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video_test.py
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video_test.py
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import os
import cv2
import time
import argparse
import torch
import warnings
import numpy as np
from Object_Detection import build_detector
from Object_Track 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
from PIL import Image
import os
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
class VideoTracker(object):
def __init__(self, args, video_path):
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()
self.deepsort = build_tracker(use_cuda=True)
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)
# 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
while self.vdo.grab():
idx_frame += 1
if idx_frame % self.args.frame_interval:
continue
start = time.time()
ref, ori_im = self.vdo.retrieve()
# 检测
if ref is True:
# 格式转变，BGRtoRGB
im = cv2.cvtColor(ori_im, cv2.COLOR_BGR2RGB)
# ----- do detection
# 转变成Image
frame = Image.fromarray(np.uint8(im))
bbox_xywh, cls_conf, cls_ids = self.detector.new_detect(frame)
if cls_conf is not None:
# -----copy
list_fin = []
for i in bbox_xywh:
temp = []
temp.append(i[0])
temp.append(i[1])
temp.append(i[2] * 1.)
temp.append(i[3] * 1.)
list_fin.append(temp)
new_bbox = np.array(list_fin).astype(np.float32)
# -----#-----mask processing filter the useless part
mask = [0, 1, 2, 3, 5, 7] # keep specific classes the indexes are corresponded to coco_classes
mask_filter = []
for i in cls_ids:
if i in mask:
mask_filter.append(1)
else:
mask_filter.append(0)
new_cls_conf = []
new_new_bbox = []
new_cls_ids = []
for i in range(len(mask_filter)):
if mask_filter[i] == 1:
new_cls_conf.append(cls_conf[i])
new_new_bbox.append(new_bbox[i])
new_cls_ids.append(cls_ids[i])
new_bbox = np.array(new_new_bbox).astype(np.float32)
cls_conf = np.array(new_cls_conf).astype(np.float32)
cls_ids = np.array(new_cls_ids).astype(np.float32)
# -----#-----
# do tracking
outputs = self.deepsort.update(new_bbox, 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)
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), new_bbox.shape[0], len(outputs)))
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--display", action="store_true", default=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()
with VideoTracker(args, video_path='./03.mp4') as vdo_trk:
vdo_trk.run()