I am going to perform ridge segmentation on an input image using OpenCV. From the internet, I found a Matlab code as follows, which fits quite well with my goal:
function [normim, mask, maskind] = ridgesegment(im, blksze, thresh)
im = normalise(im,0,1); % normalise to have zero mean, unit std dev
fun = inline('std(x(:))*ones(size(x))');
stddevim = blkproc(im, [blksze blksze], fun);
mask = stddevim > thresh;
maskind = find(mask);
% Renormalise image so that the *ridge regions* have zero mean, unit
% standard deviation.
im = im - mean(im(maskind));
normim = im/std(im(maskind));
end
So I tried to convert it to C++. Up to now, I can only finish these parts:
cv::Mat ridgeSegment(cv::Mat inputImg, int blockSize, double thresh)
{
cv::normalize(inputImg, inputImg, 0, 1.0, cv::NORM_MINMAX, CV_8UC1);
blkproc(inputImg, cv::Size(blockSize, blockSize), thresh);
...// how to do the next steps ????
}
cv::Mat blkproc(cv::Mat img, cv::Size size, double thresh)
{
cv::Mat croppedImg;
for (int i = 0; i < im.cols; i += size.width)
{
for (int j = 0; j < im.rows; j += size.height)
{
croppedImg = im(cv::Rect(i, j, size.width, size.height)).clone();
//perform standard deviation calculation here???
}
}
return croppedImg;
}
I don't know how to proceed further here. Especially that stddevim and its later parts. Could someone explain and show me the rest? Thank you in advance.
Related
I am currently making a project for school on image processing in visual Studio 2013, using Open CV 3.1. My goal (for now) is to transform an image, using affine transform, so that the trapezoidal board will be transformed into a rectangle.
To do that I have substracted certain channels and thresholded the image so that now I have a binary image with white blocks in the corners of the board.
Now I need to pick 4 white points that are closest to each corner and (using affine transform) set them as corners of the transformed image.
And since this is my first time using Open CV, I am stuck.
Here's my code:
#include <iostream>
#include <opencv2\core.hpp>
#include <opencv2\highgui.hpp>
#include<opencv2/imgproc.hpp>
#include <stdlib.h>
#include <stdio.h>
#include <vector>
int main(){
double dist;
cv::Mat image;
image = cv::imread("C:\\Users\\...\\ideal.png");
cv::Mat imagebin;
imagebin = cv::imread("C:\\Users\\...\\ideal.png");
cv::Mat imageerode;
//cv::imshow("Test", image);
cv::Mat src = cv::imread("C:\\Users\\...\\ideal.png");
std::vector<cv::Mat>img_rgb;
cv::split(src, img_rgb);
//cv::imshow("ideal.png", img_rgb[2] - img_rgb[1]);
cv::threshold(img_rgb[2] - 0.5*img_rgb[1], imagebin , 20, 255, CV_THRESH_BINARY);
cv::erode(imagebin, imageerode, cv::Mat(), cv::Point(1, 1), 2, 1, 1);
cv::erode(imageerode, imageerode, cv::Mat(), cv::Point(1, 1), 2, 1, 1);
// cv::Point2f array[4];
// std::vector<cv::Point2f> array;
for (int i = 0; i < imageerode.cols; i++)
{
for (int j = 0; j < imageerode.rows; j++)
{
if (imageerode.at<uchar>(i,j) > 0)
{
dist = std::min(dist, i + j);
}
}
}
//cv::imshow("Test binary", imagebin);
cv::namedWindow("Test", CV_WINDOW_NORMAL);
cv::imshow("Test", imageerode);
cv::waitKey(0);
std::cout << "Hello world!";
return 0;
}
As you can see I don't know how to loop over each white pixel using image.at and save the distance to each corner.
I would appreciate some help.
Also: I don't want to just do this. I really want to learn how to do that. But I'm currently having some mindstuck.
