390 lines
14 KiB
Python
390 lines
14 KiB
Python
# 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 time
|
|
import os
|
|
import sys
|
|
import ast
|
|
import argparse
|
|
|
|
|
|
def argsparser():
|
|
parser = argparse.ArgumentParser(description=__doc__)
|
|
parser.add_argument(
|
|
"--model_dir",
|
|
type=str,
|
|
default=None,
|
|
help=("Directory include:'model.pdiparams', 'model.pdmodel', "
|
|
"'infer_cfg.yml', created by tools/export_model.py."),
|
|
required=True)
|
|
parser.add_argument(
|
|
"--image_file", type=str, default=None, help="Path of image file.")
|
|
parser.add_argument(
|
|
"--image_dir",
|
|
type=str,
|
|
default=None,
|
|
help="Dir of image file, `image_file` has a higher priority.")
|
|
parser.add_argument(
|
|
"--batch_size", type=int, default=1, help="batch_size for inference.")
|
|
parser.add_argument(
|
|
"--video_file",
|
|
type=str,
|
|
default=None,
|
|
help="Path of video file, `video_file` or `camera_id` has a highest priority."
|
|
)
|
|
parser.add_argument(
|
|
"--camera_id",
|
|
type=int,
|
|
default=-1,
|
|
help="device id of camera to predict.")
|
|
parser.add_argument(
|
|
"--threshold", type=float, default=0.5, help="Threshold of score.")
|
|
parser.add_argument(
|
|
"--output_dir",
|
|
type=str,
|
|
default="output",
|
|
help="Directory of output visualization files.")
|
|
parser.add_argument(
|
|
"--run_mode",
|
|
type=str,
|
|
default='paddle',
|
|
help="mode of running(paddle/trt_fp32/trt_fp16/trt_int8)")
|
|
parser.add_argument(
|
|
"--device",
|
|
type=str,
|
|
default='cpu',
|
|
help="Choose the device you want to run, it can be: CPU/GPU/XPU/NPU, default is CPU."
|
|
)
|
|
parser.add_argument(
|
|
"--use_gpu",
|
|
type=ast.literal_eval,
|
|
default=False,
|
|
help="Deprecated, please use `--device`.")
|
|
parser.add_argument(
|
|
"--run_benchmark",
|
|
type=ast.literal_eval,
|
|
default=False,
|
|
help="Whether to predict a image_file repeatedly for benchmark")
|
|
parser.add_argument(
|
|
"--enable_mkldnn",
|
|
type=ast.literal_eval,
|
|
default=False,
|
|
help="Whether use mkldnn with CPU.")
|
|
parser.add_argument(
|
|
"--cpu_threads", type=int, default=1, help="Num of threads with CPU.")
|
|
parser.add_argument(
|
|
"--trt_min_shape", type=int, default=1, help="min_shape for TensorRT.")
|
|
parser.add_argument(
|
|
"--trt_max_shape",
|
|
type=int,
|
|
default=1280,
|
|
help="max_shape for TensorRT.")
|
|
parser.add_argument(
|
|
"--trt_opt_shape",
|
|
type=int,
|
|
default=640,
|
|
help="opt_shape for TensorRT.")
|
|
parser.add_argument(
|
|
"--trt_calib_mode",
|
|
type=bool,
|
|
default=False,
|
|
help="If the model is produced by TRT offline quantitative "
|
|
"calibration, trt_calib_mode need to set True.")
|
|
parser.add_argument(
|
|
'--save_images',
|
|
action='store_true',
|
|
help='Save visualization image results.')
|
|
parser.add_argument(
|
|
'--save_mot_txts',
|
|
action='store_true',
|
|
help='Save tracking results (txt).')
|
|
parser.add_argument(
|
|
'--save_mot_txt_per_img',
|
|
action='store_true',
|
|
help='Save tracking results (txt) for each image.')
|
|
parser.add_argument(
|
|
'--scaled',
|
|
type=bool,
|
|
default=False,
|
|
help="Whether coords after detector outputs are scaled, False in JDE YOLOv3 "
|
|
"True in general detector.")
|
|
parser.add_argument(
|
|
"--tracker_config", type=str, default=None, help=("tracker donfig"))
|
|
parser.add_argument(
|
|
"--reid_model_dir",
|
|
type=str,
|
|
default=None,
|
|
help=("Directory include:'model.pdiparams', 'model.pdmodel', "
|
|
"'infer_cfg.yml', created by tools/export_model.py."))
|
|
parser.add_argument(
|
|
"--reid_batch_size",
|
|
type=int,
|
|
default=50,
|
|
help="max batch_size for reid model inference.")
|
|
parser.add_argument(
|
|
'--use_dark',
|
|
type=ast.literal_eval,
|
|
default=True,
|
|
help='whether to use darkpose to get better keypoint position predict ')
|
|
parser.add_argument(
|
|
'--skip_frame_num',
|
|
type=int,
|
|
default=-1,
|
|
help='Skip frames to speed up the process of getting mot results.')
