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# Copyright (c) 2021 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 time
import yaml
import cv2
import numpy as np
from collections import defaultdict
import paddle
from benchmark_utils import PaddleInferBenchmark
from preprocess import decode_image
from mot_utils import argsparser, Timer, get_current_memory_mb
from det_infer import Detector, get_test_images, print_arguments, bench_log, PredictConfig
# add python path
import sys
parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 2)))
sys.path.insert(0, parent_path)
from mot import JDETracker
from mot.utils import MOTTimer, write_mot_results, flow_statistic
from mot.visualize import plot_tracking, plot_tracking_dict
# Global dictionary
MOT_JDE_SUPPORT_MODELS = {
'JDE',
'FairMOT',
}
class JDE_Detector(Detector):
"""
Args:
model_dir (str): root path of model.pdiparams, model.pdmodel and infer_cfg.yml
device (str): Choose the device you want to run, it can be: CPU/GPU/XPU, default is CPU
run_mode (str): mode of running(paddle/trt_fp32/trt_fp16)
batch_size (int): size of pre batch in inference
trt_min_shape (int): min shape for dynamic shape in trt
trt_max_shape (int): max shape for dynamic shape in trt
trt_opt_shape (int): opt shape for dynamic shape in trt
trt_calib_mode (bool): If the model is produced by TRT offline quantitative
calibration, trt_calib_mode need to set True
cpu_threads (int): cpu threads
enable_mkldnn (bool): whether to open MKLDNN
output_dir (string): The path of output, default as 'output'
threshold (float): Score threshold of the detected bbox, default as 0.5
save_images (bool): Whether to save visualization image results, default as False
save_mot_txts (bool): Whether to save tracking results (txt), default as False
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
skip_frame_num (int): Skip frame num to get faster MOT results, default as -1
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.
do_break_in_counting(bool): Whether counting the numbers of identifiers break in
the area, default as False，only support single class counting in MOT,
and the video should be taken by a static camera.
region_type (str): Area type for entrance counting or break in counting, 'horizontal'
and 'vertical' used when do entrance counting. 'custom' used when do break in counting.
Note that only support single-class MOT, and the video should be taken by a static camera.
region_polygon (list): Clockwise point coords (x0,y0,x1,y1...) of polygon of area when
do_break_in_counting. Note that only support single-class MOT and
the video should be taken by a static camera.
"""
def __init__(self,
model_dir,
tracker_config=None,
device='CPU',
run_mode='paddle',
batch_size=1,
trt_min_shape=1,
trt_max_shape=1088,
trt_opt_shape=608,
trt_calib_mode=False,
cpu_threads=1,
enable_mkldnn=False,
output_dir='output',
threshold=0.5,
save_images=False,
save_mot_txts=False,
draw_center_traj=False,
secs_interval=10,
skip_frame_num=-1,
do_entrance_counting=False,
do_break_in_counting=False,
region_type='horizontal',
region_polygon=[]):
super(JDE_Detector, self).__init__(
model_dir=model_dir,
device=device,
run_mode=run_mode,
batch_size=batch_size,
trt_min_shape=trt_min_shape,
trt_max_shape=trt_max_shape,
trt_opt_shape=trt_opt_shape,
trt_calib_mode=trt_calib_mode,
cpu_threads=cpu_threads,
enable_mkldnn=enable_mkldnn,
output_dir=output_dir,
threshold=threshold, )
self.save_images = save_images
self.save_mot_txts = save_mot_txts
self.draw_center_traj = draw_center_traj
self.secs_interval = secs_interval
self.skip_frame_num = skip_frame_num
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
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.'
assert batch_size == 1, "MOT model only supports batch_size=1."
self.det_times = Timer(with_tracker=True)
self.num_classes = len(self.pred_config.labels)
if self.skip_frame_num > 1:
self.previous_det_result = None
# tracker config
assert self.pred_config.tracker, "The exported JDE Detector model should have tracker."
cfg = self.pred_config.tracker
min_box_area = cfg.get('min_box_area', 0.0)
vertical_ratio = cfg.get('vertical_ratio', 0.0)
conf_thres = cfg.get('conf_thres', 0.0)
tracked_thresh = cfg.get('tracked_thresh', 0.7)
metric_type = cfg.get('metric_type', 'euclidean')
self.tracker = JDETracker(
num_classes=self.num_classes,
min_box_area=min_box_area,
vertical_ratio=vertical_ratio,
conf_thres=conf_thres,
tracked_thresh=tracked_thresh,
metric_type=metric_type)
def postprocess(self, inputs, result):
# postprocess output of predictor
np_boxes = result['pred_dets']
if np_boxes.shape[0] <= 0:
print('[WARNNING] No object detected.')