Thank you
EDIT:
I think I'm done with finding the coordinates of the 4 points. But I can't really get the idea of the warpAffine syntax.
Code:
for (int i = 0; i < imageerode.cols; i++)
{
for (int j = 0; j < imageerode.rows; j++)
{
if (imageerode.at<uchar>(i, j) > 0)
{
if (i + j < distances[0])
{
distances[0] = i + j;
coordinates[0] = i;
coordinates[1] = j;
}
if (i + imageerode.cols-j < distances[1])
{
distances[1] = i + imageerode.cols-j;
coordinates[2] = i;
coordinates[3] = j;
}
if (imageerode.rows-i + j < distances[2])
{
distances[2] = imageerode.rows - i + j;
coordinates[4] = i;
coordinates[5] = j;
}
if (imageerode.rows-i + imageerode.cols-j < distances[3])
{
distances[3] = imageerode.rows - i + imageerode.cols - j;
coordinates[6] = i;
coordinates[7] = j;
}
}
}
Where I set all of the distances values to imageerode.cols+imageerode.rows since it's the maximum value it can get.
Also: note that I'm using taxicab geometry. I was told it's faster and the results are pretty much the same.
If anyone could help me with warpAffine it would be great. I don't understand where do I put the coordinates I have found.
Thank you
I am not sure how your "trapezoidal board" looks like but if it has a perspective transform like when you capture a rectangle with a camera, then an affine transform is not enough. Use perspective transform. I think Features2D + Homography to find a known object is very close to what you want to do.
On Windows 10, running Visual Studio 2015. Opencv 3.0
Using Opencv to first correlate two images and determine translation between them using matchTemplate. I want to get subpixel estimate so I am going to input an 11X11 window of values from the correlation output and fit a quadratic surface to those points.
void Sector1::ResampSector(cv::Mat In, cv::Mat R, cv::Mat Out, cv::Point Loc)
{
// first get fractional offset
int lsq = 5;
// Ax^2 + B xy + Cy^2 + Dx +Ey + F = R
cv::setBreakOnError(true);
cv::Mat A( 121, 6, CV_32F);
cv::Mat B( 121, 1, CV_32F);
cv::Mat C (6, 1, CV_32F);
int L = 0;
for (int i = Loc.y-lsq; i <= Loc.y+lsq; i++) {
for (int j = Loc.x-lsq; j <= Loc.x+lsq; j++) {
A.at<float>(L, 0) = float(i*i);
A.at<float>(L, 1) = (float)i*j;
A.at<float>(L, 2) = (float)j*j;
A.at<float>(L, 3) = (float)i;
A.at<float>(L, 4) = (float)j;
A.at<float>(L, 5) = 1.f;
B.at<float>(L) = R.at<float>(i, j); // since is 3 band stuff ?
L++;
} // for j
} // for i
bool rc = cv::solve(A, B, C);
the call to cv::solve returns false and there are two cv::Exceptions at same address which is outside of any of the image matrices or other variables. I have looked at the contents of A, B and C using memory window and they all appear correct. A,B,C structures all appear correct. I have tried to step into solve but i do not have the library with symbolic tables.
Any clue where i have gone wrong? suggestions for further tracking the problem?
Lapack complains that the default method will not work. correction is to add the flag=DECOMP_QR as the 4th, optional, arguement to the call to solve()
So, I've been trying to get a 3D cloud point from a sequence of images of an object. I have successfully obtained a decent point cloud with two images. I got that from matching features on both images, finding the fundamental matrix and from that, extracting P' (the camera matrix for the second view). For the first view, I set P = K(I | 0), where K is the matrix for the camera intrinsics. But I haven't been able to extend this approach to several images. My idea was to do this sliding the two image window through the sequence of images(e.g. match image1 with image2, find 3d points, match image2 with image3 and then find the more 3d points, and so on). For the following image pairs, P would be made of a cumulative rotation matrix and a cumulative translation vector (this would allow me to keep bringing the points to the first camera coordinate system). But this is not working at all. I'm using OpenCV. What I wanna know is if this approach makes sense at all.
