import os

import cv2
from scipy.fftpack import fftshift, fft2
import numpy as np


def variance_of_laplacian(image):
    # 计算输入图像的拉普拉斯响应的方差
    return cv2.Laplacian(image, cv2.CV_64F).var()

def frequency_domain_analysis(gray):

    # 计算傅里叶变换并移动到中心
    f_transform = fftshift(fft2(gray))
    # 计算频谱幅度
    magnitude_spectrum = 20 * np.log(np.abs(f_transform))
    # 计算高频成分的比例
    high_frequency_magnitude = np.sum(magnitude_spectrum > np.mean(magnitude_spectrum))
    total_magnitude = magnitude_spectrum.size
    ratio = high_frequency_magnitude / total_magnitude
    return ratio
def edge_gradient(gray):

    sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=3)
    sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=3)
    magnitude = np.sqrt(sobelx**2 + sobely**2)
    return np.mean(magnitude)

def testOne():
    # 加载图像并转换为灰度图
    image = cv2.imread('H:/images/1/20250114193113.jpg')
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # 计算图像的清晰度分数
    score = variance_of_laplacian(gray)
    ratio = frequency_domain_analysis(gray)
    edge_gradient_score = edge_gradient(gray)

    print("Image clarity score: ", score)
    print("High frequency ratio: ", ratio)
    print("Edge gradient score: ", edge_gradient_score)

    # 根据经验设置阈值来判断图像是否模糊
    threshold = 100  # 这个阈值可以根据实际情况调整
    if score < threshold:
        print("The image is considered blurry.")
    else:
        print("The image is considered clear.")


def batch_score():
    global name, join
    # 读取文件夹下的所有图像分别计算分数
    folder_path = 'H:/images/2'
    for filename in os.listdir(folder_path):
        if filename.endswith('.jpg') or filename.endswith('.png'):
            image_path = os.path.join(folder_path, filename)
            image = cv2.imread(image_path)
            gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
            score = variance_of_laplacian(gray)
            ratio = frequency_domain_analysis(gray)
            edge_gradient_score = edge_gradient(gray)
            # 所有结果保留两位小数
            score = round(score, 2)
            ratio = round(ratio, 2)
            edge_gradient_score = round(edge_gradient_score, 2)
            name = "score=" + str(score) + "ratio=" + str(ratio) + "edge_gradient_score=" + str(
                edge_gradient_score) + ".jpg"
            path_ = folder_path + "/22/"
            # 判断文件夹是否存在，不存在则新建
            if not os.path.exists(path_):
                os.makedirs(path_)
            join = path_ + name
            print(join)
            cv2.imwrite(join, image)


def cam_score():
    # 调用摄像头计算采集到图像的得分
    camera_index = 2
    print("第二个摄像头索引：" + str(camera_index))
    # 打开摄像头
    capture = cv2.VideoCapture(camera_index, cv2.CAP_DSHOW)
    # 设置分辨率
    capture.set(cv2.CAP_PROP_FRAME_WIDTH, 1921)
    capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 1081)
    # 检查摄像头是否成功打开
    if not capture.isOpened():
        print("无法打开摄像头2")
        exit()
    # 循环读取画面
    while True:
        ret, frame = capture.read()
        if not ret:
            print("无法读取摄像头画面")
            break
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

        score = variance_of_laplacian(gray)
        # ratio = frequency_domain_analysis(gray)
        # edge_gradient_score = edge_gradient(gray)
        # 将FPS绘制在图像上
        cv2.putText(frame, f"score={str(score)}"
                    , (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)
        # cv2.putText(frame, f"ratio={str(ratio)}"
        #             , (10, 70), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)
        # cv2.putText(frame, f"edge_gradient_score={str(edge_gradient_score)}"
        #             , (10, 110), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)
        # 显示画面
        cv2.imshow("Camera", frame)
        # 检测按键，如果按下q键则退出循环
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
    # 关闭摄像头
    capture.release()
    # 关闭所有窗口
    cv2.destroyAllWindows()


if __name__ == '__main__':
    testOne()
    # batch_score()
    # cam_score()