# 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 yaml import glob from collections import defaultdict import cv2 import numpy as np import math import paddle import sys import copy from collections import Sequence from reid import ReID from datacollector import DataCollector, Result from mtmct import mtmct_process # add deploy path of PadleDetection to sys.path parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 2))) sys.path.insert(0, parent_path) from python.infer import Detector, DetectorPicoDet from python.attr_infer import AttrDetector from python.keypoint_infer import KeyPointDetector from python.keypoint_postprocess import translate_to_ori_images from python.action_infer import ActionRecognizer from python.action_utils import KeyPointBuff, ActionVisualHelper from pipe_utils import argsparser, print_arguments, merge_cfg, PipeTimer from pipe_utils import get_test_images, crop_image_with_det, crop_image_with_mot, parse_mot_res, parse_mot_keypoint from python.preprocess import decode_image from python.visualize import visualize_box_mask, visualize_attr, visualize_pose, visualize_action from pptracking.python.mot_sde_infer import SDE_Detector from pptracking.python.mot.visualize import plot_tracking_dict from pptracking.python.mot.utils import flow_statistic class Pipeline(object): """ Pipeline Args: cfg (dict): config of models in pipeline image_file (string|None): the path of image file, default as None image_dir (string|None): the path of image directory, if not None, then all the images in directory will be predicted, default as None video_file (string|None): the path of video file, default as None camera_id (int): the device id of camera to predict, default as -1 enable_attr (bool): whether use attribute recognition, default as false enable_action (bool): whether use action recognition, default as false device (string): the device to predict, options are: CPU/GPU/XPU, default as CPU run_mode (string): the mode of prediction, options are: paddle/trt_fp32/trt_fp16, default as paddle trt_min_shape (int): min shape for dynamic shape in trt, default as 1 trt_max_shape (int): max shape for dynamic shape in trt, default as 1280 trt_opt_shape (int): opt shape for dynamic shape in trt, default as 640 trt_calib_mode (bool): If the model is produced by TRT offline quantitative calibration, trt_calib_mode need to set True. default as False cpu_threads (int): cpu threads, default as 1 enable_mkldnn (bool): whether to open MKLDNN, default as False output_dir (string): The path of output, default as 'output' 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. """ def __init__(self, cfg, image_file=None, image_dir=None, video_file=None, video_dir=None, camera_id=-1, enable_attr=False, enable_action=True, device='CPU', run_mode='paddle', 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', draw_center_traj=False, secs_interval=10, do_entrance_counting=False): self.multi_camera = False self.is_video = False self.output_dir = output_dir self.vis_result = cfg['visual'] self.input = self._parse_input(image_file, image_dir, video_file, video_dir, camera_id) if self.multi_camera: self.predictor = [] for name in self.input: predictor_item = PipePredictor( cfg, is_video=True, multi_camera=True, enable_attr=enable_attr, enable_action=enable_action, device=device, run_mode=run_mode, trt_min_shape=trt_min_shape, trt_max_shape=trt_max_shape, trt_opt_shape=trt_opt_shape, cpu_threads=cpu_threads, enable_mkldnn=enable_mkldnn, output_dir=output_dir) predictor_item.set_file_name(name) self.predictor.append(predictor_item) else: self.predictor = PipePredictor( cfg, self.is_video, enable_attr=enable_attr, enable_action=enable_action, device=device, run_mode=run_mode, 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, draw_center_traj=draw_center_traj, secs_interval=secs_interval, do_entrance_counting=do_entrance_counting) if self.is_video: self.predictor.set_file_name(video_file) self.output_dir = output_dir self.draw_center_traj = draw_center_traj self.secs_interval = secs_interval self.do_entrance_counting = do_entrance_counting def _parse_input(self, image_file, image_dir, video_file, video_dir, camera_id): # parse input as is_video and multi_camera if image_file is not None or image_dir is not None: input = get_test_images(image_dir, image_file) self.is_video = False self.multi_camera = False elif video_file is not None: assert os.path.exists(video_file), "video_file not exists." self.multi_camera = False input = video_file self.is_video = True elif video_dir is not None: videof = [os.path.join(video_dir, x) for x in os.listdir(video_dir)] if len(videof) > 1: self.multi_camera = True videof.sort() input = videof else: input = videof[0] self.is_video = True elif camera_id != -1: self.multi_camera = False input = camera_id self.is_video = True else: raise ValueError( "Illegal Input, please set one of ['video_file'，'camera_id'，'image_file', 'image_dir']" ) return input def run(self): if self.multi_camera: multi_res = [] for predictor, input in zip(self.predictor, self.input): predictor.run(input) collector_data = predictor.get_result() multi_res.append(collector_data) mtmct_process( multi_res, self.input, mtmct_vis=self.vis_result, output_dir=self.output_dir) else: self.predictor.run(self.input) class PipePredictor(object): """ Predictor in single camera The pipeline for image input: 1. Detection 2. Detection -> Attribute The pipeline for video input: 1. Tracking 2. Tracking -> Attribute 3. Tracking -> KeyPoint -> Action Recognition Args: cfg (dict): config of models in pipeline is_video (bool): whether the input is video, default as False multi_camera (bool): whether to use multi camera in pipeline, default as False camera_id (int): the device id of camera to predict, default as -1 enable_attr (bool): whether use attribute recognition, default as false enable_action (bool): whether use action recognition, default as false device (string): the device to predict, options are: CPU/GPU/XPU, default as CPU run_mode (string): the mode of prediction, options are: paddle/trt_fp32/trt_fp16, default as paddle trt_min_shape (int): min shape for dynamic shape in trt, default as 1 trt_max_shape (int): max shape for dynamic shape in trt, default as 1280 trt_opt_shape (int): opt shape for dynamic shape in trt, default as 640 trt_calib_mode (bool): If the model is produced by TRT offline quantitative calibration, trt_calib_mode need to set True. default as False cpu_threads (int): cpu threads, default as 1 enable_mkldnn (bool): whether to open MKLDNN, default as False output_dir (string): The path of output, default as 'output' 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. """ def __init__(self, cfg, is_video=True, multi_camera=False, enable_attr=False, enable_action=False, device='CPU', run_mode='paddle', 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', draw_center_traj=False, secs_interval=10, do_entrance_counting=False): if enable_attr and not cfg.get('ATTR', False): ValueError( 'enable_attr is set to True, please set ATTR in config file') if enable_action and (not cfg.get('ACTION', False) or not cfg.get('KPT', False)): ValueError( 'enable_action is set to True, please set KPT and ACTION in config file' ) self.with_attr = cfg.get('ATTR', False) and enable_attr self.with_action = cfg.get('ACTION', False) and enable_action self.with_mtmct = cfg.get('REID', False) and multi_camera if self.with_attr: print('Attribute Recognition enabled') if self.with_action: print('Action Recognition enabled') if multi_camera: if not self.with_mtmct: print( 'Warning!!! MTMCT enabled, but cannot find REID config in [infer_cfg.yml], please check!' ) else: print("MTMCT enabled") self.is_video = is_video self.multi_camera = multi_camera self.cfg = cfg self.output_dir = output_dir self.draw_center_traj = draw_center_traj self.secs_interval = secs_interval