I'm working in opencv 2.4.0 and C++
I'm trying to do an exercise that says I should load an RGB image, convert it to gray scale and save the new image. The next step is to make the grayscale image into a binary image and store that image. This much I have working.
My problem is in counting the amount of black pixels in the binary image.
So far I've searched the web and looked in the book. The method that I've found that seems the most useful is.
int TotalNumberOfPixels = width * height;
int ZeroPixels = TotalNumberOfPixels - cvCountNonZero(cv_image);
But I don't know how to store these values and use them in cvCountNonZero(). When I pass the the image I want counted from to this function I get an error.
int main()
{
Mat rgbImage, grayImage, resizedImage, bwImage, result;
rgbImage = imread("C:/MeBGR.jpg");
cvtColor(rgbImage, grayImage, CV_RGB2GRAY);
resize(grayImage, resizedImage, Size(grayImage.cols/3,grayImage.rows/4),
0, 0, INTER_LINEAR);
imwrite("C:/Jakob/Gray_Image.jpg", resizedImage);
bwImage = imread("C:/Jakob/Gray_Image.jpg");
threshold(bwImage, bwImage, 120, 255, CV_THRESH_BINARY);
imwrite("C:/Jakob/Binary_Image.jpg", bwImage);
imshow("Original", rgbImage);
imshow("Resized", resizedImage);
imshow("Resized Binary", bwImage);
waitKey(0);
return 0;
}
So far this code is very basic but it does what it's supposed to for now. Some adjustments will be made later to clean it up :)
You can use countNonZero to count the number of pixels that are not black (>0) in an image. If you want to count the number of black (==0) pixels, you need to subtract the number of pixels that are not black from the number of pixels in the image (the image width * height).
This code should work:
int TotalNumberOfPixels = bwImage.rows * bwImage.cols;
int ZeroPixels = TotalNumberOfPixels - countNonZero(bwImage);
cout<<"The number of pixels that are zero is "<<ZeroPixels<<endl;
Related
Firstly I integrate OpenCV framework to XCode and All the OpenCV code is on ObjectiveC and I am using in Swift Using bridging header. I am new to OpenCV Framework and trying to achieve count of vertical lines from the image.
Here is my code:
First I am converting the image to GrayScale
+ (UIImage *)convertToGrayscale:(UIImage *)image {
cv::Mat mat;
UIImageToMat(image, mat);
cv::Mat gray;
cv::cvtColor(mat, gray, CV_RGB2GRAY);
UIImage *grayscale = MatToUIImage(gray);
return grayscale;
}
Then, I am detecting edges so I can find the line of gray color
+ (UIImage *)detectEdgesInRGBImage:(UIImage *)image {
cv::Mat mat;
UIImageToMat(image, mat);
//Prepare the image for findContours
cv::threshold(mat, mat, 128, 255, CV_THRESH_BINARY);
//Find the contours. Use the contourOutput Mat so the original image doesn't get overwritten
std::vector<std::vector<cv::Point> > contours;
cv::Mat contourOutput = mat.clone();
cv::findContours( contourOutput, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE );
NSLog(#"Count =>%lu", contours.size());
//For Blue
/*cv::GaussianBlur(mat, gray, cv::Size(11, 11), 0); */
UIImage *grayscale = MatToUIImage(mat);
return grayscale;
}
This both Function is written on Objective C
Here, I am calling both function Swift
override func viewDidLoad() {
super.viewDidLoad()
let img = UIImage(named: "imagenamed")
let img1 = Wrapper.convert(toGrayscale: img)
self.capturedImageView.image = Wrapper.detectEdges(inRGBImage: img1)
}
I was doing this for some days and finding some useful documents(Reference Link)
OpenCV - how to count objects in photo?
How to count number of lines (Hough Trasnform) in OpenCV
OPENCV Documents
https://docs.opencv.org/2.4/modules/imgproc/doc/structural_analysis_and_shape_descriptors.html?#findcontours
Basically, I understand the first we need to convert this image to black and white, and then using cvtColor, threshold and findContours we can find the colors or lines.
I am attaching the image that vertical Lines I want to get.
Original Image
Output Image that I am getting
I got number of lines count =>10
I am not able to get accurate count here.
Please guide me on this. Thank You!
Since you want to detect the number of the vertical lines, there is a very simple approach I can suggest for you. You already got a clear output and I used this output in my code. Here are the steps before the code:
Preprocess the input image to get the lines clearly
Check each row and check until get a pixel whose value is higher than 100(threshold value I chose)
Then increase the line counter for that row
Continue on that line until get a pixel whose value is lower than 100
Restart from step 3 and finish the image for each row
At the end, check the most repeated element in the array which you assigned line numbers for each row. This number will be the number of vertical lines.
Note: If the steps are difficult to understand, think like this way:
" I am checking the first row, I found a pixel which is higher than
100, now this is a line edge starting, increase the counter for this
row. Search on this row until get a pixel smaller than 100, and then
research a pixel bigger than 100. when row is finished, assign the
line number for this row to a big array. Do this for all image. At the
end, since some lines looks like two lines at the top and also some
noises can occur, you should take the most repeated element in the big
array as the number of lines."
Here is the code part in C++:
#include <vector>
#include <iostream>
#include <opencv2/opencv.hpp>
#include <opencv2/highgui/highgui.hpp>
int main()
{
cv::Mat img = cv::imread("/ur/img/dir/img.jpg",cv::IMREAD_GRAYSCALE);
std::vector<int> numberOfVerticalLinesForEachRow;
cv::Rect r(0,0,img.cols-10,200);
img = img(r);
bool blackCheck = 1;
for(int i=0; i<img.rows; i++)
{
int numberOfLines = 0;
for(int j=0; j<img.cols; j++)
{
if((int)img.at<uchar>(cv::Point(j,i))>100 && blackCheck)
{
numberOfLines++;
blackCheck = 0;
}
if((int)img.at<uchar>(cv::Point(j,i))<100)
blackCheck = 1;
}
numberOfVerticalLinesForEachRow.push_back(numberOfLines);
}
// In this part you need a simple algorithm to check the most repeated element
for(int k:numberOfVerticalLinesForEachRow)
std::cout<<k<<std::endl;
cv::namedWindow("WinWin",0);
cv::imshow("WinWin",img);
cv::waitKey(0);
}
Here's another possible approach. It relies mainly on the cv::thinning function from the extended image processing module to reduce the lines at a width of 1 pixel. We can crop a ROI from this image and count the number of transitions from 255 (white) to 0 (black). These are the steps:
Threshold the image using Otsu's method
Apply some morphology to clean up the binary image
Get the skeleton of the image
Crop a ROI from the center of the image
Count the number of jumps from 255 to 0
This is the code, be sure to include the extended image processing module (ximgproc) and also link it before compiling it:
#include <iostream>
#include <opencv2/opencv.hpp>
#include <opencv2/ximgproc.hpp> // The extended image processing module
// Read Image:
std::string imagePath = "D://opencvImages//";
cv::Mat inputImage = cv::imread( imagePath+"IN2Xh.png" );
// Convert BGR to Grayscale:
cv::cvtColor( inputImage, inputImage, cv::COLOR_BGR2GRAY );
// Get binary image via Otsu:
cv::threshold( inputImage, inputImage, 0, 255, cv::THRESH_OTSU );
The above snippet produces the following image:
Note that there's a little bit of noise due to the thresholding, let's try to remove those isolated blobs of white pixels by applying some morphology. Maybe an opening, which is an erosion followed by dilation. The structuring elements and iterations, though, are not the same, and these where found by experimentation. I wanted to remove the majority of the isolated blobs without modifying too much the original image:
// Apply Morphology. Erosion + Dilation:
// Set rectangular structuring element of size 3 x 3:
cv::Mat SE = cv::getStructuringElement( cv::MORPH_RECT, cv::Size(3, 3) );
// Set the iterations:
int morphoIterations = 1;
cv::morphologyEx( inputImage, inputImage, cv::MORPH_ERODE, SE, cv::Point(-1,-1), morphoIterations);
// Set rectangular structuring element of size 5 x 5:
SE = cv::getStructuringElement( cv::MORPH_RECT, cv::Size(5, 5) );
// Set the iterations:
morphoIterations = 2;
cv::morphologyEx( inputImage, inputImage, cv::MORPH_DILATE, SE, cv::Point(-1,-1), morphoIterations);
This combination of structuring elements and iterations yield the following filtered image:
