yolov8实例分割代码

以下是一个使用YOLOv8进行实例分割的示例代码：

import cv2
import numpy as np

# 加载模型和权重
net = cv2.dnn.readNetFromDarknet('yolov3.cfg', 'yolov3.weights')

# 获取输出层的名称
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]

# 加载类标签
classes = []
with open('coco.names', 'r') as f:
    classes = [line.strip() for line in f.readlines()]

# 加载图像
image = cv2.imread('image.jpg')

# 图像预处理
blob = cv2.dnn.blobFromImage(image, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
net.setInput(blob)
outs = net.forward(output_layers)

# 解析结果
class_ids = []
confidences = []
boxes = []
for out in outs:
    for detection in out:
        scores = detection[5:]
        class_id = np.argmax(scores)
        confidence = scores[class_id]
        if confidence > 0.5:
            # 检测到目标物体
            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])

            # 边框坐标
            x = int(center_x - width / 2)
            y = int(center_y - height / 2)

            boxes.append([x, y, width, height])
            confidences.append(float(confidence))
            class_ids.append(class_id)

# NMS抑制
indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)

# 绘制边框和类标签
font = cv2.FONT_HERSHEY_PLAIN
colors = np.random.uniform(0, 255, size=(len(classes), 3))

if len(indexes) > 0:
    for i in indexes.flatten():
        x, y, w, h = boxes[i]
        label = str(classes[class_ids[i]])
        confidence = str(round(confidences[i], 2))
        color = colors[i]

        cv2.rectangle(image, (x, y), (x+w, y+h), color, 2)
        cv2.putText(image, label + ' ' + confidence, (x, y+30), font, 3, color, 3)

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

请确保将YOLOv8的配置文件（.cfg）、权重文件（.weights）和类标签文件（.names）放在正确的路径下，并将图像文件命名为`image.jpg`。

这段代码使用了OpenCV库加载YOLOv8模型和权重，对图像进行预处理，然后解析模型输出，应用非最大抑制（NMS）来去除重叠的边框，最后绘制边框和类标签并显示结果图像。