Content: Image Processing in OpenCV C++.
The Requirement is to create tiles of Mat pattern of size 256 X 256 on an outer Mat Image. The user specifies the width and the height of the outer Mat Image.
To do this , I created the below OpenCV C++ function:
Mat GenerateDiagonalFade(int width, int height)
{
// Creating a Mat Image in user defined dimension
Mat image(height, width, CV_8UC1, Scalar(0));
//Looping through all rows and columns of the outer Image
for (int row = 0; row < image.rows; row ++)
{
for (int col = 0; col < image.cols; col ++)
{
//Here, I am giving the condition to access the pixel values
//The pattern should be 255 X 255 and they must fill in the entire image
if ((row % 256 + col % 256) <= 255)
{
image.at<uchar>(row, col) = (row % 256 + col % 256);
}
else
{
//Here is where I get error
image.at<uchar>(row, col) = abs(row % 256 - col % 256);
}
}
}
return image;
}
If you can see the else statement above, I tried to make the inverse of the first condition and make the value absolute.
The output I get is as seen below:
The Expected Output is the inverse of the first part of the diagonal. Darker to lighter shade towards the diagonal.
I tried replacing abs(row % 256 - col % 256);
with many statements. I am struct with the output. The changes should be made only in the else statement. Rest of my code is correct as half of my output( top diagonal) is right.
I appreciate any help from you in order to solve this. Trust me, it's quite interesting to work out all graphical[XY axis] and mathematical calculations[pixel access] to get the desired output.
I would begin by splitting the problem into two parts:
The goal is to generate a 256x256 grayscale image containing a gradient such that:
You got the part above the diagonal right, but let's inspect that anyway.
The coordinates of the top left corner are (0, 0) and we expect intensity of 0. --> row + col == 0
The coordinates of one end of the diagonal are (255, 0) and we expect intensity of 255. --> row + col == 255
The other end of the diagonal is at (0, 255) -> row + col == 255
Let's try another point on the diagonal, (254,1) --> again row + col == 255
OK, now a point just above the diagonal, (254,0) -> row + col == 254
-- slightly less white, as we would expect.
Next, let's try a point just below the diagonal, say (255, 1) --> row + col == 256
. If we cast this to an 8 bit integer, we get a 0, yet we expect 254, just like in the previous case.
Finally, bottom right corner (255, 255) -> row + col == 510
. If we cast this to an 8 bit integer, we get a 254, yet we expect 0.
Let's try something:
256 + 254 == 510
510 + 0 == 510
And we see an algorithm: * If the sum of row + col
is less than 256, then use the sum * Otherwise subtract the sum from 510 and use the result
Sample code:
cv::Mat make_tile()
{
int32_t const TILE_SIZE(256);
cv::Mat image(TILE_SIZE, TILE_SIZE, CV_8UC1);
for (int32_t r(0); r < TILE_SIZE; ++r) {
for (int32_t c(0); c < TILE_SIZE; ++c) {
int32_t sum(r + c);
if (sum < TILE_SIZE) {
image.at<uint8_t>(r, c) = static_cast<uint8_t>(sum);
} else {
image.at<uint8_t>(r, c) = static_cast<uint8_t>(2 * (TILE_SIZE - 1) - sum);
}
}
}
return image;
}
Single tile:
Now that we have a complete tile, we can simply generate the full image by iterating over tile-sized ROIs of the target image, and copying a tile ROI of identical size to them.
Sample code:
#include <opencv2/opencv.hpp>
#include <cstdint>
cv::Mat make_tile()
{
int32_t const TILE_SIZE(256);
cv::Mat image(TILE_SIZE, TILE_SIZE, CV_8UC1);
for (int32_t r(0); r < TILE_SIZE; ++r) {
for (int32_t c(0); c < TILE_SIZE; ++c) {
int32_t sum(r + c);
if (sum < TILE_SIZE) {
image.at<uint8_t>(r, c) = static_cast<uint8_t>(sum);
} else {
image.at<uint8_t>(r, c) = static_cast<uint8_t>(2 * (TILE_SIZE - 1) - sum);
}
}
}
return image;
}
int main()
{
cv::Mat tile(make_tile());
cv::Mat result(600, 800, CV_8UC1);
for (int32_t r(0); r < result.rows; r += tile.rows) {
for (int32_t c(0); c < result.cols; c += tile.cols) {
// Handle incomplete tiles
int32_t end_r(std::min(r + tile.rows, result.rows));
int32_t end_c(std::min(c + tile.cols, result.cols));
// Get current target tile ROI and source ROI of same size
cv::Mat target_roi(result(cv::Range(r, end_r), cv::Range(c, end_c)));
cv::Mat source_roi(tile(cv::Range(0, target_roi.rows), cv::Range(0, target_roi.cols)));
// Copy the tile
source_roi.copyTo(target_roi);
}
}
cv::imwrite("gradient.png", tile);
cv::imwrite("gradient_big.png", result);
}
Complete image:
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