|
|
parser.add_argument(
|
|
'--warmup_frame',
|
|
type=int,
|
|
default=50,
|
|
help='Warmup frames to test speed of the process of getting mot results.'
|
|
)
|
|
parser.add_argument(
|
|
"--do_entrance_counting",
|
|
action='store_true',
|
|
help="Whether counting the numbers of identifiers entering "
|
|
"or getting out from the entrance. Note that only support single-class MOT."
|
|
)
|
|
parser.add_argument(
|
|
"--do_break_in_counting",
|
|
action='store_true',
|
|
help="Whether counting the numbers of identifiers break in "
|
|
"the area. Note that only support single-class MOT and "
|
|
"the video should be taken by a static camera.")
|
|
parser.add_argument(
|
|
"--region_type",
|
|
type=str,
|
|
default='horizontal',
|
|
help="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."
|
|
)
|
|
parser.add_argument(
|
|
'--region_polygon',
|
|
nargs='+',
|
|
type=int,
|
|
default=[],
|
|
help="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.")
|
|
parser.add_argument(
|
|
"--secs_interval",
|
|
type=int,
|
|
default=2,
|
|
help="The seconds interval to count after tracking")
|
|
parser.add_argument(
|
|
"--draw_center_traj",
|
|
action='store_true',
|
|
help="Whether drawing the trajectory of center")
|
|
parser.add_argument(
|
|
"--mtmct_dir",
|
|
type=str,
|
|
default=None,
|
|
help="The MTMCT scene video folder.")
|
|
parser.add_argument(
|
|
"--mtmct_cfg", type=str, default=None, help="The MTMCT config.")
|
|
return parser
|
|
|
|
|
|
class Times(object):
|
|
def __init__(self):
|
|
self.time = 0.
|
|
# start time
|
|
self.st = 0.
|
|
# end time
|
|
self.et = 0.
|
|
|
|
def start(self):
|
|
self.st = time.time()
|
|
|
|
def end(self, repeats=1, accumulative=True):
|
|
self.et = time.time()
|
|
if accumulative:
|
|
self.time += (self.et - self.st) / repeats
|
|
else:
|
|
self.time = (self.et - self.st) / repeats
|
|
|
|
def reset(self):
|
|
self.time = 0.
|
|
self.st = 0.
|
|
self.et = 0.
|
|
|
|
def value(self):
|
|
return round(self.time, 4)
|
|
|
|
|
|
class Timer(Times):
|
|
def __init__(self, with_tracker=False):
|
|
super(Timer, self).__init__()
|
|
self.with_tracker = with_tracker
|
|
self.preprocess_time_s = Times()
|
|
self.inference_time_s = Times()
|
|
self.postprocess_time_s = Times()
|
|
self.tracking_time_s = Times()
|
|
self.img_num = 0
|
|
|
|
def info(self, average=False):
|
|
pre_time = self.preprocess_time_s.value()
|
|
infer_time = self.inference_time_s.value()
|
|
post_time = self.postprocess_time_s.value()
|
|
track_time = self.tracking_time_s.value()
|
|
|
|
total_time = pre_time + infer_time + post_time
|
|
if self.with_tracker:
|
|
total_time = total_time + track_time
|
|
total_time = round(total_time, 4)
|
|
print("------------------ Inference Time Info ----------------------")
|
|
print("total_time(ms): {}, img_num: {}".format(total_time * 1000,
|
|
self.img_num))
|
|
preprocess_time = round(pre_time / max(1, self.img_num),
|
|
4) if average else pre_time
|
|
postprocess_time = round(post_time / max(1, self.img_num),
|
|
4) if average else post_time
|
|
inference_time = round(infer_time / max(1, self.img_num),
|
|
4) if average else infer_time
|
|
tracking_time = round(track_time / max(1, self.img_num),
|
|
4) if average else track_time
|
|
|
|
average_latency = total_time / max(1, self.img_num)
|
|
qps = 0
|
|
if total_time > 0:
|
|
qps = 1 / average_latency
|
|
print("average latency time(ms): {:.2f}, QPS: {:2f}".format(
|
|
average_latency * 1000, qps))
|
|
if self.with_tracker:
|
|
print(
|
|
"preprocess_time(ms): {:.2f}, inference_time(ms): {:.2f}, postprocess_time(ms): {:.2f}, tracking_time(ms): {:.2f}".
|
|
format(preprocess_time * 1000, inference_time * 1000,
|
|
postprocess_time * 1000, tracking_time * 1000))
|
|
else:
|
|
print(
|
|
"preprocess_time(ms): {:.2f}, inference_time(ms): {:.2f}, postprocess_time(ms): {:.2f}".
|
|
format(preprocess_time * 1000, inference_time * 1000,
|
|
postprocess_time * 1000))
|
|
|
|
def tracking_info(self, average=True):
|
|
pre_time = self.preprocess_time_s.value()
|
|
infer_time = self.inference_time_s.value()
|
|
post_time = self.postprocess_time_s.value()
|
|
track_time = self.tracking_time_s.value()
|
|
|
|
total_time = pre_time + infer_time + post_time
|
|
if self.with_tracker:
|
|
total_time = total_time + track_time
|
|
total_time = round(total_time, 4)
|
|
print(
|
|
"------------------ Tracking Module Time Info ----------------------"
|
|
)
|
|
|
|
preprocess_time = round(pre_time / max(1, self.img_num),
|
|
4) if average else pre_time
|
|
postprocess_time = round(post_time / max(1, self.img_num),
|
|
4) if average else post_time
|
|
inference_time = round(infer_time / max(1, self.img_num),
|
|
4) if average else infer_time
|
|
tracking_time = round(track_time / max(1, self.img_num),
|
|
4) if average else track_time
|
|
|
|
if self.with_tracker:
|
|
print(
|
|
"preprocess_time(ms): {:.2f}, inference_time(ms): {:.2f}, postprocess_time(ms): {:.2f}, tracking_time(ms): {:.2f}".
|
|
format(preprocess_time * 1000, inference_time * 1000,
|
|
postprocess_time * 1000, tracking_time * 1000))
|
|
else:
|
|
print(
|
|
"preprocess_time(ms): {:.2f}, inference_time(ms): {:.2f}, postprocess_time(ms): {:.2f}".
|
|
format(preprocess_time * 1000, inference_time * 1000,
|
|
postprocess_time * 1000))
|
|
|
|
def report(self, average=False):
|
|
dic = {}
|
|
pre_time = self.preprocess_time_s.value()
|
|
infer_time = self.inference_time_s.value()
|
|
post_time = self.postprocess_time_s.value()
|
|
track_time = self.tracking_time_s.value()
|
|
|
|
dic['preprocess_time_s'] = round(pre_time / max(1, self.img_num),
|
|
4) if average else pre_time
|
|
dic['inference_time_s'] = round(infer_time / max(1, self.img_num),
|
|
4) if average else infer_time
|
|
dic['postprocess_time_s'] = round(post_time / max(1, self.img_num),
|
|
4) if average else post_time
|
|
dic['img_num'] = self.img_num
|
|
total_time = pre_time + infer_time + post_time
|
|
if self.with_tracker:
|
|
dic['tracking_time_s'] = round(track_time / max(1, self.img_num),
|
|
4) if average else track_time
|
|
total_time = total_time + track_time
|
|
dic['total_time_s'] = round(total_time, 4)
|
|
return dic
|
|
|
|
|
|
def get_current_memory_mb():
|
|
"""
|
|
It is used to Obtain the memory usage of the CPU and GPU during the running of the program.
|
|
And this function Current program is time-consuming.
|
|
"""
|
|
import pynvml
|
|
import psutil
|
|
import GPUtil
|
|
gpu_id = int(os.environ.get('CUDA_VISIBLE_DEVICES', 0))
|
|
|
|
pid = os.getpid()
|
|
p = psutil.Process(pid)
|
|
info = p.memory_full_info()
|
|
cpu_mem = info.uss / 1024. / 1024.
|
|
gpu_mem = 0
|
|
gpu_percent = 0
|
|
gpus = GPUtil.getGPUs()
|
|
if gpu_id is not None and len(gpus) > 0:
|
|
gpu_percent = gpus[gpu_id].load
|
|
pynvml.nvmlInit()
|
|
handle = pynvml.nvmlDeviceGetHandleByIndex(0)
|
|
meminfo = pynvml.nvmlDeviceGetMemoryInfo(handle)
|
|
gpu_mem = meminfo.used / 1024. / 1024.
|
|
return round(cpu_mem, 4), round(gpu_mem, 4), round(gpu_percent, 4)
|
|
|
|
|
|
def video2frames(video_path, outpath, frame_rate=25, **kargs):
|
|
def _dict2str(kargs):
|
|
cmd_str = ''
|
|
for k, v in kargs.items():
|
|
cmd_str += (' ' + str(k) + ' ' + str(v))
|
|
return cmd_str
|
|
|
|
ffmpeg = ['ffmpeg ', ' -y -loglevel ', ' error ']
|
|
vid_name = os.path.basename(video_path).split('.')[0]
|
|
out_full_path = os.path.join(outpath, vid_name)
|
|
|
|
if not os.path.exists(out_full_path):
|
|
os.makedirs(out_full_path)
|
|
|
|
# video file name
|
|
outformat = os.path.join(out_full_path, '%05d.jpg')
|
|
|
|
cmd = ffmpeg
|
|
cmd = ffmpeg + [
|
|
' -i ', video_path, ' -r ', str(frame_rate), ' -f image2 ', outformat
|
|
]
|
|
cmd = ''.join(cmd) + _dict2str(kargs)
|
|
|
|
if os.system(cmd) != 0:
|
|
raise RuntimeError('ffmpeg process video: {} error'.format(video_path))
|
|
sys.exit(-1)
|
|
|
|
sys.stdout.flush()
|
|
return out_full_path
|
|
|
|
|
|
def _is_valid_video(f, extensions=('.mp4', '.avi', '.mov', '.rmvb', '.flv')):
|
|
return f.lower().endswith(extensions)
|