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deepsort.py
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deepsort.py
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import os
import cv2
import time
import argparse
import torch
import warnings
import numpy as np
import sys
sys.path.append(os.path.join(os.path.dirname(__file__), 'thirdparty/fast-reid'))
sys.path.append(os.path.join(os.path.dirname(__file__), 'thirdparty/mmdetection'))
from detector import build_detector
from deep_sort import build_tracker
from utils.draw import draw_boxes
from utils.parser import get_config
from utils.log import get_logger
from utils.io import write_results
import pickle
class VideoTracker(object):
def __init__(self, cfg, args, video_path):
self.cfg = cfg
self.args = args
self.video_path = video_path
## self.track_class
# yolov3, person_id = 0; car_id = 2, 选择人cls_ids=0，作为跟踪; car, cls_ids=2, 具体见 coco.name;
# -1 所有目标都进行跟踪
self.track_class = cfg.DEEPSORT.TRACK_CLASS
self.logger = get_logger("root")
self.reid_feature_dic = {}
self.now_frame = 0
self.cam_id = video_path.split(os.sep)[-2] # video_path = /workspace/dataset/aic22-mcmt/S06/c112/vdo.avi
use_cuda = args.use_cuda and torch.cuda.is_available()
if not use_cuda:
warnings.warn("Running in cpu mode which maybe very slow!", UserWarning)
if args.display:
cv2.namedWindow("test", cv2.WINDOW_NORMAL)
cv2.resizeWindow("test", args.display_width, args.display_height)
if args.cam != -1:
print("Using webcam " + str(args.cam))
self.vdo = cv2.VideoCapture(args.cam)
else:
self.vdo = cv2.VideoCapture()
self.detector = build_detector(cfg, use_cuda=use_cuda)
self.deepsort = build_tracker(cfg, use_cuda=use_cuda)
self.class_names = self.detector.class_names
def reid_feature_ele_init(self, reid_dic={}):
# frame_id = 1, track_id=5):
"""
key_names = "img" + str(frame_id).zfill(6) + "_" + str(track_id).zfill(3)
unit = {
'img000011_020':{
'bbox': (1056, 448, 1262, 590),
'frame': 'img000011',
'id': 20,
'imgname': 'img000011_020.png',
'class': 2,
'conf': 0.8876953125,
'feat': array([ 0.55748284, ...e=float32) }
}
"""
frame = "img" + str(reid_dic["frame_id"]).zfill(6)
key_names = frame + "_" + str(reid_dic["track_id"]).zfill(3)
unit = {
'bbox': reid_dic["bbox"],
'frame': frame,
'id': reid_dic["track_id"],
'imgname': key_names + '.png',
'class': reid_dic["class"],
'conf': reid_dic["conf"],
'feat': reid_dic["feat"] }
return key_names, unit
def update_reid_feature_dic(self, frame=0, detections=None):
out_dict = {}
for idx, item in enumerate(detections):
idx_frame = "img" + str(frame).zfill(6)
key_names = idx_frame + "_" + str(idx).zfill(3)
out_dict[key_names] = {
'bbox': item.x1y1x2y2,
'frame':idx_frame,
'id': str(idx),
'imgname': key_names +".png",
'class': self.track_class,
'conf': item.confidence,
'feat': item.feature
}
return out_dict
def save_dic_to_pkl(self, dic={}, output_path="./", cam="c112"):
feat_pkl_file = os.path.join(self.args.save_path, f'{cam}_dets_feat.pkl')
pickle.dump(dic, open(feat_pkl_file, 'wb'), pickle.HIGHEST_PROTOCOL)
print('save pickle in %s' % feat_pkl_file)
def __enter__(self):
if self.args.cam != -1:
ret, frame = self.vdo.read()
assert ret, "Error: Camera error"
self.im_width = frame.shape[0]
self.im_height = frame.shape[1]
else:
assert os.path.isfile(self.video_path), "Path error"
self.vdo.open(self.video_path)
self.im_width = int(self.vdo.get(cv2.CAP_PROP_FRAME_WIDTH))
self.im_height = int(self.vdo.get(cv2.CAP_PROP_FRAME_HEIGHT))
assert self.vdo.isOpened()
if self.args.save_path:
os.makedirs(self.args.save_path, exist_ok=True)
# path of saved video and results
self.save_video_path = os.path.join(self.args.save_path, "results.avi")
self.save_results_path = os.path.join(self.args.save_path, "results.txt")
# create video writer
fourcc = cv2.VideoWriter_fourcc(*'MJPG')
