I am trying to figure a way of sorting a 3x3 row into a 9x1.
So i have following:
I want to end up with this:
This is what i end up doing so far:
Rect roi(y-1,x-1,kernel,kernel);
Mat image_roi = image(roi);
Mat image_sort(kernel, kernel, CV_8U);
cv::sort(image_roi, image_sort, CV_SORT_ASCENDING+CV_SORT_EVERY_ROW);
The code is not functional, currently i cannot find any data in my image_sort after its "sorted".
Sure you have single-channel grey level images? Try:
cv::Mat image_sort = cv::Mat::zeros(rect.height, rect.width, rect.type()); // allocated memory
image(roi).copyTo(image_sort); // copy data in image_sorted
std::sort(image_sort.data, image_sort.dataend); // call std::sort
cv::Mat vectorized = image_sort.reshape(1, 1); // reshaped your WxH matrix into a 1x(W*H) vector.
Related
I have a bgr image and convert to lab channels.
I tried to check if the idft image of the result of dft of L channel image is the same.
// MARK: Split LAB Channel each
cv::Mat lab_resized_host_image;
cv::cvtColor(resized_host_image, lab_resized_host_image, cv::COLOR_BGR2Lab);
imshow("lab_resized_host_image", lab_resized_host_image);
cv::Mat channel_L_host_image, channel_A_host_image, channel_B_host_image;
std::vector<cv::Mat> channel_LAB_host_image(3);
cv::split(lab_resized_host_image, channel_LAB_host_image);
// MARK: DFT the channel_L host image.
channel_L_host_image = channel_LAB_host_image[0];
imshow("channel_L_host_image", channel_L_host_image);
cv::Mat padded_L;
int rows_L = getOptimalDFTSize(channel_L_host_image.rows);
int cols_L = getOptimalDFTSize(channel_L_host_image.cols);
copyMakeBorder(channel_L_host_image, padded_L, 0, rows_L - channel_L_host_image.rows, 0, cols_L - channel_L_host_image.cols, BORDER_CONSTANT, Scalar::all(0));
Mat planes_L[] = {Mat_<float>(padded_L), Mat::zeros(padded_L.size(), CV_32F)};
Mat complexI_L;
merge(planes_L, 2, complexI_L);
dft(complexI_L, complexI_L);
// MARK: iDFT Channel_L.
Mat complexI_channel_L = complexI_L;
Mat complexI_channel_L_idft;
cv::dft(complexI_L, complexI_channel_L_idft, cv::DFT_INVERSE|cv::DFT_REAL_OUTPUT);
normalize(complexI_channel_L_idft, complexI_channel_L_idft, 0, 1, NORM_MINMAX);
imshow("complexI_channel_L_idft", complexI_channel_L_idft);
Each imshow give me different image... I think normalization would be error...
what is wrong? help!
original image
idft
OpenCV’s FFT is not normalized by default. One of the forward/backward transform pair must be normalized for the pair to reproduce the input values. Simply add cv::DFT_SCALE to the options:
cv::dft(complexI_mid_frequency_into_channel_A, iDFT_mid_frequency_into_channel_A, cv::DFT_INVERSE|cv::DFT_REAL_OUTPUT|cv::DFT_SCALE);
I want to detect the very minimal movement of a conveyor belt using image evaluation (Resolution: 31x512, image rate: 1000 per second.). The moment of belt-start is important for me.
If I do cv::absdiff between two subsequent images, I obtain very noisy result:
According to the mechanical rotation sensor of the motor, the movement starts here:
I tried to threshold the abs-diff image with a cascade of erosion and dilation, but I could detect the earliest change more than second too late in this image:
Is it possible to find the change earlier?
Here is the sequence of the Images without changes (according to motor sensor):
In this sequence the movement begins in the middle image:
Looks like I've found a solution which works in MY case.
Instead of comparing the image changes in space-domain, the cross-correlation should be applied:
I convert both images to DFT, multiply DFT-Mats and convert back. The max pixel value is the center of the correlation. As long as the images are same, the max-pix remains in the same position and moves otherwise.
