用python调用yolov8

在Python中调用YOLOv8（You Only Look Once version 8）通常需要一些库的支持，如OpenCV、Pillow以及对YOLOv8模型文件的处理。以下是基本步骤：

1. 安装必要的库：
- 首先安装`pip`，然后通过它安装`opencv-python-headless`和`imutils`，这两个库可以帮助处理图像：

   pip install opencv-python-headless imutils

2. 下载YOLOv8模型：YOLOv8官方通常会提供预训练的权重文件（`.weights`），以及配置文件（`.cfg`）。可以从GitHub或其他官方网站获取。

3. 加载模型：

   import cv2
   from utils.utils import get_classes, plot_one_box

   # 加载模型配置文件
   config_path = 'path/to/yolov8.cfg'
   # 加载权重文件
   weight_path = 'path/to/yolov8.weights'
   
   # 获取类别列表
   classes_path = 'path/to/coco.names'
   class_names = get_classes(classes_path)
   model = cv2.dnn.readNetFromDarknet(config_path, weight_path)
   model.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)
   model.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)  # 或者DNN_TARGET_OPENCL如果支持GPU加速

4. 运行检测：

   img = cv2.imread('image.jpg')
   height, width, _ = img.shape
   blob = cv2.dnn.blobFromImage(img, 1 / 255.0, (416, 416), swapRB=True, crop=False)

   # 设置输入并运行前向传播
   model.setInput(blob)
   layer_names = [layer.name for layer in model.getLayerNames() if 'output' in layer.name]
   output_layers = [model.getLayerByName(layer_name).output for layer_name in layer_names]
   outputs = model.forward(output_layers)

   # 解析检测结果并绘制在图像上
   boxes, confidences, class_ids = [], [], []
   for output in outputs:
       for detection in output:
           scores = detection[5:]
           class_id = np.argmax(scores)
           confidence = scores[class_id]
           if confidence > 0.5:  # 可自定义阈值
               center_x, center_y = int(detection[0] * width), int(detection[1] * height)
               w, h = int(detection[2] * width), int(detection[3] * height)
               x, y = center_x - w / 2, center_y - h / 2
               boxes.append([x, y, w, h])
               confidences.append(float(confidence))
               class_ids.append(class_id)

   # 绘制边界框
   plot_one_box(boxes, img, color=(0, 255, 0), label='person', line_thickness=2)

5. 显示结果：

   cv2.imshow("YOLOv8 Detection", img)
   cv2.waitKey(0)
   cv2.destroyAllWindows()