pyside6将yolov8目标检测结果写入文件

要将YOLOv8目标检测结果写入文件，需要进行以下步骤：

1. 导入必要的库和模块：

import cv2
import numpy as np
import os

2. 加载YOLOv8模型和配置文件：

net = cv2.dnn.readNet('yolov8.weights', 'yolov8.cfg')
classes = []
with open('coco.names', 'r') as f:
    classes = [line.strip() for line in f.readlines()]
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]

3. 加载图片并进行目标检测：

img = cv2.imread('test.jpg')
height, width, channels = img.shape
blob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
net.setInput(blob)
outs = net.forward(output_layers)

4. 解析检测结果并将结果写入文件：

# 类别ID、置信度和边界框坐标
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:
            # 中心坐标和宽高
            center_x = int(detection[0] * width)
            center_y = int(detection[1] * height)
            w = int(detection[2] * width)
            h = int(detection[3] * height)
            # 边界框左上角坐标
            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)
# 非极大值抑制
indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
# 写入文件
with open('result.txt', 'w') as f:
    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]
            f.write(f'{label} {confidence:.2f} {x} {y} {x+w} {y+h}\n')

以上代码将检测到的目标类别、置信度和边界框坐标写入result.txt文件中。每行的格式为：类别名称 置信度 左上角x坐标 左上角y坐标 右下角x坐标 右下角y坐标。