self.writer = cv2.VideoWriter(self.save_video_path, fourcc, 15, (self.im_width, self.im_height)) # defualt, fps=20
# logging
self.logger.info("Save results to {}".format(self.args.save_path))
return self
def __exit__(self, exc_type, exc_value, exc_traceback):
if exc_type:
print(exc_type, exc_value, exc_traceback)
def run(self):
results = []
idx_frame = 0
while self.vdo.grab():
self.now_frame = idx_frame
idx_frame += 1
if idx_frame % self.args.frame_interval:
continue
start = time.time()
_, ori_im = self.vdo.retrieve()
im = cv2.cvtColor(ori_im, cv2.COLOR_BGR2RGB)
# do detection
bbox_xywh, cls_conf, cls_ids = self.detector(im)
# 画框到一张图片上，并保存
# imga = draw_boxes(im, bbox_xywh, cls_ids, cls_conf , class_name_map=self.detector.class_names)
# cv2.imwrite("./1.png", imga[:, :, (2, 1, 0)])
if self.track_class == -1:
# track all id
mask = cls_ids == cls_ids
else:
# t rack specify id
mask = cls_ids == self.track_class
bbox_xywh = bbox_xywh[mask]
# bbox dilation just in case bbox too small, delete this line if using a better pedestrian detector
bbox_xywh[:, 3:] *= 1.2 #
cls_conf = cls_conf[mask]
# do tracking
outputs, detections = self.deepsort.update(bbox_xywh, cls_conf, im)
# draw boxes for visualization
if len(outputs) > 0:
bbox_tlwh = []
bbox_xyxy = outputs[:, :4]
identities = outputs[:, -1]
ori_im = draw_boxes(ori_im, bbox_xyxy, identities) # 画框到原始图片上
for bb_xyxy in bbox_xyxy:
bbox_tlwh.append(self.deepsort._xyxy_to_tlwh(bb_xyxy))
results.append((idx_frame - 1, bbox_tlwh, identities))
end = time.time()
if self.args.display:
cv2.imshow("test", ori_im)
cv2.waitKey(1)
if self.args.save_path:
self.writer.write(ori_im)
# save results
write_results(self.save_results_path, results, 'mot')
# logging
self.logger.info("time: {:.03f}s, fps: {:.03f}, detection numbers: {}, tracking numbers: {}" \
.format(end - start, 1 / (end - start), bbox_xywh.shape[0], len(outputs)))
# reid feature
if len(detections) >0:
out_dic = self.update_reid_feature_dic(frame=self.now_frame, detections= detections)
self.reid_feature_dic.update(out_dic)
self.save_dic_to_pkl(dic=self.reid_feature_dic, output_path=self.args.save_path, cam=self.cam_id)
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("VIDEO_PATH", type=str)
parser.add_argument("--config_mmdetection", type=str, default="./configs/mmdet.yaml")
parser.add_argument("--config_detection", type=str, default="./configs/yolov3.yaml")
parser.add_argument("--config_deepsort", type=str, default="./configs/deep_sort.yaml")
parser.add_argument("--config_fastreid", type=str, default="./configs/fastreid.yaml")
parser.add_argument("--detect_model", type=str, default="yolov3")
parser.add_argument("--fastreid", action="store_true")
parser.add_argument("--mmdet", action="store_true")
# parser.add_argument("--ignore_display", dest="display", action="store_false", default=True)
parser.add_argument("--display", action="store_true")
parser.add_argument("--frame_interval", type=int, default=1)
parser.add_argument("--display_width", type=int, default=800)
parser.add_argument("--display_height", type=int, default=600)
parser.add_argument("--save_path", type=str, default="./output/")
parser.add_argument("--cpu", dest="use_cuda", action="store_false", default=True)
parser.add_argument("--camera", action="store", dest="cam", type=int, default="-1")
return parser.parse_args()
if __name__ == "__main__":
args = parse_args()
cfg = get_config()
print(args)
if args.mmdet:
cfg.merge_from_file(args.config_mmdetection)
cfg.USE_MMDET = True
else:
cfg.merge_from_file(args.config_detection)
cfg.USE_MMDET = False
cfg.DETECT_MODEL = args.detect_model
cfg.merge_from_file(args.config_deepsort)
if args.fastreid:
cfg.merge_from_file(args.config_fastreid)
cfg.USE_FASTREID = True
else:
cfg.USE_FASTREID = False
with VideoTracker(cfg, args, video_path=args.VIDEO_PATH) as vdo_trk:
vdo_trk.run()