# Copyright (c) 2021 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 numpy as np from collections import defaultdict import paddle from benchmark_utils import PaddleInferBenchmark from preprocess import decode_image from utils import argsparser, Timer, get_current_memory_mb from infer import Detector, get_test_images, print_arguments, bench_log, PredictConfig, load_predictor # add python path import sys parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 2))) sys.path.insert(0, parent_path) from pptracking.python.mot import JDETracker, DeepSORTTracker from pptracking.python.mot.utils import MOTTimer, write_mot_results, get_crops, clip_box from pptracking.python.mot.visualize import plot_tracking, plot_tracking_dict 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 reid_model_dir (str): reid model dir, default None for ByteTrack, but set for DeepSORT """ 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, reid_model_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 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, ) 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'] 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 = [], [], [] 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) tracking_outs = { 'online_tlwhs': online_tlwhs, 'online_scores': online_scores, 'online_ids': online_ids, } return tracking_outs else: # use ByteTracker, support multiple class online_tlwhs = defaultdict(list) online_scores = defaultdict(list) online_ids = 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) 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 mot_results = [] for frame_id, img_file in enumerate(image_list): 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'] 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) 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 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[:, :, ::-1]], visual=False, seq_name=seq_name) timer.toc() # bs=1 in MOT model online_tlwhs, online_scores, online_ids = mot_results[0] 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) 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) 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) writer.release() 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, ) # 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) 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()