y_0_out = self.deconv_0_1(y_0_3) - - if self.skip_connections: - output_head_0 = self.heads_0(y_0_out + x_0_1) - else: - output_head_0 = self.heads_0(y_0_out) - - else: - y_0_5 = self.upsample_0_4(decoder_input) - - if self.skip_connections: - y_0_out = self.upsample_0_3(y_0_5 + x_0_3_d) - else: - y_0_out = self.upsample_0_3(y_0_5) - - if self.full_output: - y_0_out = self.upsample_0_2(y_0_out) - y_0_out = self.upsample_0_1(y_0_out) - - output_head_0 = self.heads_0(y_0_out) - - # SECOND HEAD - y_1_5 = self.upsample_1_4(decoder_input) - - if self.skip_connections: - y_1_out = self.upsample_1_3(y_1_5 + x_1_3_d) - else: - y_1_out = self.upsample_1_3(y_1_5) - - if self.full_output: - y_1_out = self.upsample_1_2(y_1_out) - y_1_out = self.upsample_1_1(y_1_out) - - output_head_1 = self.heads_1(y_1_out) - - # Output heads - outputs = [] - outputs.append(output_head_0) - - # Return outputs - return output_head_0,output_head_1 - - -if __name__ == '__main__': - import torch - # n_keypoints = 7 - # n_joints = 10 - batch_size = 2 - - # print('ResnetSimple') - # net = ResnetSimple().cuda() - # y = net(torch.zeros(batch_size, 3, 400, 400).cuda()) - # print(y[0][-1].shape) - # print() - # del net, y - a = torch.sum(torch.zeros(2,9,50,50),dim=1) - print(a.shape) - # net = BoundaryAwareNet().cuda() - net = DopeMobileNet().cuda() - y = net(torch.zeros(batch_size, 3, 400, 400).cuda()) - - # print(y.shape) \ No newline at end of file diff --git a/scripts/train2/readme.md b/scripts/train2/readme.md deleted file mode 100755 index 0b5585ed..00000000 --- a/scripts/train2/readme.md +++ /dev/null @@ -1,21 +0,0 @@ -# Training - -This is the training code used for the NViSII [paper](). You can also use this training script on the training data generated by the training script in `scripts/nvisii_data_gen/` - -``` -python -m torch.distributed.launch --nproc_per_node=1 train.py --network dope --epochs 2 --batchsize 10 --outf tmp/ --data ../nvisii_data_gen/output/output_example/ -``` - -There is an accompanying dataset you can also use to train DOPE on the meat can with the shiny top. [link here](https://drive.google.com/file/d/1Q5VLnlt1gu2pKIAcUo9uzSyWw1nGlSF8/view?usp=sharing). - -# Inference - -I also made an inference script that runs without any ROS components. - -``` -python inference.py -``` - -Look at the file for more information, similar to the ROS node everything is run through the yaml files in `config_inference`. It is very similar to the original code with some changes. - -Check `models.py` as we are proposing different architectures. \ No newline at end of file diff --git a/scripts/train2/requirements.txt b/scripts/train2/requirements.txt deleted file mode 100755 index 4accddba..00000000 --- a/scripts/train2/requirements.txt +++ /dev/null @@ -1,16 +0,0 @@ -opencv-python-headless<4.3 -albumentations -matplotlib -simplejson -numpy -opencv_python -photutils -scipy -torch -pyquaternion -tqdm -pyrr -Pillow==5.2.0 -torchvision -PyYAML -tensorboardX diff --git a/scripts/train2/train.py b/scripts/train2/train.py deleted file mode 100755 index 828c705e..00000000 --- a/scripts/train2/train.py +++ /dev/null @@ -1,630 +0,0 @@ -""" -python train.py --data data/path/to/images -Use the NDDS format for images and json. - -""" - - -from __future__ import print_function - -import argparse -import os -import random -import shutil - -try: - import configparser as configparser -except ImportError: - import ConfigParser as configparser - -# import cv2 -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.nn.parallel -import torch.backends.cudnn as cudnn -import torch.optim as optim -import torch.utils.data -import torchvision.datasets as dset -import torchvision.transforms as transforms -import torchvision.utils as vutils -from torch.autograd import Variable -import torchvision.models as models -import datetime -import json - -from tensorboardX import SummaryWriter - - -from PIL import Image -from PIL import ImageDraw - -from models import * -from utils_dope import * - - -import warnings -warnings.filterwarnings("ignore") - - -os.environ["CUDA_VISIBLE_DEVICES"]="0,1,2,3,4,5,6,7" - -torch.autograd.set_detect_anomaly(False) -torch.autograd.profiler.profile(False) -torch.autograd.gradcheck = False -torch.backends.cudnn.benchmark = True - -# import ctypes - -# _libcudart = ctypes.CDLL('libcudart.so') -# # Set device limit on the current device -# # cudaLimitMaxL2FetchGranularity = 0x05 -# pValue = ctypes.cast((ctypes.c_int*1)(), ctypes.POINTER(ctypes.c_int)) -# _libcudart.cudaDeviceSetLimit(ctypes.c_int(0x05), ctypes.c_int(128)) -# _libcudart.cudaDeviceGetLimit(pValue, ctypes.c_int(0x05)) -# assert pValue.contents.value == 128 - - - - -# os.environ["CUDA_VISIBLE_DEVICES"]="0,1,2,3" -# os.environ["CUDA_VISIBLE_DEVICES"]="0" - -print ("start:" , datetime.datetime.now().time()) - -conf_parser = argparse.ArgumentParser( - description=__doc__, # printed with -h/--help - # Don't mess with format of description - formatter_class=argparse.RawDescriptionHelpFormatter, - # Turn off help, so we print all options in response to -h - add_help=False - ) -conf_parser.add_argument("-c", "--config", - help="Specify config file", metavar="FILE") - -parser = argparse.ArgumentParser() -parser.add_argument('--data', nargs='+', help='path to training data') -parser.add_argument('--datatest', nargs='+', default="", help='path to data testing set') -parser.add_argument('--testonly', action='store_true', help='only run inference') -parser.add_argument('--testbatchsize', default=1,type=int, help='size of the batchsize for testing') -parser.add_argument('--freeze', action='store_true', help='use affinity maps for training') -parser.add_argument('--test', action='store_true', help='use affinity maps for training') -parser.add_argument('--debug', action='store_true', help='gt vs. prediction show difference') -parser.add_argument('--savetest', action='store_true', help='Save the output of the testing -- this might slow things') -parser.add_argument('--horovod', action='store_true', help='run the network with horovod') -parser.add_argument('--objects',nargs='+', type=str, default=None, - help='In the dataset which objets of interest') -parser.add_argument('--optimizer', default='adam', - help='which optimizer to use, default=adam') -parser.add_argument('--workers', type=int, help='number of data loading workers', default=8) -parser.add_argument('--batchsize', type=int, default=32, help='input batch size') -parser.add_argument('--imagesize', type=int, default=448, help='the height / width of the input image to network') -parser.add_argument('--lr', type=float, default=0.0001, help='learning rate, default=0.001') -parser.add_argument('--noise', type=float, default=2.0, help='gaussian noise added to the image') -parser.add_argument('--net', default='', help="path