# 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 import cv2 import numpy as np import math import paddle import sys import copy from collections import Sequence, defaultdict from datacollector import DataCollector, Result # 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 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.infer import Detector, DetectorPicoDet from python.keypoint_infer import KeyPointDetector from python.keypoint_postprocess import translate_to_ori_images from python.preprocess import decode_image, ShortSizeScale from python.visualize import visualize_box_mask, visualize_attr, visualize_pose, visualize_action, visualize_vehicleplate 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 from pphuman.attr_infer import AttrDetector from pphuman.video_action_infer import VideoActionRecognizer from pphuman.action_infer import SkeletonActionRecognizer, DetActionRecognizer, ClsActionRecognizer from pphuman.action_utils import KeyPointBuff, ActionVisualHelper from pphuman.reid import ReID from pphuman.mtmct import mtmct_process from ppvehicle.vehicle_plate import PlateRecognizer from ppvehicle.vehicle_attr import VehicleAttr from download import auto_download_model 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 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, args, cfg): self.multi_camera = False reid_cfg = cfg.get('REID', False) self.enable_mtmct = reid_cfg['enable'] if reid_cfg else False self.is_video = False self.output_dir = args.output_dir self.vis_result = cfg['visual'] self.input = self._parse_input(args.image_file, args.image_dir, args.video_file, args.video_dir, args.camera_id) if self.multi_camera: self.predictor = [] for name in self.input: predictor_item = PipePredictor( args, cfg, is_video=True, multi_camera=True) predictor_item.set_file_name(name) self.predictor.append(predictor_item) else: self.predictor = PipePredictor(args, cfg, self.is_video) if self.is_video: self.predictor.set_file_name(args.video_file) self.output_dir = args.output_dir self.draw_center_traj = args.draw_center_traj self.secs_interval = args.secs_interval self.do_entrance_counting = args.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) if self.enable_mtmct: mtmct_process( multi_res, self.input, mtmct_vis=self.vis_result, output_dir=self.output_dir) else: self.predictor.run(self.input) def get_model_dir(cfg): # auto download inference model model_dir_dict = {} for key in cfg.keys(): if type(cfg[key]) == dict and \ ("enable" in cfg[key].keys() and cfg[key]['enable'] or "enable" not in cfg[key].keys()): if "model_dir" in cfg[key].keys(): model_dir = cfg[key]["model_dir"] downloaded_model_dir = auto_download_model(model_dir) if downloaded_model_dir: model_dir = downloaded_model_dir model_dir_dict[key] = model_dir print(key, " model dir:", model_dir) elif key == "VEHICLE_PLATE": det_model_dir = cfg[key]["det_model_dir"] downloaded_det_model_dir = auto_download_model(det_model_dir) if downloaded_det_model_dir: det_model_dir = downloaded_det_model_dir model_dir_dict["det_model_dir"] = det_model_dir print("det_model_dir model dir:", det_model_dir) rec_model_dir = cfg[key]["rec_model_dir"] downloaded_rec_model_dir = auto_download_model(rec_model_dir) if downloaded_rec_model_dir: rec_model_dir = downloaded_rec_model_dir model_dir_dict["rec_model_dir"] = rec_model_dir print("rec_model_dir model dir:", rec_model_dir) elif key == "MOT": # for idbased and skeletonbased actions model_dir = cfg[key]["model_dir"] downloaded_model_dir = auto_download_model(model_dir) if downloaded_model_dir: model_dir = downloaded_model_dir model_dir_dict[key] = model_dir return model_dir_dict 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 -> SkeletonAction Recognition 4. VideoAction 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 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, args, cfg, is_video=True, multi_camera=False): device = args.device run_mode = args.run_mode trt_min_shape = args.trt_min_shape trt_max_shape = args.trt_max_shape trt_opt_shape = args.trt_opt_shape trt_calib_mode = args.trt_calib_mode cpu_threads = args.cpu_threads enable_mkldnn = args.enable_mkldnn output_dir = args.output_dir draw_center_traj = args.draw_center_traj secs_interval = args.secs_interval do_entrance_counting = args.do_entrance_counting # general module for pphuman and ppvehicle self.with_mot = cfg.get('MOT', False)['enable'] if cfg.get( 'MOT', False) else False self.with_human_attr = cfg.get('ATTR', False)['enable'] if cfg.get( 'ATTR', False) else False if self.with_mot: print('Multi-Object Tracking enabled') if self.with_human_attr: print('Human Attribute Recognition enabled') # only for pphuman self.with_skeleton_action = cfg.get( 'SKELETON_ACTION', False)['enable'] if cfg.get('SKELETON_ACTION', False) else False self.with_video_action = cfg.get( 'VIDEO_ACTION', False)['enable'] if cfg.get('VIDEO_ACTION', False) else False self.with_idbased_detaction = cfg.get( 'ID_BASED_DETACTION', False)['enable'] if cfg.get( 'ID_BASED_DETACTION', False) else False self.with_idbased_clsaction = cfg.get( 'ID_BASED_CLSACTION', False)['enable'] if cfg.get( 'ID_BASED_CLSACTION', False) else False self.with_mtmct = cfg.get('REID', False)['enable'] if cfg.get( 'REID', False) else False if self.with_skeleton_action: print('SkeletonAction Recognition enabled') if self.with_video_action: print('VideoAction Recognition enabled') if self.with_idbased_detaction: print('IDBASED Detection Action Recognition enabled') if self.with_idbased_clsaction: print('IDBASED Classification Action Recognition enabled') if self.with_mtmct: print("MTMCT enabled") # only for ppvehicle self.with_vehicleplate = cfg.get( 'VEHICLE_PLATE', False)['enable'] if cfg.get('VEHICLE_PLATE', False) else False if self.with_vehicleplate: print('Vehicle Plate Recognition enabled') self.with_vehicle_attr = cfg.get( 'VEHICLE_ATTR', False)['enable'] if cfg.get('VEHICLE_ATTR', False) else False if self.with_vehicle_attr: print('Vehicle Attribute Recognition enabled') self.modebase = { "framebased": False, "videobased": False, "idbased": False, "skeletonbased": False } 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() # auto download inference model model_dir_dict = get_model_dir(self.cfg) if not is_video: det_cfg = self.cfg['DET'] model_dir = model_dir_dict['DET'] 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_human_attr: attr_cfg = self.cfg['ATTR'] model_dir = model_dir_dict['ATTR'] batch_size = attr_cfg['batch_size'] basemode = attr_cfg['basemode'] self.modebase[basemode] = True 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_vehicle_attr: vehicleattr_cfg = self.cfg['VEHICLE_ATTR'] model_dir = model_dir_dict['VEHICLE_ATTR'] batch_size = vehicleattr_cfg['batch_size'] color_threshold = vehicleattr_cfg['color_threshold'] type_threshold = vehicleattr_cfg['type_threshold'] basemode = vehicleattr_cfg['basemode'] self.modebase[basemode] = True self.vehicle_attr_predictor = VehicleAttr( model_dir, device, run_mode, batch_size, trt_min_shape, trt_max_shape, trt_opt_shape, trt_calib_mode, cpu_threads, enable_mkldnn, color_threshold, type_threshold) else: if self.with_human_attr: attr_cfg = self.cfg['ATTR'] model_dir = model_dir_dict['ATTR'] batch_size = attr_cfg['batch_size'] basemode = attr_cfg['basemode'] self.modebase[basemode] = True 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_idbased_detaction: idbased_detaction_cfg = self.cfg['ID_BASED_DETACTION'] model_dir = model_dir_dict['ID_BASED_DETACTION'] batch_size = idbased_detaction_cfg['batch_size'] basemode = idbased_detaction_cfg['basemode'] threshold = idbased_detaction_cfg['threshold'] display_frames = idbased_detaction_cfg['display_frames'] skip_frame_num = idbased_detaction_cfg['skip_frame_num'] self.modebase[basemode] = True self.det_action_predictor = DetActionRecognizer( model_dir, device, run_mode, batch_size, trt_min_shape, trt_max_shape, trt_opt_shape, trt_calib_mode, cpu_threads, enable_mkldnn, threshold=threshold, display_frames=display_frames, skip_frame_num=skip_frame_num) self.det_action_visual_helper = ActionVisualHelper(1) if self.with_idbased_clsaction: idbased_clsaction_cfg = self.cfg['ID_BASED_CLSACTION'] model_dir = model_dir_dict['ID_BASED_CLSACTION'] batch_size = idbased_clsaction_cfg['batch_size'] basemode = idbased_clsaction_cfg['basemode'] threshold = idbased_clsaction_cfg['threshold'] self.modebase[basemode] = True display_frames = idbased_clsaction_cfg['display_frames'] skip_frame_num = idbased_clsaction_cfg['skip_frame_num'] self.cls_action_predictor = ClsActionRecognizer( model_dir, device, run_mode, batch_size, trt_min_shape, trt_max_shape, trt_opt_shape, trt_calib_mode, cpu_threads, enable_mkldnn, threshold=threshold, display_frames=display_frames, skip_frame_num=skip_frame_num) self.cls_action_visual_helper = ActionVisualHelper(1) if self.with_skeleton_action: skeleton_action_cfg = self.cfg['SKELETON_ACTION'] skeleton_action_model_dir = model_dir_dict['SKELETON_ACTION'] skeleton_action_batch_size = skeleton_action_cfg['batch_size'] skeleton_action_frames = skeleton_action_cfg['max_frames'] display_frames = skeleton_action_cfg['display_frames'] self.coord_size = skeleton_action_cfg['coord_size'] basemode = skeleton_action_cfg['basemode'] self.modebase[basemode] = True self.skeleton_action_predictor = SkeletonActionRecognizer( skeleton_action_model_dir, device, run_mode, skeleton_action_batch_size, trt_min_shape, trt_max_shape, trt_opt_shape, trt_calib_mode, cpu_threads, enable_mkldnn, window_size=skeleton_action_frames) self.skeleton_action_visual_helper = ActionVisualHelper( display_frames) if self.modebase["skeletonbased"]: kpt_cfg = self.cfg['KPT'] kpt_model_dir = model_dir_dict['KPT'] kpt_batch_size = kpt_cfg['batch_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(skeleton_action_frames) if self.with_vehicleplate: vehicleplate_cfg = self.cfg['VEHICLE_PLATE'] self.vehicleplate_detector = PlateRecognizer(args, vehicleplate_cfg) basemode = vehicleplate_cfg['basemode'] self.modebase[basemode] = True if self.with_vehicle_attr: vehicleattr_cfg = self.cfg['VEHICLE_ATTR'] model_dir = model_dir_dict['VEHICLE_ATTR'] batch_size = vehicleattr_cfg['batch_size'] color_threshold = vehicleattr_cfg['color_threshold'] type_threshold = vehicleattr_cfg['type_threshold'] basemode = vehicleattr_cfg['basemode'] self.modebase[basemode] = True self.vehicle_attr_predictor = VehicleAttr( model_dir, device, run_mode, batch_size, trt_min_shape, trt_max_shape, trt_opt_shape, trt_calib_mode, cpu_threads, enable_mkldnn, color_threshold, type_threshold) if self.with_mtmct: reid_cfg = self.cfg['REID'] model_dir = model_dir_dict['REID'] batch_size = reid_cfg['batch_size'] basemode = reid_cfg['basemode'] self.modebase[basemode] = True 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) if self.with_mot or self.modebase["idbased"] or self.modebase[ "skeletonbased"]: mot_cfg = self.cfg['MOT'] model_dir = model_dir_dict['MOT'] tracker_config = mot_cfg['tracker_config'] batch_size = mot_cfg['batch_size'] basemode = mot_cfg['basemode'] self.modebase[basemode] = True 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_video_action: video_action_cfg = self.cfg['VIDEO_ACTION'] basemode = video_action_cfg['basemode'] self.modebase[basemode] = True video_action_model_dir = model_dir_dict['VIDEO_ACTION'] video_action_batch_size = video_action_cfg['batch_size'] short_size = video_action_cfg["short_size"] target_size = video_action_cfg["target_size"] self.video_action_predictor = VideoActionRecognizer( model_dir=video_action_model_dir, short_size=short_size, target_size=target_size, device=device, run_mode=run_mode, batch_size=video_action_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) 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_human_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') if self.with_vehicle_attr: crop_inputs = crop_image_with_det(batch_input, det_res) vehicle_attr_res_list = [] if i > self.warmup_frame: self.pipe_timer.module_time['vehicle_attr'].start() for crop_input in crop_inputs: attr_res = self.vehicle_attr_predictor.predict_image( crop_input, visual=False) vehicle_attr_res_list.extend(attr_res['output']) if i > self.warmup_frame: self.pipe_timer.module_time['vehicle_attr'].end() attr_res = {'output': vehicle_attr_res_list} self.pipeline_res.update(attr_res, 'vehicle_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 video_action_imgs = [] if self.with_video_action: short_size = self.cfg["VIDEO_ACTION"]["short_size"] scale = ShortSizeScale(short_size) while (1): if frame_id % 10 == 0: print('frame id: ', frame_id) ret, frame = capture.read() if not ret: break frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) if self.modebase["idbased"] or self.modebase["skeletonbased"]: 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_rgb)], 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, entrance, records, center_traj) # visualize writer.write(im) if self.file_name is None: # use camera_id cv2.imshow('Paddle-Pipeline', im) if cv2.waitKey(1) & 0xFF == ord('q'): break continue self.pipeline_res.update(mot_res, 'mot') crop_input, new_bboxes, ori_bboxes = crop_image_with_mot( frame_rgb, mot_res) if self.with_vehicleplate: if frame_id > self.warmup_frame: self.pipe_timer.module_time['vehicleplate'].start() platelicense = self.vehicleplate_detector.get_platelicense( crop_input) if frame_id > self.warmup_frame: self.pipe_timer.module_time['vehicleplate'].end() self.pipeline_res.update(platelicense, 'vehicleplate') if self.with_human_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_vehicle_attr: if frame_id > self.warmup_frame: self.pipe_timer.module_time['vehicle_attr'].start() attr_res = self.vehicle_attr_predictor.predict_image( crop_input, visual=False) if frame_id > self.warmup_frame: self.pipe_timer.module_time['vehicle_attr'].end() self.pipeline_res.update(attr_res, 'vehicle_attr') if self.with_idbased_detaction: if frame_id > self.warmup_frame: self.pipe_timer.module_time['det_action'].start() det_action_res = self.det_action_predictor.predict( crop_input, mot_res) if frame_id > self.warmup_frame: self.pipe_timer.module_time['det_action'].end() self.pipeline_res.update(det_action_res, 'det_action') if self.cfg['visual']: self.det_action_visual_helper.update(det_action_res) if self.with_idbased_clsaction: if frame_id > self.warmup_frame: self.pipe_timer.module_time['cls_action'].start() cls_action_res = self.cls_action_predictor.predict_with_mot( crop_input, mot_res) if frame_id > self.warmup_frame: self.pipe_timer.module_time['cls_action'].end() self.pipeline_res.update(cls_action_res, 'cls_action') if self.cfg['visual']: self.cls_action_visual_helper.update(cls_action_res) if self.with_skeleton_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 skeleton_action_res = {} if state: if frame_id > self.warmup_frame: self.pipe_timer.module_time[ 'skeleton_action'].start() collected_keypoint = self.kpt_buff.get_collected_keypoint( ) # reoragnize kpt output with ID skeleton_action_input = parse_mot_keypoint( collected_keypoint, self.coord_size) skeleton_action_res = self.skeleton_action_predictor.predict_skeleton_with_mot( skeleton_action_input) if frame_id > self.warmup_frame: self.pipe_timer.module_time['skeleton_action'].end() self.pipeline_res.update(skeleton_action_res, 'skeleton_action') if self.cfg['visual']: self.skeleton_action_visual_helper.update( skeleton_action_res) if self.with_mtmct