result = {'pred_dets': np.zeros([0, 6]), 'pred_embs': None}
result = {k: v for k, v in result.items() if v is not None}
return result
def tracking(self, det_results):
pred_dets = det_results['pred_dets'] # cls_id, score, x0, y0, x1, y1
pred_embs = det_results['pred_embs']
online_targets_dict = self.tracker.update(pred_dets, pred_embs)
online_tlwhs = defaultdict(list)
online_scores = defaultdict(list)
online_ids = defaultdict(list)
for cls_id in range(self.num_classes):
online_targets = online_targets_dict[cls_id]
for t in online_targets:
tlwh = t.tlwh
tid = t.track_id
tscore = t.score
if tlwh[2] * tlwh[3] <= self.tracker.min_box_area: continue
if self.tracker.vertical_ratio > 0 and tlwh[2] / tlwh[
3] > self.tracker.vertical_ratio:
continue
online_tlwhs[cls_id].append(tlwh)
online_ids[cls_id].append(tid)
online_scores[cls_id].append(tscore)
return online_tlwhs, online_scores, online_ids
def predict(self, repeats=1):
'''
Args:
repeats (int): repeats number for prediction
Returns:
result (dict): include 'pred_dets': np.ndarray: shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
FairMOT(JDE)'s result include 'pred_embs': np.ndarray:
shape: [N, 128]
'''
# model prediction
np_pred_dets, np_pred_embs = None, None
for i in range(repeats):
self.predictor.run()
output_names = self.predictor.get_output_names()
boxes_tensor = self.predictor.get_output_handle(output_names[0])
np_pred_dets = boxes_tensor.copy_to_cpu()
embs_tensor = self.predictor.get_output_handle(output_names[1])
np_pred_embs = embs_tensor.copy_to_cpu()
result = dict(pred_dets=np_pred_dets, pred_embs=np_pred_embs)
return result
def predict_image(self,
image_list,
run_benchmark=False,
repeats=1,
visual=True,
seq_name=None,
reuse_det_result=False):
mot_results = []
num_classes = self.num_classes
image_list.sort()
ids2names = self.pred_config.labels
data_type = 'mcmot' if num_classes > 1 else 'mot'
for frame_id, img_file in enumerate(image_list):
batch_image_list = [img_file] # bs=1 in MOT model
if run_benchmark:
# preprocess
inputs = self.preprocess(batch_image_list) # warmup
self.det_times.preprocess_time_s.start()
inputs = self.preprocess(batch_image_list)
self.det_times.preprocess_time_s.end()
# model prediction
result_warmup = self.predict(repeats=repeats) # warmup
self.det_times.inference_time_s.start()
result = self.predict(repeats=repeats)
self.det_times.inference_time_s.end(repeats=repeats)
# postprocess
result_warmup = self.postprocess(inputs, result) # warmup
self.det_times.postprocess_time_s.start()
det_result = self.postprocess(inputs, result)
self.det_times.postprocess_time_s.end()
# tracking
result_warmup = self.tracking(det_result)
self.det_times.tracking_time_s.start()
online_tlwhs, online_scores, online_ids = self.tracking(
det_result)
self.det_times.tracking_time_s.end()
self.det_times.img_num += 1
cm, gm, gu = get_current_memory_mb()
self.cpu_mem += cm
self.gpu_mem += gm
self.gpu_util += gu
else:
self.det_times.preprocess_time_s.start()
if not reuse_det_result:
inputs = self.preprocess(batch_image_list)
self.det_times.preprocess_time_s.end()
self.det_times.inference_time_s.start()
if not reuse_det_result:
result = self.predict()
self.det_times.inference_time_s.end()
self.det_times.postprocess_time_s.start()
if not reuse_det_result:
det_result = self.postprocess(inputs, result)
self.previous_det_result = det_result
else:
assert self.previous_det_result is not None
det_result = self.previous_det_result
self.det_times.postprocess_time_s.end()
# tracking process
self.det_times.tracking_time_s.start()
online_tlwhs, online_scores, online_ids = self.tracking(
det_result)
self.det_times.tracking_time_s.end()
self.det_times.img_num += 1
if visual:
if len(image_list) > 1 and frame_id % 10 == 0:
print('Tracking frame {}'.format(frame_id))
frame, _ = decode_image(img_file, {})
im = plot_tracking_dict(
frame,
num_classes,
online_tlwhs,
online_ids,
online_scores,
frame_id=frame_id,
ids2names=ids2names)
if seq_name is None:
seq_name = image_list[0].split('/')[-2]
save_dir = os.path.join(self.output_dir, seq_name)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
cv2.imwrite(
os.path.join(save_dir, '{:05d}.jpg'.format(frame_id)), im)
mot_results.append([online_tlwhs, online_scores, online_ids])
return mot_results
def predict_video(self, video_file, camera_id):
video_out_name = 'mot_output.mp4'
if camera_id != -1:
capture = cv2.VideoCapture(camera_id)
else:
capture = cv2.VideoCapture(video_file)