to net (to continue training)") -parser.add_argument('--net_dope', default=None, help="path to pretrained dope_network") -parser.add_argument('--network', default='dream_full', help="[dream_full,dope,mobile]") -parser.add_argument('--namefile', default='epoch', help="name to put on the file of the save weightss") -parser.add_argument('--manualseed', type=int, help='manual seed') -parser.add_argument('--epochs', type=int, default=60) -parser.add_argument('--loginterval', type=int, default=100) -# parser.add_argument('--gpuids',nargs='+', type=int, default=[0,1,2,3], help='GPUs to use') -parser.add_argument('--gpuids',nargs='+', type=int, default=[0], help='GPUs to use') -parser.add_argument('--extensions',nargs='+', type=str, default=["png"], - help='Extensions to use, you can have multiple entries seperated by space, e.g., png jpeg. ') -parser.add_argument('--outf', default='tmp', help='folder to output images and model checkpoints') -parser.add_argument('--sigma', default=4, help='keypoint creation sigma') -parser.add_argument('--keypoints', default=None, - help='list of keypoints to load from the json files.') -parser.add_argument('--no_affinity', default=False, - help='remove the affinity part of DOPE') -parser.add_argument('--datastyle', default='json', help='What data style are you using: nvdl,lov,pascal') -parser.add_argument('--save', action="store_true", help='save a batch and quit') -parser.add_argument('--verbose', action="store_true", help='speak out your mind program') -parser.add_argument('--dontsave', action="store_true", default=False,help='dont_save_weights') - -parser.add_argument("--pretrained",default=True,help='do you want to have pretrained weights') - -# Feature extractor -parser.add_argument('--features', default="vgg", help='vgg or resnet') - -# datasize is used -parser.add_argument('--datasize', default=None, help='the size in absolute to use, e.g. 2000') -parser.add_argument('--nbupdates', default=None, help='nb max update to network') - -# These are used when you want to mix two datasets with different percentage. -parser.add_argument('--data1', default=None, help='path to dataset1') -parser.add_argument('--data2', default=None, help='path to dataset2') -parser.add_argument('--size1', default=None, help='size of dataset1 in percentage (0,1)') -parser.add_argument('--size2', default=None, help='size of dataset2 in percentage (0,1)') -parser.add_argument("--local_rank", type=int) - -# Read the config but do not overwrite the args written -args, remaining_argv = conf_parser.parse_known_args() -defaults = { "option":"default" } - -if args.config: - config = configparser.SafeConfigParser() - config.read([args.config]) - defaults.update(dict(config.items("defaults"))) - -parser.set_defaults(**defaults) -parser.add_argument("--option") -opt = parser.parse_args(remaining_argv) - -if opt.keypoints: - opt.keypoints = eval(opt.keypoints) - -if isinstance(opt.extensions, str): - opt.extensions = eval(opt.extensions) - -if isinstance(opt.objects, str): - opt.objects = eval(opt.objects) - -try: - for i,o in enumerate(opt.objects): - opt.objects[i] = opt.objects[i].lower() -except: - pass - -train_sampler = None -if opt.horovod: - import horovod.torch as hvd - import torch.utils.data.distributed - hvd.init() - torch.cuda.set_device(hvd.local_rank()) - -if opt.testonly: - opt.epochs = 1 - -if not "/" in opt.outf: - opt.outf = "train_{}".format(opt.outf) - -try: - os.makedirs(opt.outf) -except OSError: - pass - -# Delete the folder for testing -try: - shutil.rmtree("{}/test/".format(opt.outf)) - print ('deleted the test folder') -except: - pass - -# Delete the folder for testing -try: - shutil.rmtree("{}/test/".format(opt.outf)) - print ('deleted the test folder') -except: - pass - - -# Create the folders for debugging -if opt.debug: - try: - shutil.rmtree("{}/debug/".format(opt.outf)) - # print ('deleted the debug folder') - except: - pass - try: - os.makedirs("{}/debug/".format(opt.outf)) - # print ("created debug dir") - except OSError: - pass - - -with open (opt.outf+'/header.txt','w') as file: - file.write(str(opt)+"\n") - -if opt.manualseed is None: - opt.manualseed = random.randint(1, 10000) - -with open (opt.outf+'/header.txt','w') as file: - file.write(str(opt)) - file.write("seed: "+ str(opt.manualseed)+'\n') -with open (opt.outf+'/test_metric.csv','w') as file: - file.write("epoch, passed, total \n") - - -# Data check from args -# if opt.testbatchsize > 1: -# print ('ERROR: test batchsize has to be one') -# opt.testbatchsize = 1 -if opt.local_rank == 0: - writer = SummaryWriter(opt.outf+"/runs/") - -random.seed(opt.manualseed) - - -torch.cuda.set_device(opt.local_rank) -torch.distributed.init_process_group(backend='NCCL', - init_method='env://') - - -torch.manual_seed(opt.manualseed) - -torch.cuda.manual_seed_all(opt.manualseed) - -if not opt.save: - contrast = 0.2 - brightness = 0.2 - noise = 0.1 - normal_imgs = [0.59,0.25] - transform = transforms.Compose([ - AddRandomContrast(0.2), - AddRandomBrightness(0.2), - transforms.Scale(opt.imagesize), - ]) -else: - contrast = 0.00001 - brightness = 0.00001 - noise = 0.00001 - normal_imgs = None - transform = transforms.Compose([ - transforms.Resize(opt.imagesize), - transforms.ToTensor()]) - - -if opt.network == 'resnetsimple': - net = ResnetSimple() - output_size = 208 - -elif opt.network == 'dope': - net = DopeNetwork() - output_size = 50 - opt.sigma = 0.5 - -elif opt.network == 'full': - net = () - output_size = 400 - opt.sigma = 2 - net = DreamHourglassMultiStage( - 9, - n_stages = 2, - internalize_spatial_softmax = False, - deconv_decoder = False, - full_output = True) - -elif opt.network == 'mobile': - net = () - output_size = 50 - opt.sigma = 0.5 - net = DopeMobileNet() - -elif opt.network == 'boundary': - - # if not opt.net_dope is None: - # net_dope = DopeNetwork() - # net_dope = torch.nn.DataParallel(net_dope).cuda() - # tmp = torch.load(opt.net_dope) - # net_dope.load_state_dict(tmp) - - net = BoundaryAwareNet(opt.net_dope) - output_size = 50 - opt.sigma = 1 - -else: - print(f'network {opt.network} does not exists') - quit() - - -print (f"load data: {opt.data}") -print (f"load data: {opt.datatest}") -#load the dataset using the loader in utils_pose -trainingdata = None - -if not opt.data == "": - train_dataset = CleanVisiiDopeLoader( - opt.data, - sigma = opt.sigma, - output_size = output_size, - objects = opt.objects - ) - trainingdata = torch.utils.data.DataLoader(train_dataset, - batch_size = opt.batchsize, - shuffle = True, - num_workers = opt.workers, - pin_memory = True - ) - -testingdata = None - -if not opt.datatest == "": - test_dataset = CleanVisiiDopeLoader( - opt.datatest, - sigma = opt.sigma, - output_size = output_size, - objects = opt.objects - ) - testingdata = torch.utils.data.DataLoader(test_dataset, - batch_size = opt.testbatchsize, - shuffle = True, - num_workers = opt.workers, - pin_memory = True - ) - -if not trainingdata is None: - print('training data: {} batches'.format(len(trainingdata))) -if not testingdata is None: - print('testing data: {} batches'.format(len(testingdata))) -print('load models') - -# Might want to modfify to include the cropped features from -# yolov3 - -# net = DopeHourglassBlock(input_size_channels=3,output_size_channels = 9) -# net = DopeHourglass(num_stages = 2) - - - -# net = torch.nn.DataParallel(net,device_ids=opt.gpuids).cuda() -# net = torch.nn.DataParallel(net).cuda() -net = torch.nn.parallel.DistributedDataParallel(net.cuda(), - device_ids=[opt.local_rank], - output_device=opt.local_rank) -# print(net) - -if opt.net != '': - net.load_state_dict(torch.load(opt.net)) - -parameters = filter(lambda p: p.requires_grad, net.parameters()) -optimizer = optim.Adam(parameters,lr=opt.lr) - - -with open (opt.outf+'/loss_train.txt','w') as file: - file.write('epoch,batchid,loss\n') - -with open (opt.outf+'/loss_test.txt','w') as file: - file.write('epoch,batchid,loss,l1,l1std\n') - -print("ready to train!") - -nb_update_network = 0 -best_results = {"epoch":None,'passed':None,'add_mean':None,"add_std":None} - -scaler = torch.cuda.amp.GradScaler() - -def _runnetwork(epoch,train_loader,train=True,syn=False): - global nb_update_network - # net - if train: - net.train() - else: - net.eval() - - config_detect = lambda: None - config_detect.mask_edges = 1 - config_detect.mask_faces = 1 - config_detect.vertex = 1 - config_detect.treshold = 0.5 - config_detect.softmax = 1000 - config_detect.thresh_angle = 0.5 - config_detect.thresh_map = 0.01 - config_detect.sigma = 4 - config_detect.thresh_points = 0.1 - config_detect.threshold = 0.1 - - # todo opt.model path to the 3d model - - #Create the folders for output - # try: - # namefile = '/test_{}.csv'.format(epoch) - # with open (opt.outf+namefile,'w') as file: - # file.write("img, trans_gu, trans_gt, agg_error \n") - # os.makedirs(opt.outf+"/test/{}/".format(str(epoch).zfill(3))) - all_data_to_save = [] - - loss_avg_to_log = {} - loss_avg_to_log['loss'] = [] - loss_avg_to_log['loss_affinities'] = [] - loss_avg_to_log['loss_belief'] = [] - loss_avg_to_log['loss_class'] = [] - for batch_idx, targets in enumerate(train_loader): - optimizer.zero_grad() - logged = 0 - - data = Variable(targets['img'].cuda()) - target_belief = Variable(targets['beliefs'].cuda()) - target_affinities = Variable(targets['affinities'].cuda()) - - # target_segmentation = Variable(targets['segmentation'].cuda()) - - # target_affinity_map = Variable(targets['affinity_map'][:,2,:,:]).cuda() - # print(target_belief.min(),target_belief.max()) - # print(target_affinities.min(),target_affinities.max()) - # print(target_classification.min(),target_classification.max()) - - - # print (f'data: {data.shape}') - # print (f'target_belief: {target_belief.shape}') - # print (f'target_affinities: {target_affinities.shape}') - # print (f'target_segmentation: {target_segmentation.shape}') - output_belief, output_aff = net(data) - - loss = None - - # print(f'len: {len(output_net)}') - # print(f'1: {output_net[0][0].shape}') - # print(f'2: {output_net[0][1].shape}') - # print(f'3: {output_net[0][2].shape}') - # len: 2 - # 1: torch.Size([4, 9, 100, 100]) - # 2: torch.Size([4, 16, 100, 100]) - # 3: torch.Size([4, 21, 100, 100]) - - # raise() - - loss_belief = torch.tensor(0).float().cuda() - loss_affinities = torch.tensor(0).float().cuda() - loss_class = torch.tensor(0).float().cuda() - # loss_segmentation = torch.tensor(0).float().cuda() - - for stage in range(len(output_aff)): #output, each belief map layers. - # print(stage[0].shape) - # print(target_affinity_map.shape) - # raise() - # loss_tmp = (( - target_affinity_map) * (stage[0]-target_affinity_map)).mean() - - - - loss_affinities += ((output_aff[stage] - target_affinities)*(output_aff[stage] - target_affinities)).mean() - - # print(output_belief[stage].shape) - # print(target_belief.shape) - - loss_belief += ((output_belief[stage] - target_belief)*(output_belief[stage] - target_belief)).mean() - - # loss_tmp = ((stage[1] - target_affinities) * (stage[1]-target_affinities)).mean() - # loss_affinities += loss_tmp - - # loss_tmp = ((stage[2] - target_segmentation) * (stage[2]-target_segmentation)).mean() - # loss_segmentation += loss_tmp - - # loss = loss_belief + loss_affinities * 0.9 + loss_segmentation * 0.00001 - - # compute classification loss - # loss_class = ((target_classification.flatten(1) - output_classification) * (target_classification.flatten(1) - output_classification)).mean() - # print(loss_class.item(),loss_belief.item(),loss_affinities.item() ) - loss = loss_affinities + loss_belief - - #save one output of the network and one gt - # if False : - if batch_idx == 0 : - - if train: - post = "train" - else: - post = 'test' - - if opt.local_rank == 0: - - for i_output in range(1): - - # input images - writer.add_image(f"{post}_input_{i_output}", - targets['img_original'][i_output], - epoch, - dataformats="CWH", - ) - - # belief maps gt - imgs = VisualizeBeliefMap(target_belief[i_output]) - img,grid = save_image(imgs, "some_img.png", - mean=0, std=1, nrow=3, save=False) - writer.add_image(f"{post}_belief_ground_truth_{i_output}", - grid, - epoch, - dataformats="CWH") - - # belief maps guess - imgs = VisualizeBeliefMap(output_belief[-1][i_output]) - img,grid = save_image(imgs, "some_img.png", - mean=0, std=1, nrow=3, save=False) - writer.add_image(f"{post}_belief_guess_{i_output}", - grid, - epoch, - dataformats="CWH") - - if not train: - # TODO look into using batchsize > 1 when the input data is - # constant - # comes as Batch size x nb object x 3 or 4 - - # save the images then continue - data_to_save = {} - data_to_save['loss_test'] = float(loss.item()) - - - # @ray.remote - from multiprocessing import Process - - - if train: - # optimizer.zero_grad() - # for param in net.parameters(): - # param.grad = None - - loss.backward() - # scaler.scale(loss).backward() - - optimizer.step() - # scaler.step(optimizer) - - # scaler.update() - nb_update_network+=1 - - - # namefile = '/loss_train.txt' - # with open (opt.outf+namefile,'a') as file: - # s = '{}, {},{:.15f}\n'.format( - # epoch,batch_idx,loss.item()) - # file.write(s) - - # log the loss - loss_avg_to_log["loss"].append(loss.item()) - loss_avg_to_log["loss_class"].append(loss_class.item()) - loss_avg_to_log["loss_affinities"].append(loss_affinities.item()) - loss_avg_to_log["loss_belief"].append(loss_belief.item()) - - if batch_idx % opt.loginterval == 0: - if not opt.horovod or hvd.rank() == 0: - if train: - print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.15f}'.format( - epoch, batch_idx * len(data), len(train_loader.dataset), - 