In the code, P_prev is P and Pl is P'. This is just the part that I think it's relevant.
Mat combinedPointCloud;
Mat P_prev;
P_prev = (Mat_<double>(3,4) << cameraMatrix.at<double>(0,0), cameraMatrix.at<double>(0,1), cameraMatrix.at<double>(0,2), 0,
cameraMatrix.at<double>(1,0), cameraMatrix.at<double>(1,1), cameraMatrix.at<double>(1,2), 0,
cameraMatrix.at<double>(2,0), cameraMatrix.at<double>(2,1), cameraMatrix.at<double>(2,2), 0);
for(int i = 1; i < images.size(); i++) {
Mat points3D;
image1 = images[i-1];
image2 = images[i];
matchTwoImages(image1, image2, imgpts1, imgpts2);
P = findSecondProjectionMatrix(cameraMatrix, imgpts1, imgpts2);
P.col(0).copyTo(R.col(0));
P.col(1).copyTo(R.col(1));
P.col(2).copyTo(R.col(2));
P.col(3).copyTo(t.col(0));
if(i == 1) {
Pl = P;
triangulatePoints(P_prev, Pl, imgpts1, imgpts2, points3D); //points3D is 4xN
//Transforming to euclidean by hand, because couldn't make
// opencv's convertFromHomogeneous work
aux.create(3, points3D.cols, CV_64F);// aux is 3xN
for(int i = 0; i < points3D.cols; i++) {
aux.at<float>(0, i) = points3D.at<float>(0, i)/points3D.at<float>(3, i);
aux.at<float>(1, i) = points3D.at<float>(1, i)/points3D.at<float>(3, i);
aux.at<float>(2, i) = points3D.at<float>(2, i)/points3D.at<float>(3, i);
}
points3D.create(3, points3D.cols, CV_64F);
aux.copyTo(points3D);
}
else {
R_aux = R_prev * R;
t_aux = t_prev + t;
R_aux.col(0).copyTo(Pl.col(0));
R_aux.col(1).copyTo(Pl.col(1));
R_aux.col(2).copyTo(Pl.col(2));
t_aux.col(0).copyTo(Pl.col(3));
triangulatePoints(P_prev, Pl, imgpts1, imgpts2, points3D);
//Transforming to euclidean by hand, because couldn't make
// opencv's convertFromHomogeneous work
aux.create(3, points3D.cols, CV_64F);// aux is 3xN
for(int i = 0; i < points3D.cols; i++) {
aux.at<float>(0, i) = points3D.at<float>(0, i)/points3D.at<float>(3, i);
aux.at<float>(1, i) = points3D.at<float>(1, i)/points3D.at<float>(3, i);
aux.at<float>(2, i) = points3D.at<float>(2, i)/points3D.at<float>(3, i);
}
points3D.create(3, points3D.cols, CV_64F);
aux.copyTo(points3D);
}
Pl.col(0).copyTo(R_prev.col(0));
Pl.col(1).copyTo(R_prev.col(1));
Pl.col(2).copyTo(R_prev.col(2));
Pl.col(3).copyTo(t_prev.col(0));
P_prev = Pl;
if(i==1) {
points3D.copyTo(combinedPointCloud);
} else {
hconcat(combinedPointCloud, points3D, combinedPointCloud);
}
}
show3DCloud(comninedPointCloud);
I am trying to use the vl_slic_segment function of the VLFeat library using an input image stored in an OpenCV Mat. My code is compiling and running, but the output superpixel values do not make sense. Here is my code so far :
Mat bgrUChar = imread("/pathtowherever/image.jpg");
Mat bgrFloat;
bgrUChar.convertTo(bgrFloat, CV_32FC3, 1.0/255);
cv::Mat labFloat;
cvtColor(bgrFloat, labFloat, CV_BGR2Lab);
Mat labels(labFloat.size(), CV_32SC1);
vl_slic_segment(labels.ptr<vl_uint32>(),labFloat.ptr<const float>(),labFloat.cols,labFloat.rows,labFloat.channels(),30,0.1,25);
I have tried not converting it to the Lab colorspace and setting different regionSize/regularization, but the output is always very glitchy. I am able to retrieve the label values correctly, the thing is the every labels is usually scattered on a little non-contiguous area.
