example-pipeline.yml

# A pipeline describes the list of detector and recognizer components to run on
# input files. A pipeline can have any number of 
# - deep detectors, which use YOLO for multiple object detection. Each
#      deep detector can specify its own convolutional neural network configuration and 
#      model obtained by training it on different datasets such as Pascal VOC, COCO, ImageNet, etc.
#
# - simple detectors, which use traditional cascade of weak classifiers
#
# - simple recognizers, which use Eigen faces, Fischer faces or LBPH recognition 
#
# All the component outputs for one input file - be it image or video - are saved in a single JSON report. Each input
#   generated its own separate report file.
#
# Every component which is configured to output images outputs only its own relevant annotations
#   in an image. So there may be multiple annotated image outputs for each input. A video input is considered
#   as a sequence of frames and each frame results in separate output images. Output frames of a video can be combined back into
#   a video.
---
pipeline:
  # A name for this pipeline component
- name: coco-detector

  # type can be one of [deepdetector, simpledetector, recognizer]
  type: deepdetector
  
  # "files" is a special keyword, meaning the inputs for this detector
  # are the raw photo or video files. If this is the name of another pipeline component
  # then the component uses the outputs of that component instead of the
  # raw source files.
  inputs: 
  - files 
  
  # Parameters for the component
  params: 
    model: /root/darkflow/cfg/yolo.cfg
    weights: /root/darkflow/cfg/yolo.weights
  
    
    
    
    
- name: human-face-detector-haar
  type: simpledetector

  inputs: 
  - coco-detector

  params:
    model: /root/data/haarcascades/haarcascade_frontalface_default.xml
    
    outputlabel: humanface
    
    # triggerlabels: The list of labels to filter on. Only ROIs with one of these
    # labels are considered for detection.
    triggerlabels:
    - person
    
    
    
    
- name: cat-face-detector-lbp
  type: simpledetector
  inputs: 
  - coco-detector

  params:
    model: /root/data/lbpcascades/lbpcascade_frontalcatface.xml
    
    # Label to give to ROIs of this detector.
    outputlabel: catface
    
    # Detection is run only on ROIs which match these labels.
    triggerlabels:
    - cat
    
    # Optional params for cascade classifier to adjust false positive and false negative
    # rates.
    scaledown_factor: 1.1
    min_neighbors: 3
    
    
    
    
- name: cat-face-recognizer
  type: recognizer
  inputs: 
  - cat-face-detector-lbp
  
  params:
  
    # The model directory where model files were generated during training.
    model: /root/models/mycats/
    
    triggerlabels:
    - catface
    # strategies: list of recognizers to run. one or more of [eigen, fischer, lbp]
    strategies:
    - eigen
    - fischer
    - lbp
    # output : which labels to output. Can be one of [all, mostvotes, <strategies>] where
    # 
    # "all" means outputs of all strategies
    # "mostvotes" means single output which has most votes from multiple strategies. If there'
    #       no clear majority, it'll output all.
    # "<strategies>" means output of one or more of the strategies above. 
    #       For example, 
    #           output: 
    #           - lbp
    #           - eigen
    #       means include only outputs of lbp and eigen in the outputs.
    outputlabel: 
    - mostvotes
    
    # If recognizer was trained with histogram equalization enabled, you
    # should enable it during recognition too.
    equalizehist: false
    
  
- name: combinedreporter
  type: jsonreportwriter
  inputs: 
  - coco-detector
  - human-face-detector-haar
  - cat-face-detector-lbp
  - cat-face-recognizer
  
  
  
- name: combinedvideowriter
  type: videowriter 
  inputs:
  - coco-detector
  - human-face-detector-haar
  - cat-face-detector-lbp
  - cat-face-recognizer
  
  params:
    format: mp4
    size: 
      width: 640
      height: 480
  
  
  
- name: combinedannotatedphotowriter
  type: photowriter 
  inputs:
  - coco-detector
  - human-face-detector-haar
  - cat-face-detector-lbp
  - cat-face-recognizer
  
  params:
    format: jpg
    size: 
      width: 640
      height: 480


- name: combinedannotatedframewriter
  type: framewriter 
  inputs:
  - coco-detector
  - human-face-detector-haar
  - cat-face-detector-lbp
  - cat-face-recognizer
  
  params:
    format: jpg
    size: 
      width: 128
      height: 96