100. * batch_idx / len(train_loader), loss.item())) - - else: - print('Test Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.15f}'.format( - epoch, batch_idx * len(data), len(train_loader.dataset), - 100. * batch_idx / len(train_loader), loss.item())) - - # break - # if not opt.nbupdates is None and nb_update_network > int(opt.nbupdates): - # torch.save(net.state_dict(), '{}/net_{}.pth'.format(opt.outf, opt.namefile)) - # break - - # log the loss values - if opt.local_rank == 0: - - if train: - writer.add_scalar('loss/train_loss',np.mean(loss_avg_to_log["loss"]),epoch) - writer.add_scalar('loss/train_cls',np.mean(loss_avg_to_log["loss_class"]),epoch) - writer.add_scalar('loss/train_aff',np.mean(loss_avg_to_log["loss_affinities"]),epoch) - writer.add_scalar('loss/train_bel',np.mean(loss_avg_to_log["loss_belief"]),epoch) - else: - # import pandas as pd - # add the loss - - writer.add_scalar('loss/test_loss',np.mean(loss_avg_to_log["loss"]),epoch) - writer.add_scalar('loss/test_cls',np.mean(loss_avg_to_log["loss_class"]),epoch) - writer.add_scalar('loss/test_aff',np.mean(loss_avg_to_log["loss_affinities"]),epoch) - writer.add_scalar('loss/test_bel',np.mean(loss_avg_to_log["loss_belief"]),epoch) - -for epoch in range(1, opt.epochs + 1): - - if not trainingdata is None and not opt.testonly: - _runnetwork(epoch,trainingdata) - if opt.optimizer == 'sgd': - scheduler.step() - - if not opt.datatest == "": - _runnetwork(epoch,testingdata,train = False) - if opt.data == "": - break # lets get out of this if we are only testing - try: - if opt.local_rank == 0: - if not opt.dontsave is True: - torch.save(net.state_dict(), f'{opt.outf}/net_{opt.namefile}_{str(epoch).zfill(2)}.pth') - else: - torch.save(net.state_dict(), f'{opt.outf}/net_{opt.namefile}.pth') - except: - pass - - if not opt.nbupdates is None and nb_update_network > int(opt.nbupdates): - break -# print(best_results) -if opt.local_rank == 0: - torch.save(net.state_dict(), f'{opt.outf}/net_{opt.namefile}_{str(epoch).zfill(2)}.pth') -print ("end:" , datetime.datetime.now().time()) - diff --git a/scripts/train2/utils_dope.py b/scripts/train2/utils_dope.py deleted file mode 100755 index 642ac079..00000000 --- a/scripts/train2/utils_dope.py +++ /dev/null @@ -1,1114 +0,0 @@ -""" -NVIDIA from jtremblay@gmail.com -""" -import numpy as np -import scipy.ndimage -import torch - -import argparse -import os -import random -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.nn.parallel -import torch.backends.cudnn as cudnn -import torch.optim as optim -import torch.utils.data -import torchvision.datasets as dset -import torchvision.transforms as transforms -import torchvision.utils as vutils -from torch.autograd import Variable -import torchvision.models as models -import torch.utils.data as data -import glob -import os -import copy -import pickle - -from PIL import Image -from PIL import ImageFilter -from PIL import ImageOps -from PIL import ImageDraw -from PIL import ImageFont -from PIL import ImageEnhance - -from math import acos -from math import sqrt -from math import pi - -from os.path import exists, basename -import json -from os.path import join - -import cv2 -import albumentations as A - -def default_loader(path): - return Image.open(path).convert('RGB') - -def length(v): - return sqrt(v[0]**2+v[1]**2) - -def dot_product(v,w): - return v[0]*w[0]+v[1]*w[1] - -def normalize(v): - norm=np.linalg.norm(v, ord=1) - if norm==0: - norm=np.finfo(v.dtype).eps - return v/norm - -def determinant(v,w): - return v[0]*w[1]-v[1]*w[0] - -def inner_angle(v,w): - cosx=dot_product(v,w)/(length(v)*length(w)) - rad=acos(cosx) # in radians - return rad*180/pi # returns degrees - -def py_ang(A, B=(1,0)): - inner=inner_angle(A,B) - det = determinant(A,B) - if det<0: #this is a property of the det. If the det < 0 then B is clockwise of A - return inner - else: # if the det > 0 then A is immediately clockwise of B - return 360-inner -import colorsys,math - -def getAffinity(width,height,p1,p2,tickness=12,tensor=None,img_affinity=None): - """ - take as input image width, and height or a tensor - and returns the affinity field from p1 to p2. - when tensor added then return into the tensor. - tensor here is pytorch tensor not a numpy array - """ - - if tensor is None: - tensor = torch.zeros(2,width,height).float() - - # create the canvas for the afinity output - imgAffinity = Image.new("RGB", (width,height), "black") - draw = ImageDraw.Draw(imgAffinity) - draw.line([p1,p2],fill=(255/4,255/4,255/4),width=tickness) - draw.line([p1,p2],fill=(255/2,255/2,255/2),width=2*tickness/3) - draw.line([p1,p2],fill=(255,255,255),width=tickness/3) - del draw - - # compute the array to add the afinity - array = (np.array(imgAffinity)/255)[:,:,0] - angle_vector = np.array(p2) - np.array(p1) - angle_vector = normalize(angle_vector) - - affinity = np.concatenate([[array*angle_vector[0]],[array*angle_vector[1]]]) - # print (tensor) - if not img_affinity is None: - # find the angle vector - # print (angle_vector) - if length(angle_vector) >0: - angle=py_ang(angle_vector) - else: - angle = 0 - # print(angle) - c = np.array(colorsys.hsv_to_rgb(angle/360,1,1)) * 255 - draw = ImageDraw.Draw(img_affinity) - draw.line([p1,p2],fill=(int(c[0]/4),int(c[1]/4),int(c[2]/4)),width=tickness) - draw.line([p1,p2],fill=(int(c[0]/2),int(c[1]/2),int(c[2]/2)),width=2*tickness/3) - draw.line([p1,p2],fill=(int(c[0]),int(c[1]),int(c[2])),width=tickness/3) - del draw - re = torch.from_numpy(affinity).float() + tensor - return re, img_affinity - -def loadimages(root,datastyle = "json",extensions= ['png']): - imgs = [] - loadimages.extensions = extensions - - def add_json_files(path,): - - # print (path) - # print(len(glob.glob(path+"/*.json"))) - # for file_json in glob.glob(path+"/*.json"): - # if "setting" in file_json or "right" in file_json: - # continue - # data_jsons.append(file_json) - for ext in loadimages.extensions: - # for imgpath in glob.glob(path+"/*.rgb.{}".format(ext.replace('.',''))): - # if exists(imgpath) and exists(imgpath.replace(ext,"json").replace('.rgb','')): - # imgs.append((imgpath,imgpath.replace(path,"").replace("/",""), - # imgpath.replace(ext,"json").replace(".rgb",''))) - for imgpath in glob.glob(path+"/*.