基于yolov5下ByteTrack算法实现计数的代码
时间: 2023-06-11 10:06:29 浏览: 85
以下是基于 YOLOv5 和 ByteTrack 算法实现计数的 Python 代码:
```python
import cv2
import torch
import argparse
from models.experimental import attempt_load
from utils.general import non_max_suppression
from utils.torch_utils import select_device
from trackers.byte_tracker import ByteTracker
def detect(source, weights, conf_thres, iou_thres, device):
model = attempt_load(weights, map_location=device) # load FP32 model
stride = int(model.stride.max()) # model stride
imgsz = check_img_size(640, s=stride) # check image size
if device.type != 'cpu':
model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once
cap = cv2.VideoCapture(source)
# ByteTrack 初始化
byte_tracker = ByteTracker()
while cap.isOpened():
ret, img0 = cap.read()
if not ret:
break
img = letterbox(img0, new_shape=imgsz)[0]
img = img[:, :, ::-1].transpose(2, 0, 1)
img = np.ascontiguousarray(img)
img = torch.from_numpy(img).to(device)
img = img.float() # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
# YOLOv5 推理
pred = model(img, augment=False)[0]
pred = non_max_suppression(pred, conf_thres, iou_thres, classes=None, agnostic=False)
# ByteTrack 计数
byte_tracker.track(img0, pred[0], 0.5)
cv2.destroyAllWindows()
cap.release()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--source', type=str, default='0', help='source') # file/folder, 0 for webcam
parser.add_argument('--weights', type=str, default='yolov5s.pt', help='model.pt path')
parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
args = parser.parse_args()
detect(args.source, args.weights, args.conf_thres, args.iou_thres, args.device)
```
其中,`ByteTracker` 是一个基于 ByteTrack 算法的目标跟踪器,可以参考 [这个链接](https://github.com/STVIR/pysot/tree/master/pysot/tracker) 获取代码。`detect()` 函数中,首先加载 YOLOv5 模型,并对视频帧进行预处理,然后进行 YOLOv5 的目标检测。接着,将检测结果传递给 `ByteTracker` 进行目标跟踪,并进行计数。最后,关闭视频流并释放资源。
相关推荐
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![-](https://csdnimg.cn/download_wenku/file_type_column_c1.png)
![-](https://csdnimg.cn/download_wenku/file_type_column_c1.png)
![-](https://csdnimg.cn/download_wenku/file_type_column_c1.png)
![-](https://csdnimg.cn/download_wenku/file_type_column_c1.png)
![-](https://csdnimg.cn/download_wenku/file_type_column_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)