def predict_video(detector, reid_model, video_file, scaled, threshold, output_dir, save_images=True, save_mot_txts=True, draw_center_traj=False, secs_interval=10, do_entrance_counting=False, camera_id=-1): video_name = 'mot_output.mp4' if camera_id != -1: capture = cv2.VideoCapture(camera_id) else: capture = cv2.VideoCapture(video_file) video_name = os.path.split(video_file)[-1] # Get Video info : resolution, fps, frame count width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH)) height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)) fps = int(capture.get(cv2.CAP_PROP_FPS)) frame_count = int(capture.get(cv2.CAP_PROP_FRAME_COUNT)) print("fps: %d, frame_count: %d" % (fps, frame_count)) if not os.path.exists(output_dir): os.makedirs(output_dir) out_path = os.path.join(output_dir, video_name) if not save_images: video_format = 'mp4v' fourcc = cv2.VideoWriter_fourcc(*video_format) writer = cv2.VideoWriter(out_path, fourcc, fps, (width, height)) frame_id = 0 timer = MOTTimer() results = defaultdict(list) id_set = set() interval_id_set = set() in_id_list = list() out_id_list = list() prev_center = dict() records = list() entrance = [0, height / 2., width, height / 2.] video_fps = fps while (1): ret, frame = capture.read() if not ret: break timer.tic() ori_image_shape = list(frame.shape[:2]) pred_dets, pred_xyxys = detector.predict([frame], ori_image_shape, threshold, scaled) if len(pred_dets) == 1 and np.sum(pred_dets) == 0: print('Frame {} has no object, try to modify score threshold.'. format(frame_id)) timer.toc() im = frame else: # reid process crops = reid_model.get_crops(pred_xyxys, frame) tracking_outs = reid_model.predict(crops, pred_dets) online_tlwhs = tracking_outs['online_tlwhs'] online_scores = tracking_outs['online_scores'] online_ids = tracking_outs['online_ids'] results[0].append( (frame_id + 1, online_tlwhs, online_scores, online_ids)) # NOTE: just implement flow statistic for one class result = (frame_id + 1, online_tlwhs, online_scores, online_ids) statistic = flow_statistic(result, secs_interval, do_entrance_counting, video_fps, entrance, id_set, interval_id_set, in_id_list, out_id_list, prev_center, records) id_set = statistic['id_set'] interval_id_set = statistic['interval_id_set'] in_id_list = statistic['in_id_list'] out_id_list = statistic['out_id_list'] prev_center = statistic['prev_center'] records = statistic['records'] timer.toc() fps = 1. / timer.duration im = plot_tracking(frame, online_tlwhs, online_ids, online_scores, frame_id=frame_id, fps=fps, do_entrance_counting=do_entrance_counting, entrance=entrance) if save_images: save_dir = os.path.join(output_dir, video_name.split('.')[-2]) if not os.path.exists(save_dir): os.makedirs(save_dir) cv2.imwrite(os.path.join(save_dir, '{:05d}.jpg'.format(frame_id)), im) else: writer.write(im) frame_id += 1 print('detect frame:%d, fps: %f' % (frame_id, fps)) if camera_id != -1: cv2.imshow('Tracking Detection', im) if cv2.waitKey(1) & 0xFF == ord('q'): break if save_mot_txts: result_filename = os.path.join(output_dir, video_name.split('.')[-2] + '.txt') write_mot_results(result_filename, results) result_filename = os.path.join( output_dir, video_name.split('.')[-2] + '_flow_statistic.txt') f = open(result_filename, 'w') for line in records: f.write(line) print('Flow statistic save in {}'.format(result_filename)) f.close() if save_images: save_dir = os.path.join(output_dir, video_name.split('.')[-2]) cmd_str = 'ffmpeg -f image2 -i {}/%05d.jpg {}'.format( save_dir, out_path) os.system(cmd_str) print('Save video in {}.'.format(out_path)) else: writer.release()
def predict_video(detector, video_file, threshold, output_dir, save_images=True, save_mot_txts=True, draw_center_traj=False, secs_interval=10, do_entrance_counting=False, camera_id=-1): video_name = 'mot_output.mp4' if camera_id != -1: capture = cv2.VideoCapture(camera_id) else: capture = cv2.VideoCapture(video_file) video_name = os.path.split(video_file)[-1] # Get Video info : resolution, fps, frame count width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH)) height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)) fps = int(capture.get(cv2.CAP_PROP_FPS)) frame_count = int(capture.get(cv2.CAP_PROP_FRAME_COUNT)) print("fps: %d, frame_count: %d" % (fps, frame_count)) if not os.path.exists(output_dir): os.makedirs(output_dir) out_path = os.path.join(output_dir, video_name) if not save_images: video_format = 'mp4v' fourcc = cv2.VideoWriter_fourcc(*video_format) writer = cv2.VideoWriter(out_path, fourcc, fps, (width, height)) frame_id = 0 timer = MOTTimer() results = defaultdict(list) # support single class and multi classes num_classes = detector.num_classes data_type = 'mcmot' if num_classes > 1 else 'mot' ids2names = detector.pred_config.labels center_traj = None entrance = None records = None if draw_center_traj: center_traj = [{} for i in range(num_classes)] if num_classes == 1: id_set = set() interval_id_set = set() in_id_list = list() out_id_list = list() prev_center = dict() records = list() entrance = [0, height / 2., width, height / 2.] video_fps = fps while (1): ret, frame = capture.read() if not ret: break timer.tic() online_tlwhs, online_scores, online_ids = detector.predict([frame], threshold) timer.toc() for cls_id in range(num_classes): results[cls_id].append((frame_id + 1, online_tlwhs[cls_id], online_scores[cls_id], online_ids[cls_id])) fps = 1. / timer.duration # NOTE: just implement flow statistic for one class if num_classes == 1: result = (frame_id + 1, online_tlwhs[0], online_scores[0], online_ids[0]) statistic = flow_statistic(result, secs_interval, do_entrance_counting, video_fps, entrance, id_set, interval_id_set, in_id_list, out_id_list, prev_center, records, data_type, num_classes) id_set = statistic['id_set'] interval_id_set = statistic['interval_id_set'] in_id_list = statistic['in_id_list'] out_id_list = statistic['out_id_list'] prev_center = statistic['prev_center'] records = statistic['records'] elif num_classes > 1 and do_entrance_counting: raise NotImplementedError( 'Multi-class flow counting is not implemented now!') im = plot_tracking_dict(frame, num_classes, online_tlwhs, online_ids, online_scores, frame_id=frame_id, fps=fps, ids2names=ids2names, do_entrance_counting=do_entrance_counting, entrance=entrance, records=records, center_traj=center_traj) if save_images: save_dir = os.path.join(output_dir, video_name.split('.')[-2]) if not os.path.exists(save_dir): os.makedirs(save_dir) cv2.imwrite(os.path.join(save_dir, '{:05d}.jpg'.format(frame_id)), im) else: writer.write(im) frame_id += 1 print('detect frame: %d, fps: %f' % (frame_id, fps)) if camera_id != -1: cv2.imshow('Tracking Detection', im) if cv2.waitKey(1) & 0xFF == ord('q'): break if save_mot_txts: result_filename = os.path.join(output_dir, video_name.split('.')[-2] + '.txt') write_mot_results(result_filename, results, data_type, num_classes) if num_classes == 1: result_filename = os.path.join( output_dir, video_name.split('.')[-2] + '_flow_statistic.txt') f = open(result_filename, 'w') for line in records: f.write(line) print('Flow statistic save in {}'.format(result_filename)) f.close() if save_images: save_dir = os.path.join(output_dir, video_name.split('.')[-2]) cmd_str = 'ffmpeg -f image2 -i {}/%05d.jpg {}'.format( save_dir, out_path) os.system(cmd_str) print('Save video in {}.'.format(out_path)) else: writer.release()
def predict_video(self, video_file, camera_id): video_out_name = 'mot_output.mp4' if camera_id != -1: capture = cv2.VideoCapture(camera_id) else: capture = cv2.VideoCapture(video_file) video_out_name = os.path.split(video_file)[-1] # Get Video info : resolution, fps, frame count width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH)) height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)) fps = int(capture.get(cv2.CAP_PROP_FPS)) frame_count = int(capture.get(cv2.CAP_PROP_FRAME_COUNT)) print("fps: %d, frame_count: %d" % (fps, frame_count)) if not os.path.exists(self.output_dir): os.makedirs(self.output_dir) out_path = os.path.join(self.output_dir, video_out_name) video_format = 'mp4v' fourcc = cv2.VideoWriter_fourcc(*video_format) writer = cv2.VideoWriter(out_path, fourcc, fps, (width, height)) frame_id = 1 timer = MOTTimer() results = defaultdict(list) # support single class and multi classes num_classes = self.num_classes data_type = 'mcmot' if num_classes > 1 else 'mot' ids2names = self.pred_config.labels center_traj = None entrance = None records = None if self.draw_center_traj: center_traj = [{} for i in range(num_classes)] if num_classes == 1: id_set = set() interval_id_set = set() in_id_list = list() out_id_list = list() prev_center = dict() records = list() entrance = [0, height / 2., width, height / 2.] video_fps = fps while (1): ret, frame = capture.read() if not ret: break if frame_id % 10 == 0: print('Tracking frame: %d' % (frame_id)) frame_id += 1 timer.tic() seq_name = video_out_name.split('.')[0] mot_results = self.predict_image([frame], visual=False, seq_name=seq_name) timer.toc() online_tlwhs, online_scores, online_ids = mot_results[0] for cls_id in range(num_classes): results[cls_id].append( (frame_id + 1, online_tlwhs[cls_id], online_scores[cls_id], online_ids[cls_id])) # NOTE: just implement flow statistic for single class if num_classes == 1: result = (frame_id + 1, online_tlwhs[0], online_scores[0], online_ids[0]) statistic = flow_statistic(result, self.secs_interval, self.do_entrance_counting, video_fps, entrance, id_set, interval_id_set, in_id_list, out_id_list, prev_center, records, data_type, num_classes) records = statistic['records'] fps = 1. / timer.duration im = plot_tracking_dict( frame, num_classes, online_tlwhs, online_ids, online_scores, frame_id=frame_id, fps=fps, ids2names=ids2names, do_entrance_counting=self.do_entrance_counting, entrance=entrance, records=records, center_traj=center_traj) writer.write(im) if camera_id != -1: cv2.imshow('Mask Detection', im) if cv2.waitKey(1) & 0xFF == ord('q'): break if self.save_mot_txts: result_filename = os.path.join( self.output_dir, video_out_name.split('.')[-2] + '.txt') write_mot_results(result_filename, results, data_type, num_classes) if num_classes == 1: result_filename = os.path.join( self.output_dir, video_out_name.split('.')[-2] + '_flow_statistic.txt') f = open(result_filename, 'w') for line in records: f.write(line) print('Flow statistic save in {}'.format(result_filename)) f.close() writer.release()