# 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 mot_utils import argsparser, Timer, get_current_memory_mb from det_infer import Detector, get_test_images, print_arguments, bench_log, PredictConfig # add python path import sys parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 2))) sys.path.insert(0, parent_path) from mot import JDETracker from mot.utils import MOTTimer, write_mot_results, flow_statistic from mot.visualize import plot_tracking, plot_tracking_dict # Global dictionary MOT_JDE_SUPPORT_MODELS = { 'JDE', 'FairMOT', } class JDE_Detector(Detector): """ Args: model_dir (str): root path of model.pdiparams, model.pdmodel and infer_cfg.yml 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 skip_frame_num (int): Skip frame num to get faster MOT results, default as -1 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. do_break_in_counting(bool): Whether counting the numbers of identifiers break in the area, default as False，only support single class counting in MOT, and the video should be taken by a static camera. region_type (str): Area type for entrance counting or break in counting, 'horizontal' and 'vertical' used when do entrance counting. 'custom' used when do break in counting. Note that only support single-class MOT, and the video should be taken by a static camera. region_polygon (list): Clockwise point coords (x0,y0,x1,y1...) of polygon of area when do_break_in_counting. Note that only support single-class MOT and the video should be taken by a static camera. """ def __init__(self, model_dir, tracker_config=None, device='CPU', run_mode='paddle', batch_size=1, trt_min_shape=1, trt_max_shape=1088, trt_opt_shape=608, 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, skip_frame_num=-1, do_entrance_counting=False, do_break_in_counting=False, region_type='horizontal', region_polygon=[]): super(JDE_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.skip_frame_num = skip_frame_num self.do_entrance_counting = do_entrance_counting self.do_break_in_counting = do_break_in_counting self.region_type = region_type self.region_polygon = region_polygon if self.region_type == 'custom': assert len( self.region_polygon ) > 6, 'region_type is custom, region_polygon should be at least 3 pairs of point coords.' 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) if self.skip_frame_num > 1: self.previous_det_result = None # tracker config assert self.pred_config.tracker, "The exported JDE Detector model should have tracker." cfg = self.pred_config.tracker min_box_area = cfg.get('min_box_area', 0.0) vertical_ratio = cfg.get('vertical_ratio', 0.0) conf_thres = cfg.get('conf_thres', 0.0) tracked_thresh = cfg.get('tracked_thresh', 0.7) metric_type = cfg.get('metric_type', 'euclidean') self.tracker = JDETracker( num_classes=self.num_classes, min_box_area=min_box_area, vertical_ratio=vertical_ratio, conf_thres=conf_thres, tracked_thresh=tracked_thresh, metric_type=metric_type) def postprocess(self, inputs, result): # postprocess output of predictor np_boxes = result['pred_dets'] if np_boxes.shape[0] <= 0: print('[WARNNING] No object detected.') result = {'pred_dets': np.zeros([0, 6]), 'pred_embs': None} result = {k: v for k, v in result.items() if v is not None} return result def tracking(self, det_results): pred_dets = det_results['pred_dets'] # cls_id, score, x0, y0, x1, y1 pred_embs = det_results['pred_embs'] online_targets_dict = self.tracker.update(pred_dets, pred_embs) online_tlwhs = defaultdict(list) online_scores = defaultdict(list) online_ids = defaultdict(list) 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) return online_tlwhs, online_scores, online_ids def predict(self, repeats=1): ''' Args: repeats (int): repeats number for prediction Returns: result (dict): include 'pred_dets': np.ndarray: shape:[N,6], N: number of box, matix element:[class, score, x_min, y_min, x_max, y_max] FairMOT(JDE)'s result include 'pred_embs': np.ndarray: shape: [N, 128] ''' # model prediction np_pred_dets, np_pred_embs = None, None for i in range(repeats): self.predictor.run() output_names = self.predictor.get_output_names() boxes_tensor = self.predictor.get_output_handle(output_names[0]) np_pred_dets = boxes_tensor.copy_to_cpu() embs_tensor = self.predictor.get_output_handle(output_names[1]) np_pred_embs = embs_tensor.copy_to_cpu() result = dict(pred_dets=np_pred_dets, pred_embs=np_pred_embs) return result def predict_image(self, image_list, run_benchmark=False, repeats=1, visual=True, seq_name=None, reuse_det_result=False): mot_results = [] num_classes = self.num_classes image_list.sort() ids2names = self.pred_config.labels data_type = 'mcmot' if num_classes > 1 else 'mot' for frame_id, img_file in enumerate(image_list): batch_image_list = [img_file] # bs=1 in MOT model 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 result_warmup = self.tracking(det_result) self.det_times.tracking_time_s.start() online_tlwhs, online_scores, online_ids = 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() if not reuse_det_result: inputs = self.preprocess(batch_image_list) self.det_times.preprocess_time_s.end() self.det_times.inference_time_s.start() if not reuse_det_result: result = self.predict() self.det_times.inference_time_s.end() self.det_times.postprocess_time_s.start() if not reuse_det_result: det_result = self.postprocess(inputs, result) self.previous_det_result = det_result else: assert self.previous_det_result is not None det_result = self.previous_det_result self.det_times.postprocess_time_s.end() # tracking process self.det_times.tracking_time_s.start() online_tlwhs, online_scores, online_ids = self.tracking( det_result) self.det_times.tracking_time_s.end() self.det_times.img_num += 1 if visual: if len(image_list) > 1 and frame_id % 10 == 0: print('Tracking frame {}'.format(frame_id)) frame, _ = decode_image(img_file, {}) im = plot_tracking_dict( frame, num_classes, online_tlwhs, online_ids, online_scores, frame_id=frame_id, ids2names=ids2names) if seq_name is None: seq_name = image_list[0].split('/')[-2] 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) mot_results.append([online_tlwhs, online_scores, online_ids]) return mot_results 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 = 0 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() if self.do_entrance_counting or self.do_break_in_counting: if self.region_type == 'horizontal': entrance = [0, height / 2., width, height / 2.] elif self.region_type == 'vertical': entrance = [width / 2, 0., width / 2, height] elif self.region_type == 'custom': entrance = [] assert len( self.region_polygon ) % 2 == 0, "region_polygon should be pairs of coords points when do break_in counting." for i in range(0, len(self.region_polygon), 2): entrance.append([ self.region_polygon[i], self.region_polygon[i + 1] ]) entrance.append([width, height]) else: raise ValueError("region_type:{} is not supported.".format( self.region_type)) video_fps = fps while (1): ret, frame = capture.read() if not ret: break if frame_id % 10 == 0: print('Tracking frame: %d' % (frame_id)) timer.tic() mot_skip_frame_num = self.skip_frame_num reuse_det_result = False if mot_skip_frame_num > 1 and frame_id > 0 and frame_id % mot_skip_frame_num > 0: reuse_det_result = True seq_name = video_out_name.split('.')[0] mot_results = self.predict_image( [frame], visual=False, seq_name=seq_name, reuse_det_result=reuse_det_result) 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, self.do_break_in_counting, self.region_type, 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 frame_id += 1 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() def main(): detector = JDE_Detector( FLAGS.model_dir, tracker_config=None, 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, skip_frame_num=FLAGS.skip_frame_num, do_entrance_counting=FLAGS.do_entrance_counting, do_break_in_counting=FLAGS.do_break_in_counting, region_type=FLAGS.region_type, region_polygon=FLAGS.region_polygon) # 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 img_list = get_test_images(FLAGS.image_dir, FLAGS.image_file) detector.predict_image(img_list, FLAGS.run_benchmark, repeats=10) 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()