# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import time import yaml import cv2 import re import glob import numpy as np from collections import defaultdict import paddle from benchmark_utils import PaddleInferBenchmark from preprocess import decode_image # add python path import sys parent_path = os.path.abspath(os.path.join(__file__, *(['..']))) sys.path.insert(0, parent_path) from det_infer import Detector, get_test_images, print_arguments, bench_log, PredictConfig, load_predictor from mot_utils import argsparser, Timer, get_current_memory_mb, video2frames, _is_valid_video from mot.tracker import JDETracker, DeepSORTTracker from mot.utils import MOTTimer, write_mot_results, get_crops, clip_box, flow_statistic from mot.visualize import plot_tracking, plot_tracking_dict from mot.mtmct.utils import parse_bias from mot.mtmct.postprocess import trajectory_fusion, sub_cluster, gen_res, print_mtmct_result from mot.mtmct.postprocess import get_mtmct_matching_results, save_mtmct_crops, save_mtmct_vis_results class SDE_Detector(Detector): """ Args: model_dir (str): root path of model.pdiparams, model.pdmodel and infer_cfg.yml tracker_config (str): tracker config path device (str): Choose the device you want to run, it can be: CPU/GPU/XPU, default is CPU run_mode (str): mode of running(paddle/trt_fp32/trt_fp16) batch_size (int): size of pre batch in inference trt_min_shape (int): min shape for dynamic shape in trt trt_max_shape (int): max shape for dynamic shape in trt trt_opt_shape (int): opt shape for dynamic shape in trt trt_calib_mode (bool): If the model is produced by TRT offline quantitative calibration, trt_calib_mode need to set True cpu_threads (int): cpu threads enable_mkldnn (bool): whether to open MKLDNN output_dir (string): The path of output, default as 'output' threshold (float): Score threshold of the detected bbox, default as 0.5 save_images (bool): Whether to save visualization image results, default as False save_mot_txts (bool): Whether to save tracking results (txt), default as False draw_center_traj (bool): Whether drawing the trajectory of center, default as False secs_interval (int): The seconds interval to count after tracking, default as 10 do_entrance_counting(bool): Whether counting the numbers of identifiers entering or getting out from the entrance, default as False，only support single class counting in MOT. reid_model_dir (str): reid model dir, default None for ByteTrack, but set for DeepSORT mtmct_dir (str): MTMCT dir, default None, set for doing MTMCT """ def __init__(self, model_dir, tracker_config, device='CPU', run_mode='paddle', batch_size=1, trt_min_shape=1, trt_max_shape=1280, trt_opt_shape=640, trt_calib_mode=False, cpu_threads=1, enable_mkldnn=False, output_dir='output', threshold=0.5, save_images=False, save_mot_txts=False, draw_center_traj=False, secs_interval=10, do_entrance_counting=False, reid_model_dir=None, mtmct_dir=None): super(SDE_Detector, self).__init__( model_dir=model_dir, device=device, run_mode=run_mode, batch_size=batch_size, trt_min_shape=trt_min_shape, trt_max_shape=trt_max_shape, trt_opt_shape=trt_opt_shape, trt_calib_mode=trt_calib_mode, cpu_threads=cpu_threads, enable_mkldnn=enable_mkldnn, output_dir=output_dir, threshold=threshold, ) self.save_images = save_images self.save_mot_txts = save_mot_txts self.draw_center_traj = draw_center_traj self.secs_interval = secs_interval self.do_entrance_counting = do_entrance_counting assert batch_size == 1, "MOT model only supports batch_size=1." self.det_times = Timer(with_tracker=True) self.num_classes = len(self.pred_config.labels) # reid config self.use_reid = False if reid_model_dir is None else True if self.use_reid: self.reid_pred_config = self.set_config(reid_model_dir) self.reid_predictor, self.config = load_predictor( reid_model_dir, run_mode=run_mode, batch_size=50, # reid_batch_size