I think the problem is the format of my input data is wrong but I can't figure out how to send it properly to the vl_slic_segment function.
Thank you in advance!
EDIT
Thank you David, as you helped me understand, vl_slic_segment wants data ordered as [LLLLLAAAAABBBBB] whereas OpenCV is ordering its data [LABLABLABLABLAB] for the LAB color space.
In the course of my bachelor thesis I have to use VLFeat's SLIC implementation as well. You can find a short example applying VLFeat's SLIC on Lenna.png on GitHub: https://github.com/davidstutz/vlfeat-slic-example.
Maybe, a look at main.cpp will help you figuring out how to convert the images obtained by OpenCV to the right format:
// OpenCV can be used to read images.
#include <opencv2/opencv.hpp>
// The VLFeat header files need to be declared external.
extern "C" {
#include "vl/generic.h"
#include "vl/slic.h"
}
int main() {
// Read the Lenna image. The matrix 'mat' will have 3 8 bit channels
// corresponding to BGR color space.
cv::Mat mat = cv::imread("Lenna.png", CV_LOAD_IMAGE_COLOR);
// Convert image to one-dimensional array.
float* image = new float[mat.rows*mat.cols*mat.channels()];
for (int i = 0; i < mat.rows; ++i) {
for (int j = 0; j < mat.cols; ++j) {
// Assuming three channels ...
image[j + mat.cols*i + mat.cols*mat.rows*0] = mat.at<cv::Vec3b>(i, j)[0];
image[j + mat.cols*i + mat.cols*mat.rows*1] = mat.at<cv::Vec3b>(i, j)[1];
image[j + mat.cols*i + mat.cols*mat.rows*2] = mat.at<cv::Vec3b>(i, j)[2];
}
}
// The algorithm will store the final segmentation in a one-dimensional array.
vl_uint32* segmentation = new vl_uint32[mat.rows*mat.cols];
vl_size height = mat.rows;
vl_size width = mat.cols;
vl_size channels = mat.channels();
// The region size defines the number of superpixels obtained.
// Regularization describes a trade-off between the color term and the
// spatial term.
vl_size region = 30;
float regularization = 1000.;
vl_size minRegion = 10;
vl_slic_segment(segmentation, image, width, height, channels, region, regularization, minRegion);
// Convert segmentation.
int** labels = new int*[mat.rows];
for (int i = 0; i < mat.rows; ++i) {
labels[i] = new int[mat.cols];
for (int j = 0; j < mat.cols; ++j) {
labels[i][j] = (int) segmentation[j + mat.cols*i];
}
}
// Compute a contour image: this actually colors every border pixel
// red such that we get relatively thick contours.
int label = 0;
int labelTop = -1;
int labelBottom = -1;
int labelLeft = -1;
int labelRight = -1;
for (int i = 0; i < mat.rows; i++) {
for (int j = 0; j < mat.cols; j++) {
label = labels[i][j];
labelTop = label;
if (i > 0) {
labelTop = labels[i - 1][j];
}
labelBottom = label;
if (i < mat.rows - 1) {
labelBottom = labels[i + 1][j];
}
labelLeft = label;
if (j > 0) {
labelLeft = labels[i][j - 1];
}
labelRight = label;
if (j < mat.cols - 1) {
labelRight = labels[i][j + 1];
}
if (label != labelTop || label != labelBottom || label!= labelLeft || label != labelRight) {
mat.at<cv::Vec3b>(i, j)[0] = 0;
mat.at<cv::Vec3b>(i, j)[1] = 0;
mat.at<cv::Vec3b>(i, j)[2] = 255;
}
}
}
// Save the contour image.
cv::imwrite("Lenna_contours.png", mat);
return 0;
}
In addition, have a look at README.md within the GitHub repository. The following figures show some example outputs of setting the regularization to 1 (100,1000) and setting the region size to 30 (20,40).
Figure 1: Superpixel segmentation with region size set to 30 and regularization set to 1.
Figure 2: Superpixel segmentation with region size set to 30 and regularization set to 100.
Figure 3: Superpixel segmentation with region size set to 30 and regularization set to 1000.
Figure 4: Superpixel segmentation with region size set to 20 and regularization set to 1000.
Figure 5: Superpixel segmentation with region size set to 20 and regularization set to 1000.
How we can make vignette filter in opencv? Do we need to implement any algorithm for it or only to play with the values of BGR ? How we can make this type of filters. I saw its implementation here but i didn't understand it clearly . Anyone with complete algorithms guidance and implementation guidance is highly appriciated.
After Abid rehman K answer I tried this in c++
int main()
{
Mat v;
Mat img = imread ("D:\\2.jpg");
img.convertTo(v, CV_32F);
Mat a,b,c,d,e;
c.create(img.rows,img.cols,CV_32F);
d.create(img.rows,img.cols,CV_32F);
e.create(img.rows,img.cols,CV_32F);
a = getGaussianKernel(img.cols,300,CV_32F);
b = getGaussianKernel(img.rows,300,CV_32F);
c = b*a.t();
double minVal;
double maxVal;
cv::minMaxLoc(c, &minVal, &maxVal);
d = c/maxVal;
e = v*d ; // This line causing error
imshow ("venyiet" , e);
cvWaitKey();
}
d is displaying right but e=v*d line is causing runtime error of
OpenCV Error: Assertion failed (type == B.type() && (type == CV_32FC1 || type ==
CV_64FC1 || type == CV_32FC2 || type == CV_64FC2)) in unknown function, file ..
\..\..\src\opencv\modules\core\src\matmul.cpp, line 711
First of all, Abid Rahman K describes the easiest way to go about this filter. You should seriously study his answer with time and attention. Wikipedia's take on Vignetting is also quite clarifying for those that had never heard about this filter.
Browny's implementation of this filter is considerably more complex. However, I ported his code to the C++ API and simplified it so you can follow the instructions yourself.
#include <math.h>
#include <vector>
#include <cv.hpp>
#include <highgui/highgui.hpp>
// Helper function to calculate the distance between 2 points.
double dist(CvPoint a, CvPoint b)
{
return sqrt(pow((double) (a.x - b.x), 2) + pow((double) (a.y - b.y), 2));
}
// Helper function that computes the longest distance from the edge to the center point.
double getMaxDisFromCorners(const cv::Size& imgSize, const cv::Point& center)
{
// given a rect and a line
// get which corner of rect is farthest from the line
std::vector<cv::Point> corners(4);
corners[0] = cv::Point(0, 0);
corners[1] = cv::Point(imgSize.width, 0);
corners[2] = cv::Point(0, imgSize.height);
corners[3] = cv::Point(imgSize.width, imgSize.height);
double maxDis = 0;
for (int i = 0; i < 4; ++i)
{
double dis = dist(corners[i], center);
if (maxDis < dis)
maxDis = dis;
}
return maxDis;
}
// Helper function that creates a gradient image.
