具有拉普拉斯公式的OpenCV用於檢測圖像在iOS中是否模糊

Question

提前感謝您的幫助。

我有很多研發和搜索，但我找不到任何檢測模糊圖像的解決方案。

我使用過這個https://github.com/BloodAxe/OpenCV-Tutorial並使用拉普拉斯公式進行模糊檢測，但無法在圖像中獲得模糊檢測

- （void）checkForBurryImage：（UIImage *）image {

 cv::Mat matImage = [image toMat]; cv::Mat matImageGrey; cv::cvtColor(matImage, matImageGrey, CV_BGRA2GRAY); cv::Mat dst2 =[image toMat]; cv::Mat laplacianImage; dst2.convertTo(laplacianImage, CV_8UC1); cv::Laplacian(matImageGrey, laplacianImage, CV_8U); cv::Mat laplacianImage8bit; laplacianImage.convertTo(laplacianImage8bit, CV_8UC1); //------------------------------------------------------------- //------------------------------------------------------------- unsigned char *pixels = laplacianImage8bit.data; //------------------------------------------------------------- //------------------------------------------------------------- // unsigned char *pixels = laplacianImage8bit.data; int maxLap = -16777216; for (int i = 0; i < ( laplacianImage8bit.elemSize()*laplacianImage8bit.total()); i++) { if (pixels[i] > maxLap) maxLap = pixels[i]; } int soglia = -6118750; printf("\\n maxLap : %i",maxLap); if (maxLap < soglia || maxLap == soglia) { printf("\\n\\n***** blur image *****"); }else printf("\\nNOT a blur image"); } 

我使用相同的代碼作為Android和它的工作正常，但在iOS，它給我總是積極的價值所以我認為它不起作用，

所以請給我想法或鏈接或任何建議。

Answer 1

用這個 ：

Laplacian(gray, laplacianImage, CV_64F);
Scalar mean, stddev; // 0:1st channel, 1:2nd channel and 2:3rd channel
meanStdDev(laplacianImage, mean, stddev, Mat());
double variance = stddev.val[0] * stddev.val[0];

double threshold = 2900;

if (variance <= threshold) {
    // Blurry
} else {
    // Not blurry
}

Answer 2

用這個

-(BOOL) checkForBurryImage:(cv::Mat) matImage {// Output:(cv::Mat &) outputFrame {

cv::Mat finalImage;


cv::Mat matImageGrey;
cv::cvtColor(matImage, matImageGrey, CV_BGRA2GRAY);
matImage.release();

cv::Mat newEX;
const int MEDIAN_BLUR_FILTER_SIZE = 15; // odd number
cv::medianBlur(matImageGrey, newEX, MEDIAN_BLUR_FILTER_SIZE);
matImageGrey.release();

cv::Mat laplacianImage;
cv::Laplacian(newEX, laplacianImage, CV_8U); // CV_8U
newEX.release();

cv::Mat laplacianImage8bit;
laplacianImage.convertTo(laplacianImage8bit, CV_8UC1);
laplacianImage.release();
cv::cvtColor(laplacianImage8bit,finalImage,CV_GRAY2BGRA);
laplacianImage8bit.release();

int rows = finalImage.rows;
int cols= finalImage.cols;
char *pixels = reinterpret_cast<char *>( finalImage.data);
int maxLap = -16777216;
for (int i = 0; i < (rows*cols); i++) {
    if (pixels[i] > maxLap)
        maxLap = pixels[i];
}

int soglia = -6118750;    

pixels=NULL;
finalImage.release();

BOOL isBlur = (maxLap < kBlurThreshhold)?  YES :  NO;
return isBlur;

}

Answer 3

以下方法使用OpenCV ：

- (BOOL) isImageBlurry:(UIImage *) image {
    // converting UIImage to OpenCV format - Mat
    cv::Mat matImage = [self convertUIImageToCVMat:image];
    cv::Mat matImageGrey;
    // converting image's color space (RGB) to grayscale
    cv::cvtColor(matImage, matImageGrey, CV_BGR2GRAY);

    cv::Mat dst2 = [self convertUIImageToCVMat:image];
    cv::Mat laplacianImage;
    dst2.convertTo(laplacianImage, CV_8UC1);

    // applying Laplacian operator to the image
    cv::Laplacian(matImageGrey, laplacianImage, CV_8U);
    cv::Mat laplacianImage8bit;
    laplacianImage.convertTo(laplacianImage8bit, CV_8UC1);

    unsigned char *pixels = laplacianImage8bit.data;

    // 16777216 = 256*256*256
    int maxLap = -16777216;
    for (int i = 0; i < ( laplacianImage8bit.elemSize()*laplacianImage8bit.total()); i++) {
        if (pixels[i] > maxLap) {
            maxLap = pixels[i];
        }
    }
    // one of the main parameters here: threshold sets the sensitivity for the blur check
    // smaller number = less sensitive; default = 180
    int threshold = 100;         

    return (maxLap <= threshold);
}

將 UIImage 轉換 為 OpenCV::Mat

- (cv::Mat)convertUIImageToCVMat:(UIImage *)image {
    CGColorSpaceRef colorSpace = CGImageGetColorSpace(image.CGImage);
    CGFloat cols = image.size.width;
    CGFloat rows = image.size.height;

    cv::Mat cvMat(rows, cols, CV_8UC4); // 8 bits per component, 4 channels (color channels + alpha)

    CGContextRef contextRef = CGBitmapContextCreate(cvMat.data,                 // Pointer to  data
                                                    cols,                       // Width of bitmap
                                                    rows,                       // Height of bitmap
                                                    8,                          // Bits per component
                                                    cvMat.step[0],              // Bytes per row
                                                    colorSpace,                 // Colorspace
                                                    kCGImageAlphaNoneSkipLast |
                                                    kCGBitmapByteOrderDefault); // Bitmap info flags

    CGContextDrawImage(contextRef, CGRectMake(0, 0, cols, rows), image.CGImage);
    CGContextRelease(contextRef);

    return cvMat;
}

Answer 4

試試有沒有辦法檢測圖像是否模糊？

並閱讀： http ： //www.cs.cmu.edu/~htong/pdf/ICME04_tong.pdf 。

基本上，如果圖像中沒有很多高頻分量，則模糊。