Remove Boxes/rectangles from image - opencv

Question

I have the following image.
this image
I would like to remove the orange boxes/rectangle around numbers and keep the original image clean without any orange grid/rectangle.
Below is my current code but it does not remove it.
Mat mask = new Mat();
Mat src = new Mat();
src = Imgcodecs.imread("enveloppe.jpg",Imgcodecs.CV_LOAD_IMAGE_COLOR);
Imgproc.cvtColor(src, hsvMat, Imgproc.COLOR_BGR2HSV);
Scalar lowerThreshold = new Scalar(0, 50, 50);
Scalar upperThreshold = new Scalar(25, 255, 255);
Mat mask = new Mat();
Core.inRange(hsvMat, lowerThreshold, upperThreshold, mask);
//src.setTo(new scalar(255,255,255),mask);
what to do next ?
How can i remove the orange boxes/rectangle from the original images ?
Update:
For information , the mask contains exactly all the boxes/rectangle that i want to remove. I don't know how to use this mask to remove boxes/rectangle from the source (src) image as if they were not present.

Answer 1

This is what I did to solve the problem. I solved the problem in C++ and I used OpenCV.
Part 1: Find box candidates
Firstly I wanted to isolate the signal that was specific for red channel. I splitted the image into three channels. I then subtracted the red channel from blue channel and the red from green channel. After that I subtracted both previous subtraction results from one another. The final subtraction result is shown on the image below.
using namespace cv;
using namespace std;
Mat src_rgb = imread("image.jpg");
std::vector<Mat> channels;
split(src_rgb, channels);
Mat diff_rb, diff_rg;
subtract(channels[2], channels[0], diff_rb);
subtract(channels[2], channels[1], diff_rg);
Mat diff;
subtract(diff_rb, diff_rg, diff);
My next goal was to divide the parts of obtained image into separate "groups". To do that, I smoothed the image a little bit with a Gaussian filter. Then I applied a threshold to obtain a binary image; finally I looked for external contours within that image.
GaussianBlur(diff, diff, cv::Size(11, 11), 2.0, 2.0);
threshold(diff, diff, 5, 255, THRESH_BINARY);
vector<vector<Point>> contours;
findContours(diff, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);
Click to see subtraction result, Gaussian blurred image, thresholded image and detected contours.
Part 2: Inspect box candidates
After that, I had to make an estimate whether the interior of each contour contained a number or something else. I made an assumption that numbers will always be printed with black ink and that they will have sharp edges. Therefore I took a blue channel image and I applied just a little bit of Gaussian smoothing and convolved it with a Laplacian operator.
Mat blurred_ch2;
GaussianBlur(channels[2], blurred_ch2, cv::Size(7, 7), 1, 1);
Mat laplace_result;
Laplacian(blurred_ch2, laplace_result, -1, 1);
I then took the resulting image and applied the following procedure for every contour separately. I computed a standard deviation of the pixel values within the contour interior. Standard deviation was high inside the contours that surrounded numbers; and it was low inside the two contours that surrounded the dog's head and the letters on top of the stamp.
That is why I could appliy the standard deviation threshold. Standard deviation was approx. twice larger for contours containing numbers so this was an easy way to only select the contours that contained numbers. Then I drew the contour interior mask. I used erosion and subtraction to obtain the "box edge mask".
The final step was fairly easy. I computed an estimate of average pixel value nearby the box on every channel of the image. Then I changed all pixel values under the "box edge mask" to those values on every channel. After I repeated that procedure for every box contour, I merged all three channels into one.
Mat mask(src_rgb.size(), CV_8UC1);
for (int i = 0; i < contours.size(); ++i)
{
mask.setTo(0);
drawContours(mask, contours, i, cv::Scalar(200), -1);
Scalar mean, stdev;
meanStdDev(laplace_result, mean, stdev, mask);
if (stdev.val[0] < 10.0) continue;
Mat eroded;
erode(mask, eroded, cv::Mat(), cv::Point(-1, -1), 6);
subtract(mask, eroded, mask);
for (int c = 0; c < src_rgb.channels(); ++c)
{
erode(mask, eroded, cv::Mat());
subtract(mask, eroded, eroded);
Scalar mean, stdev;
meanStdDev(channels[c], mean, stdev, eroded);
channels[c].setTo(mean, mask);
}
}
Mat final_result;
merge(channels, final_result);
imshow("Final Result", final_result);
Click to see red channel of the image, the result of convolution with Laplacian operator, drawn mask of the box edges and the final result.
Please note
This code is far from being optimal, especially the last loop does quite a lot of unnecessary work. But I think that in this case readability is more important (and the author of the question did not request an optimized solution anyway).
Looking towards more general solution
After I posted the initial reply, the author of the question noted that the digits can be of any color and their edges are not necessarily sharp. That means that above procedure can fail because of various reasons. I altered the input image so that it contains different kinds of numbers (click to see the image) and you can run my algorithm on this input and analyze what goes wrong.
The way I see it, one of these approaches is needed (or perhaps a mixture of both) to obtain a more "general" solution:
concentrate only on rectangle shape and color (confirm that the box candidate is really an orange box and remove it regardless of what is inside)
concentrate on numbers only (run a proper number detection algorithm inside the interior of every box candidate; if it contains a single number, remove the box)
I will give a trivial example of the first approach. If you can assume that orange box size will always be the same, just check the box size instead of standard deviation of the signal in the last loop of the algorithm:
Rect rect = boundingRect(contours[i]);
float area = rect.area();
if (area < 1000 || area > 1200) continue;
Warning: actual area of rectangles is around 600Px^2, but I took into account the Gaussian Blurring, which caused the contour to expand. Please also note that if you use this approach you no longer need to perform blurring or laplace operations on blue channel image.
You can also add other simple constraints to that condition; ratio between width and height is the first one that comes to my mind. Geometric properties can also be a good option (right angles, straight edges, convexness ...).