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val.py
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val.py
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import os
import sys
import torch
import logging
import subprocess
from subprocess import Popen
import argparse
import zipfile
from pathlib import Path
import shutil
import threading
FILE = Path(__file__).resolve()
ROOT = FILE.parents[0] # yolov5 strongsort root directory
WEIGHTS = ROOT / 'weights'
if str(ROOT) not in sys.path:
sys.path.append(str(ROOT)) # add ROOT to PATH
if str(ROOT / 'yolov5') not in sys.path:
sys.path.append(str(ROOT / 'yolov5')) # add yolov5 ROOT to PATH
if str(ROOT / 'strong_sort') not in sys.path:
sys.path.append(str(ROOT / 'strong_sort')) # add strong_sort ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
from yolov5.utils.general import LOGGER, check_requirements, print_args, increment_path
from yolov5.utils.torch_utils import select_device
from trackPass import run
def download_official_mot_eval_tool(dst_val_tools_folder):
# source: https://github.com/JonathonLuiten/TrackEval#official-evaluation-code
val_tools_url = "https://github.com/JonathonLuiten/TrackEval"
try:
Repo.clone_from(val_tools_url, dst_val_tools_folder)
LOGGER.info('Official MOT evaluation repo downloaded')
except git.exc.GitError as err:
LOGGER.info('Eval repo already downloaded')
def download_mot_dataset(dst_val_tools_folder, benchmark):
gt_data_url = 'https://omnomnom.vision.rwth-aachen.de/data/TrackEval/data.zip'
subprocess.run(["wget", "-nc", gt_data_url, "-O", dst_val_tools_folder / 'data.zip']) # python module has no -nc nor -N flag
if not (dst_val_tools_folder / 'data').is_dir():
with zipfile.ZipFile(dst_val_tools_folder / 'data.zip', 'r') as zip_ref:
zip_ref.extractall(dst_val_tools_folder)
LOGGER.info('MOTs ground truth downloaded')
else:
LOGGER.info('gt already downloaded')
mot_gt_data_url = 'https://motchallenge.net/data/' + benchmark + '.zip'
subprocess.run(["wget", "-nc", mot_gt_data_url, "-O", dst_val_tools_folder / (benchmark + '.zip')]) # python module has no -nc nor -N flag
if not (dst_val_tools_folder / 'data' / benchmark).is_dir():
with zipfile.ZipFile(dst_val_tools_folder / (benchmark + '.zip'), 'r') as zip_ref:
if opt.benchmark == 'MOT16':
zip_ref.extractall(dst_val_tools_folder / 'data' / 'MOT16')
else:
zip_ref.extractall(dst_val_tools_folder / 'data')
LOGGER.info(f'{benchmark} images downloaded')
else:
LOGGER.info(f'{benchmark} data already downloaded')
def parse_opt():
parser = argparse.ArgumentParser()
parser.add_argument('--yolo-weights', type=str, default=WEIGHTS / 'crowdhuman_yolov5m.pt', help='model.pt path(s)')
parser.add_argument('--reid-weights', type=str, default=WEIGHTS / 'osnet_x1_0_dukemtmcreid.pt')
parser.add_argument('--tracking-method', type=str, default='strongsort', help='strongsort, ocsort')
parser.add_argument('--name', default='exp', help='save results to project/name')
parser.add_argument('--project', default=ROOT / 'runs/track', help='save results to project/name')
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
parser.add_argument('--benchmark', type=str, default='MOT17', help='MOT16, MOT17, MOT20')
parser.add_argument('--split', type=str, default='train', help='existing project/name ok, do not increment')
parser.add_argument('--eval-existing', type=str, default='', help='evaluate existing tracker results under mot_callenge/MOTXX-YY/...')
