使用拉普拉斯公式检测图像的OpenCV在iOS中模糊或不模糊
提前感谢您的帮助。
我有很多的研发和search，但我找不到任何检测模糊图像的解决scheme。
我已经使用这个https://github.com/BloodAxe/OpenCV-Tutorial和模糊检测使用拉普拉斯公式，但不能在图像中模糊检测
– （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"); }
我使用了与Android相同的代码，并且工作正常，但是在iOS中，它总是给我正面的价值，所以我认为它不工作，
所以请给我想法或链接或任何build议。
用这个
-(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;
}
尝试有没有办法来检测图像是否模糊？
并阅读： http : //www.cs.cmu.edu/~htong/pdf/ICME04_tong.pdf 。
基本上，如果图像中的高频成分不多，则是模糊的。
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); }
将 UIImage 转换 为 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; }
用这个 ：
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) { BLURRR } else{ NOT BLURR }