import cv2
from PIL import Image
import numpy as np

def a_hash_a(image_path):
    # 打开图片
    image = Image.open(image_path)
    # 缩小图片尺寸到8x8
    image = image.resize((8, 8), Image.Resampling.LANCZOS)
    # 转换为灰度图
    image = image.convert('L')
    # 将图片转换为numpy数组
    data = np.array(image)
    # 计算所有像素的灰度平均值
    mean_value = data.mean()
    # 比较每个像素的灰度与平均值，生成hash值
    hash_str = ''.join(['1' if i > mean_value else '0' for i in data.flatten()])
    # 返回64位的hash值
    return int(hash_str, 2)


def a_hash_b(image_path):
    # 打开图片
    image = Image.open(image_path)
    # 缩小图片尺寸到32x32
    image = image.resize((32, 32), Image.Resampling.LANCZOS)
    # 转换为灰度图
    image = image.convert('L')
    # 将图片转换为numpy数组
    data = np.array(image)
    # 应用DCT变换
    dct = dct_transform(data)
    # 取左上角8x8的DCT系数
    dct_low = dct[:8, :8]
    # 计算DCT系数的平均值
    mean_value = dct_low.flatten().mean()
    # 比较每个DCT系数与平均值，生成hash值
    hash_str = ''.join(['1' if i >= mean_value else '0' for i in dct_low.flatten()])
    # 返回64位的hash值
    return int(hash_str, 2)

def dct_transform(image):
    # 将图片转换为灰度图
    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    # 应用2D离散余弦变换
    dct = cv2.dct(np.float32(gray_image))
    return dct


if __name__ == '__main__':

    hash_value1 = a_hash_a('../images/3.jpeg')
    hash_value2 = a_hash_a('../images/2.jpg')
    print(f'running 1... {hash_value1}  2... {hash_value2}')