rgb_ext = {'primesense': 'png', 'kinect': 'png', 'canon': 'jpg'}
obj_colors_path = os.path.join('data', 'obj_rgb.txt')
vis_rgb_path_mask = os.path.join(output_dir, '{:02d}_{}_{}_{:04d}_rgb.png')
vis_depth_path_mask = os.path.join(output_dir,
'{:02d}_{}_{}_{:04d}_depth_diff.png')
misc.ensure_dir(output_dir)
obj_colors = inout.load_colors(obj_colors_path)
plt.ioff() # Turn interactive plotting off
for scene_id in scene_ids:
# Load info about the test images (including camera parameters etc.)
scene_info_path = scene_info_path_mask.format(device, scene_id)
scene_info = inout.load_info(scene_info_path)
scene_gt_path = scene_gt_path_mask.format(device, scene_id)
scene_gt = inout.load_gt(scene_gt_path)
# Load models of objects present in the scene
scene_obj_ids = set()
for gt in scene_gt[0]:
scene_obj_ids.add(gt['obj_id'])
models = {}
for scene_obj_id in scene_obj_ids:
model_path = model_path_mask.format(scene_obj_id)
models[scene_obj_id] = inout.load_ply(model_path)
for im_id, im_info in scene_info.items():
if im_id % im_step != 0:
'{:02d}_{}_{}_{:04d}_depth_diff.png')
misc.ensure_dir(output_dir)
obj_colors = inout.load_colors(obj_colors_path)
plt.ioff() # Turn interactive plotting off
for obj_id in obj_ids:
# Load object model
model_path = model_path_mask.format(obj_id)
model = inout.load_ply(model_path)
# Load info about the templates (including camera parameters etc.)
obj_info_path = obj_info_path_mask.format(device, obj_id)
obj_info = inout.load_info(obj_info_path)
obj_gt_path = obj_gt_path_mask.format(device, obj_id)
obj_gt = inout.load_gt(obj_gt_path)
for im_id in obj_info.keys():
if im_id % im_step != 0:
continue
print('obj: ' + str(obj_id) + ', device: ' + device + ', im_id: ' +
str(im_id))
im_info = obj_info[im_id]
im_gt = obj_gt[im_id]
# Get intrinsic camera parameters and object pose
K = im_info['cam_K']
depth_path_mask = os.path.join(data_path, 'test_{}', '{:02d}', 'depth', '{:04d}.png')
rgb_ext = {'primesense': 'png', 'kinect': 'png', 'canon': 'jpg'}
obj_colors_path = os.path.join('data', 'obj_rgb.txt')
vis_rgb_path_mask = os.path.join(output_dir, '{:02d}_{}_{}_{:04d}_rgb.png')
vis_depth_path_mask = os.path.join(output_dir, '{:02d}_{}_{}_{:04d}_depth_diff.png')
misc.ensure_dir(output_dir)
obj_colors = inout.load_colors(obj_colors_path)
plt.ioff() # Turn interactive plotting off
for scene_id in scene_ids:
# Load info about the test images (including camera parameters etc.)
scene_info_path = scene_info_path_mask.format(device, scene_id)
scene_info = inout.load_info(scene_info_path)
scene_gt_path = scene_gt_path_mask.format(device, scene_id)
scene_gt = inout.load_gt(scene_gt_path)
# Load models of objects present in the scene
scene_obj_ids = set()
for gt in scene_gt[0]:
scene_obj_ids.add(gt['obj_id'])
models = {}
for scene_obj_id in scene_obj_ids:
model_path = model_path_mask.format(scene_obj_id)
models[scene_obj_id] = inout.load_ply(model_path)
for im_id, im_info in scene_info.items():
if im_id % im_step != 0: