I just want to get my concept clear that - is accessing all the matrix elements of cv::Mat means I am actually accessing all the pixel values of an image (grayscale - 1 channel and for colour - 3 channels)? Like suppose my code for printing the values of matrix of gray scale that is 1 channel image loaded and type CV_32FC1, is as shown below, then does that mean that I am accessing only the members of the cv::mat or I am accessing the pixel values of the image (with 1 channel - grayscale and type CV_32FC1) also?
cv::Mat img = cv::imread("lenna.png");
for(int j=0;j<img.rows;j++)
{
for (int i=0;i<img.cols;i++)
{
std::cout << "Matrix of image loaded is: " << img.at<uchar>(i,j);
}
}
I am quite new to image processing with OpenCV and want to clear my idea. If I am wrong, then how can I access each pixel value of an image?
You are accessing the elements of the matrix and you are accessing the image itself also. In your code, after you do this:
cv::Mat img = cv::imread("lenna.png");
the matrix img represents the image lenna.png. ( if it is successfully opened )
Why don't you experiment yourself by changing some of the pixel values:
cv::Mat img = cv::imread("lenna.png");
//Before changing
cv::imshow("Before",img);
//change some pixel value
for(int j=0;j<img.rows;j++)
{
for (int i=0;i<img.cols;i++)
{
if( i== j)
img.at<uchar>(j,i) = 255; //white
}
}
//After changing
cv::imshow("After",img);
Note: this only changes the image values in volatile memory, that is where the mat img is currently loaded. Modifying the values of the mat img, not going to change value in your actual image "lenna.png",which is stored in your disk, (unless you do imwrite)
But in case of 1-channel grayscale image it is CV_8UC1 not CV_32FC1
In order to get the pixel value of the grayscale image (an integer between 0 and 255), the answer also needs to be typecasted.
int pixelValue = (int)img.at<uchar>(i,j);
Related
I'm attempting to read the pixels in an image and convert them into another format by iterating through the pixels.
After my conversion I only seem to be getting 1/3rd of the image and I'm certain it's because of the way I'm accessing the pixels using the .at() function.
I'm reading in the following image:
Mat image = imread("cameraman.jpg");
I then iterate through the images rows and columns:
for (int i = 0; i < image.rows; i++)
{
for (int j = 0; j < image.cols; j++)
{
placeGrayValue((double)image.at<uchar>(i, j));
}
}
Note: placedGrayValue() is just a placeholder here so that I can share only the code that is relevant.
The resulting image is only the first third of the image:
You're loading your image with cv::imread, which with default value (cv::IMREAD_COLOR) will load it as a 3 channel image of type CV_8UC3 (aka cv::Mat3b).
If your original image is grayscale, when loading as a 3 channel image you have the same intensity value for each channel.
So when you scan the image you should access pixels with .at<cv::Vec3b>(...).
If you want to copy only the first channel to the placeGrayValue matrix you should do it as:
placeGrayValue((double)image.at<cv::Vec3b>(i, j)[0]);
^^^^^^^^^ ^^^
3 channel first channel
If your input is not a grayscale image, then you shouldn't just copy the first channel, since the grayscale value is a linear combination of the three R,G,B channels.
So it's better to first convert to grayscale, and then copy:
cv::Mat grayscale;
cv::cvtColor(image, grayscale, cv::COLOR_BGR2GRAY);
...
placeGrayValue((double)grayscale.at<uchar>(i, j));
^^^^^
1 channel
Or you can load the image already as a grayscale image:
Mat grayscale = imread("cameraman.jpg", cv::IMREAD_GRAYSCALE);
At the end, you want to have placeGrayValue with the grayscale values as double.
You should not scan the image for this kind of easy operations. You can just:
cv::Mat placeGrayValue;
grayscale.convertTo(placeGrayValue, CV_64F);
^^^^^^
to double type
Summing up:
cv::Mat grayscale = cv::imread("cameraman.jpg", cv::IMREAD_GRAYSCALE);
cv::Mat placeGrayValue;
grayscale.convertTo(placeGrayValue, CV_64F);
I'll post what ended up working as an answer, though it makes little sense to me and I'd still like to understand why.
The image has 3 channels. When iterate through an image using a for loop and extract pixel data with (double)image.at<uchar>(i, j) it goes through each channel as if they were individual pixels.
The solution (at least with this grayscale image) is to iterate and multiply by 3. In other words, (double)image.at<uchar>(i*3, j) ended up giving me the full image.
I'm wondering if there is a way to convert a grayscale image to one color image? Like if I have an image in grayscale and I want to convert it to shades of blue instead? Is that possible in OpenCV?
Thank you very much!
According to the opencv community answer, you should of creating a 3-channel image by yourself.
Mat empty_image = Mat::zeros(src.rows, src.cols, CV_8UC1);//initial empty layer
Mat result_blue(src.rows, src.cols, CV_8UC3); //initial blue result
Mat in1[] = { ***GRAYINPUT***, empty_image, empty_image }; //construct 3 layer Matrix
int from_to1[] = { 0,0, 1,1, 2,2 };
mixChannels( in1, 3, &result_blue, 1, from_to1, 3 ); //combine image
After that, you can get your blue channel image. Normally, the blue channel of an colour image in opencv is the first layer (cuz they put 3 channels as BGR).
By the way, if you wanna use the copy each pixel method, you can initial an empty image
Mat result_blue(src.rows, src.cols, CV_8UC3); //blue result
for (int i =0; i<src.rows; i++)
for (int j=0; j<src.cols; j++){
Vec3b temp = result_blue.at<Vec3b>(Point(i,j));//get each pixel
temp[0] = gray.at<uchar>(i,j); //give value to blue channel
result_blue.at<Vec3b>(Point(x,y)) = temp; //copy back to img
}
However, it will take longer as there are two loops!
A gray scale image is usually just one dimensional. Usually what I do if I want to pass in a gray scale image into a function that accepts RGB (3-dimensional), I replicate the the matrix 3 times to create a MxNx3 matrix. If you wish to only use the Blue channel, just concat MxN of zeros in the 1st dimension and the 2nd dimension while putting the original gray scale values in the 3rd dimension.
To accomplish this you would essentially just need to iterate over all the pixels in the grayscale image and map the values over to a color range. Here is pseudo-code:
grayImage:imageObject;
tintedImage:imageObject;
//Define your color tint here
myColorR:Int = 115;
myColorG:Int = 186;
myColorB:Int = 241;
for(int i=0; i<imagePixelArray.length; i++){
float pixelBrightness = grayImage.getPixelValueAt(i)/255;
int pixelColorR = myColorR*pixelBrightness;
int pixelColorG = myColorG*pixelBrightness;
int pixelColorB = myColorB*pixelBrightness;
tintedImage.setPixelColorAt(i, pixelColorR, pixelColorG, pixelColorB);
}
Hope that helps!
I'm using an STMap to map a .jpg image using remap().
I loaded my STMap, split the channels and converted each channel matrix to CV_32FC1.
I checked them and it worked - each matrix displays correctly and all of its values are between 0.0 and 1.0.
However, when i try to use the remap() function:
Mat dst;
remap(image4, dst,map_x,map_y,INTER_LINEAR,BORDER_CONSTANT,Scalar(0,0,0));
imshow( "Result", dst );
It just displays a black image.
image4 = my .jpg image
map_x = grayscale CV_32FC1 (red channel
of the original STMap)
map_y = grayscale CV_32FC1 (green channel
of the original STMap)
What could be the problem?
Thanks!
Black image when using cv::remap is due to using offsets instead of absolute locations in the passed map(s).
Optical flow algorithms usually export motion vectors, not absolute positions, whereas cv::remap expects the absolute coordinate (subpixel) to sample from.
To convert between the two, starting with a CV_32FC2 flow matrix we can do something like this:
// Convert from offsets to absolute locations.
Mat mapx(flow.size(), CV_32FC1);
Mat mapy(flow.size(), CV_32FC1);
for (int row = 0; row < flow.rows; row++)
{
for (int col = 0; col < flow.cols; col++)
{
Point2f f = flow.at<Point2f>(row, col);
mapx.at<float>(row, col) = col + f.x;
mapy.at<float>(row, col) = row + f.y;
}
}
Then mapx and mapy can be used in remap.
I create a Bird-View-Image with the warpPerspective()-function like this:
warpPerspective(frame, result, H, result.size(), CV_WARP_INVERSE_MAP, BORDER_TRANSPARENT);
The result looks very good and also the border is transparent:
Bird-View-Image
Now I want to put this image on top of another image "out". I try doing this with the function warpAffine like this:
warpAffine(result, out, M, out.size(), CV_INTER_LINEAR, BORDER_TRANSPARENT);
I also converted "out" to a four channel image with alpha channel according to a question which was already asked on stackoverflow:
Convert Image
This is the code: cvtColor(out, out, CV_BGR2BGRA);
I expected to see the chessboard but not the gray background. But in fact, my result looks like this:
Result Image
What am I doing wrong? Do I forget something to do? Is there another way to solve my problem? Any help is appreciated :)
Thanks!
Best regards
DamBedEi
I hope there is a better way, but here it is something you could do:
Do warpaffine normally (without the transparency thing)
Find the contour that encloses the image warped
Use this contour for creating a mask (white values inside the image warped, blacks in the borders)
Use this mask for copy the image warped into the other image
Sample code:
// load images
cv::Mat image2 = cv::imread("lena.png");
cv::Mat image = cv::imread("IKnowOpencv.jpg");
cv::resize(image, image, image2.size());
// perform warp perspective
std::vector<cv::Point2f> prev;
prev.push_back(cv::Point2f(-30,-60));
prev.push_back(cv::Point2f(image.cols+50,-50));
prev.push_back(cv::Point2f(image.cols+100,image.rows+50));
prev.push_back(cv::Point2f(-50,image.rows+50 ));
std::vector<cv::Point2f> post;
post.push_back(cv::Point2f(0,0));
post.push_back(cv::Point2f(image.cols-1,0));
post.push_back(cv::Point2f(image.cols-1,image.rows-1));
post.push_back(cv::Point2f(0,image.rows-1));
cv::Mat homography = cv::findHomography(prev, post);
cv::Mat imageWarped;
cv::warpPerspective(image, imageWarped, homography, image.size());
// find external contour and create mask
std::vector<std::vector<cv::Point> > contours;
cv::Mat imageWarpedCloned = imageWarped.clone(); // clone the image because findContours will modify it
cv::cvtColor(imageWarpedCloned, imageWarpedCloned, CV_BGR2GRAY); //only if the image is BGR
cv::findContours (imageWarpedCloned, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);
// create mask
cv::Mat mask = cv::Mat::zeros(image.size(), CV_8U);
cv::drawContours(mask, contours, 0, cv::Scalar(255), -1);
// copy warped image into image2 using the mask
cv::erode(mask, mask, cv::Mat()); // for avoid artefacts
imageWarped.copyTo(image2, mask); // copy the image using the mask
//show images
cv::imshow("imageWarpedCloned", imageWarpedCloned);
cv::imshow("warped", imageWarped);
cv::imshow("image2", image2);
cv::waitKey();
One of the easiest ways to approach this (not necessarily the most efficient) is to warp the image twice, but set the OpenCV constant boundary value to different values each time (i.e. zero the first time and 255 the second time). These constant values should be chosen towards the minimum and maximum values in the image.
Then it is easy to find a binary mask where the two warp values are close to equal.
More importantly, you can also create a transparency effect through simple algebra like the following:
new_image = np.float32((warp_const_255 - warp_const_0) *
preferred_bkg_img) / 255.0 + np.float32(warp_const_0)
The main reason I prefer this method is that openCV seems to interpolate smoothly down (or up) to the constant value at the image edges. A fully binary mask will pick up these dark or light fringe areas as artifacts. The above method acts more like true transparency and blends properly with the preferred background.
