After the scanning the document, I am applying the image processing using OpenCV library. I am not getting the quality of the scanned document like the Scannable iOS app.
I am using below code for image processing:
- (UIImage *)applyImageProcessing:(UIImage *)aImage
{
cv::Mat originalMat = [self cvMatFromUIImage:aImage];
cv::Mat dest_mat(aImage.size.width, aImage.size.height, CV_8UC4);
cv::Mat intermediate_mat(aImage.size.width, aImage.size.height, CV_8UC4);
cv::multiply(originalMat, 0.5, intermediate_mat);
cv::add(originalMat, intermediate_mat, dest_mat);
return [self UIImageFromCVMat:dest_mat];
}
- (cv::Mat)cvMatFromUIImage:(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, // Color space
kCGImageAlphaNoneSkipLast
| kCGBitmapByteOrderDefault); // Bitmap info flags
CGContextDrawImage(contextRef, CGRectMake(0, 0, cols, rows), image.CGImage);
CGContextRelease(contextRef);
return cvMat;
}
- (UIImage *)UIImageFromCVMat:(cv::Mat)cvMat
{
NSData *data = [NSData dataWithBytes:cvMat.data length:cvMat.elemSize()*cvMat.total()];
CGColorSpaceRef colorspace;
if (cvMat.elemSize() == 1)
{
colorspace = CGColorSpaceCreateDeviceGray();
}
else
{
colorspace = CGColorSpaceCreateDeviceRGB();
}
CGDataProviderRef provider = CGDataProviderCreateWithCFData((__bridge CFDataRef)data);
// Create CGImage from cv::Mat
CGImageRef imageRef = CGImageCreate(cvMat.cols, cvMat.rows, 8, 8 * cvMat.elemSize(), cvMat.step[0], colorspace, kCGImageAlphaNone | kCGBitmapByteOrderDefault, provider, NULL, false, kCGRenderingIntentDefault);
// get uiimage from cgimage
UIImage *finalImage = [UIImage imageWithCGImage:imageRef];
CGImageRelease(imageRef);
CGDataProviderRelease(provider);
CGColorSpaceRelease(colorspace);
return finalImage;
}
My App Scanned document quality & clearity
Scannable iOS App Scanned document quality & clearity
How can I get result of my scanned document like as scannble app?
Original image:
Scannable app original image:
You need to estimate the light fall off on the paper to be able to make it uniform. A simple, non-local estimate for a white paper background is local maximum. By choosing the kernel size carefully large enough to not be contained within any character, you can filter out text (Fig. @middle). Subsequently you can estimate the per-pixel-gain.
If needed, you can use Canny detector to detect spots where localmax does not apply -- in this case the pins top of the image -- and maybe process them differently.
Finally, you can apply a global lut operation for maximal contrast, e.g., one that you'd do with Photoshop curves tool.
cv::Mat src; // input image
if( src.type()!=CV_8UC3 )
CV_Error(CV_StsError,"not impl");
cv::Mat median;
// remove highlight pixels e.g., those from debayer-artefacts and noise
cv::medianBlur(src,median,5);
cv::Mat localmax;
// find local maximum
cv::morphologyEx( median,localmax,
cv::MORPH_CLOSE,cv::getStructuringElement(cv::MORPH_RECT,cv::Size(15,15) ),
cv::Point(-1,-1),1,cv::BORDER_REFLECT101 );
// compute the per pixel gain such that the localmax goes to monochromatic 255
cv::Mat dst = cv::Mat(src.size(),src.type() );
for ( int y=0;y<src.rows;++y){
for ( int x=0;x<src.cols;++x){
const cv::Vec3b & v1=src.at<cv::Vec3b>(y,x);
const cv::Vec3b & v2=localmax.at<cv::Vec3b>(y,x);
cv::Vec3b & v3=dst.at<cv::Vec3b>(y,x);
for ( int i=0;i<3;++i )
{
double gain = 255.0/(double)v2[i];
v3[i] = cv::saturate_cast<unsigned char>( gain * v1[i] );
}
}
}
// and dst is the result
:::EDIT:::
For papers containing not just text, I modified the algorithm to use a simple Gaussian model. Particularly, I used the detectLetters
by @William Extracting text OpenCV
and truncated the localmax
into mean +/- 1 standard deviation away from what is estimated inside the text rectangles.
