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mot_demo.py
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mot_demo.py
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###################################################################
# File Name: mot_demo.py
# Author: Zhongdao Wang
# mail: wcd17@mails.tsinghua.edu.cn
# Created Time: Sat Jul 24 16:07:23 2021
###################################################################
import os
import sys
import yaml
import argparse
import os.path as osp
from loguru import logger
import cv2
import torch
import numpy as np
from torchvision.transforms import transforms as T
sys.path[0] = os.getcwd()
from data.video import LoadVideo
from utils.meter import Timer
from utils import visualize as vis
from detector.YOLOX.yolox.exp import get_exp
from detector.YOLOX.yolox.utils import get_model_info
from detector.YOLOX.yolox.data.datasets import COCO_CLASSES
from detector.YOLOX.tools.demo import Predictor
from utils.box import scale_box_input_size
from tracker.mot.box import BoxAssociationTracker
def make_parser():
parser = argparse.ArgumentParser("YOLOX + UniTrack MOT demo")
# Common arguments
parser.add_argument('--demo', default='video',
help='demo type, eg. video or webcam')
parser.add_argument('--path', default='./docs/test_video.mp3',
help='path to images or video')
parser.add_argument('--camid', type=int, default=0,
help='webcam demo camera id')
parser.add_argument('--save_result', action='store_true',
help='whether to save result')
parser.add_argument("--nms", default=None, type=float,
help="test nms threshold")
parser.add_argument("--tsize", default=[640, 480], type=int, nargs='+',
help="test img size")
parser.add_argument("--exp_file", type=str,
default='./detector/YOLOX/exps/default/yolox_x.py',
help="pls input your expriment description file")
parser.add_argument('--output-root', default='./results/mot_demo',
help='output directory')
parser.add_argument('--classes', type=int, nargs='+',
default=list(range(90)), help='COCO_CLASSES')
# Detector related
parser.add_argument("-c", "--ckpt", type=str,
default='./detector/YOLOX/weights/yolox_x.pth',
help="model weights of the detector")
parser.add_argument("--conf", default=0.65, type=float,
help="detection confidence threshold")
# UniTrack related
parser.add_argument('--config', type=str, help='tracker config file',
default='./config/imagenet_resnet18_s3.yaml')
return parser
def dets2obs(dets, imginfo, cls):
if dets is None or len(dets) == 0:
return np.array([])
obs = dets.cpu().numpy()
h, w = imginfo['height'], imginfo['width']
# To xywh
ret = np.zeros((len(obs), 6))
ret[:, 0] = (obs[:, 0] + obs[:, 2]) * 0.5 / w
ret[:, 1] = (obs[:, 1] + obs[:, 3]) * 0.5 / h
ret[:, 2] = (obs[:, 2] - obs[:, 0]) / w
ret[:, 3] = (obs[:, 3] - obs[:, 1]) / h
ret[:, 4] = obs[:, 4] * obs[:, 5]
ret[:, 5] = obs[:, 6]
ret = [r for r in ret if int(r[5]) in cls]
ret = np.array(ret)
return ret
def eval_seq(opt, dataloader, detector, tracker,
result_filename, save_dir=None,
show_image=True):
transforms = T.Compose([T.ToTensor(),
T.Normalize(opt.im_mean, opt.im_std)])
if save_dir:
os.makedirs(save_dir, exist_ok=True)
timer = Timer()
results = []
for frame_id, (_, img, img0) in enumerate(dataloader):
if frame_id % 20 == 0:
logger.info('Processing frame {} ({:.2f} fps)'.format(
frame_id, 1./max(1e-5, timer.average_time)))
# run tracking
timer.tic()
det_outputs, img_info = detector.inference(img)
img = img / 255.
img = transforms(img)
obs = dets2obs(det_outputs[0], img_info, opt.classes)
if len(obs) == 0:
online_targets = []
else:
online_targets = tracker.update(img, img0, obs)
online_tlwhs = []
online_ids = []
for t in online_targets:
tlwh = t.tlwh
tid = t.track_id
online_tlwhs.append(tlwh)
online_ids.append(tid)
timer.toc()
# save results
results.append((frame_id + 1, online_tlwhs, online_ids))
if show_image or save_dir is not None:
online_im = vis.plot_tracking(
img0, online_tlwhs, online_ids, frame_id=frame_id,
fps=1. / timer.average_time)
if show_image:
cv2.imshow('online_im', online_im)
if save_dir is not None:
cv2.imwrite(os.path.join(
save_dir, '{:05d}.jpg'.format(frame_id)), online_im)
return frame_id, timer.average_time, timer.calls
def main(exp, args):
logger.info("Args: {}".format(args))
# Data, I/O
dataloader = LoadVideo(args.path, args.tsize)
video_name = osp.basename(args.path).split('.')[0]
result_root = osp.join(args.output_root, video_name)
result_filename = os.path.join(result_root, 'results.txt')
args.frame_rate = dataloader.frame_rate
# Detector init
det_model = exp.get_model()
logger.info("Model Summary: {}".format(
get_model_info(det_model, exp.test_size)))
det_model.cuda()
det_model.eval()
logger.info("loading checkpoint")
ckpt = torch.load(args.ckpt, map_location="cpu")
# load the model state dict
det_model.load_state_dict(ckpt["model"])
logger.info("loaded checkpoint done.")
detector = Predictor(det_model, exp, COCO_CLASSES, None, None, 'gpu')
# Tracker init
tracker = BoxAssociationTracker(args)
frame_dir = osp.join(result_root, 'frame')
try:
eval_seq(args, dataloader, detector, tracker, result_filename,
save_dir=frame_dir, show_image=False)
except Exception as e:
print(e)
output_video_path = osp.join(result_root, video_name+'.avi')
cmd_str = 'ffmpeg -f image2 -i {}/%05d.jpg -c:v copy {}'.format(
osp.join(result_root, 'frame'), output_video_path)
os.system(cmd_str)
if __name__ == '__main__':
args = make_parser().parse_args()
with open(args.config) as f:
common_args = yaml.load(f)
for k, v in common_args['common'].items():
setattr(args, k, v)
for k, v in common_args['mot'].items():
setattr(args, k, v)
exp = get_exp(args.exp_file, None)
if args.conf is not None:
args.conf_thres = args.conf
exp.test_conf = args.conf
if args.nms is not None:
exp.nmsthre = args.nms
if args.tsize is not None:
exp.test_size = args.tsize[::-1]
args.img_size = args.tsize
args.classes = [x+1 for x in args.classes]
main(exp, args)