基于yolov3的红绿灯检测

红绿灯检测是计算机视觉中的一个经典问题，可以应用于自动驾驶、交通监管等领域。基于YOLOv3的红绿灯检测可以实现实时的目标检测，下面是一个基于YOLOv3的红绿灯检测的示例代码：

import cv2
import numpy as np

# Load YOLOv3 weights and configuration files
net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")

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

# Set input and output layers
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]

# Load image
img = cv2.imread("traffic.jpg")

# Resize image
img = cv2.resize(img, None, fx=0.4, fy=0.4)

# Convert image to blob
blob = cv2.dnn.blobFromImage(img, 1/255.0, (416, 416), swapRB=True, crop=False)

# Set input to network
net.setInput(blob)

# Forward pass
outputs = net.forward(output_layers)

# Process outputs
boxes = []
confidences = []
class_ids = []
for output in outputs:
    for detection in output:
        scores = detection[5:]
        class_id = np.argmax(scores)
        confidence = scores[class_id]
        if confidence > 0.5 and class_id == 9:
            center_x = int(detection[0] * img.shape[1])
            center_y = int(detection[1] * img.shape[0])
            w = int(detection[2] * img.shape[1])
            h = int(detection[3] * img.shape[0])
            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)

# Apply non-max suppression
indices = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)

# Draw boxes and labels
for i in indices:
    i = i[0]
    box = boxes[i]
    x, y, w, h = box
    cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)

# Display image
cv2.imshow("Image", img)
cv2.waitKey(0)
cv2.destroyAllWindows()

这段代码使用了YOLOv3目标检测网络来检测图像中的红绿灯，其中：

- `cv2.dnn.readNet`函数用于加载YOLOv3网络的权重和配置文件；
- `cv2.dnn.blobFromImage`函数用于将图像转换为网络输入的blob格式；
- `net.setInput`函数用于将blob作为网络的输入；
- `net.forward`函数用于进行前向传播，得到网络的输出；
- `cv2.dnn.NMSBoxes`函数用于对网络输出进行非极大值抑制，得到目标框；
- 最后使用`cv2.rectangle`函数将目标框画在图像上。

需要注意的是，上述代码是基于COCO数据集训练的YOLOv3模型，因此`classes`中包含了80个目标类别。在这里，我们只需要检测红绿灯，因此需要将目标类别为9的目标框保留下来。另外，由于YOLOv3网络的输出是一个张量，需要对张量进行处理才能得到目标框的坐标。