cv::Mat input = cv::imread(ss.str()+".jpg", CV_LOAD_IMAGE_COLOR );
int maxdim = input.cols; //std::max(input.rows,input.cols);
const int dim = 1024;
if ( maxdim > dim )
{
double scale = (double)dim/(double)maxdim;
cv::Mat t;
cv::resize( input, t, cv::Size(), scale,scale );
input = t;
}
if ( input.type()!=CV_8UC3 )
CV_Error(CV_StsError,"!bgr");
cv::Mat result;
input.copyTo( result ); // result is just for drawing the text rectangles
// as previously...
cv::Mat median;
// remove highlight pixels e.g., those from debayer-artefacts and noise
cv::medianBlur(input,median,5);
cv::Mat localmax;
// find local maximum
cv::Mat kernel = cv::getStructuringElement(cv::MORPH_RECT,cv::Size(15,15) );
cv::morphologyEx( median,localmax,cv::MORPH_CLOSE,kernel,cv::Point(-1,-1),1,cv::BORDER_REFLECT101 );
std::vector< cv::Rect > bb;
// detectLetters by @William, modified to internally do the grayscale conversion if necessary
// https://stackoverflow.com/questions/23506105/extracting-text-opencv?rq=1
detectLetters( input, bb );
// compose a simple Gaussian model for text background (still assumed white)
cv::Mat mask( input.size(),CV_8UC1,cv::Scalar( 0 ) );
if ( bb.empty() )
return; // TODO; none found
for ( size_t i=0;i<bb.size(); ++i )
{
cv::rectangle( result, bb[i], cv::Scalar(0,0,255),2,8 ); // visualize only
cv::rectangle( mask, bb[i], cv::Scalar( 1 ), -1 ); // create a mask for cv::meanStdDev
}
cv::Mat mean,dev;
cv::meanStdDev( localmax, mean, dev, mask );
if ( mean.type()!=CV_64FC1 || dev.type()!=CV_64FC1 || mean.size()!=cv::Size(1,3) || dev.size()!=cv::Size(1,3) )
CV_Error(CV_StsError, "should never happen");
double minimum[3];
double maximum[3];
// simply truncate the localmax according to our simple Gaussian model (+/- one standard deviation)
for ( unsigned int u=0;u<3;++u )
{
minimum[u] = mean.at<double>(u ) - dev.at<double>( u );
maximum[u] = mean.at<double>(u ) + dev.at<double>( u );
}
for ( int y=0;y<mask.rows;++y){
for ( int x=0;x<mask.cols;++x){
cv::Vec3b & col = localmax.at<cv::Vec3b>(y,x);
for ( unsigned int u=0;u<3;++u )
{
if ( col[u]>maximum[u] )
col[u]=maximum[u];
else if ( col[u]<minimum[u] )
col[u]=minimum[u];
}
}
}
// do the per pixel gain then
cv::Mat dst;
input.copyTo( dst );
for ( int y=0;y<input.rows;++y){
for ( int x=0;x<input.cols;++x){
const cv::Vec3b & v1=input.at<cv::Vec3b>(y,x);
const cv::Vec3b & v2=localmax.at<cv::Vec3b>(y,x);
cv::Vec3b & v3=dst.at<cv::Vec3b>(y,x);
for ( int i=0;i<3;++i )
{
double gain = 255.0/(double)v2[i];
v3[i] = cv::saturate_cast<unsigned char>( gain * v1[i] );
}
}
}
// and dst is the result
A NEW sample result can be found here:
https://i.imgur.com/FL1xcUF.jpg
:::
:::EDIT::: If you want to increase the performance of this filter you can combine the two nested-for-loops over cols and rows for the max/min truncation as well as the final loop for the gain into one loop using OpenCV's wrapper for Intel TBB multi-threading technology. This will parallelize the loop function into small bits optimized for available CPU threads that can work simultaneously and therefor faster:
cv::parallel_for_(cv::Range(0, input.cols * input.rows), [&](const cv::Range& range) {
for (int y=range.start;y<range.end;++y){
int newY = y / input.cols;
int newX = y % input.cols;
const cv::Vec4b & v1=input.at<cv::Vec4b>(newY,newX);
auto & v2 = localmax.at<cv::Vec4b>(newY,newX);
auto & v3=input.at<cv::Vec4b>(newY,newX);
for (int i=0;i<3;++i)
{
if (v2[i]>maximum[i])
v2[i]=maximum[i];
else if (v2[i]<minimum[i])
v2[i]=minimum[i];
double gain = 255.0/(double)v2[i];
v3[i] = cv::saturate_cast<unsigned char>( gain * v1[i] );
}
v3[3] = 1;
}
});
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