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pipeline.py
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# 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 cfg_utils import argsparser, print_arguments, merge_cfg
from pipe_utils import 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, update_object_info
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:
args (argparse.Namespace): arguments in pipeline, which contains environment and runtime settings
cfg (dict): config of models in pipeline
"""
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, args.rtsp)
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(self.input)
def _parse_input(self, image_file, image_dir, video_file, video_dir,
camera_id, rtsp):
# 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
) or 'rtsp' in video_file, "video_file not exists and not an rtsp site."
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 rtsp is not None:
if len(rtsp) > 1:
rtsp = [rtsp_item for rtsp_item in rtsp if 'rtsp' in rtsp_item]
self.multi_camera = True
input = rtsp
else:
self.multi_camera = False
input = rtsp[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_multithreads(self):
import threading
if self.multi_camera:
multi_res = []
threads = []
for idx, (predictor,
input) in enumerate(zip(self.predictor, self.input)):
thread = threading.Thread(
name=str(idx).zfill(3),
target=predictor.run,
args=(input, idx))
threads.append(thread)
for thread in threads:
thread.start()
for predictor, thread in zip(self.predictor, threads):
thread.join()
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 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 if the model_path is a url link.
Otherwise it will use the model_path directly.
"""
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
cfg[key]["model_dir"] = 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
cfg[key]["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
cfg[key]["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
cfg[key]["model_dir"] = model_dir
print("mot_model_dir model_dir: ", model_dir)
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:
args (argparse.Namespace): arguments in pipeline, which contains environment and runtime settings
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
"""
def __init__(self, args, cfg, is_video=True, multi_camera=False):
# 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.basemode = {
"MOT": "idbased",
"ATTR": "idbased",
"VIDEO_ACTION": "videobased",
"SKELETON_ACTION": "skeletonbased",
"ID_BASED_DETACTION": "idbased",
"ID_BASED_CLSACTION": "idbased",
"REID": "idbased",
"VEHICLE_PLATE": "idbased",
"VEHICLE_ATTR": "idbased",
}
self.is_video = is_video
self.multi_camera = multi_camera
self.cfg = cfg
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
self.do_break_in_counting = args.do_break_in_counting
self.region_type = args.region_type
self.region_polygon = args.region_polygon
self.illegal_parking_time = args.illegal_parking_time
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
get_model_dir(self.cfg)
if self.with_vehicleplate:
vehicleplate_cfg = self.cfg['VEHICLE_PLATE']
self.vehicleplate_detector = PlateRecognizer(args, vehicleplate_cfg)
basemode = self.basemode['VEHICLE_PLATE']
self.modebase[basemode] = True
if self.with_human_attr:
attr_cfg = self.cfg['ATTR']
basemode = self.basemode['ATTR']
self.modebase[basemode] = True
self.attr_predictor = AttrDetector.init_with_cfg(args, attr_cfg)
if self.with_vehicle_attr:
vehicleattr_cfg = self.cfg['VEHICLE_ATTR']
basemode = self.basemode['VEHICLE_ATTR']
self.modebase[basemode] = True
self.vehicle_attr_predictor = VehicleAttr.init_with_cfg(
args, vehicleattr_cfg)
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, args.device, args.run_mode, batch_size,
args.trt_min_shape, args.trt_max_shape, args.trt_opt_shape,
args.trt_calib_mode, args.cpu_threads, args.enable_mkldnn)
else:
if self.with_idbased_detaction:
idbased_detaction_cfg = self.cfg['ID_BASED_DETACTION']
basemode = self.basemode['ID_BASED_DETACTION']
self.modebase[basemode] = True
self.det_action_predictor = DetActionRecognizer.init_with_cfg(
args, idbased_detaction_cfg)
self.det_action_visual_helper = ActionVisualHelper(1)
if self.with_idbased_clsaction:
idbased_clsaction_cfg = self.cfg['ID_BASED_CLSACTION']
basemode = self.basemode['ID_BASED_CLSACTION']
self.modebase[basemode] = True
self.cls_action_predictor = ClsActionRecognizer.init_with_cfg(
args, idbased_clsaction_cfg)
self.cls_action_visual_helper = ActionVisualHelper(1)
if self.with_skeleton_action:
skeleton_action_cfg = self.cfg['SKELETON_ACTION']
display_frames = skeleton_action_cfg['display_frames']
self.coord_size = skeleton_action_cfg['coord_size']
basemode = self.basemode['SKELETON_ACTION']
self.modebase[basemode] = True
skeleton_action_frames = skeleton_action_cfg['max_frames']
self.skeleton_action_predictor = SkeletonActionRecognizer.init_with_cfg(
args, skeleton_action_cfg)
self.skeleton_action_visual_helper = ActionVisualHelper(
display_frames)
kpt_cfg = self.cfg['KPT']
kpt_model_dir = kpt_cfg['model_dir']
kpt_batch_size = kpt_cfg['batch_size']
self.kpt_predictor = KeyPointDetector(
kpt_model_dir,
args.device,
args.run_mode,
kpt_batch_size,
args.trt_min_shape,
args.trt_max_shape,
args.trt_opt_shape,
args.trt_calib_mode,
args.cpu_threads,
args.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 = self.basemode['VEHICLE_PLATE']
self.modebase[basemode] = True
if self.with_mtmct:
reid_cfg = self.cfg['REID']
basemode = self.basemode['REID']
self.modebase[basemode] = True
self.reid_predictor = ReID.init_with_cfg(args, reid_cfg)
if self.with_mot or self.modebase["idbased"] or self.modebase[
"skeletonbased"]:
mot_cfg = self.cfg['MOT']
model_dir = mot_cfg['model_dir']
