bodymocap/utils/imutils.py [228:320]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - def bbox_from_openpose(openpose_file, rescale=1.2, detection_thresh=0.2): """Get center and scale for bounding box from openpose detections.""" with open(openpose_file, 'r') as f: data = json.load(f) if 'people' not in data or len(data['people'])==0: return None, None # keypoints = json.load(f)['people'][0]['pose_keypoints_2d'] keypoints = data['people'][0]['pose_keypoints_2d'] keypoints = np.reshape(np.array(keypoints), (-1,3)) valid = keypoints[:,-1] > detection_thresh valid_keypoints = keypoints[valid][:,:-1] #(25,2) # min_pt = np.min(valid_keypoints, axis=0) # max_pt = np.max(valid_keypoints, axis=0) # bbox= [ min_pt[0], min_pt[1], max_pt[0] - min_pt[0], max_pt[1] - min_pt[1]] center = valid_keypoints.mean(axis=0) bbox_size = (valid_keypoints.max(axis=0) - valid_keypoints.min(axis=0)).max() # adjust bounding box tightness scale = bbox_size / 200.0 scale *= rescale return center, scale#, bbox # keypoints: (Nx3) def bbox_from_keypoint2d(keypoints, rescale=1.2, detection_thresh=0.2): """ output: center: bbox center scale: scale_n2o: 224x224 -> original bbox size (max length if not a square bbox) """ # """Get center and scale for bounding box from openpose detections.""" if len(keypoints.shape)==2 and keypoints.shape[1]==2: #(X,2) valid_keypoints = keypoints else: keypoints = np.reshape(np.array(keypoints), (-1,3)) valid = keypoints[:,-1] > detection_thresh valid_keypoints = keypoints[valid][:,:-1] #(25,2) # min_pt = np.min(valid_keypoints, axis=0) # max_pt = np.max(valid_keypoints, axis=0) # bbox= [ min_pt[0], min_pt[1], max_pt[0] - min_pt[0], max_pt[1] - min_pt[1]] center = valid_keypoints.mean(axis=0) bbox_size = (valid_keypoints.max(axis=0) - valid_keypoints.min(axis=0)).max() # adjust bounding box tightness scale = bbox_size / 200.0 scale *= rescale return center, scale#, bbox # def bbox_from_bboxXYXY(bboxXYXY, rescale=1.2): # """ # bboxXYXY # """ # pass def bbox_from_keypoints(keypoints, rescale=1.2, detection_thresh=0.2, imageHeight= None): """Get center and scale for bounding box from openpose detections.""" # with open(openpose_file, 'r') as f: # data = json.load(f) # if 'people' not in data or len(data['people'])==0: # return None, None # # keypoints = json.load(f)['people'][0]['pose_keypoints_2d'] # keypoints = data['people'][0]['pose_keypoints_2d'] keypoints = np.reshape(np.array(keypoints), (-1,3)) valid = keypoints[:,-1] > detection_thresh # if g_debugUpperBodyOnly: #Intentionally remove lower bodies # valid[ [ 9,10,11,12,13,14, 22,23,24, 19,20,21] ] = False valid_keypoints = keypoints[valid][:,:-1] #(25,2) if len(valid_keypoints)<2: return None, None, None if False: #Should have all limbs and nose if np.sum(valid[ [ 2,3,4, 5,6,7, 9,10, 12,13,1,0] ]) <12: return None, None, None min_pt = np.min(valid_keypoints, axis=0) max_pt = np.max(valid_keypoints, axis=0) bbox= [ min_pt[0], min_pt[1], max_pt[0] - min_pt[0], max_pt[1] - min_pt[1]] - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - mocap_utils/coordconv.py [83:158]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - def bbox_from_openpose(openpose_file, rescale=1.2, detection_thresh=0.2): """Get center and scale for bounding box from openpose detections.""" with open(openpose_file, 'r') as f: data = json.load(f) if 'people' not in data or len(data['people'])==0: return None, None # keypoints = json.load(f)['people'][0]['pose_keypoints_2d'] keypoints = data['people'][0]['pose_keypoints_2d'] keypoints = np.reshape(np.array(keypoints), (-1,3)) valid = keypoints[:,-1] > detection_thresh valid_keypoints = keypoints[valid][:,:-1] #(25,2) # min_pt = np.min(valid_keypoints, axis=0) # max_pt = np.max(valid_keypoints, axis=0) # bbox= [ min_pt[0], min_pt[1], max_pt[0] - min_pt[0], max_pt[1] - min_pt[1]] center = valid_keypoints.mean(axis=0) bbox_size = (valid_keypoints.max(axis=0) - valid_keypoints.min(axis=0)).max() # adjust bounding box tightness scale = bbox_size / 200.0 scale *= rescale return center, scale#, bbox # keypoints: (Nx3) def bbox_from_keypoint2d(keypoints, rescale=1.2, detection_thresh=0.2): """ output: center: bbox center scale: scale_n2o: 224x224 -> original bbox size (max length if not a square bbox) """ # """Get center and scale for bounding box from openpose detections.""" if len(keypoints.shape)==2 and keypoints.shape[1]==2: #(X,2) valid_keypoints = keypoints else: keypoints = np.reshape(np.array(keypoints), (-1,3)) valid = keypoints[:,-1] > detection_thresh valid_keypoints = keypoints[valid][:,:-1] #(25,2) # min_pt = np.min(valid_keypoints, axis=0) # max_pt = np.max(valid_keypoints, axis=0) # bbox= [ min_pt[0], min_pt[1], max_pt[0] - min_pt[0], max_pt[1] - min_pt[1]] center = valid_keypoints.mean(axis=0) bbox_size = (valid_keypoints.max(axis=0) - valid_keypoints.min(axis=0)).max() # adjust bounding box tightness scale = bbox_size / 200.0 scale *= rescale return center, scale#, bbox def bbox_from_keypoints(keypoints, rescale=1.2, detection_thresh=0.2, imageHeight= None): """Get center and scale for bounding box from openpose detections.""" keypoints = np.reshape(np.array(keypoints), (-1,3)) valid = keypoints[:,-1] > detection_thresh valid_keypoints = keypoints[valid][:,:-1] #(25,2) if len(valid_keypoints)<2: return None, None, None if False: #Should have all limbs and nose if np.sum(valid[ [ 2,3,4, 5,6,7, 9,10, 12,13,1,0] ]) <12: return None, None, None min_pt = np.min(valid_keypoints, axis=0) max_pt = np.max(valid_keypoints, axis=0) bbox= [ min_pt[0], min_pt[1], max_pt[0] - min_pt[0], max_pt[1] - min_pt[1]] - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -