在YOLOv7中使用tri-layer插件的python代码
时间: 2023-06-14 14:03:31 浏览: 117
以下是使用tri-layer插件的YOLOv7的Python代码示例:
首先,需要安装 `torch` 和 `opencv-python` 库。然后,下载 `yolov7.weights` 模型文件和 `yolov7.cfg` 配置文件。
```python
import cv2
import torch
import numpy as np
# Load the model
model = torch.hub.load('ultralytics/yolov5', 'custom', path='yolov7.weights', yaml='yolov7.cfg')
# Load an image
img = cv2.imread('image.jpg')
# Preprocess the image
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img_tensor = torch.from_numpy(img / 255.0).float().permute(2, 0, 1).unsqueeze(0)
# Run the model
model.eval()
with torch.no_grad():
output = model(img_tensor)
# Postprocess the result
output = output.cpu().numpy()[0]
boxes = output[:, :4]
scores = output[:, 4]
classes = output[:, 5].astype(int)
# Draw the bounding boxes
colors = np.random.randint(0, 255, size=(len(boxes), 3), dtype=np.uint8)
for i, box in enumerate(boxes):
x1, y1, x2, y2 = box.astype(int)
cv2.rectangle(img, (x1, y1), (x2, y2), colors[i].tolist(), 2)
cv2.putText(img, str(classes[i]), (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, colors[i].tolist(), 2)
# Show the result
cv2.imshow('result', img)
cv2.waitKey(0)
```