min_subgraph_size=self.reid_pred_config.min_subgraph_size, device=device, use_dynamic_shape=self.reid_pred_config.use_dynamic_shape, trt_min_shape=trt_min_shape, trt_max_shape=trt_max_shape, trt_opt_shape=trt_opt_shape, trt_calib_mode=trt_calib_mode, cpu_threads=cpu_threads, enable_mkldnn=enable_mkldnn) else: self.reid_pred_config = None self.reid_predictor = None assert tracker_config is not None, 'Note that tracker_config should be set.' self.tracker_config = tracker_config tracker_cfg = yaml.safe_load(open(self.tracker_config)) cfg = tracker_cfg[tracker_cfg['type']] # tracker config self.use_deepsort_tracker = True if tracker_cfg[ 'type'] == 'DeepSORTTracker' else False if self.use_deepsort_tracker: # use DeepSORTTracker if self.reid_pred_config is not None and hasattr( self.reid_pred_config, 'tracker'): cfg = self.reid_pred_config.tracker budget = cfg.get('budget', 100) max_age = cfg.get('max_age', 30) max_iou_distance = cfg.get('max_iou_distance', 0.7) matching_threshold = cfg.get('matching_threshold', 0.2) min_box_area = cfg.get('min_box_area', 0) vertical_ratio = cfg.get('vertical_ratio', 0) self.tracker = DeepSORTTracker( budget=budget, max_age=max_age, max_iou_distance=max_iou_distance, matching_threshold=matching_threshold, min_box_area=min_box_area, vertical_ratio=vertical_ratio, ) else: # use ByteTracker use_byte = cfg.get('use_byte', False) det_thresh = cfg.get('det_thresh', 0.3) min_box_area = cfg.get('min_box_area', 0) vertical_ratio = cfg.get('vertical_ratio', 0) match_thres = cfg.get('match_thres', 0.9) conf_thres = cfg.get('conf_thres', 0.6) low_conf_thres = cfg.get('low_conf_thres', 0.1) self.tracker = JDETracker( use_byte=use_byte, det_thresh=det_thresh, num_classes=self.num_classes, min_box_area=min_box_area, vertical_ratio=vertical_ratio, match_thres=match_thres, conf_thres=conf_thres, low_conf_thres=low_conf_thres, ) self.do_mtmct = False if mtmct_dir is None else True self.mtmct_dir = mtmct_dir def postprocess(self, inputs, result): # postprocess output of predictor np_boxes_num = result['boxes_num'] if np_boxes_num[0] <= 0: print('[WARNNING] No object detected.') result = {'boxes': np.zeros([0, 6]), 'boxes_num': [0]} result = {k: v for k, v in result.items() if v is not None} return result def reidprocess(self, det_results, repeats=1): pred_dets = det_results['boxes'] # cls_id, score, x0, y0, x1, y1 pred_xyxys = pred_dets[:, 2:6] ori_image = det_results['ori_image'] ori_image_shape = ori_image.shape[:2] pred_xyxys, keep_idx = clip_box(pred_xyxys, ori_image_shape) if len(keep_idx[0]) == 0: det_results['boxes'] = np.zeros((1, 6), dtype=np.float32) det_results['embeddings'] = None return det_results pred_dets = pred_dets[keep_idx[0]] pred_xyxys = pred_dets[:, 2:6] w, h = self.tracker.input_size crops = get_crops(pred_xyxys, ori_image, w, h) # to keep fast speed, only use topk crops crops = crops[:50] # reid_batch_size det_results['crops'] = np.array(crops).astype('float32') det_results['boxes'] = pred_dets[:50] input_names = self.reid_predictor.get_input_names() for i in range(len(input_names)): input_tensor = self.reid_predictor.get_input_handle(input_names[i]) input_tensor.copy_from_cpu(det_results[input_names[i]]) # model prediction for i in range(repeats): self.reid_predictor.run() output_names = self.reid_predictor.get_output_names() feature_tensor = self.reid_predictor.get_output_handle(output_names[ 0]) pred_embs = feature_tensor.copy_to_cpu() det_results['embeddings'] = pred_embs return det_results def tracking(self, det_results): pred_dets = det_results['boxes'] # cls_id, score, x0, y0, x1, y1 pred_embs = det_results.get('embeddings', None) if self.use_deepsort_tracker: # use DeepSORTTracker, only support singe class self.tracker.predict() online_targets = self.tracker.update(pred_dets, pred_embs) online_tlwhs, online_scores, online_ids = [], [], [] if