Here's a small test program that warps with transparent "border", then copies the warped image to a solid background.
int main()
{
cv::Mat input = cv::imread("../inputData/Lenna.png");
cv::Mat transparentInput, transparentWarped;
cv::cvtColor(input, transparentInput, CV_BGR2BGRA);
//transparentInput = input.clone();
// create sample transformation mat
cv::Mat M = cv::Mat::eye(2,3, CV_64FC1);
// as a sample, just scale down and translate a little:
M.at<double>(0,0) = 0.3;
M.at<double>(0,2) = 100;
M.at<double>(1,1) = 0.3;
M.at<double>(1,2) = 100;
// warp to same size with transparent border:
cv::warpAffine(transparentInput, transparentWarped, M, transparentInput.size(), CV_INTER_LINEAR, cv::BORDER_TRANSPARENT);
// NOW: merge image with background, here I use the original image as background:
cv::Mat background = input;
// create output buffer with same size as input
cv::Mat outputImage = input.clone();
for(int j=0; j<transparentWarped.rows; ++j)
for(int i=0; i<transparentWarped.cols; ++i)
{
cv::Scalar pixWarped = transparentWarped.at<cv::Vec4b>(j,i);
cv::Scalar pixBackground = background.at<cv::Vec3b>(j,i);
float transparency = pixWarped[3] / 255.0f; // pixel value: 0 (0.0f) = fully transparent, 255 (1.0f) = fully solid
outputImage.at<cv::Vec3b>(j,i)[0] = transparency * pixWarped[0] + (1.0f-transparency)*pixBackground[0];
outputImage.at<cv::Vec3b>(j,i)[1] = transparency * pixWarped[1] + (1.0f-transparency)*pixBackground[1];
outputImage.at<cv::Vec3b>(j,i)[2] = transparency * pixWarped[2] + (1.0f-transparency)*pixBackground[2];
}
cv::imshow("warped", outputImage);
cv::imshow("input", input);
cv::imwrite("../outputData/TransparentWarped.png", outputImage);
cv::waitKey(0);
return 0;
}
I use this as input:
and get this output:
which looks like ALPHA channel isn't set to ZERO by warpAffine but to something like 205...
But in general this is the way I would do it (unoptimized)
I am using Ubuntu 12.04 and OpenCV 2
I have written the following code :
IplImage* img =0;
img = cvLoadImage("nature.jpg");
if(img != 0)
{
Mat Img_mat(img);
std::vector<Mat> RGB;
split(Img_mat, RGB);
int data = (RGB[0]).at<int>(i,j)); /*Where i, j are inside the bounds of the matrix size .. i have checked this*/
}
The problem is I am getting negative values and very large values in the data variable. I think I have made some mistake somewhere. Can you please point it out.
I have been reading the documentation (I have not finished it fully.. it is quite large. ) But from what I have read, this should work. But it isnt. What is going wrong here?
Img_mat is a 3 channeled image. Each channel consists of pixel values uchar in data type.
So with split(Img_mat, BGR) the Img_mat is split into 3 planes of blue, green and red which are collectively stored in a vector BGR. So BGR[0] is the first (blue) plane with uchar data type pixels...hence it will be
int dataB = (int)BGR[0].at<uchar>(i,j);
int dataG = (int)BGR[1].at<uchar>(i,j);
so on...
You have to specify the correct type for cv::Mat::at(i,j). You are accessing the pixel as int, while it should be a vector of uchar. Your code should look something like this:
IplImage* img = 0;
img = cvLoadImage("nature.jpg");
if(img != 0)
{
Mat Img_mat(img);
std::vector<Mat> BGR;
split(Img_mat, BGR);
Vec3b data = BGR[0].at<Vec3b>(i,j);
// data[0] -> blue
// data[1] -> green
// data[2] -> red
}
Why are you loading an IplImage first? You are mixing the C and C++ interfaces.
Loading a cv::Mat with imread directly would be more straight-forward.
This way you can also specify the type and use the according type in your at call.