and frame_id % 10 == 0: crop_input, img_qualities, rects = self.reid_predictor.crop_image_with_mot( frame_rgb, 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') if self.with_video_action: # get the params frame_len = self.cfg["VIDEO_ACTION"]["frame_len"] sample_freq = self.cfg["VIDEO_ACTION"]["sample_freq"] if sample_freq * frame_len > frame_count: # video is too short sample_freq = int(frame_count / frame_len) # filter the warmup frames if frame_id > self.warmup_frame: self.pipe_timer.module_time['video_action'].start() # collect frames if frame_id % sample_freq == 0: # Scale image scaled_img = scale(frame_rgb) video_action_imgs.append(scaled_img) # the number of collected frames is enough to predict video action if len(video_action_imgs) == frame_len: classes, scores = self.video_action_predictor.predict( video_action_imgs) if frame_id > self.warmup_frame: self.pipe_timer.module_time['video_action'].end() video_action_res = {"class": classes[0], "score": scores[0]} self.pipeline_res.update(video_action_res, 'video_action') print("video_action_res:", video_action_res) video_action_imgs.clear() # next clip 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('Paddle-Pipeline', 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 if mot_res is not None: 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) human_attr_res = result.get('attr') if human_attr_res is not None: boxes = mot_res['boxes'][:, 1:] human_attr_res = human_attr_res['output'] image = visualize_attr(image, human_attr_res, boxes) image = np.array(image) vehicle_attr_res = result.get('vehicle_attr') if vehicle_attr_res is not None: boxes = mot_res['boxes'][:, 1:] vehicle_attr_res = vehicle_attr_res['output'] image = visualize_attr(image, vehicle_attr_res, boxes) image = np.array(image) vehicleplate_res = result.get('vehicleplate') if vehicleplate_res: boxes = mot_res['boxes'][:, 1:] image = visualize_vehicleplate(image, vehicleplate_res['plate'], 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) video_action_res = result.get('video_action') if video_action_res is not None: video_action_score = None if video_action_res and video_action_res["class"] == 1: video_action_score = video_action_res["score"] mot_boxes = None if mot_res: mot_boxes = mot_res['boxes'] image = visualize_action( image, mot_boxes, action_visual_collector=None, action_text="SkeletonAction", video_action_score=video_action_score, video_action_text="Fight") visual_helper_for_display = [] action_to_display = [] skeleton_action_res = result.get('skeleton_action') if skeleton_action_res is not None: visual_helper_for_display.append(self.skeleton_action_visual_helper) action_to_display.append("Falling") det_action_res = result.get('det_action') if det_action_res is not None: visual_helper_for_display.append(self.det_action_visual_helper) action_to_display.append("Smoking") cls_action_res = result.get('cls_action') if cls_action_res is not None: visual_helper_for_display.append(self.cls_action_visual_helper) action_to_display.append("Calling") if len(visual_helper_for_display) > 0: image = visualize_action(image, mot_res['boxes'], visual_helper_for_display, action_to_display) return image def visualize_image(self, im_files, images, result): start_idx, boxes_num_i = 0, 0 det_res = result.get('det') human_attr_res = result.get('attr') vehicle_attr_res = result.get('vehicle_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 human_attr_res is not None: human_attr_res_i = human_attr_res['output'][start_idx:start_idx + boxes_num_i] im = visualize_attr(im, human_attr_res_i, det_res_i['boxes']) if vehicle_attr_res is not None: vehicle_attr_res_i = vehicle_attr_res['output'][ start_idx:start_idx + boxes_num_i] im = visualize_attr(im, vehicle_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(FLAGS, cfg) 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()