i'm trying to get a set of values in a binary image for inverting it.. but i'm having troubles to index the matrix, the first lines of my code are.
std::string path = "img/lena.jpg";
//Our color image
cv::Mat imageMat = cv::imread(path, CV_LOAD_IMAGE_GRAYSCALE);
if(imageMat.empty())
{
std::cerr << "ERROR: Could not read image " << argv[1] << std::endl;
return 1;
}
//Grayscale matrix
cv::Mat grayscaleMat (imageMat.size(), CV_8U);
//Convert BGR to Gray
cv::cvtColor( imageMat, grayscaleMat, CV_BGR2GRAY );
//Binary image
cv::Mat binaryMat(grayscaleMat.size(), grayscaleMat.type());
//Apply thresholding
cv::threshold(grayscaleMat, binaryMat, 100, 255, cv::THRESH_BINARY);
Now i need to work with the values in binaryMat, but i don't know how get it...
1: with opencv's c++ api, you don't need to allocate output/result Mat's. just leave them empty.
//Convert BGR to Gray
cv::Mat grayscaleMat;
cv::cvtColor( imageMat, grayscaleMat, CV_BGR2GRAY );
//Apply thresholding
cv::Mat binaryMat;
cv::threshold(grayscaleMat, binaryMat, 100, 255, cv::THRESH_BINARY);
2: now access the pixels:
uchar p = binaryMat.at<uchar>(y,x); // row,col world !
binaryMat.at<uchar>(5,5) = 17;
Related
I set my mask from BGR2HSV. I have my image:
How I can change the white color in the mask? So I want to change the white parts with other colors.
Mat mask;
mask = imread("C:\\Users\\...\\Desktop\\...\\mask.png");
if (!img.data)
{
cout << "Could not find the image";
return -1;
}
cvtColor(mask, mask, COLOR_BGR2HSV);
cvtColor(mask, mask, COLOR_HSV2BGR);
imshow("Ergebnis", mask);
waitKey(0);
Between two cvtColor functions, you need to split the image into its 3 channels with split. Looking at the conversion between RGB and HSV, make S channel 0 and choose an H value between [0-180]. Then, merge the channels back.
cv::Mat hsv = mask.clone(); // from your code
std::vector<cv::Mat> hsv_vec;
cv::split(hsv, hsv_vec);
cv::Mat &H = hsv_vec[0];
cv::Mat &S = hsv_vec[1];
cv::Mat &V = hsv_vec[2];
S = 0;
mask = (V > 10); // non-zero pixels in the original image
H(mask) = your_H_value_here; // H is between 0-180 in OpenCV
cv::merge(hsv_vec, hsv);
mask = hsv; // according to your code
As a side note, I suggest using convenient names for variables.
i'm trying to make an AR app, using aruco and Opencv (i'm a newbie). It detects aruco marker, and puts an image on it. I have tried to use wrapPerstective() function, however somethig is wrong, it returns Opencv error assertion failed ((m0.type() == cv_32f m0.type() == cv_64f) in wrapPerspective. Please give me a way to solve it
int main() {
cv::VideoCapture inputVideo;
inputVideo.open("gal.mp4");
cv::Ptr<cv::aruco::Dictionary> dictionary = cv::aruco::getPredefinedDictionary(cv::aruco::DICT_4X4_50);
cv::Mat sq = imread("zhuz.jpg", CV_LOAD_IMAGE_UNCHANGED);
while (inputVideo.grab()) {
vector<Point2f> sqPoints;
vector<Point2f> p;
sqPoints.push_back(Point2f(0, 0));
sqPoints.push_back(Point2f(sq.cols, 0));
sqPoints.push_back(Point2f(sq.cols, sq.rows));
sqPoints.push_back(Point2f(0, sq.rows));
cv::Mat image, warp_matrix;
inputVideo.retrieve(image);
Mat cpy_img(image.rows, image.cols, image.type());
Mat neg_img(image.rows, image.cols, image.type());
Mat gray;
Mat blank(sq.rows, sq.cols, sq.type());
std::vector<int> ids;
std::vector<std::vector<cv::Point2f>> corners;
cv::aruco::detectMarkers(image, dictionary, corners, ids);
if (ids.size() > 0) {
p.push_back(corners[0][0]);
p.push_back(corners[0][1]);
p.push_back(corners[0][2]);
p.push_back(corners[0][3]);
Mat wrap_matrix = getPerspectiveTransform(sqPoints, p);
blank = Scalar(0);
neg_img = Scalar(0); // Image is white when pixel values are zero
cpy_img = Scalar(0); // Image is white when pixel values are zero
bitwise_not(blank, blank);
warpPerspective(sq, neg_img, warp_matrix, Size(neg_img.cols, neg_img.rows)); // Transform overlay Image to the position - [ITEM1]
warpPerspective(blank, cpy_img, warp_matrix, Size(cpy_img.cols, neg_img.rows)); // Transform a blank overlay image to position
bitwise_not(cpy_img, cpy_img); // Invert the copy paper image from white to black
bitwise_and(cpy_img, image, cpy_img); // Create a "hole" in the Image to create a "clipping" mask - [ITEM2]
bitwise_or(cpy_img, neg_img, image); // Finally merge both items [ITEM1 & ITEM2]
}
cv::imshow("out", image);
}
}
I'm new to image processing and development. I need to take the inside triangle pixels of the image. In order to do it I used the following code. Unfortunately I obtain unwanted black pixels. get rid of that problem i tried to remove background[0] pixels by giving alfa value.(tranparent background) But it gives following Error. Any help is appreciated.
My code:
Mat img = cv::imread("/home/fabio/code/lena.jpg", cv::IMREAD_GRAYSCALE);
Mat alpha(img.size(), CV_8UC1, Scalar(0));
//triangle definition (example points)
vector<Point> points;
points.push_back(Point(200, 70));
points.push_back(Point(60, 150));
points.push_back(Point(500, 500));
//apply triangle to mask
fillConvexPoly(alpha, points, Scalar(255));
cv::Mat finalImage = cv::Mat::zeros(img.size(), img.type());
img.copyTo(finalImage, alpha);
imshow("image", finalImage);
Mat dst;
Mat rgb[1];
split(finalImage, rgb);
Mat rgba[2] = { finalImage, alpha };
merge(rgba, 2, dst);
imshow("dst", dst);
Error: OpenCV Error: Bad number of channels (Source image must have 1, 3 or 4 channels) in cvConvertImage, file C:\builds\2_4_PackSlave-win64-vc12-shared\opencv\modules\highgui\src\utils.cpp, line 611
use this instead of your last block:
std::vector<cv::Mat> channels;
cv::split(finalImage,m channels);
if(channels.size() == 0)
{
std::cout << "unexpected error" << std::endl;
return 1;
}
// fill up to reach 3 channels
while(channels,size() < 3)
{
channels.push_back(channels[0]);
}
// add alpha channel
channels.push_back(alpha);
cv::merge(channels, dst);
I didn't test it but this should be what you want?
I'm currently working on a project that uses a Lacatan Banana, and I would like to know how to further separate the foreground from the background:
I already got a segmented image of it using erosion, dilation, and thresholding only. The problem is that it is still not properly segmented.
