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import os
import cv2
import time
import argparse
import torch
import warnings
import json
import sys
sys.path.append(os.path.join(os.path.dirname(__file__), 'thirdparty/fast-reid'))
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
class VideoTracker(object):
def __init__(self, cfg, args, video_path):
self.cfg = cfg
self.args = args
self.video_path = video_path
self.logger = get_logger("root")
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, segment=self.args.segment)
self.deepsort = build_tracker(cfg, use_cuda=use_cuda)
self.class_names = self.detector.class_names
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)
# TODO save masks
# 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, 20, (self.im_width, self.im_height))
# 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
with open('coco_classes.json', 'r') as f:
idx_to_class = json.load(f)
while self.vdo.grab():
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
if self.args.segment:
bbox_xywh, cls_conf, cls_ids, seg_masks = self.detector(im)
else:
bbox_xywh, cls_conf, cls_ids = self.detector(im)
# select person class
mask = cls_ids == 0
bbox_xywh = bbox_xywh[mask]
# bbox dilation just in case bbox too small, delete this line if using a better pedestrian detector
bbox_xywh[:, 2:] *= 1.2
cls_conf = cls_conf[mask]
cls_ids = cls_ids[mask]
# do tracking
if self.args.segment:
seg_masks = seg_masks[mask]
outputs, mask_outputs = self.deepsort.update(bbox_xywh, cls_conf, cls_ids, im, seg_masks)
else:
outputs, _ = self.deepsort.update(bbox_xywh, cls_conf, cls_ids, im)
# draw boxes for visualization
if len(outputs) > 0:
bbox_tlwh = []
bbox_xyxy = outputs[:, :4]
identities = outputs[:, -1]
cls = outputs[:, -2]
names = [idx_to_class[str(label)] for label in cls]
ori_im = draw_boxes(ori_im, bbox_xyxy, names, identities, None if not self.args.segment else mask_outputs)
for bb_xyxy in bbox_xyxy:
bbox_tlwh.append(self.deepsort._xyxy_to_tlwh(bb_xyxy))
results.append((idx_frame - 1, bbox_tlwh, identities, cls))
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)))
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--VIDEO_PATH", type=str, default='demo.avi')
parser.add_argument("--config_mmdetection", type=str, default="./configs/mmdet.yaml")
parser.add_argument("--config_detection", type=str, default="./configs/mask_rcnn.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("--fastreid", action="store_true")
parser.add_argument("--mmdet", action="store_true")
parser.add_argument("--segment", 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()
if args.segment:
cfg.USE_SEGMENT = True
else:
cfg.USE_SEGMENT = False
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.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()
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