Its looking alright. Now comes the main idea of the algorithm. If we compute the skeleton of this image, we would "normalize" all the lines to a width of 1 pixel, which is very handy, because we could reduce the image to a 1 x 1 (row) matrix and count the number of jumps. Since the lines are "normalized" we could get rid of possible overlaps between lines. Now, skeletonized images sometimes produce artifacts near the borders of the image. These artifacts resemble thickened anchors at the first and last row of the image. To prevent these artifacts we can extend borders prior to computing the skeleton:
// Extend borders to avoid skeleton artifacts, extend 5 pixels in all directions:
cv::copyMakeBorder( inputImage, inputImage, 5, 5, 5, 5, cv::BORDER_CONSTANT, 0 );
// Get the skeleton:
cv::Mat imageSkelton;
cv::ximgproc::thinning( inputImage, imageSkelton );
This is the skeleton obtained:
Nice. Before we count jumps, though, we must observe that the lines are skewed. If we reduce this image directly to a one row, some overlapping could indeed happen between to lines that are too skewed. To prevent this, I crop a middle section of the skeleton image and count transitions there. Let's crop the image:
// Crop middle ROI:
cv::Rect linesRoi;
linesRoi.x = 0;
linesRoi.y = 0.5 * imageSkelton.rows;
linesRoi.width = imageSkelton.cols;
linesRoi.height = 1;
cv::Mat imageROI = imageSkelton( linesRoi );
This would be the new ROI, which is just the middle row of the skeleton image:
Let me prepare a BGR copy of this just to draw some results:
// BGR version of the Grayscale ROI:
cv::Mat colorROI;
cv::cvtColor( imageROI, colorROI, cv::COLOR_GRAY2BGR );
Ok, let's loop through the image and count the transitions between 255 and 0. That happens when we look at the value of the current pixel and compare it with the value obtained an iteration earlier. The current pixel must be 0 and the past pixel 255. There's more than a way to loop through a cv::Mat in C++. I prefer to use cv::MatIterator_s and pointer arithmetic:
// Set the loop variables:
cv::MatIterator_<cv::Vec3b> it, end;
uchar pastPixel = 0;
int jumpsCounter = 0;
int i = 0;
// Loop thru image ROI and count 255-0 jumps:
for (it = imageROI.begin<cv::Vec3b>(), end = imageROI.end<cv::Vec3b>(); it != end; ++it) {
// Get current pixel
uchar ¤tPixel = (*it)[0];
// Compare it with past pixel:
if ( (currentPixel == 0) && (pastPixel == 255) ){
// We have a jump:
jumpsCounter++;
// Draw the point on the BGR version of the image:
cv::line( colorROI, cv::Point(i, 0), cv::Point(i, 0), cv::Scalar(0, 0, 255), 1 );
}
// current pixel is now past pixel:
pastPixel = currentPixel;
i++;
}
// Show image and print number of jumps found:
cv::namedWindow( "Jumps Found", CV_WINDOW_NORMAL );
cv::imshow( "Jumps Found", colorROI );
cv::waitKey( 0 );
std::cout<<"Jumps Found: "<<jumpsCounter<<std::endl;
The points where the jumps were found are drawn in red, and the number of total jumps printed is:
Jumps Found: 9
I have some misunderstanding about OpenCV 4.1.0 and memcpy in C++. The question is why the image is zoomed in a lot?
I read an image like this:
Mat img = imread("lena512.bmp", 1); // Black and White Image
namedWindow("Display window", WINDOW_AUTOSIZE);
imshow("Display window", img);
After this I have 2 byte array:
int inputSize = width * height * channels;
byte* pixels = new byte[width * height * channels];
byte* out = new byte[width * height * channels];
I copy the img to pixels array:
memcpy(pixels, img.data, inputSize * sizeof(byte));
And then I want to check if retrieving image is the same as input:
Mat image = Mat(width, height , CV_8U);
memcpy(image.data, out, inputSize * sizeof(byte));
Mat img = imread("lena512.bmp", 1); // Black and White Image
That's the problem, the comment is a lie, and by using a magic number instead of a named constant, you can't easily tell that's the case. 1 in this context means IMREAD_COLOR -- i.e. the image is always read as a 3 channel BGR image.
However, after the shenanigans with memcpy and raw pointers, you create new Mat in the following manner:
Mat image = Mat(width, height , CV_8U);
Note that CV_8U is equivalent to CV_8UC1. Hence, you create a single channel (grayscale) Mat, but give it 3-channel data.
Getting garbage as a result is the lesser issue. The much more serious issue is that you copy 3x as much data as the target pixel buffer can hold -- basically you clobber half a megabyte of memory that doesn't belong to the Mat. That can either end with a segfault, or some really hard to find bugs (in case you overwrite some memory used by other data structures).