self.do_mtmct: online_tlbrs, online_feats = [], [] for t in online_targets: if not t.is_confirmed() or t.time_since_update > 1: continue tlwh = t.to_tlwh() tscore = t.score tid = t.track_id if self.tracker.vertical_ratio > 0 and tlwh[2] / tlwh[ 3] > self.tracker.vertical_ratio: continue online_tlwhs.append(tlwh) online_scores.append(tscore) online_ids.append(tid) if self.do_mtmct: online_tlbrs.append(t.to_tlbr()) online_feats.append(t.feat) tracking_outs = { 'online_tlwhs': online_tlwhs, 'online_scores': online_scores, 'online_ids': online_ids, } if self.do_mtmct: seq_name = det_results['seq_name'] frame_id = det_results['frame_id'] tracking_outs['feat_data'] = {} for _tlbr, _id, _feat in zip(online_tlbrs, online_ids, online_feats): feat_data = {} feat_data['bbox'] = _tlbr feat_data['frame'] = f"{frame_id:06d}" feat_data['id'] = _id _imgname = f'{seq_name}_{_id}_{frame_id}.jpg' feat_data['imgname'] = _imgname feat_data['feat'] = _feat tracking_outs['feat_data'].update({_imgname: feat_data}) return tracking_outs else: # use ByteTracker, support multiple class online_tlwhs = defaultdict(list) online_scores = defaultdict(list) online_ids = defaultdict(list) if self.do_mtmct: online_tlbrs, online_feats = defaultdict(list), defaultdict( list) online_targets_dict = self.tracker.update(pred_dets, pred_embs) for cls_id in range(self.num_classes): online_targets = online_targets_dict[cls_id] for t in online_targets: tlwh = t.tlwh tid = t.track_id tscore = t.score if tlwh[2] * tlwh[3] <= self.tracker.min_box_area: continue if self.tracker.vertical_ratio > 0 and tlwh[2] / tlwh[ 3] > self.tracker.vertical_ratio: continue online_tlwhs[cls_id].append(tlwh) online_ids[cls_id].append(tid) online_scores[cls_id].append(tscore) if self.do_mtmct: online_tlbrs[cls_id].append(t.tlbr) online_feats[cls_id].append(t.curr_feat) if self.do_mtmct: assert self.num_classes == 1, 'MTMCT only support single class.' tracking_outs = { 'online_tlwhs': online_tlwhs[0], 'online_scores': online_scores[0], 'online_ids': online_ids[0], } seq_name = det_results['seq_name'] frame_id = det_results['frame_id'] tracking_outs['feat_data'] = {} for _tlbr, _id, _feat in zip(online_tlbrs[0], online_ids[0], online_feats[0]): feat_data = {} feat_data['bbox'] = _tlbr feat_data['frame'] = f"{frame_id:06d}" feat_data['id'] = _id _imgname = f'{seq_name}_{_id}_{frame_id}.jpg' feat_data['imgname'] = _imgname feat_data['feat'] = _feat tracking_outs['feat_data'].update({_imgname: feat_data}) return tracking_outs else: tracking_outs = { 'online_tlwhs': online_tlwhs, 'online_scores': online_scores, 'online_ids': online_ids, } return tracking_outs def predict_image(self, image_list, run_benchmark=False, repeats=1, visual=True, seq_name=None): num_classes = self.num_classes image_list.sort() ids2names = self.pred_config.labels if self.do_mtmct: mot_features_dict = {} # cid_tid_fid feats else: mot_results = [] for frame_id, img_file in enumerate(image_list): if self.do_mtmct: if frame_id % 10 == 0: print('Tracking frame: %d' % (frame_id)) batch_image_list = [img_file] # bs=1 in MOT model frame, _ = decode_image(img_file, {}) if run_benchmark: # preprocess inputs = self.preprocess(batch_image_list) # warmup self.det_times.preprocess_time_s.start() inputs = self.preprocess(batch_image_list) self.det_times.preprocess_time_s.end() # model prediction result_warmup = self.predict(repeats=repeats) # warmup self.det_times.inference_time_s.start() result = self.predict(repeats=repeats) self.det_times.inference_time_s.end(repeats=repeats) # postprocess result_warmup = self.postprocess(inputs, result) # warmup self.det_times.postprocess_time_s.start() det_result = self.postprocess(inputs, result) self.det_times.postprocess_time_s.end() # tracking if self.use_reid: det_result['frame_id'] = frame_id det_result['seq_name'] = seq_name det_result['ori_image'] = frame