tracker_config = mot_cfg['tracker_config']
batch_size = mot_cfg['batch_size']
skip_frame_num = mot_cfg.get('skip_frame_num', -1)
basemode = self.basemode['MOT']
self.modebase[basemode] = True
self.mot_predictor = SDE_Detector(
model_dir,
tracker_config,
args.device,
args.run_mode,
batch_size,
args.trt_min_shape,
args.trt_max_shape,
args.trt_opt_shape,
args.trt_calib_mode,
args.cpu_threads,
args.enable_mkldnn,
skip_frame_num=skip_frame_num,
draw_center_traj=self.draw_center_traj,
secs_interval=self.secs_interval,
do_entrance_counting=self.do_entrance_counting,
do_break_in_counting=self.do_break_in_counting,
region_type=self.region_type,
region_polygon=self.region_polygon)
if self.with_video_action:
video_action_cfg = self.cfg['VIDEO_ACTION']
basemode = self.basemode['VIDEO_ACTION']
self.modebase[basemode] = True
self.video_action_predictor = VideoActionRecognizer.init_with_cfg(
args, video_action_cfg)
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, thread_idx=0):
if self.is_video:
self.predict_video(input, thread_idx=thread_idx)
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.pipe_timer.track_num += len(det_res['boxes'])
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')
if self.with_vehicleplate:
if i > self.warmup_frame:
self.pipe_timer.module_time['vehicleplate'].start()
crop_inputs = crop_image_with_det(batch_input, det_res)
platelicenses = []
for crop_input in crop_inputs:
platelicense = self.vehicleplate_detector.get_platelicense(
crop_input)
platelicenses.extend(platelicense['plate'])
if i > self.warmup_frame:
self.pipe_timer.module_time['vehicleplate'].end()
vehicleplate_res = {'vehicleplate': platelicenses}
self.pipeline_res.update(vehicleplate_res, 'vehicleplate')
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, thread_idx=0):
# 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
if "rtsp" in video_file:
video_out_name = video_out_name + "_t" + str(thread_idx).zfill(
2) + "_rtsp.mp4"
# 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()
if self.do_entrance_counting or self.do_break_in_counting or self.illegal_parking_time != -1:
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."
assert len(
self.region_polygon
) > 6, 'region_type is custom, region_polygon should be at least 3 pairs of point coords.'
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:{} unsupported.".format(
self.region_type))
video_fps = fps
video_action_imgs = []
if self.with_video_action:
short_size = self.cfg["VIDEO_ACTION"]["short_size"]
scale = ShortSizeScale(short_size)
object_in_region_info = {
} # store info for vehicle parking in region
illegal_parking_dict = None
while (1):
if frame_id % 10 == 0:
print('Thread: {}; frame id: {}'.format(thread_idx, frame_id))
ret, frame = capture.read()
if not ret:
break
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
if frame_id > self.warmup_frame:
self.pipe_timer.total_time.start()
if self.modebase["idbased"] or self.modebase["skeletonbased"]:
if frame_id > self.warmup_frame:
self.pipe_timer.module_time['mot'].start()
mot_skip_frame_num = self.mot_predictor.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
res = self.mot_predictor.predict_image(
[copy.deepcopy(frame_rgb)],
visual=False,
reuse_det_result=reuse_det_result)
# mot output format: id, class, score, xmin, ymin, xmax, ymax
mot_res = parse_mot_res(res)
if frame_id > self.warmup_frame:
self.pipe_timer.module_time['mot'].end()
self.pipe_timer.track_num += len(mot_res['boxes'])
if frame_id % 10 == 0:
print("Thread: {}; trackid number: {}".format(
thread_idx, len(mot_res['boxes'])))
# 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,
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,
ids2names=self.mot_predictor.pred_config.labels)
records = statistic['records']
if self.illegal_parking_time != -1:
object_in_region_info, illegal_parking_dict = update_object_info(
object_in_region_info, mot_result, self.region_type,
entrance, video_fps, self.illegal_parking_time)
if len(illegal_parking_dict) != 0:
# build relationship between id and plate
for key, value in illegal_parking_dict.items():
plate = self.collector.get_carlp(key)
illegal_parking_dict[key]['plate'] = plate
# 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 and frame_id % 10 == 0:
if frame_id > self.warmup_frame:
self.pipe_timer.module_time['vehicleplate'].start()
plate_input, _, _ = crop_image_with_mot(
frame_rgb, mot_res, expand=False)
platelicense = self.vehicleplate_detector.get_platelicense(
plate_input)
if frame_id > self.warmup_frame:
self.pipe_timer.module_time['vehicleplate'].end()
self.pipeline_res.update(platelicense, 'vehicleplate')
else:
self.pipeline_res.clear('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,
self.collector, frame_id, fps,
entrance, records, center_traj,
self.illegal_parking_time != -1,
illegal_parking_dict) # 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,
collector,
frame_id,
fps,
entrance=None,
records=None,
center_traj=None,
do_illegal_parking_recognition=False,
illegal_parking_dict=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,
ids2names=self.mot_predictor.pred_config.labels,
do_entrance_counting=self.do_entrance_counting,
do_break_in_counting=self.do_break_in_counting,
do_illegal_parking_recognition=do_illegal_parking_recognition,
illegal_parking_dict=illegal_parking_dict,
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)
if mot_res is not None:
vehicleplate = False
plates = []
for trackid in mot_res['boxes'][:, 0]:
plate = collector.get_carlp(trackid)
if plate != None:
vehicleplate = True
plates.append(plate)
else:
plates.append("")
if vehicleplate:
boxes = mot_res['boxes'][:, 1:]
image = visualize_vehicleplate(image, plates, 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')