Update: There's another issue that I've missed (thanks to #Micka for catching that). The order of parameters of the cv::Mat constructor is rows, columns, datatype. It appears you switched width and height, although since your input image appears to be square (i.e. width == height) it didn't matter.
The correct way to allocate the second Mat would be
Mat image = Mat(height, width, CV_8UC3);
I have a 200x200 pixels image and I want to keep only the data for a certain region inside it.
Check the following image:
The whole out square is 200x200 pixels.I want to remove from it the smaller square(white).So ,keep only the information that is included in the blue area.But , I want to keep the 200x200 dimensions.
I tried:
Mat whiteArea;
whiteArea = ImageInitial( Range(50,200) , Range(50,200) );
Size size(200,200);
Mat dst;
resize(whiteArea,dst,size);
Mat FinalImage;
subtract(ImageInitial,dst,FinalImage);
I am resizing the white area because I want to substract it from the initial image.
My problem is that it gives me the initial image.
Maybe the resize is the problem .but then how to substract 2 different sized images?
try to use subimages or use a mask:
// use a roi (nice if your target area is rectangular and you know the position)
Rect whiteArea = Rect(50,50, 200,200); // creates a roi of the inner rect
Mat FinalImage = ImageInitial.clone();
// now set the roi area to zero:
FinalImage (whiteArea).setTo(Scalar(0,0,0));
// or FinalImage(whiteArea) = FinalImage(whiteArea) - FinalImage(whiteArea);
imshow("version 1 with subimage", FinalImage);
waitkey(0);
// or use a mask (nice if that region can has arbitrary shape etc and you have to extract it first):
Scalar lowerColorBound = Scalar(x,y,z); //some BGR values to find the color you want to eliminate
Scalar upperColorBound = Scalar(a,b,c); //some BGR values to find the color you want to eliminate
Mat mask;
inRange(ImageInitial, lowerColorBound, upperColorBound mask)
// use the mask for subtraction:
subtract(ImageInitial, ImageInitial, FinalImage , mask);
imshow("version 2 with mask", FinalImage);
waitkey(0);
I'm trying to load and display a .PGM image using OpenCV(2.4.0) for C++.
void open(char* location, int flag, int windowFlag)
{
Mat image = imread(location, flag);
namedWindow("Image window", windowFlag);
imshow("Image window", image);
waitKey(0);
}
I'm calling open like this:
open("./img_00245_c1.pgm", IMREAD_UNCHANGED, CV_WINDOW_AUTOSIZE);
The problem is that the image shown when the window is opened is darker than if I'm opening the file with IrfanView.
Also if I'm trying to write this image to another file like this:
Mat imgWrite;
imgWrite = image;
imwrite("newImage.pgm", imgWrite)
I will get a different file content than the original one and IrfanView will display this as my function displays with imshow.
Is there a different flag in imread for .PGM files such that I can get the original file to be displayed and saved ?
EDIT: Image pgm file
EDIT 2 : Remarked that: IrfanView normalizes the image to a maximum pixel value of 255 .
In order to see the image clearly using OpenCV I should normalize the image also when loading in Mat. Is this possible directly with OpenCV functions without iterating through pixels and modifying their values ?
The problem is not in the way data are loaded, but in the way they are displayed.
Your image is a CV_16UC1, and both imshow and imwrite normalize the values from original range [0, 65535] to the range [0, 255] to fit the range of the type CV_8U.
Since your PGM image has max_value of 4096:
P2
1176 640 // width height
4096 // max_value
it should be normalized from range [0, 4096] instead of [0, 65535].
You can do this with:
Mat img = imread("path_to_image", IMREAD_UNCHANGED);
img.convertTo(img, CV_8U, 255.0 / 4096.0);
imshow("Image", img);
waitKey();
Please note that the values range in your image doesn't correspond to [0, 4096], but:
double minv, maxv;
minMaxLoc(img, &minv, &maxv);
// minv = 198
// maxv = 2414
So the straightforward normalization in [0,255] like:
normalize(img, img, 0, 255, NORM_MINMAX);
img.convertTo(img, CV_8U);
won't work, as it will produce an image brighter than it should be.
This means that to properly show your image you need to know the max_value (here 4096). If it changes every time, you can retrieve it parsing the .pgm file.
Again, it's just a problem with visualization. Data are correct.
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)