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Minor changes to get the code running #1

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8e4b67e
Add requirements.txt
buesma Apr 4, 2023
b2fc15d
Replaced constant davis dataset path in depth_video_new_cache.py with…
buesma Apr 4, 2023
2aacb49
Replaced deprecated numpy np.float datatype by native python float da…
buesma Apr 4, 2023
7a13761
Switched from pytorch to torch in dependencies.
buesma Apr 13, 2023
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9 changes: 9 additions & 0 deletions requirements.txt
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Original file line number Diff line number Diff line change
@@ -0,0 +1,9 @@
torch
scikit-image
pillow
imageio
configargparse
tqdm
matplotlib
opencv-python
yacs
56 changes: 28 additions & 28 deletions slam/depth_video_new_cache.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,7 @@
from third_party.raft import load_RAFT, get_input_padder
from util.util_3dvideo import save_ply, depth_to_points_Rt, Video_3D_Webpage
from networks.MiDaS import MidasNet, MidasNet_featopt
from configs import midas_pretrain_path
from configs import midas_pretrain_path, davis_path
from networks.goem_opt import _so3_exp_map, CameraPoseDeltaCollection, DepthScaleShiftCollection, DepthBasedWarping, CameraIntrinsics, get_relative_transform
from skimage.transform import resize as imresize
from tqdm import tqdm
Expand Down Expand Up @@ -463,20 +463,20 @@ def read_images(self):
H_output, W_output = self.output_shape
H_opt, W_opt = self.opt_shape
img = imresize(img, (H_opt, W_opt),
preserve_range=True).astype(np.float)
preserve_range=True).astype(float)
img_orig = imresize(img, (H_output, W_output),
preserve_range=True).astype(np.float)
preserve_range=True).astype(float)
self.images.append(img)
self.images_orig.append(img_orig)
for i in range(1, self.number_of_frames+1):
if self.opt.not_load_mask:
seg = np.ones([H_opt, W_opt], dtype=np.float)
seg = np.ones([H_opt, W_opt], dtype=float)
else:
seg = util_sintel_io.seg_read(
join(self.paths['seg'], f'frame_{i:04d}.png'))
seg = 1-seg
seg = imresize(seg, (H_opt, W_opt),
preserve_range=True).astype(np.float)
preserve_range=True).astype(float)
seg = np.where(seg > 0.99, 1, 0)
self.masks.append(seg[None, ...])
self.images = np.stack(self.images, axis=0)
Expand Down Expand Up @@ -513,7 +513,7 @@ def read_gt_info(self):
depth = util_sintel_io.depth_read(
join(self.paths['depth'], f'frame_{i:04d}.dpt'))
depth = imresize(depth, (H_output, W_output),
preserve_range=True).astype(np.float)
preserve_range=True).astype(float)
self.depth_gt.append(depth)
for i in range(1, self.number_of_frames+1):
cam = util_sintel_io.cam_read(
Expand Down Expand Up @@ -543,8 +543,8 @@ def __init__(self, opt):

def get_paths(self, opt):
track_name = opt.track_name
data_list_root = "/mnt/localssd1/ztzhang/davis/JPEGImages/Full-Resolution"
mask_root = "/mnt/localssd1/ztzhang/davis/Annotations/Full-Resolution"
data_list_root = davis_path + '/JPEGImages/Full-Resolution'
mask_root = davis_path + '/Annotations/Full-Resolution'
image_path = join(data_list_root, f'{track_name}')
self.paths = {'image_path': image_path,
'mask_path': join(mask_root, f'{track_name}')}
Expand All @@ -561,7 +561,7 @@ def read_images(self):
self.images_orig = []
for img_path in tqdm(image_paths):
img_raw = np.asarray(Image.open(
img_path).convert('RGB')).astype(np.float)/255
img_path).convert('RGB')).astype(float)/255
if not hasattr(self, 'original_shape'):
self.original_shape = img_raw.shape[:2]
H_output, W_output = self.get_output_shape(
Expand All @@ -573,9 +573,9 @@ def read_images(self):
H_output, W_output = self.output_shape
H_opt, W_opt = self.opt_shape
img = imresize(img_raw, (H_opt, W_opt),
preserve_range=True).astype(np.float)
preserve_range=True).astype(float)
img_orig = imresize(img_raw, (H_output, W_output),
preserve_range=True).astype(np.float)
preserve_range=True).astype(float)
self.images.append(img.copy())
self.images_orig.append(img_orig.copy())
self.number_of_frames = len(self.images)
Expand Down Expand Up @@ -603,9 +603,9 @@ def read_gt_info(self):
mask_paths = mask_paths[::self.opt.image_sequence_stride]
for mask_path in mask_paths:
mask = np.asarray(Image.open(
mask_path).convert('L')).astype(np.float)/255
mask_path).convert('L')).astype(float)/255
mask = imresize(mask, self.opt_shape,
preserve_range=True).astype(np.float)
preserve_range=True).astype(float)
mask = np.where(mask > 0.001, 1, 0)
mask = 1-mask
mask = torch.from_numpy(mask[None, ...]).float().pin_memory()
Expand All @@ -623,7 +623,7 @@ def read_gt_info(self):
# np_file = np.load(depth_and_pose_path[x], allow_pickle=True)
# depth_gt = np_file['depth']+1e-6
# depth_gt = imresize(depth_gt, (H, W), preserve_range=True)
# extrinsics = np.linalg.inv(np_file['w2c']).astype(np.float)
# extrinsics = np.linalg.inv(np_file['w2c']).astype(float)
# self.R_gt.append(extrinsics[:3, :3])
# self.t_gt.append(extrinsics[:3, 3:4])
# self.depth_gt.append(depth_gt)
Expand All @@ -649,8 +649,8 @@ def __init__(self, opt):