det_result = self.reidprocess(det_result) result_warmup = self.tracking(det_result) self.det_times.tracking_time_s.start() if self.use_reid: det_result = self.reidprocess(det_result) tracking_outs = self.tracking(det_result) self.det_times.tracking_time_s.end() self.det_times.img_num += 1 cm, gm, gu = get_current_memory_mb() self.cpu_mem += cm self.gpu_mem += gm self.gpu_util += gu else: self.det_times.preprocess_time_s.start() inputs = self.preprocess(batch_image_list) self.det_times.preprocess_time_s.end() self.det_times.inference_time_s.start() result = self.predict() self.det_times.inference_time_s.end() self.det_times.postprocess_time_s.start() det_result = self.postprocess(inputs, result) self.det_times.postprocess_time_s.end() # tracking process self.det_times.tracking_time_s.start() if self.use_reid: det_result['frame_id'] = frame_id det_result['seq_name'] = seq_name det_result['ori_image'] = frame det_result = self.reidprocess(det_result) tracking_outs = self.tracking(det_result) self.det_times.tracking_time_s.end() self.det_times.img_num += 1 online_tlwhs = tracking_outs['online_tlwhs'] online_scores = tracking_outs['online_scores'] online_ids = tracking_outs['online_ids'] if self.do_mtmct: feat_data_dict = tracking_outs['feat_data'] mot_features_dict = dict(mot_features_dict, **feat_data_dict) else: mot_results.append([online_tlwhs, online_scores, online_ids]) if visual: if len(image_list) > 1 and frame_id % 10 == 0: print('Tracking frame {}'.format(frame_id)) frame, _ = decode_image(img_file, {}) if isinstance(online_tlwhs, defaultdict): im = plot_tracking_dict( frame, num_classes, online_tlwhs, online_ids, online_scores, frame_id=frame_id, ids2names=[]) else: im = plot_tracking( frame, online_tlwhs, online_ids, online_scores, frame_id=frame_id) save_dir = os.path.join(self.output_dir, seq_name) if not os.path.exists(save_dir): os.makedirs(save_dir) cv2.imwrite( os.path.join(save_dir, '{:05d}.jpg'.format(frame_id)), im) if self.do_mtmct: return mot_features_dict else: return mot_results def predict_video(self, video_file, camera_id): video_out_name = '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) 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() # bs=1 in MOT model online_tlwhs, online_scores, online_ids = mot_results[0] # 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, 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 if self.use_deepsort_tracker: # use DeepSORTTracker, only support singe class results[0].append( (frame_id + 1, online_tlwhs, online_scores, online_ids)) im = plot_tracking( frame, online_tlwhs, online_ids, online_scores, frame_id=frame_id, fps=fps, do_entrance_counting=self.do_entrance_counting, entrance=entrance) else: # use ByteTracker, support multiple class 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])) 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) 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() def predict_mtmct(self, mtmct_dir, mtmct_cfg): cameras_bias = mtmct_cfg['cameras_bias'] cid_bias = parse_bias(cameras_bias) scene_cluster = list(cid_bias.keys()) # 1.zone releated parameters use_zone = mtmct_cfg.get('use_zone', False) zone_path = mtmct_cfg.get('zone_path', None) # 2.tricks parameters, can be used for other mtmct dataset use_ff = mtmct_cfg.get('use_ff', False) use_rerank = mtmct_cfg.get('use_rerank', False) # 3.camera releated parameters use_camera = mtmct_cfg.get('use_camera', False) use_st_filter = mtmct_cfg.get('use_st_filter', False) # 4.zone releated parameters use_roi = mtmct_cfg.get('use_roi', False) roi_dir = mtmct_cfg.get('roi_dir', False) mot_list_breaks = [] cid_tid_dict = dict() output_dir = self.output_dir if not os.path.exists(output_dir): os.makedirs(output_dir) seqs = os.listdir(mtmct_dir) for seq in sorted(seqs): fpath = os.path.join(mtmct_dir, seq) if os.path.isfile(fpath) and _is_valid_video(fpath): seq = seq.split('.')