cv2.dnn.readNet如何加载yolov5模型

可以按照以下步骤使用`cv2.dnn.readNet`加载yolov5模型：

1. 下载yolov5模型权重文件和配置文件。

2. 加载权重文件和配置文件：

model_weights = "yolov5s.pt"
model_config = "yolov5s.yaml"

net = cv2.dnn.readNet(model_weights, model_config)

3. 设置预测输入尺寸：

net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)
net.setPreferableLayout(cv2.dnn.DNN_TARGET_CUDA_FP16)
net.setInputSize(640, 640)

4. 设置类别标签：

class_labels = ["person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"]

5. 进行预测：

image = cv2.imread("image.jpg")
blob = cv2.dnn.blobFromImage(image, 1/255.0, (640, 640), swapRB=True, crop=False)
net.setInput(blob)
outputs = net.forward(net.getUnconnectedOutLayersNames())

6. 处理预测结果：

confidence_threshold = 0.5
nms_threshold = 0.4

for output in outputs:
    for detection in output:
        scores = detection[5:]
        class_id = np.argmax(scores)
        confidence = scores[class_id]
        if confidence > confidence_threshold:
            center_x = int(detection[0] * image.shape[1])
            center_y = int(detection[1] * image.shape[0])
            width = int(detection[2] * image.shape[1])
            height = int(detection[3] * image.shape[0])
            left = int(center_x - width / 2)
            top = int(center_y - height / 2)
            cv2.rectangle(image, (left, top), (left + width, top + height), (0, 255, 0), 2)
            cv2.putText(image, class_labels[class_id], (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

以上代码仅为示例，具体实现可能需要根据自己的需求进行修改。