yolov8预测代码

以下是一个基本的 YOLOv8 预测代码示例：

import cv2
import numpy as np
# 加载模型和类别列表
model = cv2.dnn.readNet("yolov8.weights", "yolov8.cfg")
classes = []

with open("coco.names", "r") as f:
    classes = [line.strip() for line in f.readlines()]

# 设置输入图像尺寸和缩放因子
input_size = (416, 416)
scale = 1 / 255.0

# 读取输入图像
image = cv2.imread("image.jpg")

# 将图像调整为指定尺寸并进行预处理
blob = cv2.dnn.blobFromImage(image, scale, input_size, (0, 0, 0), swapRB=True, crop=False)

# 设置网络的输入
model.setInput(blob)

# 运行网络并获取预测结果
output_layers_names = model.getUnconnectedOutLayersNames()
outputs = model.forward(output_layers_names)

# 解析预测结果
class_ids = []
confidences = []
boxes = []

for output in 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])
            width = int(detection[2] * image.shape[1])
            height = int(detection[3] * image.shape[0])

            x = int(center_x - width / 2)
            y = int(center_y - height / 2)

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

# 应用非最大抑制
indices = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)

# 绘制边界框和标签
font_scale = 1
thickness = 1

for i in indices:
    i = i[0]
    box = boxes[i]
    x, y, width, height = box

    label = f"{classes[class_ids[i]]}: {confidences[i]:.2f}"
    color = (255, 0, 0)
    cv2.rectangle(image, (x, y), (x + width, y + height), color, thickness)
    cv2.putText(image, label, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, font_scale, color, thickness)

# 显示结果图像
cv2.imshow("YOLOv8", image)
cv2.waitKey(0)
cv2.destroyAllWindows()

请确保在运行代码之前已经下载了 `yolov8.weights`、`yolov8.cfg` 和 `coco.names` 文件，并将其放置在正确的路径下。此外，你还需要安装 OpenCV 库来运行此代码。这段代码将加载预训练的 YOLOv8 模型，并在给定的图像上进行物体检测和标记。