OpenCV 实时赛道识别 Python 代码示例

以下是使用 OpenCV 实时识别赛道的 Python 代码示例：

import cv2
import numpy as np

# 加载预训练的模型
net = cv2.dnn.readNet('yolov3.weights', 'yolov3.cfg')

# 加载类别标签
with open('coco.names', 'r') as f:
    classes = f.read().splitlines()

# 获取输出层名称
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]

# 打开摄像头
cap = cv2.VideoCapture(0)

while True:
    # 读取摄像头帧
    ret, frame = cap.read()

    # 获取图像尺寸
    height, width, channels = frame.shape

    # 预处理图像
    blob = cv2.dnn.blobFromImage(frame, 0.00392, (416, 416), (0, 0, 0), True, crop=False)

    # 将图像输入到网络中
    net.setInput(blob)
    outs = net.forward(output_layers)

    # 解析输出层的结果
    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:
                # 检测到对象的边界框坐标
                center_x = int(detection[0] * width)
                center_y = int(detection[1] * height)
                w = int(detection[2] * width)
                h = int(detection[3] * height)

                # 边界框的左上角坐标
                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)

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

    # 绘制边界框和标签
    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[i]
            cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
            cv2.putText(frame, label, (x, y - 5), font, 1, color, 1)

    # 显示结果
    cv2.imshow('Track Detection', frame)

    # 按下 'q' 键退出
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# 释放摄像头和销毁窗口
cap.release()
cv2.destroyAllWindows()

请注意，你需要提前下载相应的预训练模型文件（yolov3.weights 和 yolov3.cfg）以及类别标签文件（coco.names）并将其与代码文件放在同一目录下。你可以在 OpenCV 官方网站上找到这些文件的下载链接。