Remove unused functions plot_keypoint, write, and load_json from utilitys.py to streamline the code and improve maintainability.

Author: xiu-cs

fmpose3d/lib/hrnet/lib/utils/utilitys.py

@@ -30,83 +30,6 @@
           [170, 0, 255], [255, 0, 255]]
 
 
-def plot_keypoint(image, coordinates, confidence, keypoint_thresh=0.3):
-    # USE cv2
-    joint_visible = confidence[:, :, 0] > keypoint_thresh
-    coordinates = coco_h36m(coordinates)
-    for i in range(coordinates.shape[0]):
-        pts = coordinates[i]
-
-        for joint in pts:
-            cv2.circle(image, (int(joint[0]), int(joint[1])), 8, (255, 255, 255), 1)
-
-        for color_i, jp in zip(colors, h36m_pairs):
-            if joint_visible[i, jp[0]] and joint_visible[i, jp[1]]:
-                pt0 = pts[jp, 0]
-                pt1 = pts[jp, 1]
-                pt0_0, pt0_1, pt1_0, pt1_1 = int(pt0[0]), int(pt0[1]), int(pt1[0]), int(pt1[1])
-
-                cv2.line(image, (pt0_0, pt1_0), (pt0_1, pt1_1), color_i, 6)
-                #  cv2.circle(image,(pt0_0, pt0_1), 2, color_i, thickness=-1)
-                #  cv2.circle(image,(pt1_0, pt1_1), 2, color_i, thickness=-1)
-    return image
-
-
-def write(x, img):
-    x = [int(i) for i in x]
-    c1 = tuple(x[0:2])
-    c2 = tuple(x[2:4])
-
-    color = [0, 97, 255]
-    label = 'People {}'.format(x[-1])
-    cv2.rectangle(img, c1, c2, color, 2)
-    t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 1, 1)[0]
-    c2 = c1[0] + t_size[0] + 3, c1[1] + t_size[1] + 4
-    cv2.rectangle(img, c1, c2, [0, 128, 255], -1)
-    cv2.putText(img, label, (c1[0], c1[1] + t_size[1] + 4), cv2.FONT_HERSHEY_PLAIN, 1, [225, 255, 255], 1)
-    return img
-
-
-def load_json(file_path):
-    with open(file_path, 'r') as fr:
-        video_info = json.load(fr)
-
-    label = video_info['label']
-    label_index = video_info['label_index']
-
-    num_frames = video_info['data'][-1]['frame_index']
-    keypoints = np.zeros((2, num_frames, 17, 2), dtype=np.float32)  # (M, T, N, 2)
-    scores = np.zeros((2, num_frames, 17), dtype=np.float32)  # (M, T, N)
-
-    for frame_info in video_info['data']:
-        frame_index = frame_info['frame_index']
-
-        for index, skeleton_info in enumerate(frame_info['skeleton']):
-            pose = skeleton_info['pose']
-            score = skeleton_info['score']
-            bbox = skeleton_info['bbox']
-
-            if len(bbox) == 0 or index+1 > 2:
-                continue
-
-            pose = np.asarray(pose, dtype=np.float32)
-            score = np.asarray(score, dtype=np.float32)
-            score = score.reshape(-1)
-
-            keypoints[index, frame_index-1] = pose
-            scores[index, frame_index-1] = score
-
-    new_kpts = []
-    for i in range(keypoints.shape[0]):
-        kps = keypoints[i]
-        if np.sum(kps) != 0.:
-            new_kpts.append(kps)
-
-    new_kpts = np.asarray(new_kpts, dtype=np.float32)
-    scores = np.asarray(scores, dtype=np.float32)
-    scores = scores[:, :, :, np.newaxis]
-    return new_kpts, scores, label, label_index
-
 
 def box_to_center_scale(box, model_image_width, model_image_height):
     """convert a box to center,scale information required for pose transformation