opencv yolov8 部署

以下是在Win10下使用Opencv+CUDA部署yolov8算法的步骤：

1.安装CUDA和cuDNN

2.安装Opencv

3.下载yolov8的权重文件和配置文件

4.使用Opencv的DNN模块加载yolov8的权重文件和配置文件

5.读取图像并将其传递给网络进行目标检测

具体的代码实现可以参考以下范例：

import cv2

# 加载yolov8的权重文件和配置文件
model = cv2.dnn.readNetFromDarknet('yolov8.cfg', 'yolov8.weights')

# 设置CUDA作为后端
model.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
model.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)

# 读取图像并将其传递给网络进行目标检测
image = cv2.imread('test.jpg')
blob = cv2.dnn.blobFromImage(image, 1/255.0, (416, 416), swapRB=True, crop=False)
model.setInput(blob)
output_layers = model.getUnconnectedOutLayersNames()
layer_outputs = model.forward(output_layers)

# 处理网络输出结果
boxes = []
confidences = []
class_ids = []
for output in layer_outputs:
    for detection in output:
        scores = detection[5:]
        class_id = np.argmax(scores)
        confidence = scores[class_id]
        if confidence > 0.5:
            center_x = int(detection[0] * image.shape[1])
            center_y = int(detection[1] * image.shape[0])
            w = int(detection[2] * image.shape[1])
            h = int(detection[3] * image.shape[0])
            x = int(center_x - w / 2)
            y = int(center_y - h / 2)
            boxes.append([x, y, w, h])
            confidences.append(float(confidence))
            class_ids.append(class_id)

# 绘制检测结果
indices = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
for i in indices:
    i = i[0]
    box = boxes[i]
    x, y, w, h = box[0], box[1], box[2], box[3]
    cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)
    label = str(classes[class_ids[i]])
    cv2.putText(image, label, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

# 显示结果
cv2.imshow('image', image)
cv2.waitKey(0)
cv2.destroyAllWindows()