# 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 copy import math import time import yaml import cv2 import numpy as np from collections import defaultdict import paddle from benchmark_utils import PaddleInferBenchmark from utils import gaussian_radius, gaussian2D, draw_umich_gaussian from preprocess import preprocess, decode_image, WarpAffine, NormalizeImage, Permute from utils import argsparser, Timer, get_current_memory_mb from infer import Detector, get_test_images, print_arguments, bench_log, PredictConfig from keypoint_preprocess import get_affine_transform # 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 CenterTracker from pptracking.python.mot.utils import MOTTimer, write_mot_results from pptracking.python.mot.visualize import plot_tracking def transform_preds_with_trans(coords, trans): target_coords = np.ones((coords.shape[0], 3), np.float32) target_coords[:, :2] = coords target_coords = np.dot(trans, target_coords.transpose()).transpose() return target_coords[:, :2] def affine_transform(pt, t): new_pt = np.array([pt[0], pt[1], 1.]).T new_pt = np.dot(t, new_pt) return new_pt[:2] def affine_transform_bbox(bbox, trans, width, height): bbox = np.array(copy.deepcopy(bbox), dtype=np.float32) bbox[:2] = affine_transform(bbox[:2], trans) bbox[2:] = affine_transform(bbox[2:], trans) bbox[[0, 2]] = np.clip(bbox[[0, 2]], 0, width - 1) bbox[[1, 3]] = np.clip(bbox[[1, 3]], 0, height - 1) return bbox class CenterTrack(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 """ def __init__( self, model_dir, tracker_config=None, device='CPU', run_mode='paddle', batch_size=1, trt_min_shape=1, trt_max_shape=960, trt_opt_shape=544, trt_calib_mode=False, cpu_threads=1, enable_mkldnn=False, output_dir='output', threshold=0.5, save_images=False, save_mot_txts=False, ): super(CenterTrack, 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) # tracker config cfg = self.pred_config.tracker min_box_area = cfg.get('min_box_area', -1) vertical_ratio = cfg.get('vertical_ratio', -1) track_thresh = cfg.get('track_thresh', 0.4) pre_thresh = cfg.get('pre_thresh', 0.5) self.tracker = CenterTracker( num_classes=self.num_classes, min_box_area=min_box_area, vertical_ratio=vertical_ratio, track_thresh=track_thresh, pre_thresh=pre_thresh) self.pre_image = None def get_additional_inputs(self, dets, meta, with_hm=True): # Render input heatmap from previous trackings. trans_input = meta['trans_input'] inp_width, inp_height = int(meta['inp_width']), int(meta['inp_height']) input_hm = np.zeros((1, inp_height, inp_width), dtype=np.float32) for det in dets: if det['score'] < self.tracker.pre_thresh: continue bbox = affine_transform_bbox(det['bbox'], trans_input, inp_width, inp_height) h, w = bbox[3] - bbox[1], bbox[2] - bbox[0] if (h > 0 and w > 0): radius = gaussian_radius( (math.ceil(h), math.ceil(w)), min_overlap=0.7) radius = max(0, int(radius)) ct = np.array( [(bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2], dtype=np.float32) ct_int = ct.astype(np.int32) if with_hm: input_hm[0] = draw_umich_gaussian(input_hm[0], ct_int, radius) if with_hm: input_hm = input_hm[np.newaxis] return input_hm def preprocess(self, image_list): preprocess_ops = [] for op_info in self.pred_config.preprocess_infos: new_op_info = op_info.copy() op_type = new_op_info.pop('type') preprocess_ops.append(eval(op_type)(**new_op_info)) assert len(image_list) == 1, 'MOT only support bs=1' im_path = image_list[0] im, im_info = preprocess(im_path, preprocess_ops) #inputs = create_inputs(im, im_info) inputs = {} inputs['image'] = np.array((im, )).astype('float32') inputs['im_shape'] = np.array( (im_info['im_shape'], )).astype('float32') inputs['scale_factor'] = np.array( (im_info['scale_factor'], )).astype('float32') inputs['trans_input'] = im_info['trans_input'] inputs['inp_width'] = im_info['inp_width'] inputs['inp_height'] = im_info['inp_height'] inputs['center'] = im_info['center'] inputs['scale'] = im_info['scale'] inputs['out_height'] = im_info['out_height'] inputs['out_width'] = im_info['out_width'] if self.pre_image is None: self.pre_image = inputs['image'] # initializing tracker for the first frame self.tracker.init_track([]) inputs['pre_image'] = self.pre_image self.pre_image = inputs['image'] # Note: update for next image # render input heatmap from tracker status pre_hm = self.get_additional_inputs( self.tracker.tracks, inputs, with_hm=True) inputs['pre_hm'] = pre_hm #.to_tensor(pre_hm) input_names = self.predictor.get_input_names() for i in range(len(input_names)): input_tensor = self.predictor.get_input_handle(input_names[i]) if input_names[i] == 'x': input_tensor.copy_from_cpu(inputs['image']) else: input_tensor.copy_from_cpu(inputs[input_names[i]]) return inputs def postprocess(self, inputs, result): # postprocess output of predictor np_bboxes = result['bboxes'] if np_bboxes.shape[0] <= 0: print('[WARNNING] No object detected and tracked.') result = {'bboxes': np.zeros([0, 6]), 'cts': None, 'tracking': None} return result result = {k: v for k, v in result.items() if v is not None} return result def centertrack_post_process(self, dets, meta, out_thresh): if not ('bboxes' in dets): return [{}] preds = [] c, s = meta['center'], meta['scale'] h, w = meta['out_height'], meta['out_width'] trans = get_affine_transform( center=c, input_size=s, rot=0, output_size=[w, h], shift=(0., 0.), inv=True).astype(np.float32) for i, dets_bbox in enumerate(dets['bboxes']): if dets_bbox[1] < out_thresh: break item = {} item['score'] = dets_bbox[1] item['class'] = int(dets_bbox[0]) + 1 item['ct'] = transform_preds_with_trans( dets['cts'][i].reshape([1, 2]), trans).reshape(2) if 'tracking' in dets: tracking = transform_preds_with_trans( (dets['tracking'][i] + dets['cts'][i]).reshape([1, 2]), trans).reshape(2) item['tracking'] = tracking - item['ct'] if 'bboxes' in dets: bbox = transform_preds_with_trans( dets_bbox[2:6].reshape([2, 2]), trans).reshape(4) item['bbox'] = bbox preds.append(item) return preds def tracking(self, inputs, det_results): result = self.centertrack_post_process( det_results, inputs, self.tracker.out_thresh) online_targets = self.tracker.update(result) online_tlwhs, online_scores, online_ids = [], [], [] for t in online_targets: bbox = t['bbox'] tlwh = [bbox[0], bbox[1], bbox[2] - bbox[0], bbox[3] - bbox[1]] tscore = float(t['score']) tid = int(t['tracking_id']) if tlwh[2] * tlwh[3] > 0: online_tlwhs.append(tlwh) online_ids.append(tid) online_scores.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 'bboxes', 'cts' and 'tracking': np.ndarray: shape:[N,6],[N,2] and [N,2], N: number of box ''' # model prediction np_bboxes, np_cts, np_tracking = None, None, None for i in range(repeats): self.predictor.run() output_names = self.predictor.get_output_names() bboxes_tensor = self.predictor.get_output_handle(output_names[0]) np_bboxes = bboxes_tensor.copy_to_cpu() cts_tensor = self.predictor.get_output_handle(output_names[1]) np_cts = cts_tensor.copy_to_cpu() tracking_tensor = self.predictor.get_output_handle(output_names[2]) np_tracking = tracking_tensor.copy_to_cpu() result = dict( bboxes=np_bboxes, cts=np_cts, tracking=np_tracking) return result def predict_image(self, image_list, run_benchmark=False, repeats=1, visual=True, seq_name=None): 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(inputs, det_result) self.det_times.tracking_time_s.start() online_tlwhs, online_scores, online_ids = self.tracking(inputs, 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() online_tlwhs, online_scores, online_ids = self.tracking(inputs, 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( frame, 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 = 1 timer = MOTTimer() results = defaultdict(list) # centertrack onpy support single class 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() fps = 1. / timer.duration online_tlwhs, online_scores, online_ids = mot_results[0] 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, 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, data_type, num_classes) writer.release() def main(): detector = CenterTrack( 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) # 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()