video_out_name = os.path.split(video_file)[-1]
# Get Video info : resolution, fps, frame count
width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(capture.get(cv2.CAP_PROP_FPS))
frame_count = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
print("fps: %d, frame_count: %d" % (fps, frame_count))
if not os.path.exists(self.output_dir):
os.makedirs(self.output_dir)
out_path = os.path.join(self.output_dir, video_out_name)
video_format = 'mp4v'
fourcc = cv2.VideoWriter_fourcc(*video_format)
writer = cv2.VideoWriter(out_path, fourcc, fps, (width, height))
frame_id = 0
timer = MOTTimer()
results = defaultdict(list) # support single class and multi classes
num_classes = self.num_classes
data_type = 'mcmot' if num_classes > 1 else 'mot'
ids2names = self.pred_config.labels
center_traj = None
entrance = None
records = None
if self.draw_center_traj:
center_traj = [{} for i in range(num_classes)]
if num_classes == 1:
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:{} is not supported.".format(
self.region_type))
video_fps = fps
while (1):
ret, frame = capture.read()
if not ret:
break
if frame_id % 10 == 0:
print('Tracking frame: %d' % (frame_id))
timer.tic()
mot_skip_frame_num = self.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
seq_name = video_out_name.split('.')[0]
mot_results = self.predict_image(
[frame],
visual=False,
seq_name=seq_name,
reuse_det_result=reuse_det_result)
timer.toc()
online_tlwhs, online_scores, online_ids = mot_results[0]
for cls_id in range(num_classes):
results[cls_id].append(
(frame_id + 1, online_tlwhs[cls_id], online_scores[cls_id],
online_ids[cls_id]))
# NOTE: just implement flow statistic for single class
if num_classes == 1:
result = (frame_id + 1, online_tlwhs[0], online_scores[0],
online_ids[0])
statistic = flow_statistic(
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,
data_type,
ids2names=self.pred_config.labels)
records = statistic['records']
fps = 1. / timer.duration
im = plot_tracking_dict(
frame,
num_classes,
online_tlwhs,
online_ids,
online_scores,
frame_id=frame_id,
fps=fps,
ids2names=ids2names,
do_entrance_counting=self.do_entrance_counting,
entrance=entrance,
records=records,
center_traj=center_traj)
writer.write(im)
if camera_id != -1:
cv2.imshow('Mask Detection', im)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
frame_id += 1
if self.save_mot_txts:
result_filename = os.path.join(
self.output_dir, video_out_name.split('.')[-2] + '.txt')
write_mot_results(result_filename, results, data_type, num_classes)
if num_classes == 1:
result_filename = os.path.join(
self.output_dir,
video_out_name.split('.')[-2] + '_flow_statistic.txt')
f = open(result_filename, 'w')
for line in records:
f.write(line)
print('Flow statistic save in {}'.format(result_filename))
f.close()
writer.release()
def main():
detector = JDE_Detector(
FLAGS.model_dir,
tracker_config=None,
device=FLAGS.device,
run_mode=FLAGS.run_mode,
batch_size=1,
trt_min_shape=FLAGS.trt_min_shape,
trt_max_shape=FLAGS.trt_max_shape,
trt_opt_shape=FLAGS.trt_opt_shape,
trt_calib_mode=FLAGS.trt_calib_mode,
cpu_threads=FLAGS.cpu_threads,
enable_mkldnn=FLAGS.enable_mkldnn,
output_dir=FLAGS.output_dir,
threshold=FLAGS.threshold,
save_images=FLAGS.save_images,
save_mot_txts=FLAGS.save_mot_txts,
draw_center_traj=FLAGS.draw_center_traj,
secs_interval=FLAGS.secs_interval,
skip_frame_num=FLAGS.skip_frame_num,
do_entrance_counting=FLAGS.do_entrance_counting,
do_break_in_counting=FLAGS.do_break_in_counting,
region_type=FLAGS.region_type,
region_polygon=FLAGS.region_polygon)
# predict from video file or camera video stream
if FLAGS.video_file is not None or FLAGS.camera_id != -1:
detector.predict_video(FLAGS.video_file, FLAGS.camera_id)
else:
# predict from image
img_list = get_test_images(FLAGS.image_dir, FLAGS.image_file)
detector.predict_image(img_list, FLAGS.run_benchmark, repeats=10)
if not FLAGS.run_benchmark:
detector.det_times.info(average=True)
else:
mode = FLAGS.run_mode
model_dir = FLAGS.model_dir
model_info = {
'model_name': model_dir.strip('/').split('/')[-1],
'precision': mode.split('_')[-1]
}
bench_log(detector, img_list, model_info, name='MOT')
if __name__ == '__main__':
paddle.enable_static()
parser = argsparser()
FLAGS = parser.parse_args()
print_arguments(FLAGS)
FLAGS.device = FLAGS.device.upper()
assert FLAGS.device in ['CPU', 'GPU', 'XPU'
], "device should be CPU, GPU or XPU"
main()
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