// firstPt, radius and power, are variables that control the artistic effect of the filter.
void generateGradient(cv::Mat& mask)
{
cv::Point firstPt = cv::Point(mask.size().width/2, mask.size().height/2);
double radius = 1.0;
double power = 0.8;
double maxImageRad = radius * getMaxDisFromCorners(mask.size(), firstPt);
mask.setTo(cv::Scalar(1));
for (int i = 0; i < mask.rows; i++)
{
for (int j = 0; j < mask.cols; j++)
{
double temp = dist(firstPt, cv::Point(j, i)) / maxImageRad;
temp = temp * power;
double temp_s = pow(cos(temp), 4);
mask.at<double>(i, j) = temp_s;
}
}
}
// This is where the fun starts!
int main()
{
cv::Mat img = cv::imread("stack-exchange-chefs.jpg");
if (img.empty())
{
std::cout << "!!! Failed imread\n";
return -1;
}
/*
cv::namedWindow("Original", cv::WINDOW_NORMAL);
cv::resizeWindow("Original", img.size().width/2, img.size().height/2);
cv::imshow("Original", img);
*/
What img looks like:
cv::Mat maskImg(img.size(), CV_64F);
generateGradient(maskImg);
/*
cv::Mat gradient;
cv::normalize(maskImg, gradient, 0, 255, CV_MINMAX);
cv::imwrite("gradient.png", gradient);
*/
What maskImg looks like:
cv::Mat labImg(img.size(), CV_8UC3);
cv::cvtColor(img, labImg, CV_BGR2Lab);
for (int row = 0; row < labImg.size().height; row++)
{
for (int col = 0; col < labImg.size().width; col++)
{
cv::Vec3b value = labImg.at<cv::Vec3b>(row, col);
value.val[0] *= maskImg.at<double>(row, col);
labImg.at<cv::Vec3b>(row, col) = value;
}
}
cv::Mat output;
cv::cvtColor(labImg, output, CV_Lab2BGR);
//cv::imwrite("vignette.png", output);
cv::namedWindow("Vignette", cv::WINDOW_NORMAL);
cv::resizeWindow("Vignette", output.size().width/2, output.size().height/2);
cv::imshow("Vignette", output);
cv::waitKey();
return 0;
}
What output looks like:
As stated in the code above, by changing the values of firstPt, radius and power you can achieve stronger/weaker artistic effects.
Good luck!
You can do a simple implementation using Gaussian Kernels available in OpenCV.
Load the image, Get its number of rows and columns
Create two Gaussian Kernels of size rows and columns, say A,B. Its variance depends upon your needs.
C = transpose(A)*B, ie multiply a column vector with a row vector such that result array should be same size as that of the image.
D = C/C.max()
E = img*D
See the implementation below (for a grayscale image):
import cv2
import numpy as np
from matplotlib import pyplot as plt
img = cv2.imread('temp.jpg',0)
row,cols = img.shape
a = cv2.getGaussianKernel(cols,300)
b = cv2.getGaussianKernel(rows,300)
c = b*a.T
d = c/c.max()
e = img*d
cv2.imwrite('vig2.png',e)
Below is my result:
Similarly for Color image:
NOTE : Of course, it is centered. You will need to make additional modifications to move focus to other places.
Similar one close to Abid's Answer. But the code is for the colored image
import cv2
import numpy as np
from matplotlib import pyplot as plt
img = cv2.imread('turtle.jpg',1)
rows,cols = img.shape[:2]
zeros = np.copy(img)
zeros[:,:,:] = 0
a = cv2.getGaussianKernel(cols,900)
b = cv2.getGaussianKernel(rows,900)
c = b*a.T
d = c/c.max()
zeros[:,:,0] = img[:,:,0]*d
zeros[:,:,1] = img[:,:,1]*d
zeros[:,:,2] = img[:,:,2]*d
cv2.imwrite('vig2.png',zeros)
Original Image (Taken from Pexels under CC0 Licence)
After Applying Vignette with a sigma of 900 (i.e `cv2.getGaussianKernel(cols,900))
After Applying Vignette with a sigma of 300 (i.e `cv2.getGaussianKernel(cols,300))
Additionally you can focus the vignette effect to the cordinates of your wish by simply shifting the mean of the gaussian to your focus point as follows.
