如何用opencv dnn加载训练好的网络进行识别

1. 下载训练好的网络模型和对应的配置文件（如caffe或tensorflow格式）。

2. 加载模型和配置文件：

```python
import cv2
net = cv2.dnn.readNetFromCaffe("model.prototxt", "model.caffemodel")
```

3. 加载图片并将其转换为网络所需的格式：

```python
image = cv2.imread("image.jpg")
blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 0.007843, (300, 300), 127.5)
```

4. 将图片输入到网络中进行识别：

```python
net.setInput(blob)
detections = net.forward()
```

5. 解析识别结果并进行操作，如绘制框和标签：

```python
for i in range(detections.shape[2]):
    confidence = detections[0, 0, i, 2]
    if confidence > 0.5:
        box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
        (startX, startY, endX, endY) = box.astype("int")
        label = "{}: {:.2f}%".format(CLASSES[int(detections[0, 0, i, 1])], confidence * 100)
        cv2.rectangle(image, (startX, startY), (endX, endY), (0, 255, 0), 2)
        y = startY - 15 if startY - 15 > 15 else startY + 15
        cv2.putText(image