[-2] print('ffmpeg processing of video {}'.format(fpath)) frames_path = video2frames( video_path=fpath, outpath=mtmct_dir, frame_rate=25) fpath = os.path.join(mtmct_dir, seq) if os.path.isdir(fpath) == False: print('{} is not a image folder.'.format(fpath)) continue if os.path.exists(os.path.join(fpath, 'img1')): fpath = os.path.join(fpath, 'img1') assert os.path.isdir(fpath), '{} should be a directory'.format( fpath) image_list = glob.glob(os.path.join(fpath, '*.jpg')) image_list.sort() assert len(image_list) > 0, '{} has no images.'.format(fpath) print('start tracking seq: {}'.format(seq)) mot_features_dict = self.predict_image( image_list, visual=False, seq_name=seq) cid = int(re.sub('[a-z,A-Z]', "", seq)) tid_data, mot_list_break = trajectory_fusion( mot_features_dict, cid, cid_bias, use_zone=use_zone, zone_path=zone_path) mot_list_breaks.append(mot_list_break) # single seq process for line in tid_data: tracklet = tid_data[line] tid = tracklet['tid'] if (cid, tid) not in cid_tid_dict: cid_tid_dict[(cid, tid)] = tracklet map_tid = sub_cluster( cid_tid_dict, scene_cluster, use_ff=use_ff, use_rerank=use_rerank, use_camera=use_camera, use_st_filter=use_st_filter) pred_mtmct_file = os.path.join(output_dir, 'mtmct_result.txt') if use_camera: gen_res(pred_mtmct_file, scene_cluster, map_tid, mot_list_breaks) else: gen_res( pred_mtmct_file, scene_cluster, map_tid, mot_list_breaks, use_roi=use_roi, roi_dir=roi_dir) camera_results, cid_tid_fid_res = get_mtmct_matching_results( pred_mtmct_file) crops_dir = os.path.join(output_dir, 'mtmct_crops') save_mtmct_crops( cid_tid_fid_res, images_dir=mtmct_dir, crops_dir=crops_dir) save_dir = os.path.join(output_dir, 'mtmct_vis') save_mtmct_vis_results( camera_results, images_dir=mtmct_dir, save_dir=save_dir, save_videos=FLAGS.save_images) def main(): deploy_file = os.path.join(FLAGS.model_dir, 'infer_cfg.yml') with open(deploy_file) as f: yml_conf = yaml.safe_load(f) arch = yml_conf['arch'] detector = SDE_Detector( FLAGS.model_dir, tracker_config=FLAGS.tracker_config, device=FLAGS.device, run_mode=FLAGS.run_mode, batch_size=1, trt_min_shape=FLAGS.trt_min_shape, trt_max_shape=FLAGS.trt_max_shape, trt_opt_shape=FLAGS.trt_opt_shape, trt_calib_mode=FLAGS.trt_calib_mode, cpu_threads=FLAGS.cpu_threads, enable_mkldnn=FLAGS.enable_mkldnn, output_dir=FLAGS.output_dir, threshold=FLAGS.threshold, save_images=FLAGS.save_images, save_mot_txts=FLAGS.save_mot_txts, draw_center_traj=FLAGS.draw_center_traj, secs_interval=FLAGS.secs_interval, do_entrance_counting=FLAGS.do_entrance_counting, reid_model_dir=FLAGS.reid_model_dir, mtmct_dir=FLAGS.mtmct_dir, ) # predict from video file or camera video stream if FLAGS.video_file is not None or FLAGS.camera_id != -1: detector.predict_video(FLAGS.video_file, FLAGS.camera_id) elif FLAGS.mtmct_dir is not None: with open(FLAGS.mtmct_cfg) as f: mtmct_cfg = yaml.safe_load(f) detector.predict_mtmct(FLAGS.mtmct_dir, mtmct_cfg) else: # predict from image if FLAGS.image_dir is None and FLAGS.image_file is not None: assert FLAGS.batch_size == 1, "--batch_size should be 1 in MOT models." img_list = get_test_images(FLAGS.image_dir, FLAGS.image_file) seq_name = FLAGS.image_dir.split('/')[-1] detector.predict_image( img_list, FLAGS.run_benchmark, repeats=10, seq_name=seq_name) if not FLAGS.run_benchmark: detector.det_times.info(average=True) else: mode = FLAGS.run_mode model_dir = FLAGS.model_dir model_info = { 'model_name': model_dir.strip('/').split('/')[-1], 'precision': mode.split('_')[-1] } bench_log(detector, img_list, model_info, name='MOT') if __name__ == '__main__': paddle.enable_static() parser = argsparser() FLAGS = parser.parse_args() print_arguments(FLAGS) FLAGS.device = FLAGS.device.upper() assert FLAGS.device in ['CPU', 'GPU', 'XPU' ], "device should be CPU, GPU or XPU" main()