def get_paths(self, opt):
track_name = opt.track_name
data_list_root = "/data/vision/billf/scratch/ztzhang/data/layered-video/DAVIS/JPEGImages/Full-Resolution"
mask_root = "/data/vision/billf/scratch/ztzhang/data/layered-video/DAVIS/Annotations/Full-Resolution"
data_list_root = davis_path + '/JPEGImages/Full-Resolution'
mask_root = davis_path + '/Annotations/Full-Resolution'
image_path = join(data_list_root, f'{track_name}')
self.paths = {'image_path': image_path,
'mask_path': join(mask_root, f'{track_name}')}
Expand All @@ -667,7 +667,7 @@ def read_images(self):
self.images_orig = []
for img_path in tqdm(image_paths):
img_raw = np.asarray(Image.open(
img_path).convert('RGB')).astype(np.float)/255
img_path).convert('RGB')).astype(float)/255
if not hasattr(self, 'original_shape'):
self.original_shape = img_raw.shape[:2]
H_output, W_output = self.get_output_shape(
Expand All @@ -679,9 +679,9 @@ def read_images(self):
H_output, W_output = self.output_shape
H_opt, W_opt = self.opt_shape
img = imresize(img_raw, (H_opt, W_opt),
preserve_range=True).astype(np.float)
preserve_range=True).astype(float)
img_orig = imresize(img_raw, (H_output, W_output),
preserve_range=True).astype(np.float)
preserve_range=True).astype(float)
self.images.append(img.copy())
self.images_orig.append(img_orig.copy())
self.number_of_frames = len(self.images)
Expand Down Expand Up @@ -709,9 +709,9 @@ def read_gt_info(self):
mask_paths = mask_paths[::self.opt.image_sequence_stride]
for mask_path in mask_paths:
mask = np.asarray(Image.open(
mask_path).convert('L')).astype(np.float)/255
mask_path).convert('L')).astype(float)/255
mask = imresize(mask, self.opt_shape,
preserve_range=True).astype(np.float)
preserve_range=True).astype(float)
mask = np.where(mask > 0.001, 1, 0)
mask = 1-mask
mask = torch.from_numpy(mask[None, ...]).float().pin_memory()
Expand All @@ -729,7 +729,7 @@ def read_gt_info(self):
# np_file = np.load(depth_and_pose_path[x], allow_pickle=True)
# depth_gt = np_file['depth']+1e-6
# depth_gt = imresize(depth_gt, (H, W), preserve_range=True)
# extrinsics = np.linalg.inv(np_file['w2c']).astype(np.float)
# extrinsics = np.linalg.inv(np_file['w2c']).astype(float)
# self.R_gt.append(extrinsics[:3, :3])
# self.t_gt.append(extrinsics[:3, 3:4])
# self.depth_gt.append(depth_gt)
Expand Down Expand Up @@ -760,7 +760,7 @@ def get_paths(self, opt):
def read_images(self):
print('reading images...')
img_path = self.paths['image_path']
self.images_raw = np.load(img_path).astype(np.float)/255
self.images_raw = np.load(img_path).astype(float)/255
if self.opt.frame_cap is not None:
self.images_raw = self.images_raw[:self.opt.frame_cap, ...]
if self.opt.track_name == 'soup_can':
Expand All @@ -780,9 +780,9 @@ def read_images(self):
H_output, W_output = self.output_shape
H_opt, W_opt = self.opt_shape
img = imresize(img_raw, (H_opt, W_opt),
preserve_range=True).astype(np.float)
preserve_range=True).astype(float)
img_orig = imresize(img_raw, (H_output, W_output),
preserve_range=True).astype(np.float)
preserve_range=True).astype(float)
self.images.append(img.copy())
self.images_orig.append(img_orig.copy())
self.number_of_frames = len(self.images)
Expand Down Expand Up @@ -834,7 +834,7 @@ def get_paths(self, opt):
def read_images(self):
print('reading images...')
img_path = self.paths['image_path']
self.images_raw = np.load(img_path).astype(np.float)/255
self.images_raw = np.load(img_path).astype(float)/255
# need to crop out the border
self.images_raw = self.images_raw[:, 16:-16, 32:-32, :]
if self.opt.frame_cap is not None:
Expand All @@ -854,9 +854,9 @@ def read_images(self):
H_output, W_output = self.output_shape
H_opt, W_opt = self.opt_shape
img = imresize(img_raw, (H_opt, W_opt),
preserve_range=True).astype(np.float)
preserve_range=True).astype(float)
img_orig = imresize(img_raw, (H_output, W_output),
preserve_range=True).astype(np.float)
preserve_range=True).astype(float)
self.images.append(img.copy())
self.images_orig.append(img_orig.copy())
self.number_of_frames = len(self.images)
Expand Down