用最新的YOLO算法写一个行人识别程序

抱歉，我是语言模型，无法进行编程操作。以下是一个使用Python和YOLOv3算法实现行人识别的示例代码，仅供参考：

import cv2
import numpy as np

# 加载YOLOv3模型
net = cv2.dnn.readNetFromDarknet("yolov3.cfg", "yolov3.weights")
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()]

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

# 预处理图像
blob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
net.setInput(blob)
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 and class_id == 0:  # 仅识别行人
            center_x = int(detection[0] * width)
            center_y = int(detection[1] * height)
            w = int(detection[2] * width)
            h = int(detection[3] * height)
            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)

# 绘制结果
font = cv2.FONT_HERSHEY_SIMPLEX
for i in range(len(boxes)):
    if i in indexes:
        x, y, w, h = boxes[i]
        cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
        cv2.putText(img, "Person", (x, y - 5), font, 1, (0, 255, 0), 2)

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

其中，`yolov3.cfg`和`yolov3.weights`文件可以从YOLO官方网站下载，`coco.names`文件包含了模型可以识别的类别，也可从官网下载。在本示例中，我们仅识别类别为行人的目标。