import cv2
import numpy as np
img = cv2.imread('turtle.jpg',1)
fx,fy = 1465,180 # Add your Focus cordinates here
fx,fy = 145,1000 # Add your Focus cordinates here
sigma = 300 # Standard Deviation of the Gaussian
rows,cols = img.shape[:2]
fxn = fx - cols//2 # Normalised temperory vars
fyn = fy - rows//2
zeros = np.copy(img)
zeros[:,:,:] = 0
a = cv2.getGaussianKernel(2*cols ,sigma)[cols-fx:2*cols-fx]
b = cv2.getGaussianKernel(2*rows ,sigma)[rows-fy:2*rows-fy]
c = b*a.T
d = c/c.max()
zeros[:,:,0] = img[:,:,0]*d
zeros[:,:,1] = img[:,:,1]*d
zeros[:,:,2] = img[:,:,2]*d
zeros = add_alpha(zeros)
cv2.imwrite('vig4.png',zeros)
The size of the turtle image is 1980x1200 (WxH). The following is an example focussing at the cordinate 1465,180 (i.e fx,fy = 1465,180) (Note that I have reduced the variance to exemplify the change in focus)
The following is an example focussing at the cordinate 145,1000 (i.e fx,fy = 145,1000)
Here is my c++ implementation of Vignette filter on Colored Image using opencv. It is faster than the accepted answer.
#include "opencv2/core/core.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>
using namespace cv;
using namespace std;
double fastCos(double x){
x += 1.57079632;
if (x > 3.14159265)
x -= 6.28318531;
if (x < 0)
return 1.27323954 * x + 0.405284735 * x * x;
else
return 1.27323954 * x - 0.405284735 * x * x;
}
double dist(double ax, double ay,double bx, double by){
return sqrt((ax - bx)*(ax - bx) + (ay - by)*(ay - by));
}
int main(int argv, char** argc){
Mat src = cv::imread("filename_of_your_image.jpg");
Mat dst = Mat::zeros(src.size(), src.type());
double radius; //value greater than 0,
//greater the value lesser the visible vignette
//for a medium vignette use a value in range(0.5-1.5)
cin << radius;
double cx = (double)src.cols/2, cy = (double)src.rows/2;
double maxDis = radius * dist(0,0,cx,cy);
double temp;
for (int y = 0; y < src.rows; y++) {
for (int x = 0; x < src.cols; x++) {
temp = fastCos(dist(cx, cy, x, y) / maxDis);
temp *= temp;
dst.at<Vec3b>(y, x)[0] =
saturate_cast<uchar>((src.at<Vec3b>(y, x)[0]) * temp);
dst.at<Vec3b>(y, x)[1] =
saturate_cast<uchar>((src.at<Vec3b>(y, x)[1]) * temp );
dst.at<Vec3b>(y, x)[2] =
saturate_cast<uchar>((src.at<Vec3b>(y, x)[2]) * temp);
}
}
imshow ("Vignetted Image", dst);
waitKey(0);
}
Here is a C++ implementation of Vignetting for Grayscale Image
#include "opencv2/opencv.hpp"
#include "opencv2/core/core.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>
using namespace cv;
using namespace std;
int main(int argv, char** argc)
{
Mat test = imread("test.jpg", IMREAD_GRAYSCALE);
Mat kernel_X = getGaussianKernel(test.cols, 100);
Mat kernel_Y = getGaussianKernel(test.rows, 100);
Mat kernel_X_transpose;
transpose(kernel_X, kernel_X_transpose);
Mat kernel = kernel_Y * kernel_X_transpose;
Mat mask_v, proc_img;
normalize(kernel, mask_v, 0, 1, NORM_MINMAX);
test.convertTo(proc_img, CV_64F);
multiply(mask_v, proc_img, proc_img);
convertScaleAbs(proc_img, proc_img);
imshow ("Vignette", proc_img);
waitKey(0);
return 0;
}