Open CV image effects - iphone

Question

I am using openCV in objective C. I want to convert image into Black and White, I already done it, but my output Black and White image is not clear, there is black shade on image.
Any one can help?
- (IBAction)blackAndWhite:(id)sender {
imageView.image=orignalImage;
cv::Mat dst;
cv::Mat src=[self cvMatFromUIImage:imageView.image];
if( !src.data )
{ cout<<"Usage: ./Histogram_Demo <path_to_image>"<<endl;
}
/// Convert to grayscale
cvtColor( src, src, CV_BGR2GRAY );
/// Apply Histogram Equalization
equalizeHist( src, dst );
imageView.image =[self UIImageFromCVMat:dst];
}
Thanks
orignal image :
Black and White image :

Answer 1

Firstly the image output you have is not a black and white (binary) image. It is a grayscale image.
This grayscale image is a single channel image with 256 colours (0-255), whereas binary images have only 0(black) or 255(white) in them.
You can use thresholding (cvThreshold) to convert it to binary from grayscale. There are many binarization algorithms available which can help you in achieving what you require as well. Local binarization methods are more adaptable and can help remove parts of the shaded regions as well.
Hope this helps.

Answer 2

Look into gamma correction. I assume that the gamma value needs to be adjusted in this case to suit the contrast of the lines in the image. Since it's a blurry image, you also find some trouble, I assume. You may also want to increase some of the contrast, while you're at it.
Links:
Understanding Gamma Correction
Wikipedia - Gamma Correction
OpenCV Gamma Correction (C++)
Changing contrast and brightness of an image - OpenCV

Answer 3

Well, I don't know why there are already 3 answers and none of them is correct.
Why do you apply image equalization after converting image to grayscale? The output without it is the following:
Here you can read about histogram equalization.

Answer 4

As already pointed out, you are not really binarizing your image but you are converting it to gray scale. You can binarize using cv::threshold but due to strong artefacts in illumination in your sample image, you'd better off with determining a local threshold value, using cv::adaptiveThreshold.
Here is a useful link that explains binarization in case of non uniform lighting conditions.
A sample program, in C++:
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <cstdlib>
int
main(int argc, char **argv)
{
cv::Mat image = cv::imread(argv[1], CV_LOAD_IMAGE_GRAYSCALE);
assert(!image.empty());
cv::Mat binary;
cv::adaptiveThreshold(image, binary, 255, CV_ADAPTIVE_THRESH_MEAN_C, CV_THRESH_BINARY, 5, 7);
cv::imshow("Orig", image);
cv::imshow("Binary", binary);
cv::waitKey(0);
cv::imwrite("binary.png", binary);
return EXIT_SUCCESS;
}
Result: