yolov8 数据导入 代码

YOLOv8是一种目标检测算法，它是YOLO（You Only Look Once）系列的最新版本。下面是一个简单的示例代码，用于演示如何导入数据并使用YOLOv8进行目标检测：

import cv2
import numpy as np

# 导入YOLOv8模型和权重
net = cv2.dnn.readNetFromDarknet('yolov3.cfg', 'yolov3.weights')

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

# 加载类别标签
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')
height, width, channels = image.shape

# 对图像进行预处理
blob = cv2.dnn.blobFromImage(image, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
net.setInput(blob)

# 运行前向传播
outs = net.forward(output_layers)

# 解析模型输出并绘制边界框
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:
            center_x = int(detection[0] * width)
            center_y = int(detection * height)
            w = int(detection * width)
            h = int(detection * height)
            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)

# 使用非最大抑制方法去除重叠边界框
indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)

# 在图像上绘制边界框和类别标签
font = cv2.FONT_HERSHEY_SIMPLEX
for i in range(len(boxes)):
    if i in indexes:
        x, y, w, h = boxes[i]
        label = str(classes[class_ids[i]])
        confidence = confidences[i]
        color = (0, 255, 0)
        cv2.rectangle(image, (x, y), (x + w, y + h), color, 2)
        cv2.putText(image, label + ' ' + str(round(confidence, 2)), (x, y - 10), font, 0.5, color, 2)

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

这段代码使用OpenCV库来导入YOLOv8模型和权重，并使用模型进行目标检测。你需要将YOLOv8的配置文件（yolov3.cfg）、权重文件（yolov3.weights）和类别标签文件（coco.names）放在同一目录下，并将待检测的图像命名为image.jpg。代码会在图像上绘制检测到的边界框和类别标签，并显示结果图像。