def show_boxes_from_standard_json(json_file_path,
classes,
img_folder_path=None,
output_folder_path=None,
track_id=-1):
dets = read_json_from_file(json_file_path)
for det in dets:
python_data = det
if img_folder_path is None:
img_path = os.path.join(python_data["image"]["folder"],
python_data["image"]["name"])
else:
img_path = os.path.join(img_folder_path,
python_data["image"]["name"])
if is_image(img_path): img = cv2.imread(img_path)
candidates = python_data["candidates"]
for candidate in candidates:
bbox = np.array(candidate["det_bbox"]).astype(int)
score = candidate["det_score"]
if score >= bbox_thresh:
img = draw_bbox(img, bbox, score, classes, track_id=track_id)
if output_folder_path is not None:
create_folder(output_folder_path)
img_output_path = os.path.join(output_folder_path,
python_data["image"]["name"])
cv2.imwrite(img_output_path, img)
return True
def show_poses_from_standard_json(json_file_path,
joint_pairs,
joint_names,
img_folder_path=None,
output_folder_path=None):
poses = read_json_from_file(json_file_path)
for pose in poses:
python_data = pose
if img_folder_path is None:
img_path = os.path.join(python_data["image"]["folder"],
python_data["image"]["name"])
else:
img_path = os.path.join(img_folder_path,
python_data["image"]["name"])
if is_image(img_path): img = cv2.imread(img_path)
candidates = python_data["candidates"]
for candidate in candidates:
pose_keypoints_2d = candidate["pose_keypoints_2d"]
joints = reshape_keypoints_into_joints(pose_keypoints_2d)
img = show_poses_from_python_data(img, joints, joint_pairs,
joint_names)
if output_folder_path is not None:
create_folder(output_folder_path)
img_output_path = os.path.join(output_folder_path,
python_data["image"]["name"])
cv2.imwrite(img_output_path, img)
return
def merge_json(gt_json_paths, save_json_path):
python_data_merged = {"annolist": []}
for gt_json_path in gt_json_paths:
python_data = read_json_from_file(gt_json_path)
python_data_merged["annolist"].extend(python_data["annolist"])
write_json_to_file(python_data_merged, save_json_path, flag_verbose=False)
def __init__(self):
#{0-Rank 1-Rkne 2-Rhip 3-Lhip 4-Lkne 5-Lank 6-Rwri 7-Relb 8-Rsho 9-Lsho 10-Lelb 11-Lwri 12-neck 13-nose 14-TopHead}
self.kp_names = [
'right_ankle', 'right_knee', 'right_pelvis', 'left_pelvis',
'left_knee', 'left_ankle', 'right_wrist', 'right_elbow',
'right_shoulder', 'left_shoulder', 'left_elbow', 'left_wrist',
'upper_neck', 'nose', 'head'
]
self.max_num_joints = 15
self.color = np.random.randint(0, 256, (self.max_num_joints, 3))
self.posetrack = []
self.test_posetrack = []
for posetrack, stage in zip([self.posetrack, self.test_posetrack],
['train', 'val']):
if stage == 'train':
self._train_gt_path = "posetrack_merged_train.json"
gt_python_data = read_json_from_file(self._train_gt_path)
anns = gt_python_data["annolist"]
else:
self._val_gt_path = "posetrack_merged_val.json"
gt_python_data = read_json_from_file(self._val_gt_path)
anns = gt_python_data["annolist"]
if stage == 'train':
for aid, ann in enumerate(anns):
if ann["is_labeled"][0] == 0: continue
if not ann["annorect"]: #if it is empty
continue
num_candidates = len(ann["annorect"])
for candidate_id in range(0, num_candidates):
if not ann["annorect"][candidate_id]["annopoints"]:
continue #list is empty
# (1) bbox
bbox = get_bbox_from_keypoints(
ann["annorect"][candidate_id]["annopoints"][0]
["point"])
bbox = x1y1x2y2_to_xywh(bbox)
if bbox == [0, 0, 2, 2]: continue
# (2) imgpath
imgname = ann["image"][0]["name"]
prefix = '../data/posetrack_data/'
imgpath = os.path.join(prefix, imgname)
# (2) joints
joints = get_joints_from_ann(
ann["annorect"][candidate_id]["annopoints"][0]
["point"])
num_points = len(ann["annorect"][candidate_id]
["annopoints"][0]["point"])
if np.sum(joints[2::3]) == 0 or num_points == 0:
continue
# (4) head_rect: useless
rect = np.array([0, 0, 1, 1], np.int32)
''' This [humanData] is what [load_data] will provide '''
humanData = dict(aid=aid,
joints=joints,
imgpath=imgpath,
headRect=rect,
bbox=bbox,
imgid=ann['imgnum'][0])
posetrack.append(humanData)
elif stage == 'val':
for aid, ann in enumerate(anns):
if ann["is_labeled"][0] == 0: continue
if not ann["annorect"]: #if it is empty
continue
num_candidates = len(ann["annorect"])
for candidate_id in range(0, num_candidates):
if not ann["annorect"][candidate_id]["annopoints"]:
continue #list is empty
imgname = ann["image"][0]["name"]
prefix = '../data/posetrack_data/'
imgpath = os.path.join(prefix, imgname)
humanData = dict(imgid=aid, imgpath=imgpath)
posetrack.append(humanData)
else:
print('PoseTrack data error, please check')
embed()
