帮我写一个HOG特征和极大值抑制的行人检测python代码

好的，以下是Python代码的实现：

import cv2

# HOG参数
winSize = (64, 128)
blockSize = (16, 16)
blockStride = (8, 8)
cellSize = (8, 8)
nbins = 9

# 加载行人检测模型
hog = cv2.HOGDescriptor(winSize, blockSize, blockStride, cellSize, nbins)
hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())

# 极大值抑制参数
threshold = 0.3
max_overlap = 0.5

# 行人检测函数
def detect_person(image):
    # 运行HOG检测器
    rects, weights = hog.detectMultiScale(image)

    # 极大值抑制
    if len(rects) > 1:
        weights = weights[:, 0]
        rects = rects[np.argsort(-weights)]

        keep = []
        while len(rects) > 0:
            x1, y1, w1, h1 = rects[0]
            keep.append(x1, y1, x1 + w1, y1 + h1)
            overlaps = compute_overlap(rects[1:], [x1, y1, x1 + w1, y1 + h1])
            inds = np.where(overlaps <= max_overlap)[0]
            rects = rects[inds + 1]
        return np.array(keep)
    else:
        return rects

# 计算两个矩形框之间的IOU(交并比)
def compute_overlap(rects, bbox):
    x1 = np.maximum(rects[:, 0], bbox[0])
    y1 = np.maximum(rects[:, 1], bbox[1])
    x2 = np.minimum(rects[:, 2], bbox[2])
    y2 = np.minimum(rects[:, 3], bbox[3])
    inter_area = np.maximum(x2 - x1, 0) * np.maximum(y2 - y1, 0)
    bbox_area = (bbox[2] - bbox[0]) * (bbox[3] - bbox[1])
    overlap = inter_area / bbox_area
    return overlap

使用方法：

# 加载图像
image = cv2.imread('person.jpg')

# 行人检测
rects = detect_person(image)

# 绘制检测结果
for (x, y, w, h) in rects:
    cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)

# 显示结果图像
cv2.imshow('detection result', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

请注意，这只是一个简单的示例，可能不是最优的行人检测算法。请根据实际需求进行修改和改进。