self.do_entrance_counting = do_entrance_counting self.warmup_frame = self.cfg['warmup_frame'] self.pipeline_res = Result() self.pipe_timer = PipeTimer() self.file_name = None self.collector = DataCollector() if not is_video: det_cfg = self.cfg['DET'] model_dir = det_cfg['model_dir'] batch_size = det_cfg['batch_size'] self.det_predictor = Detector( model_dir, device, run_mode, batch_size, trt_min_shape, trt_max_shape, trt_opt_shape, trt_calib_mode, cpu_threads, enable_mkldnn) if self.with_attr: attr_cfg = self.cfg['ATTR'] model_dir = attr_cfg['model_dir'] batch_size = attr_cfg['batch_size'] self.attr_predictor = AttrDetector( model_dir, device, run_mode, batch_size, trt_min_shape, trt_max_shape, trt_opt_shape, trt_calib_mode, cpu_threads, enable_mkldnn) else: mot_cfg = self.cfg['MOT'] model_dir = mot_cfg['model_dir'] tracker_config = mot_cfg['tracker_config'] batch_size = mot_cfg['batch_size'] self.mot_predictor = SDE_Detector( model_dir, tracker_config, device, run_mode, batch_size, trt_min_shape, trt_max_shape, trt_opt_shape, trt_calib_mode, cpu_threads, enable_mkldnn, draw_center_traj=draw_center_traj, secs_interval=secs_interval, do_entrance_counting=do_entrance_counting) if self.with_attr: attr_cfg = self.cfg['ATTR'] model_dir = attr_cfg['model_dir'] batch_size = attr_cfg['batch_size'] self.attr_predictor = AttrDetector( model_dir, device, run_mode, batch_size, trt_min_shape, trt_max_shape, trt_opt_shape, trt_calib_mode, cpu_threads, enable_mkldnn) if self.with_action: kpt_cfg = self.cfg['KPT'] kpt_model_dir = kpt_cfg['model_dir'] kpt_batch_size = kpt_cfg['batch_size'] action_cfg = self.cfg['ACTION'] action_model_dir = action_cfg['model_dir'] action_batch_size = action_cfg['batch_size'] action_frames = action_cfg['max_frames'] display_frames = action_cfg['display_frames'] self.coord_size = action_cfg['coord_size'] self.kpt_predictor = KeyPointDetector( kpt_model_dir, device, run_mode, kpt_batch_size, trt_min_shape, trt_max_shape, trt_opt_shape, trt_calib_mode, cpu_threads, enable_mkldnn, use_dark=False) self.kpt_buff = KeyPointBuff(action_frames) self.action_predictor = ActionRecognizer( action_model_dir, device, run_mode, action_batch_size, trt_min_shape, trt_max_shape, trt_opt_shape, trt_calib_mode, cpu_threads, enable_mkldnn, window_size=action_frames) self.action_visual_helper = ActionVisualHelper(display_frames) if self.with_mtmct: reid_cfg = self.cfg['REID'] model_dir = reid_cfg['model_dir'] batch_size = reid_cfg['batch_size'] self.reid_predictor = ReID(model_dir, device, run_mode, batch_size, trt_min_shape, trt_max_shape, trt_opt_shape, trt_calib_mode, cpu_threads, enable_mkldnn) def set_file_name(self, path): if path is not None: self.file_name = os.path.split(path)[-1] else: # use camera id self.file_name = None def get_result(self): return self.collector.get_res() def run(self, input): if self.is_video: self.predict_video(input) else: self.predict_image(input) self.pipe_timer.info() def predict_image(self, input): # det # det -> attr batch_loop_cnt = math.ceil( float(len(input)) / self.det_predictor.batch_size) for i in range(batch_loop_cnt): start_index = i * self.det_predictor.batch_size end_index = min((i + 1) * self.det_predictor.batch_size, len(input)) batch_file = input[start_index:end_index] batch_input = [decode_image(f, {})[0] for f in batch_file] if i > self.warmup_frame: self.pipe_timer.total_time.start() self.pipe_timer.module_time['det'].start() # det output format: class, score, xmin, ymin, xmax, ymax det_res = self.det_predictor.predict_image( batch_input, visual=False) det_res = self.det_predictor.filter_box(det_res, self.cfg['crop_thresh']) if i > self.warmup_frame: self.pipe_timer.module_time['det'].end() self.pipeline_res.update(det_res, 'det') if self.with_attr: crop_inputs = crop_image_with_det(batch_input, det_res) attr_res_list = [] if i > self.warmup_frame: self.pipe_timer.module_time['attr'].start() for crop_input in crop_inputs: attr_res = self.attr_predictor.predict_image( crop_input, visual=False) attr_res_list.extend(attr_res['output']) if i > self.warmup_frame: self.pipe_timer.module_time['attr'].end() attr_res = {'output': attr_res_list} self.pipeline_res.update(attr_res, 'attr') self.pipe_timer.img_num += len(batch_input) if i > self.warmup_frame: self.pipe_timer.total_time.end() if self.cfg['visual']: self.visualize_image(batch_file, batch_input, self.pipeline_res) def predict_video(self, video_file): # mot # mot -> attr # mot -> pose -> action capture = cv2.VideoCapture(video_file) video_out_name = 'output.mp4' if self.file_name is None else self.file_name # 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("video 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) fourcc = cv2.VideoWriter_fourcc(* 'mp4v') writer = cv2.VideoWriter(out_path, fourcc, fps, (width, height)) frame_id = 0 entrance, records, center_traj = None, None, None if self.draw_center_traj: center_traj = [{}] 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): if frame_id % 10 == 0: print('frame id: ', frame_id) ret, frame = capture.read() if not ret: break if frame_id > self.warmup_frame: self.pipe_timer.total_time.start() self.pipe_timer.module_time['mot'].start() res = self.mot_predictor.predict_image( [copy.deepcopy(frame)], visual=False) if frame_id > self.warmup_frame: self.pipe_timer.module_time['mot'].end() # mot output format: id, class, score, xmin, ymin, xmax, ymax mot_res = parse_mot_res(res) # flow_statistic only support single class MOT boxes, scores, ids = res[0] # batch size = 1 in MOT mot_result = (frame_id + 1, boxes[0], scores[0], ids[0]) # single class statistic = flow_statistic( mot_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) records = statistic['records'] # nothing detected if len(mot_res['boxes']) == 0: frame_id += 1 if frame_id > self.warmup_frame: self.pipe_timer.img_num += 1 self.pipe_timer.total_time.end() if self.cfg['visual']: _, _, fps = self.pipe_timer.get_total_time() im = self.visualize_video(frame, mot_res, frame_id, fps) # visualize writer.write(im) if self.file_name is None: # use camera_id cv2.imshow('PPHuman', im) if cv2.waitKey(1) & 0xFF == ord('q'): break continue self.pipeline_res.update(mot_res, 'mot') if self.with_attr or self.with_action: crop_input, new_bboxes, ori_bboxes = crop_image_with_mot( frame, mot_res) if self.with_attr: if frame_id > self.warmup_frame: self.pipe_timer.module_time['attr'].start() attr_res = self.attr_predictor.predict_image( crop_input, visual=False) if frame_id > self.warmup_frame: self.pipe_timer.module_time['attr'].end() self.pipeline_res.update(attr_res, 'attr') if self.with_action: if frame_id > self.warmup_frame: self.pipe_timer.module_time['kpt'].start() kpt_pred = self.kpt_predictor.predict_image( crop_input, visual=False) keypoint_vector, score_vector = translate_to_ori_images( kpt_pred, np.array(new_bboxes)) kpt_res = {} kpt_res['keypoint'] = [ keypoint_vector.tolist(), score_vector.tolist() ] if len(keypoint_vector) > 0 else [[], []] kpt_res['bbox'] = ori_bboxes if frame_id > self.warmup_frame: self.pipe_timer.module_time['kpt'].end() self.pipeline_res.update(kpt_res, 'kpt') self.kpt_buff.update(kpt_res, mot_res) # collect kpt output state = self.kpt_buff.get_state( ) # whether frame num is enough or lost tracker action_res = {} if state: if frame_id > self.warmup_frame: self.pipe_timer.module_time['action'].start() collected_keypoint = self.kpt_buff.get_collected_keypoint( ) # reoragnize kpt output with ID action_input = parse_mot_keypoint(collected_keypoint, self.coord_size) action_res = self.action_predictor.predict_skeleton_with_mot( action_input) if frame_id > self.warmup_frame: self.pipe_timer.module_time['action'].end() self.pipeline_res.update(action_res, 'action') if self.cfg['visual']: self.action_visual_helper.update(action_res) if self.with_mtmct and frame_id % 10 == 0: crop_input, img_qualities, rects = self.reid_predictor.crop_image_with_mot( frame, mot_res) if frame_id > self.warmup_frame: self.pipe_timer.module_time['reid'].start() reid_res = self.reid_predictor.predict_batch(crop_input) if frame_id > self.warmup_frame: self.pipe_timer.module_time['reid'].end() reid_res_dict = { 'features': reid_res, "qualities": img_qualities, "rects": rects } self.pipeline_res.update(reid_res_dict, 'reid') else: self.pipeline_res.clear('reid') self.collector.append(frame_id, self.pipeline_res) if frame_id > self.warmup_frame: self.pipe_timer.img_num += 1 self.pipe_timer.total_time.end() frame_id += 1 if self.cfg['visual']: _, _, fps = self.pipe_timer.get_total_time() im = self.visualize_video(frame, self.pipeline_res, frame_id, fps, entrance, records, center_traj) # visualize writer.write(im) if self.file_name is None: # use camera_id cv2.imshow('PPHuman', im) if cv2.waitKey(1) & 0xFF == ord('q'): break writer.release() print('save result to {}'.format(out_path)) def visualize_video(self, image, result, frame_id, fps, entrance=None, records=None, center_traj=None): mot_res = copy.deepcopy(result.get('mot')) if mot_res is not None: ids = mot_res['boxes'][:, 0] scores = mot_res['boxes'][:, 2] boxes = mot_res['boxes'][:, 3:] boxes[:, 2] = boxes[:, 2] - boxes[:, 0] boxes[:, 3] = boxes[:, 3] - boxes[:, 1] else: boxes = np.zeros([0, 4]) ids = np.zeros([0]) scores = np.zeros([0]) # single class, still need to be defaultdict type for ploting num_classes = 1 online_tlwhs = defaultdict(list) online_scores = defaultdict(list) online_ids = defaultdict(list) online_tlwhs[0] = boxes online_scores[0] = scores online_ids[0] = ids image = plot_tracking_dict( image, num_classes, online_tlwhs, online_ids, online_scores, frame_id=frame_id, fps=fps, do_entrance_counting=self.do_entrance_counting, entrance=entrance, records=records, center_traj=center_traj) attr_res = result.get('attr') if attr_res is not None: boxes = mot_res['boxes'][:, 1:] attr_res = attr_res['output'] image = visualize_attr(image, attr_res, boxes) image = np.array(image) kpt_res = result.get('kpt') if kpt_res is not None: image = visualize_pose( image, kpt_res, visual_thresh=self.cfg['kpt_thresh'], returnimg=True) action_res = result.get('action') if action_res is not None: image = visualize_action(image, mot_res['boxes'], self.action_visual_helper, "Falling") return image def visualize_image(self, im_files, images, result): start_idx, boxes_num_i = 0, 0 det_res = result.get('det') attr_res = result.get('attr') for i, (im_file, im) in enumerate(zip(im_files, images)): if det_res is not None: det_res_i = {} boxes_num_i = det_res['boxes_num'][i] det_res_i['boxes'] = det_res['boxes'][start_idx:start_idx + boxes_num_i, :] im = visualize_box_mask( im, det_res_i, labels=['person'], threshold=self.cfg['crop_thresh']) im = np.ascontiguousarray(np.copy(im)) im = cv2.cvtColor(im, cv2.COLOR_RGB2BGR) if attr_res is not None: attr_res_i = attr_res['output'][start_idx:start_idx + boxes_num_i] im = visualize_attr(im, attr_res_i, det_res_i['boxes']) img_name = os.path.split(im_file)[-1] if not os.path.exists(self.output_dir): os.makedirs(self.output_dir) out_path = os.path.join(self.output_dir, img_name) cv2.imwrite(out_path, im) print("save result to: " + out_path) start_idx += boxes_num_i def main(): cfg = merge_cfg(FLAGS) print_arguments(cfg) pipeline = Pipeline( cfg, FLAGS.image_file, FLAGS.image_dir, FLAGS.video_file, FLAGS.video_dir, FLAGS.camera_id, FLAGS.enable_attr, FLAGS.enable_action, FLAGS.device, FLAGS.run_mode, FLAGS.trt_min_shape, FLAGS.trt_max_shape, FLAGS.trt_opt_shape, FLAGS.trt_calib_mode, FLAGS.cpu_threads, FLAGS.enable_mkldnn, FLAGS.output_dir, FLAGS.draw_center_traj, FLAGS.secs_interval, FLAGS.do_entrance_counting) pipeline.run() if __name__ == '__main__': paddle.enable_static() parser = argsparser() FLAGS = parser.parse_args() FLAGS.device = FLAGS.device.upper() assert FLAGS.device in ['CPU', 'GPU', 'XPU' ], "device should be CPU, GPU or XPU" main()