The actual working code uses 3 images, 2 DFT multiplication result between images 1,2 and 2,3:
Mat img1_( 512, 32, CV_16UC1 );
Mat img2_( 512, 32, CV_16UC1 );
Mat img3_( 512, 32, CV_16UC1 );
//read the data in the images wohever you want. I read from MHD-file
//Set ROI (if required)
Mat img1 = img1_(cv::Rect(0,200,32,100));
Mat img2 = img2_(cv::Rect(0,200,32,100));
Mat img3 = img3_(cv::Rect(0,200,32,100));
//Float mats for DFT
Mat img1f;
Mat img2f;
Mat img3f;
//DFT and produtcts mats
Mat dft1,dft2,dft3,dftproduct,dftproduct2;
//Calculate DFT of both images
img1.convertTo(img1f, CV_32FC1);
cv::dft(img1f, dft1);
img2.convertTo(img3f, CV_32FC1);
cv::dft(img3f, dft3);
img3.convertTo(img2f, CV_32FC1);
cv::dft(img2f, dft2);
//Multiply DFT Mats
cv::mulSpectrums(dft1,dft2,dftproduct,true);
cv::mulSpectrums(dft2,dft3,dftproduct2,true);
//Convert back to space domain
cv::Mat result,result2;
cv::idft(dftproduct,result);
cv::idft(dftproduct2,result2);
//Not sure if required, I needed it for visualizing
cv::normalize( result, result, 0, 255, NORM_MINMAX, CV_8UC1);
cv::normalize( result2, result2, 0, 255, NORM_MINMAX, CV_8UC1);
//Find maxima positions
double dummy;
Point locdummy; Point maxLoc1; Point maxLoc2;
cv::minMaxLoc(result, &dummy, &dummy, &locdummy, &maxLoc1);
cv::minMaxLoc(result2, &dummy, &dummy, &locdummy, &maxLoc2);
//Calculate products simply fot having one value to compare
int maxlocProd1 = maxLoc1.x*maxLoc1.y;
int maxlocProd2 = maxLoc2.x*maxLoc2.y;
//Calculate absolute difference of the products. Not 0 means movement
int absPosDiff = std::abs(maxlocProd2-maxlocProd1);
if ( absPosDiff>0 )
{
std::cout << id<< std::endl;
break;
}
Is there a direct way to compute the column-wise standard deviation for a matrix in opencv? Similar to std in Matlab. I've found one for the mean:
cv::Mat col_mean;
reduce(A, col_mean, 1, CV_REDUCE_AVG);
but I cannot find such a function for the standard deviation.
Here's a quick answer to what you're looking for. I added both the standard deviation and mean for each column. The code can easily be modified for rows.
cv::Mat A = ...; // FILL IN THE DATA FOR YOUR INPUT MATRIX
cv::Mat meanValue, stdValue;
cv::Mat colSTD(1, A.cols, CV_64FC1);
cv::Mat colMEAN(1, A.cols, CV_64FC1);
for (int i = 0; i < A.cols; i++){
cv::meanStdDev(A.col(i), meanValue, stdValue);
colSTD.at<double>(i) = stdValue.at<double>(0);
colMEAN.at<double>(i) = meanValue.at<double>(0);
}
The following is not in a single line,but it is another version without loops:
reduce(A, meanOfEachCol, 0, CV_REDUCE_AVG); // produces single row of columnar means
Mat repColMean;
cv::repeat(meanOfEachCol, rows, 1, repColMean); // repeat mean vector 'rows' times
Mat diffMean = A - repColMean; // get difference
Mat diffMean2 = diffMean.mul(diffMean); // per element square
Mat varMeanF;
cv::reduce(diffMean2, varMeanF, 0, CV_REDUCE_AVG); // sum each column's elements to get single row
Mat stdMeanF;
cv::sqrt(varMeanF, stdMeanF); // get standard deviation
I need to use blob detection and Structural Analysis and Shape Descriptors (more specifically findContours, drawContours and moments) to detect colored circles in an image. I need to know the pros and cons of each method and which method is better. Can anyone show me the differences between those 2 methods please?
As #scap3y suggested in the comments I'd go for a much simpler approach. What I'm always doing in these cases is something similar to this:
// Convert your image to HSV color space
Mat hsv;
hsv.create(originalImage.size(), CV_8UC3);
cvtColor(originalImage,hsv,CV_RGB2HSV);
// Chose the range in each of hue, saturation and value and threshold the other pixels
Mat thresholded;
uchar loH = 130, hiH = 170;
uchar loS = 40, hiS = 255;
uchar loV = 40, hiV = 255;
inRange(hsv, Scalar(loH, loS, loV), Scalar(hiH, hiS, hiV), thresholded);
// Find contours in the image (additional step could be to
// apply morphologyEx() first)
vector<vector<Point>> contours;
findContours(thresholded,contours,CV_RETR_EXTERNAL,CHAIN_APPROX_SIMPLE);
// Draw your contours as ellipses into the original image
for(i=0;i<(int)valuable_rectangle_indices.size();i++) {
rect=minAreaRect(contours[valuable_rectangle_indices[i]]);
ellipse(originalImage, rect, Scalar(0,0,255)); // draw ellipse
}
The only thing left for you to do now is to figure out in what range your markers are in HSV color space.
hi so I've got a large 33 x 33 matrix in a text file. I've been working on an opencv project which basically reads the frames and calculates the similarities. So basically, I now have this large text file filled with numbers. How do I visualize this matrix in say a 2D grayscale image?
Is your matrix a cv::Mat object?
If so, do:
cv::Mat matrix;
//Load the matrix from the file
matrix = ...
//show the matrix
imshow("window name", matrix);
//save the image
imwrite("image.png", matrix);
If not, then do:
cv::Mat matrix = cv::Mat.create(33, 33, CV_32FC1);
float* floatPtr = matrix.ptr<float>();
for (int i=0;i<33*33;i++)
//read data from file here
*floatPtr++ = data[i] //if it's in an array
//If you have a file stream then do: file>>*floatPtr++;
//show the image
imshow("window name", matrix);
//save the image
imwrite("image.png", matrix);