yolov5 视频检测 设置检测次数
时间: 2023-09-09 19:06:06 浏览: 41
您可以使用以下代码来设置检测次数:
```python
import cv2
import torch
from models.experimental import attempt_load
from utils.datasets import LoadStreams, LoadImages
from utils.general import check_img_size, non_max_suppression, apply_classifier, scale_coords, xyxy2xywh, plot_one_box
from utils.torch_utils import select_device, time_synchronized
# 加载YOLOv5模型
weights = 'path/to/weights.pt'
device = select_device('0')
model = attempt_load(weights, map_location=device)
stride = int(model.stride.max()) # 最大步长
# 视频文件路径
video_path = 'path/to/video.mp4'
cap = cv2.VideoCapture(video_path)
# 检测次数
num_detections = 5
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
# 转换为RGB格式
img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# 图像预处理
img = cv2.resize(img, (640, 640))
img = img.transpose(2, 0, 1)
img = torch.from_numpy(img).to(device)
img = img.float() / 255.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
# 进行检测
detections = []
for i in range(num_detections):
t1 = time_synchronized()
pred = model(img, augment=False)[0]
pred = non_max_suppression(pred, 0.4, 0.5, classes=None, agnostic=True)
t2 = time_synchronized()
detections.append(pred)
print(f'Detection {i+1}: {t2 - t1:.4f} seconds')
# 绘制检测框
for i, det in enumerate(detections):
for *xyxy, conf, cls in reversed(det):
c = int(cls)
label = f'{c}'
plot_one_box(xyxy, frame, label=label, color=(0,0,255), line_thickness=3)
cv2.imshow('frame', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
```
在上述代码中,我们使用 `num_detections` 变量来设置检测次数,并在每次循环中进行多次检测。每次检测后,我们将检测结果添加到 `detections` 列表中,最终将所有检测结果绘制到帧上。
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