{}".format(ext.replace('.',''))): - if exists(imgpath) and exists(imgpath.replace(ext,"json")): - imgs.append((imgpath,imgpath.replace(path,"").replace("/",""), - imgpath.replace(ext,"json"))) - - - def explore(path): - if not os.path.isdir(path): - return - folders = [os.path.join(path, o) for o in os.listdir(path) - if os.path.isdir(os.path.join(path,o))] - if len(folders)>0: - for path_entry in folders: - - - explore(path_entry) - # raise() - else: - add_json_files(path) - - explore(root) - - return imgs - -class CleanVisiiDopeLoader(data.Dataset): - def __init__( - self, - path_dataset, - objects = None, - sigma = 1, - output_size = 400, - extensions= ["png",'jpg'], - debug = False - ): - ################### - self.path_dataset = path_dataset - self.objects_interest = objects - self.sigma = sigma - self.output_size = output_size - self.extensions = extensions - self.debug = debug - ################### - - def load_data(path,extensions): - imgs = loadimages(path,extensions = extensions) - - # Check all the folders in path - for name in os.listdir(str(path)): - imgs += loadimages(path +"/"+name,extensions = extensions) - return imgs - self.imgs = [] - for path_look in path_dataset: - self.imgs += load_data(path_look,extensions = self.extensions) - - # np.random.shuffle(self.imgs) - - if debug: - print("Debuging will be save in debug/") - if os.path.isdir("debug"): - print(f'folder {"debug"}/ exists') - else: - os.mkdir("debug") - print(f'created folder {"debug"}/') - - - def __len__(self): - return len(self.imgs) - def __getitem__(self,index): - - # load the data - path_img, img_name, path_json = self.imgs[index] - - # load the image - # img = cv2.imread(path_img,cv2.COLOR_BGR2RGB) - img = np.array(Image.open(path_img).convert('RGB')) - - # load the json file - all_projected_cuboid_keypoints = [] - with open(path_json) as f: - data_json = json.load(f) - - # load the projected cuboid keypoints - for obj in data_json['objects']: - if not self.objects_interest is None and \ - not obj['class'] in self.objects_interest\ - : - continue - # load the projected_cuboid_keypoints - if obj['visibility'] > 0: - projected_cuboid_keypoints = obj['projected_cuboid'] - else: - projected_cuboid_keypoints = [[-100,-100],[-100,-100],[-100,-100],\ - [-100,-100],[-100,-100],[-100,-100],[-100,-100],[-100,-100],[-100,-100]] - all_projected_cuboid_keypoints.append(projected_cuboid_keypoints) - - if len(all_projected_cuboid_keypoints) == 0: - all_projected_cuboid_keypoints = [[[-100,-100],[-100,-100],[-100,-100],\ - [-100,-100],[-100,-100],[-100,-100],[-100,-100],[-100,-100],[-100,-100]]] - - # flatten the keypoints - flatten_projected_cuboid = [] - for obj in all_projected_cuboid_keypoints: - for p in obj: - flatten_projected_cuboid.append(p) - - ####### - if self.debug: - img_to_save = Image.fromarray(img) - draw = ImageDraw.Draw(img_to_save) - - for ip,p in enumerate(flatten_projected_cuboid): - draw.ellipse((int(p[0])-2,int(p[1])-2,int(p[0])+2,int(p[1])+2),fill='green') - # draw.text((p[0]*2+4, p[1]*2+4),str(ip),'green',font=font) - - img_to_save.save(f"debug/{img_name.replace('.png','_original.png')}") - ####### - - - # data augmentation - transform = A.Compose( - [ - A.RandomCrop(width=400, height=400), - A.Rotate(limit=180), - A.RandomBrightnessContrast(brightness_limit=0.2,contrast_limit=0.15,p=1), - A.GaussNoise(p=1), - - ], - keypoint_params=A.KeypointParams(format='xy',remove_invisible=False) - ) - transformed = transform(image=img, keypoints=flatten_projected_cuboid) - img_transformed = transformed['image'] - flatten_projected_cuboid_transformed = transformed['keypoints'] - # img_transformed[:,:,3] = 255 - - ####### - - # transform to the final output - if not self.output_size == 400: - transform = A.Compose( - [ - A.Resize(width=self.output_size, height=self.output_size), - - ], - keypoint_params=A.KeypointParams(format='xy',remove_invisible=False) - ) - transformed = transform(image=img_transformed, keypoints=flatten_projected_cuboid_transformed) - img_transformed_output_size = transformed['image'] - flatten_projected_cuboid_transformed_output_size = transformed['keypoints'] - - else: - img_transformed_output_size = img_transformed - flatten_projected_cuboid_transformed_output_size = flatten_projected_cuboid_transformed - - - ####### - if self.debug: - img_transformed_saving = Image.fromarray(img_transformed) - - draw = ImageDraw.Draw(img_transformed_saving) - - for ip,p in enumerate(flatten_projected_cuboid_transformed): - draw.ellipse((int(p[0])-2,int(p[1])-2,int(p[0])+2,int(p[1])+2),fill='green') - # draw.text((p[0]*2+4, p[1]*2+4),str(ip),'green',font=font) - - img_transformed_saving.save(f"debug/{img_name.replace('.png','_transformed.png')}") - ####### - - # update the keypoints list - # obj x keypoint_id x (x,y) - i_all = 0 - for i_obj, obj in enumerate(all_projected_cuboid_keypoints): - for i_p, point in enumerate(obj): - all_projected_cuboid_keypoints[i_obj][i_p] = flatten_projected_cuboid_transformed_output_size[i_all] - i_all +=1 - - - # generate the belief maps - beliefs = CreateBeliefMap( - size=int(self.output_size), - pointsBelief=all_projected_cuboid_keypoints, - sigma=self.sigma, - nbpoints=9, - save=False, - ) - beliefs = torch.from_numpy(np.array(beliefs)) - # generate affinity fields with centroid. - # def GenerateMapAffinity(img,nb_vertex,pointsInterest,objects_centroid,scale): - affinities = GenerateMapAffinity( - size=int(self.output_size), - nb_vertex = 8, - pointsInterest = all_projected_cuboid_keypoints, - objects_centroid= np.array(all_projected_cuboid_keypoints)[:,-1].tolist(), - scale = 1, - # save = True, - ) - - # prepare for the image tensors - normalize_tensor = transforms.Compose( - [ - transforms.ToTensor(), - transforms.Normalize( - (0.485, 0.456, 0.406), - (0.229, 0.224, 0.225) - ) - ] - ) - to_tensor = transforms.Compose( - [ - transforms.ToTensor(), - ] - ) - img_tensor = normalize_tensor(Image.fromarray(img_transformed)) - img_original = to_tensor(img_transformed) - - ######## - if self.debug: - imgs = VisualizeBeliefMap(beliefs) - img,grid = save_image( - imgs, - f"debug/{img_name.replace('.png','_beliefs.png')}", - mean=0, std=1,nrow=3, save=True - ) - imgs = VisualizeAffinityMap(affinities) - save_image( - imgs, - f"debug/{img_name.replace('.png','_affinities.png')}", - mean=0, std=1, nrow=3, save=True - ) - ######## - img_tensor[torch.isnan(img_tensor)] = 0 - affinities[torch.isnan(affinities)] = 0 - beliefs[torch.isnan(beliefs)] = 0 - - img_tensor[torch.isinf(img_tensor)] = 0 - affinities[torch.isinf(affinities)] = 0 - beliefs[torch.isinf(beliefs)] = 0 - - - return { - 'img':img_tensor, - "affinities":torch.clamp(affinities,-1,1), - 'beliefs':torch.clamp(beliefs,0,1), - 'file_name':img_name, - 'img_original':img_original, - } - - - -def VisualizeAffinityMap( - tensor, - # tensor of (len(keypoints)*2)xwxh - threshold_norm_vector = 0.4, - # how long does the vector has to be to be drawn - points = None, - # list of points to draw in white on top of the image - factor = 1.0, - # by how much the image was reduced, scale factor - translation = (0,0) - # by how much the points were moved - - # return len(keypoints)x3xwxh # stack of images - ): - images = torch.zeros(tensor.shape[0]//2,3,tensor.shape[1],tensor.shape[2]) - for i_image in range(0,tensor.shape[0],2): #could be read as i_keypoint - # for i in range(tensor.shape[1]): - # for