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pipeline.py
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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 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
self.do_break_in_counting = args.do_break_in_counting
self.region_type = args.region_type
self.region_polygon = args.region_polygon
if self.region_type == 'custom':
assert len(
self.region_polygon
) > 6, 'region_type is custom, region_polygon should be at least 3 pairs of point coords.'
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
do_break_in_counting = args.do_break_in_counting
region_type = args.region_type
region_polygon = args.region_polygon
# 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.do_break_in_counting = do_break_in_counting
self.region_type = region_type
self.region_polygon = region_polygon
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,
do_break_in_counting=do_break_in_counting,
region_type=region_type,
region_polygon=region_polygon)
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()
if self.do_entrance_counting or self.do_break_in_counting:
if self.region_type == 'horizontal':
entrance = [0, height / 2., width, height / 2.]
elif self.region_type == 'vertical':
entrance = [width / 2, 0., width / 2, height]
elif self.region_type == 'custom':
entrance = []
assert len(
self.region_polygon
) % 2 == 0, "region_polygon should be pairs of coords points when do break_in counting."
for i in range(0, len(self.region_polygon), 2):
entrance.append(
[self.region_polygon[i], self.region_polygon[i + 1]])
entrance.append([width, height])
else:
raise ValueError("region_type:{} 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)
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,
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)
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,
ids2names=self.mot_predictor.pred_config.labels,
do_entrance_counting=self.do_entrance_counting,
do_break_in_counting=self.do_break_in_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,