yolov8计数代码

YOLOv8是一种目标检测算法，它是YOLO（You Only Look Once）系列算法的最新版本。YOLOv8的计数代码可以用于在图像或视频中检测和计数特定目标的数量。以下是一个简单的YOLOv8计数代码示例：

import cv2
import numpy as np

# 加载YOLOv8模型
net = cv2.dnn.readNetFromDarknet('yolov8.cfg', 'yolov8.weights')

# 获取输出层信息
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 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)

# 解析检测结果
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:  # 设置置信度阈值
            # 计算边界框位置
            center_x = int(detection[0] * width)
            center_y = int(detection[1] * height)
            w = int(detection[2] * width)
            h = int(detection[3] * height)

            # 计算边界框的左上角坐标
            x = int(center_x - w / 2)
            y = int(center_y - h / 2)

            # 保存检测结果
            class_ids.append(class_id)
            confidences.append(float(confidence))
            boxes.append([x, y, w, h])

# 进行非最大抑制
indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)

# 绘制边界框和标签
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]])
        cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
        cv2.putText(image, label, (x, y - 10), font, 0.5, (0, 255, 0), 2)

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

这段代码使用OpenCV库加载YOLOv8模型，并使用该模型在图像中进行目标检测和计数。你需要将YOLOv8的配置文件（yolov8.cfg）、权重文件（yolov8.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目标边界框和标签，并显示结果图像。