j in range(tensor.shape[2]): - indices = (torch.abs(tensor[i_image,:,:]) + torch.abs(tensor[i_image+1,:,:]) > threshold_norm_vector).nonzero() - # print(indices) - for indice in indices: - # print (indice) - i,j = indice - # print(tensor[i,j,i_image,0].item()) - angle_vector = np.array( - [tensor[i_image,i,j], - tensor[i_image+1,i,j]] - ) - if length(angle_vector) > threshold_norm_vector: - angle=py_ang(angle_vector) - c = colorsys.hsv_to_rgb(angle/360,1,1) - else: - c = [0,0,0] - for i_c in range(3): - images[i_image//2,i_c,i,j] = c[i_c] - if not points is None: - point = points[i_image//2] - # print (images.shape) - print ( - int(point[1]*factor+translation[1]), - int(point[0]*factor+translation[0]), - ) - images[i_image//2,:, - int(point[1]*factor+translation[1])-1:int(point[1]*factor+translation[1])+1, - int(point[0]*factor+translation[0])-1:int(point[0]*factor+translation[0])+1, - # int(point[0])-1:int(point[0])+1 - ] = 1 - - return images - -def VisualizeBeliefMap( - tensor, - # tensor of len(keypoints)xwxh - points = None, - # list of points to draw on top of the image - factor = 1.0, - # by how much the image was reduced, scale factor - translation = (0,0) - # by how much the points were moved - - # return len(keypoints)x3xwxh # stack of images in torch tensor - ): - images = torch.zeros(tensor.shape[0],3,tensor.shape[1],tensor.shape[2]) - for i_image in range(0,tensor.shape[0]): #could be read as i_keypoint - - belief = tensor[i_image].clone() - belief -= float(torch.min(belief).item()) - belief /= float(torch.max(belief).item()) - - belief = torch.clamp(belief,0,1) - belief = torch.cat([belief.unsqueeze(0),belief.unsqueeze(0),belief.unsqueeze(0)]).unsqueeze(0) - - images[i_image] = belief - - return images - - -def GenerateAffinityPoints2Tensor( - points, - # list of points in image space nx2 - tensor_mask, - # mask of the object, wxh - factor = 1.0, - # by how much the image was reduced, scale factor - translation = (0,0) - # by how much were the keypoint moved - #returns a tensor (len(points)*2)xwxh - ): - - tensor = torch.zeros(len(points)*2,tensor_mask.shape[0],tensor_mask.shape[1]) - - # find where there are ones. - indices = (tensor_mask > 0).nonzero() - for i_stack in range(0,len(points)*2,2): - i_point = i_stack//2 - for indice in indices: - i,j = indice - # j,i = indice - angle_vector = np.array([points[i_point][0] * factor + translation[0], - points[i_point][1] * factor + translation[1]]) - np.array((float(i),float(j))) - angle_vector = normalize(angle_vector) - - tensor[i_stack,i,j] = angle_vector[0] - tensor[i_stack+1,i,j] = angle_vector[1] - return tensor - - -def GenerateMapAffinity(size,nb_vertex,pointsInterest,objects_centroid,scale,save=False): - # Apply the downscale right now, so the vectors are correct. - - img_affinity = Image.new("RGB", (int(size/scale),int(size/scale)), "black") - # create the empty tensors - totensor = transforms.Compose([transforms.ToTensor()]) - - affinities = [] - for i_points in range(nb_vertex): - affinities.append(torch.zeros(2,int(size/scale),int(size/scale))) - - for i_pointsImage in range(len(pointsInterest)): - pointsImage = pointsInterest[i_pointsImage] - center = objects_centroid[i_pointsImage] - for i_points in range(nb_vertex): - point = pointsImage[i_points] - # print (pointsImage[i_points]) - affinity_pair, img_affinity = getAfinityCenter( - int(size/scale), - int(size/scale), - tuple((np.array(pointsImage[i_points])/scale).tolist()), - tuple((np.array(center)/scale).tolist()), - img_affinity = img_affinity, - radius=1) - - # affinities[i_points] = (affinities[i_points] + affinity_pair) - affinities[i_points] = (affinities[i_points] + affinity_pair)/2 - - - #Normalizing - v = affinities[i_points].numpy() - - xvec = v[0] - yvec = v[1] - - norms = np.sqrt(xvec * xvec + yvec * yvec) - nonzero = norms > 0 - - xvec[nonzero]/=norms[nonzero] - yvec[nonzero]/=norms[nonzero] - - affinities[i_points] = torch.from_numpy(np.concatenate([[xvec],[yvec]])) - affinities = torch.cat(affinities,0) - - # img_affinity.save('aff.png') - return affinities - -def getAfinityCenter(width,height,point,center,radius=7,tensor=None,img_affinity=None): - """ - Create the affinity map - """ - if tensor is None: - tensor = torch.zeros(2,height,width).float() - - # create the canvas for the afinity output - imgAffinity = Image.new("RGB", (width,height), "black") - totensor = transforms.Compose([transforms.ToTensor()]) - # raise() - draw = ImageDraw.Draw(imgAffinity) - r1 = radius - p = point - draw.ellipse((p[0]-r1,p[1]-r1,p[0]+r1,p[1]+r1),(255,255,255)) - - del draw - - # compute the array to add the afinity - array = (np.array(imgAffinity)/255)[:,:,0] - - angle_vector = np.array(center) - np.array(point) - angle_vector = normalize(angle_vector) - affinity = np.concatenate([[array*angle_vector[0]],[array*angle_vector[1]]]) - - # print (tensor) - if not img_affinity is None: - # find the angle vector - # print (angle_vector) - if length(angle_vector) >0: - angle=py_ang(angle_vector) - else: - angle = 0 - # print(angle) - c = np.array(colorsys.hsv_to_rgb(angle/360,1,1)) * 255 - draw = ImageDraw.Draw(img_affinity) - draw.ellipse((p[0]-r1,p[1]-r1,p[0]+r1,p[1]+r1),fill=(int(c[0]),int(c[1]),int(c[2]))) - del draw - re = torch.from_numpy(affinity).float() + tensor - return re, img_affinity - - -def CreateBeliefMap(size,pointsBelief,nbpoints,sigma=16,save=False): - #Create the belief maps in the points - beliefsImg = [] - # sigma = sigma - # print(img.shape) - for numb_point in range(nbpoints): - array = np.zeros([size,size]) - out = np.zeros([size,size]) - - for point in pointsBelief: - p = [point[numb_point][1],point[numb_point][0]] - w = int(sigma*2) - if p[0]-w>=0 and p[0]+w=0 and p[1]+w`_ - """ - if not (torch.is_tensor(tensor) or - (isinstance(tensor, list) and all(torch.is_tensor(t) for t in tensor))): - raise TypeError('tensor or list of tensors expected, got {}'.format(type(tensor))) - - # if list of tensors, convert to a 4D mini-batch Tensor - if isinstance(tensor, list): - tensor = torch.stack(tensor, dim=0) - - if tensor.dim() == 2: # single image H x W - tensor = tensor.view(1, tensor.size(0), tensor.size(1)) - if tensor.dim() == 3: # single image - if tensor.size(0) == 1: # if single-channel, convert to 3-channel - tensor = torch.cat((tensor, tensor, tensor), 0) - tensor = tensor.view(1, tensor.size(0), tensor.size(1), tensor.size(2)) - - if tensor.dim() == 4 and tensor.size(1) == 1: # single-channel images - tensor = torch.cat((tensor, tensor, tensor), 1) - - if normalize is True: - tensor = tensor.clone() # avoid modifying tensor in-place - if range is not None: - assert isinstance(range, tuple), \ - "range has to be a tuple (min, max) if specified. min and max are numbers" - - def norm_ip(img, min, max): - img.clamp_(min=min, max=max) - img.add_(-min).div_(max - min + 1e-5) - - def norm_range(t, range): - if range is not None: - norm_ip(t, range[0], range[1]) - else: - norm_ip(t, float(t.min()), float(t.max())) - - if scale_each is True: - for t in tensor: # loop over mini-batch dimension - norm_range(t, range) - else: - norm_range(tensor, range) - - if tensor.size(0) == 1: - return tensor.squeeze() - - # make the mini-batch of images into a grid - nmaps = tensor.size(0) - xmaps = min(nrow, nmaps) - ymaps = int(math.ceil(float(nmaps) / xmaps)) - height, width = int(tensor.size(2) + padding), int(tensor.size(3) + padding) - grid = tensor.new(3, height * ymaps + padding, width * xmaps + padding).fill_(pad_value) - k = 0 - for y in irange(ymaps): - for x in irange(xmaps): - if k >= nmaps: - break - grid.narrow(1, y * height + padding, height - padding)\ - .narrow(2, x * width + padding, width - padding)\ - .copy_(tensor[k]) - k = k + 1 - return grid - - -def save_image(tensor, filename, nrow=4, padding=2,mean=None, std=None, save=True): - """ - Saves a given Tensor into an image file. - If given a mini-batch tensor, will save the tensor as a grid of images. - """ - from PIL import Image - - tensor = tensor.cpu() - grid = make_grid(tensor, nrow=nrow, padding=10,pad_value=1) - if not mean is None: - # ndarr = grid.mul(std).add(mean).mul(255).byte().transpose(0,2).transpose(0,1).numpy() - ndarr = grid.mul(std).add(mean).mul(255).byte().transpose(0,2).transpose(0,1).numpy() - else: - ndarr = grid.mul(0.5).add(0.5).mul(255).byte().transpose(0,2).transpose(0,1).numpy() - im = Image.fromarray(ndarr) - if save is True: - im.save(filename) - return im, grid - - - - -def DrawLine(point1, point2, lineColor, lineWidth,draw): - if not point1 is None and not point2 is None: - draw.line([point1,point2],fill=lineColor,width=lineWidth) - -def DrawDot(point, pointColor, pointRadius, draw): - if not point is None: - xy = [point[0]-pointRadius, point[1]-pointRadius, point[0]+pointRadius, point[1]+pointRadius] - draw.ellipse(xy, fill=pointColor, outline=pointColor) - -def DrawCube(points, which_color = 0, color = None, draw = None): - '''Draw cube with a thick solid line across the front top edge.''' - lineWidthForDrawing = 2 - lineColor1 = (255, 215, 0) # yellow-ish - lineColor2 = (12, 115, 170) # blue-ish - lineColor3 = (45, 195, 35) # green-ish - if which_color == 3: - lineColor = lineColor3 - else: - lineColor = lineColor1 - - if not color is None: - lineColor = color - - # draw front - DrawLine(points[0], points[1], lineColor, 8, draw) #lineWidthForDrawing) - DrawLine(points[1], points[2], lineColor, lineWidthForDrawing, draw) - DrawLine(points[3], points[2], lineColor, lineWidthForDrawing, draw) - DrawLine(points[3], points[0], lineColor, lineWidthForDrawing, draw) - - # draw back - DrawLine(points[4], points[5], lineColor, lineWidthForDrawing, draw) - DrawLine(points[6], points[5], lineColor, lineWidthForDrawing, draw) - DrawLine(points[6], points[7], lineColor, lineWidthForDrawing, draw) - DrawLine(points[4], points[7], lineColor, lineWidthForDrawing, draw) - - # draw sides - DrawLine(points[0], points[4], lineColor, lineWidthForDrawing, draw) - DrawLine(points[7], points[3], lineColor, lineWidthForDrawing, draw) - DrawLine(points[5], points[1], lineColor, lineWidthForDrawing, draw) - DrawLine(points[2], points[6], lineColor, lineWidthForDrawing, draw) - - # draw dots - DrawDot(points[0], pointColor=lineColor, pointRadius = 4,draw = draw) - DrawDot(points[1], pointColor=lineColor, pointRadius = 4,draw = draw) - -def OverlayBeliefOnImage(img, beliefs, name="tmp", path="", factor=0.7, grid=3, - norm_belief = True, save = False, scale_factor=1): - """ - take as input - img: a tensor image in pytorch normalized at 0.5 - 3xwxh - belief: tensor of the same size as the image to overlay over img - nb_beliefxwxh - name: str to name the image, e.g., output.png - path: where to save, e.g., /where/to/save/ - factor: float [0,1] how much to keep the original, 1 = fully, 0 black - grid: how big the grid, e.g., 3 wide. - norm_belief: bool to normalize the values [0,1] - save: write to disk - scale_factor: how much to scale the belief map - returns the image - """ - - belief_imgs = [] - if not img is None: - in_img = img - in_img *= factor - tensor = nn.functional.upsample(beliefs.unsqueeze(0),scale_factor=scale_factor ).squeeze() - for j in range(tensor.size()[0]): - belief = tensor[j].clone() - if norm_belief: - belief -= float(torch.min(belief).data.cpu().numpy()) - belief /= float(torch.max(belief).data.cpu().numpy()) - belief = torch.clamp(belief,0,1).cpu() - - if img is None: - belief = torch.cat([ - belief.unsqueeze(0),belief.unsqueeze(0),belief.unsqueeze(0) - ]).unsqueeze(0) - else: - belief = torch.cat([ - belief.unsqueeze(0)* in_img[0,:,:] + in_img[0,:,:], - belief.unsqueeze(0)* in_img[1,:,:] + in_img[1,:,:], - belief.unsqueeze(0)* in_img[2,:,:] + in_img[2,:,:] - ]).unsqueeze(0) - - # print (in_img[0,:,:].shape) - # print ((belief.unsqueeze(0) * in_img[1,:,:] * factor + in_img[1,:,:]).shape) - # print (in_img[2,:,:].shape) - # belief = torch.cat([ - # in_img[0,:,:].unsqueeze(0), - # belief.unsqueeze(0) * in_img[1,:,:] + in_img[1,:,:], - # in_img[2,:,:].unsqueeze(0) - # ]).unsqueeze(0) - belief = torch.clamp(belief,0,1) - - belief_imgs.append(belief.data.squeeze().numpy()) - - # Create the image grid - belief_imgs = torch.tensor(np.array(belief_imgs)) - - img,grid = save_image(belief_imgs, "{}{}".format(path, name), - mean=0, std=1, nrow=grid, save=save) - - return img, grid - -def GetPoseMatrix(location,rotation): - """ - Return the rotation Matrix from a vector translation - and a quaternion rotation vector - - """ - from pyquaternion import Quaternion - - pose_matrix = np.zeros([4,4]) - q = Quaternion(x=rotation[0], y=rotation[1], z=rotation[2], w=rotation[3]) - - pose_matrix[0:3,0:3] = q.rotation_matrix - pose_matrix[0:3,3] = np.array(location) - pose_matrix[3,3] = 1 - - return pose_matrix - -def ADDErrorCuboid(pose_gu, pose_gt, cuboid): - """ - Compute the ADD error for a given cuboid. - pose_gu is the predicted pose as a matrix - pose_gt is the ground thruth pose as a matrix - cuboid is a Cuboid3D object (see inference/cuboid.py) - """ - from scipy import spatial - - #obj = self.__obj_model - vertices = np.array(cuboid._vertices) - # print (vertices.shape) - vertices = np.insert(vertices,3,1,axis=1) - vertices = np.rot90(vertices,3) - # print(vertices) - - obj = vertices - pred_obj = np.matmul(pose_gu, obj) - - # obj = self.