python实现yolo目标检测代码

以下是一个基本的Python代码示例，用于使用YOLO进行目标检测。

import cv2
import numpy as np

# 加载YOLO模型和配置文件
net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")

# 加载目标类别标签
classes = []
with open("coco.names", "r") as f:
    classes = [line.strip() for line in f.readlines()]

# 加载图像
img = cv2.imread("image.jpg")
height, width, channels = img.shape

# 构建输入图片的blob格式
blob = cv2.dnn.blobFromImage(img, 1/255.0, (416, 416), swapRB=True, crop=False)

# 设置输入层
net.setInput(blob)

# 获取每个输出层的名称和大小
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
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:
            # 中心点坐标和边界框大小
            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)

# 非最大抑制，去除重叠的边界框
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_PLAIN
colors = np.random.uniform(0, 255, size=(len(classes), 3))
for i in range(len(boxes)):
    if i in indexes:
        x, y, w, h = boxes[i]
        label = str(classes[class_ids[i]])
        color = colors[class_ids[i]]
        cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
        cv2.putText(img, label, (x, y + 30), font, 3, color, 3)

# 显示图像
cv2.imshow("Image", img)
cv2.waitKey(0)
cv2.destroyAllWindows()

其中，`yolov3.weights`和`yolov3.cfg`是预训练的权重文件和配置文件，可以从YOLO官网下载。`coco.names`是目标类别标签文件，在本例中使用了COCO数据集的标签，也可以使用其他数据集的标签。`image.jpg`是要进行目标检测的图像文件，可以替换为其他文件路径。