OpenCV with Laplacian formula to detect image is blur or not in iOS

问题

Thanks for help in advance.

I have lots of R&D and search but I can't find any solution for detect blur image or not.

I have used this https://github.com/BloodAxe/OpenCV-Tutorial and for blur detection used Laplacian formula but can't get blur detection in image

-(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"); }

And I used same code as Android and its working fine but In iOS, its give me always positive value so I think its not working,

So please give me idea or link or any suggestion.

回答1:

Use this :

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
}

回答2:

Use this

-(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;

}

回答3:

The following method uses 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);
}

Converting UIImage to 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;
}