parser.add_argument('--conf-thres', type=float, default=0.45, help='confidence threshold')
parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[1280], help='inference size h,w')
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
opt = parser.parse_args()
device = []
for a in opt.device.split(','):
try:
a = int(a)
except ValueError:
pass
device.append(a)
opt.device = device
print_args(vars(opt))
return opt
def main(opt):
check_requirements(requirements=ROOT / 'requirements.txt', exclude=('tensorboard', 'thop'))
# download eval files
dst_val_tools_folder = ROOT / 'val_utils'
download_official_mot_eval_tool(dst_val_tools_folder)
if any(opt.benchmark is s for s in ['MOT16', 'MOT17', 'MOT20']):
download_mot_dataset(dst_val_tools_folder, opt.benchmark)
# set paths
mot_seqs_path = dst_val_tools_folder / 'data' / opt.benchmark / opt.split
if opt.benchmark == 'MOT17':
# each sequences is present 3 times, one for each detector
# (DPM, FRCNN, SDP). Keep only sequences from one of them
seq_paths = sorted([str(p / 'img1') for p in Path(mot_seqs_path).iterdir() if Path(p).is_dir()])
seq_paths = [Path(p) for p in seq_paths if 'FRCNN' in p]
with open(dst_val_tools_folder / "data/gt/mot_challenge/seqmaps/MOT17-train.txt", "r") as f: #
lines = f.readlines()
# overwrite MOT17 evaluation sequences to evaluate so that they are not duplicated
with open(dst_val_tools_folder / "data/gt/mot_challenge/seqmaps/MOT17-train.txt", "w") as f:
for line in seq_paths:
f.write(str(line.parent.stem) + '\n')
else:
# this is not the case for MOT16, MOT20 or your custom dataset
seq_paths = [p / 'img1' for p in Path(mot_seqs_path).iterdir() if Path(p).is_dir()]
save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok) # increment run
MOT_results_folder = dst_val_tools_folder / 'data' / 'trackers' / 'mot_challenge' / Path(str(opt.benchmark) + '-' + str(opt.split)) / save_dir.name / 'data'
(MOT_results_folder).mkdir(parents=True, exist_ok=True) # make
# extend devices to as many sequences are available
if any(isinstance(i,int) for i in opt.device) and len(opt.device) > 1:
devices = opt.device
for a in range(0, len(opt.device) % len(seq_paths)):
opt.device.extend(devices)
opt.device = opt.device[:len(seq_paths)]
if not opt.eval_existing:
processes = []
for i, seq_path in enumerate(seq_paths):
# spawn one subprocess per GPU in increasing order.
# When max devices are reached start at 0 again
tracking_subprocess_device = opt.device[i] if len(opt.device) > 1 else opt.device[0]
dst_seq_path = seq_path.parent / seq_path.parent.name
if not dst_seq_path.is_dir():
src_seq_path = seq_path
shutil.move(str(src_seq_path), str(dst_seq_path))
p = subprocess.Popen([
"python", "track.py", \
"--yolo-weights", opt.yolo_weights, \
"--reid-weights", opt.reid_weights, \
"--tracking-method", opt.tracking_method, \
"--conf-thres", str(opt.conf_thres), \
"--imgsz", str(opt.imgsz[0]), \
"--classes", str(0), \
"--name", save_dir.name, \
"--project", opt.project, \
"--device", str(tracking_subprocess_device), \
"--source", dst_seq_path, \
"--exist-ok", \
"--save-txt", \
])
processes.append(p)
for p in processes:
p.wait()
results = (save_dir.parent / opt.eval_existing / 'tracks' if opt.eval_existing else save_dir / 'tracks').glob('*.txt')
for src in results:
if opt.eval_existing:
dst = MOT_results_folder.parent.parent / opt.eval_existing / 'data' / Path(src.stem + '.txt')
else:
dst = MOT_results_folder / Path(src.stem + '.txt')
dst.parent.mkdir(parents=True, exist_ok=True) # make
shutil.copyfile(src, dst)
# run the evaluation on the generated txts
subprocess.run([
"python", dst_val_tools_folder / "scripts/run_mot_challenge.py",\
"--BENCHMARK", opt.benchmark,\
"--TRACKERS_TO_EVAL", opt.eval_existing if opt.eval_existing else MOT_results_folder.parent.name,\
"--SPLIT_TO_EVAL", "train",\
"--METRICS", "HOTA", "CLEAR", "Identity",\
"--USE_PARALLEL", "True",\
"--NUM_PARALLEL_CORES", "4"\
])
if __name__ == "__main__":
opt = parse_opt()
main(opt)