Here is my code:
cv::Mat imggray, imgthresh, fg, bgt, bg;
cv::cvtColor(src, imggray, CV_BGR2GRAY); //Grayscaling the image from RGB color space
cv::threshold(imggray, imgthresh, 0, 255, CV_THRESH_BINARY_INV | CV_THRESH_OTSU); //Create an inverted binary image from the grayscaled image
cv::erode(imgthresh, fg, cv::Mat(), cv::Point(-1, -1), 1); //erosion of the binary image and setting it as the foreground
cv::dilate(imgthresh, bgt, cv::Mat(), cv::Point(-1, -1), 4); //dilation of the binary image to reduce the background region
cv::threshold(bgt, bg, 1, 128, CV_THRESH_BINARY); //we get the background by setting the threshold to 1
cv::Mat markers = cv::Mat::zeros(src.size(), CV_32SC1); //initializing the markers with a size same as the source image and setting its data type as 32-bit Single channel
cv::add(fg, bg, markers); //setting the foreground and background as markers
cv::Mat mask = cv::Mat::zeros(markers.size(), CV_8UC1);
markers.convertTo(mask, CV_8UC1); //converting the 32-bit single channel marker to a 8-bit single channel
cv::Mat mthresh;
cv::threshold(mask, mthresh, 0, 255, CV_THRESH_BINARY | CV_THRESH_OTSU); //threshold further the mask to reduce the noise
// cv::erode(mthresh,mthresh,cv::Mat(), cv::Point(-1,-1),2);
cv::Mat result;
cv::bitwise_and(src, src, result, mthresh); //use the mask to subtrack the banana from the background
for (int x = 0; x < result.rows; x++) { //changing the black background to white
for (int y = 0; y < result.cols; y++) {
if (result.at<Vec3b>(x, y) == Vec3b(0, 0, 0)){
result.at<Vec3b>(x, y)[0] = 255;
result.at<Vec3b>(x, y)[1] = 255;
result.at<Vec3b>(x, y)[2] = 255;
}
}
}
This is my result:
As the background is near gray-color, try using Hue channel and Saturation channel instead of grayscale image.
You can get them easily.
cv::Mat hsv;
cv::cvtColor(src, hsv, CV_BGR2HSV);
std::vector<cv::Mat> channels;
cv::split(src, channels);
cv::Mat hue = channels[0];
cv::Mat saturation = channels[1];
// If you want to combine those channels, use this code.
cv::Mat hs = cv::Mat::zeros(src.size(), CV_8U);
for(int r=0; r<src.rows; r++) {
for(int c=0; c<src.cols; c++) {
int hp = h.at<uchar>(r,c);
int sp = s.at<uchar>(r,c);
hs.at<uchar>(r, c) = static_cast<uchar>((h+s)>>1);
}
}
adaptiveThreshold() should work better than just level-cut threshold(), because it does not consider absolute color levels, but rather a change in color in small area around the point being checked.
Try replacing your thresholding with adaptive one.
Use a top-hat instead of just erosion/dilation. It will take care of the background variations at the same time.
Then in your case a simple thresholding should be good enough to have an accurate segmentation. Else, you can couple it with a watershed.
(I will share some images asap).
Thanks guys, I tried to apply your advises and was able to come up with this
However as you can see there are still bits of the background,any ideas how to "clean" these further, i tried thresholding further but it would still have the bits.The Code I came up with is below and i apologize in advance if the variables and coding style is somewhat confusing didn't have the time to properly sort them.
#include <stdio.h>
#include <iostream>
#include <opencv2\core.hpp>
#include <opencv2\opencv.hpp>
#include <opencv2\highgui.hpp>
using namespace cv;
using namespace std;
Mat COLOR_MAX(Scalar(65, 255, 255));
Mat COLOR_MIN(Scalar(15, 45, 45));
int main(int argc, char** argv){
Mat src,hsv_img,mask,gray_img,initial_thresh;
Mat second_thresh,add_res,and_thresh,xor_thresh;
Mat result_thresh,rr_thresh,final_thresh;
// Load source Image
src = imread("sample11.jpg");
imshow("Original Image", src);
cvtColor(src,hsv_img,CV_BGR2HSV);
imshow("HSV Image",hsv_img);
//imwrite("HSV Image.jpg", hsv_img);
inRange(hsv_img,COLOR_MIN,COLOR_MAX, mask);
imshow("Mask Image",mask);
cvtColor(src,gray_img,CV_BGR2GRAY);
adaptiveThreshold(gray_img, initial_thresh, 255,ADAPTIVE_THRESH_GAUSSIAN_C,CV_THRESH_BINARY_INV,257,2);
imshow("AdaptiveThresh Image", initial_thresh);
add(mask,initial_thresh,add_res);
erode(add_res, add_res, Mat(), Point(-1, -1), 1);
dilate(add_res, add_res, Mat(), Point(-1, -1), 5);
imshow("Bitwise Res",add_res);
threshold(gray_img,second_thresh,170,255,CV_THRESH_BINARY_INV | CV_THRESH_OTSU);
imshow("TreshImge", second_thresh);
bitwise_and(add_res,second_thresh,and_thresh);
imshow("andthresh",and_thresh);
bitwise_xor(add_res, second_thresh, xor_thresh);
imshow("xorthresh",xor_thresh);
bitwise_or(and_thresh,xor_thresh,result_thresh);
imshow("Result image", result_thresh);
bitwise_and(add_res,result_thresh,final_thresh);
imshow("Final Thresh",final_thresh);
erode(final_thresh, final_thresh, Mat(), Point(-1,-1),5);
bitwise_and(src,src,rr_thresh,final_thresh);
imshow("Segmented Image", rr_thresh);
imwrite("Segmented Image.jpg", rr_thresh);
waitKey(0);
return 1;
}
Basically I am trying to convert the below output image to color(RGB). The image that this code currently outputs is grayscale, however, for my application I would like it to be output as color. Please let me know where I should convert the image.
