图片模糊，清晰图，过曝光检测相关算法

 

https://blog.csdn.net/wuan584974722/article/details/51435876

此实例通过使用Halcon实现5种清晰度算法函数：
1. 方差算法函数；
2. 拉普拉斯能量函数；
3. 能量梯度函数；
4. Brenner函数；
5. Tenegrad函数；

 

https://www.cnblogs.com/arkenstone/p/7900978.html

https://www.pyimagesearch.com/2015/09/07/blur-detection-with-opencv/

https://www.semanticscholar.org/paper/Analysis-of-focus-measure-operators-for-Pertuz-Puig/8c675bf5b542b98bf81dcf70bd869ab52ab8aae9?p2df

 

https://blog.csdn.net/dcrmg/article/details/53543341 （清晰度/相机聚焦度：1，求边缘，越清晰边缘越多；傅里叶变换后，越清晰，高频率部分越多）

https://gitee.com/bug_me_not/blur-detection/tree/master

 

常用方法代码示例：

https://stackoverflow.com/questions/7765810/is-there-a-way-to-detect-if-an-image-is-blurry

// OpenCV port of 'LAPM' algorithm (Nayar89)
double modifiedLaplacian(const cv::Mat& src)
{
    cv::Mat M = (Mat_<double>(3, 1) << -1, 2, -1);
    cv::Mat G = cv::getGaussianKernel(3, -1, CV_64F);

    cv::Mat Lx;
    cv::sepFilter2D(src, Lx, CV_64F, M, G);

    cv::Mat Ly;
    cv::sepFilter2D(src, Ly, CV_64F, G, M);

    cv::Mat FM = cv::abs(Lx) + cv::abs(Ly);

    double focusMeasure = cv::mean(FM).val[0];
    return focusMeasure;
}

// OpenCV port of 'LAPV' algorithm (Pech2000)
double varianceOfLaplacian(const cv::Mat& src)
{
    cv::Mat lap;
    cv::Laplacian(src, lap, CV_64F);

    cv::Scalar mu, sigma;
    cv::meanStdDev(lap, mu, sigma);

    double focusMeasure = sigma.val[0]*sigma.val[0];
    return focusMeasure;
}

// OpenCV port of 'TENG' algorithm (Krotkov86)
double tenengrad(const cv::Mat& src, int ksize)
{
    cv::Mat Gx, Gy;
    cv::Sobel(src, Gx, CV_64F, 1, 0, ksize);
    cv::Sobel(src, Gy, CV_64F, 0, 1, ksize);

    cv::Mat FM = Gx.mul(Gx) + Gy.mul(Gy);

    double focusMeasure = cv::mean(FM).val[0];
    return focusMeasure;
}

// OpenCV port of 'GLVN' algorithm (Santos97)
double normalizedGraylevelVariance(const cv::Mat& src)
{
    cv::Scalar mu, sigma;
    cv::meanStdDev(src, mu, sigma);

    double focusMeasure = (sigma.val[0]*sigma.val[0]) / mu.val[0];
    return focusMeasure;
}

 

 

 

 python代码示例：

import numpy
import cv2
import os

def evaluate(img_col):
    numpy.seterr(all='ignore')
    assert isinstance(img_col, numpy.ndarray), 'img_col must be a numpy array'
    assert img_col.ndim == 3, 'img_col must be a color image ({0} dimensions currently)'.format(img_col.ndim)
    img_gry = cv2.cvtColor(img_col, cv2.COLOR_RGB2GRAY)
    rows, cols = img_gry.shape
    crow, ccol = int(rows/2), int(cols/2)
    f = numpy.fft.fft2(img_gry)
    fshift = numpy.fft.fftshift(f)
    fshift[crow-75:crow+75, ccol-75:ccol+75] = 0
    f_ishift = numpy.fft.ifftshift(fshift)
    img_fft = numpy.fft.ifft2(f_ishift)
    img_fft = 20*numpy.log(numpy.abs(img_fft))

    result = numpy.mean(img_fft)
    return img_fft, result


if __name__ == "__main__":
    src = r"F:\tmp2"
    for file in os.listdir(src):
        if file.endswith(".jpg"):
            img_file = os.path.join(src, file)
            img = cv2.imread(img_file)

            # 拉普拉斯方法,阈值默认为100
            img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
            # img_gray = cv2.GaussianBlur(img_gray, ksize=(3, 3), sigmaX=0)
            # blur_score = cv2.Laplacian(img_gray, ddepth=cv2.CV_64F).var()
            blur_score = cv2.Laplacian(img_gray, ddepth=cv2.CV_64F)
            print(blur_score.var(), numpy.max(blur_score))

            # 傅里叶方法, 阈值默认为10
            _, ret = evaluate(img)
            print("fft", ret)


            cv2.namedWindow("img", cv2.WINDOW_NORMAL)
            cv2.resizeWindow("img", 960, 540)
            cv2.imshow("img", img)
            cv2.waitKey(0)
            cv2.destroyAllWindows()

 

 

曝光过度检测

https://blog.csdn.net/qq249356520/article/details/78864244

https://blog.csdn.net/u010029439/article/details/103081762

https://blog.csdn.net/kingroc/article/details/97640529