__obj_model - # pred_obj = np.matmul(pose_gu, obj) - # print (pred_obj) - - actual_obj = np.matmul(pose_gt, obj) - #actual_obj = np.matmul(self.LtNT, actual_obj_l) - #print("PREDICTED OBJECT\n", pred_obj) - #print("ACTUAL OBJECT\n", actual_obj) - dist = spatial.distance.cdist(pred_obj.T, actual_obj.T, 'euclidean') - true_dist = [dist[i][i] for i in range(len(dist))] - #for i in range(len(true_dist)): - # if true_dist[i] >6000: - # print(i, true_dist[i]) - # print (true_dist) - # raise() - return np.mean(true_dist) - -if __name__ == '__main__': - # mask = torch.zeros(200,200) - # mask[50:100,50:100] = 1 - # m = GenerateAffinityPoints2Tensor([(50,50),(50,100),(100,50),(100,100)],mask) - # # print(m.shape) - # imgs = VisualizeAffinityMap(m,points = [(50,50),(50,100),(100,50),(100,100)]) - - # # save the images using the grid - # img,grid = save_image(imgs, "some_img.png", - # mean=0, std=1, nrow=2, save=True) - # raise() - - # path = '../data/fat_sugar_simple_20/' - # path = '../data/fat_sugar_simple_20/' - # path = "../../data/simple_cube/" - - # train_dataset = MultipleVertexJson( - # root = path, - # objectsofinterest = ['cube_red'], - # # random_translation = [0,0], - # # random_rotation = 0, - # # objectsofinterest = ['004_sugar_box_16k'] - # # scale_output = 2, - # debug = True, - # ) - - path = "../../data/single_object_dr_chocolat_pudding/" - # path = '/home/trump/raid/data/home_dataset/single_ndds1/AlphabetSoup/single' - path = '/home/trump/raid/data/visii/dr/dr_Ketchup_000/' - path = '/home/trump/raid/data/visii/dr/Ketchup' - path = ['../data/visii_fat/', '../data/TomatoSauce/'] - path = ['../../data/visii/meat_can_shiny/'] - # path = "../../data/home_dataset/pudding_1/" - # path = '/media/jtremblay/data/home_dataset/ndds1/alphabet_soup_small_single/' - # path = "../../data/home_dataset/single_object_dr_chocolat_pudding/" - # path = "../../data/home_dataset/pudding_1/" - # path = '/media/jtremblay/data/home_dataset/ndds1/alphabet_soup_small/' - - - train_dataset = CleanVisiiDopeLoader( - path, - output_size = 50, - sigma = 0.75, - debug = True, - # objects_interest = "TomatoSauce" - ) - - trainingdata = torch.utils.data.DataLoader(train_dataset, - batch_size = 12, - shuffle = True, - num_workers = 1, - pin_memory = False - ) - - # print(len(trainingdata)) - targets = iter(trainingdata).next() - - # raise() - - - # train_dataset = MultipleVertexJson( - # root = path, - # objectsofinterest = None, - # # random_translation = [0,0], - # # random_rotation = 0, - # # objectsofinterest = ['004_sugar_box_16k'] - # # output_size = 50, - # # sigma = 1, # for 50x50 - # output_size = 50, - # sigma = 4, # for 50x50 - # debug = False, - # ) - - # trainingdata = torch.utils.data.DataLoader(train_dataset, - # batch_size = 10, - # shuffle = False, - # num_workers = 1, - # pin_memory = False - # ) - - # print(len(trainingdata)) - # targets = iter(trainingdata).next() - - # # print(targets['pointsBelief'].shape) - # # print(targets['pointsBelief'].min()) - # # print(targets['pointsBelief'].max()) - # print(targets.keys()) - - - # print(targets['file_name']) - - # imgs = VisualizeBeliefMap(targets['beliefs'][0]) - - # img,grid = save_image(imgs, "some_img.png", - # mean=0, std=1, nrow=3, save=True) - - # imgs = VisualizeAffinityMap(targets['affinities'][0]) - - # img,grid = save_image(imgs, "some_aff.png", - # mean=0, std=1, nrow=3, save=True) - - # imgs = VisualizeAffinityMap(targets['affinities'][0]) - - # img,grid = save_image( - # torch.cat([ - # targets['img_original'], - # targets['img_original'], - # targets['img_original'], - # targets['img_original'], - # targets['img_original'], - # targets['img_original'], - # targets['img_original'], - # targets['img_original'], - # ]) - # , "imgs.png", - # mean=0, std=1, nrow=3, save=True) - - - # ####### test the inference on the data output - # import sys - # sys.path.append("inference") - - # from cuboid import Cuboid3d - # from cuboid_pnp_solver import CuboidPNPSolver - # from detector import ModelData, ObjectDetector - - # config = lambda: None - # config.thresh_angle = 0.5 - # config.thresh_map = 0.0001 - # config.sigma = 3 - # config.thresh_points = 0.01 - # scale_factor = 8 - # objects, all_peaks = ObjectDetector.find_objects( - # targets['beliefs'][0], - # targets['affinities'][0], - # config, - # scale_factor=scale_factor) - # for obj in objects: - # if obj is None: - # continue - # # Run PNP - # points = obj[1] + [(obj[0][0]*scale_factor, obj[0][1]*scale_factor)] - # cuboid2d = np.copy(points) - # try: - # print(cuboid2d/scale_factor) - # except: - # pass - # avg_dist = 0 - # denominator = 0 - # # for i_p,p in enumerate(cuboid2d): - # # if p is None: - # # continue - # # # print(p/scale_factor,targets['pointsBelief'][0][i_p].numpy(),np.linalg.norm(p/scale_factor-targets['pointsBelief'][0][i_p].numpy())) - # # avg_dist += np.linalg.norm(p/scale_factor-targets['pointsBelief'][0][i_p].numpy()) - # # denominator += 1 - # # print(avg_dist/denominator) - # # print(targets['pointsBelief'][0]) diff --git a/setup.cfg b/setup.cfg new file mode 100644 index 00000000..3c157b8a --- /dev/null +++ b/setup.cfg @@ -0,0 +1,4 @@ +[develop] +script_dir=$base/lib/dope_ros2 +[install] +install_scripts=$base/lib/dope_ros2 diff --git a/setup.py b/setup.py index 0cc02d6c..7ab7a32d 100644 --- a/setup.py +++ b/setup.py @@ -1,11 +1,33 @@ -## ! DO NOT MANUALLY INVOKE THIS setup.py, USE CATKIN INSTEAD +from setuptools import find_packages, setup +import os +from glob import glob -from distutils.core import setup -from catkin_pkg.python_setup import generate_distutils_setup +package_name = 'dope_ros2' -# fetch values from package.xml -setup_args = generate_distutils_setup( - packages=['dope', 'dope.inference'], - package_dir={'': 'src'}) - -setup(**setup_args) +setup( + name=package_name, + version='0.0.0', + packages=find_packages(exclude=['test']), + data_files=[ + ('share/ament_index/resource_index/packages', + ['resource/' + package_name]), + ('share/' + package_name, ['package.xml']), + (os.path.join('share', package_name, 'launch'), glob(os.path.join('launch', '*launch.[pxy][yma]*'))), + (os.path.join('share', package_name, 'config'), glob('config/*.yaml')), + (os.path.join('share', package_name, 'rviz'), glob('rviz/*.rviz')), + (os.path.join('share', package_name, 'meshes'), glob('meshes/*/*')), + (os.path.join('share', package_name, 'weights'), glob('weights/*.pth')), + ], + install_requires=['setuptools', 'inference_script'], + zip_safe=True, + maintainer='sfederico', + maintainer_email='sfederico@todo.todo', + description='TODO: Package description', + license='TODO: License declaration', + tests_require=['pytest'], + entry_points={ + 'console_scripts': [ + 'dope_node = dope_ros2.dope_node:main', + ], + }, +) diff --git a/weights/.gitignore b/weights/.gitignore new file mode 100644 index 00000000..209ee64a --- /dev/null +++ b/weights/.gitignore @@ -0,0 +1,2 @@ +*.pth + diff --git a/weights/readme.md b/weights/readme.md deleted file mode 100644 index 7d1e6d35..00000000 --- a/weights/readme.md +++ /dev/null @@ -1 +0,0 @@ -This is where you need to store the weights.