Also the code below is C++ and it using a function from openCV. Please keep in mind that I am using a wrapper to use this code in my iphone application.
cv::Mat CVCircles::detectedCirclesInImage(cv::Mat img, double dp, double minDist, double param1, double param2, int min_radius, int max_radius) {
//(cv::Mat img, double minDist, int min_radius, int max_radius)
if(img.empty())
{
cout << "can not open image " << endl;
return img;
}
Mat cimg;
medianBlur(img, img, 5);
cvtColor(img, cimg, CV_GRAY2RGB);
vector<Vec3f> circles;
HoughCircles( img //InputArray
, circles //OutputArray
, CV_HOUGH_GRADIENT //int method
, 1//dp //double dp=1 1 ... 20
, minDist //double minDist=10 log 1...1000
, 100//param1 //double param1=100
, 30//param2 //double param2=30 10 ... 50
, min_radius //int minRadius=1 1 ... 500
, max_radius //int maxRadius=30 1 ... 500
);
for( size_t i = 0; i < circles.size(); i++ )
{
Vec3i c = circles[i];
circle( cimg, Point(c[0], c[1]), c[2], Scalar(255,0,0), 3, CV_AA);
circle( cimg, Point(c[0], c[1]), 2, Scalar(0,255,0), 3, CV_AA);
}
return cimg;
}
This is currently set up to expect a grayscale image as input. I think that you are asking how to adapt it to accept a colour input image and return a colour output image. You don't need to change much:
cv::Mat CVCircles::detectedCirclesInImage(cv::Mat img, double dp, double minDist, double param1, double param2, int min_radius, int max_radius) {
if(img.empty())
{
cout << "can not open image " << endl;
return img;
}
Mat img;
if (img.type()==CV_8UC1) {
//input image is grayscale
cvtColor(img, cimg, CV_GRAY2RGB);
} else {
//input image is colour
cimg = img;
cvtColor(img, img, CV_RGB2GRAY);
}
the rest stays as is.
If your input image is colour, you are converting it to gray for processing by HoughCircles, and applying the found circles to the original colour image for output.
The cvtImage routine will simply copy your gray element to each of the three elements R, G, and B for each pixel. In other words if the pixel gray value is 26, then the new image will have R = 26, G = 26, B = 26.
The image presented will still LOOK grayscale even though it contains all 3 color components, all you have essentially done is to triple the space necessary to store the same image.
If indeed you want color to appear in the image (when you view it), this is truly impossible to go from grayscale back to the ORIGINAL colors. There are however means of pseudo-coloring or false coloring the image.
http://en.wikipedia.org/wiki/False_color
http://blog.martinperis.com/2011/09/opencv-pseudocolor-and-chroma-depth.html
http://podeplace.blogspot.com/2012/11/opencv-pseudocolors.html
The code you have pasted is returning colored image.
You are already doing cvtColor(img, cimg, CV_GRAY2RGB), and then I don't see cimg getting converted to grayscale anywhere !, To verify it try displaying it before returning from this function :
imshow("c",cimg);
waitKey(0);
return cimg;
You can draw circles to the input color image.
Check the documentation given in the openCV
http://docs.opencv.org/doc/tutorials/imgproc/imgtrans/hough_circle/hough_circle.html