积分图加速提取 HOG 特征

def gradient_integral_map(img, angle_step):
    H, W = img.shape[:2]

    #  梯度积分图
    angle_feature_channel_num = 360 // angle_step
    hog_matrix = np.zeros([H+1, W+1, angle_feature_channel_num], dtype='float32')
    hog_gra_matrix = np.zeros([H, W, angle_feature_channel_num], dtype='float32')

    gx = cv2.Sobel(img, cv2.CV_32F, 1, 0, ksize=1)
    gy = cv2.Sobel(img, cv2.CV_32F, 0, 1, ksize=1)

    mag, angle = cv2.cartToPolar(gx, gy, angleInDegrees=True)
    angle = np.clip(angle, None, 359.99999)
    angle_index = angle // angle_step

    for channel_index in range(angle_feature_channel_num):
        temp_map = mag * (angle_index == channel_index)
        hog_gra_matrix[:,:,channel_index] = temp_map
        sum_map = cv2.integral(temp_map)
        hog_matrix[:,:,channel_index] = sum_map

    return hog_matrix, hog_gra_matrix