A common thing to do in machine learning is to have the first column of a dataset represent the class that the corresponding row belongs to for a data point.
Basically, I have a cv::Mat and I want to efficiently create a cv::Mat containing that matrix with the first column removed. Is there a more efficient way of doing this than looping over the columns and rows and adding the elements one by one with mat.at<data_type>(row, col) = elem; ?
See Mat::operator() from OpenCV documentation.
Related
I have an opencv mat which has M rows and 3 columns, is there a way to reorder the mat such that the first and last (i.e., third) columns are switched while the middle column kept in place without copying the data?
OpenCV data is an array of pixels. Sometimes you can get a column or a rectangle view of an image (like with col() ). In which the data is not continuous, and it is calculated, as far as I know, with a step between rows. However the data is shared and is still an array.
Then the question becomes: can I swap two portions of an array without copying the data? Not as far as I know.
You can use optimized functions of OpenCV to swap them, but the data will be copied.
Also, non continuous data is way slower than continuous data in OpenCV functions. More can be read here.
You can use the OpenCV function flip for that. As an example the following code flips an image about the mid column.
int main ()
{
Mat img, flipped; //your mat
img =imread("lena.jpg");
flip(img,flipped ,1); // flipped is the output
imshow("img",flipped);
waitKey(0);
}
I have a confusion matrix but it has 0,1,2 as indexes/labels instead of actual labels. Is there any way to display the actual labels as index for confusion matrix in scikit?
If you are using inbuilt confusion-matrix function, then there is a parameter labels in it, in which you can pass the actual labels.
From the documentation:
labels : array, shape = [n_classes], optional List of labels to index
the matrix. This may be used to reorder or select a subset of labels.
If none is given, those that appear at least once in y_true or y_pred
are used in sorted order.
And if you are talking about or want to plot the confusion matrix, then you can follow this official scikit example:
http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html#sphx-glr-auto-examples-model-selection-plot-confusion-matrix-py
From time to time I have to port some Matlab Code to OpenCV.
Almost always there is a way to do it and an appropriate function in OpenCV. Nevertheless its not always easy to find.
Therefore I would like to start this summary to find and gather some equivalents between Matlab and OpenCV.
I use the Matlab function as heading and append its description from Matlab help. Afterwards a OpenCV example or links to solutions are appreciated.
Repmat
Replicate and tile an array. B = repmat(A,M,N) creates a large matrix B consisting of an M-by-N tiling of copies of A. The size of B is [size(A,1)*M, size(A,2)*N]. The statement repmat(A,N) creates an N-by-N tiling.
B = repeat(A, M, N)
OpenCV Docs
Find
Find indices of nonzero elements. I = find(X) returns the linear indices corresponding to the nonzero entries of the array X. X may be a logical expression. Use IND2SUB(SIZE(X),I) to calculate multiple subscripts from the linear indices I.
Similar to Matlab's find
Conv2
Two dimensional convolution. C = conv2(A, B) performs the 2-D convolution of matrices A and B. If [ma,na] = size(A), [mb,nb] = size(B), and [mc,nc] = size(C), then mc = max([ma+mb-1,ma,mb]) and nc = max([na+nb-1,na,nb]).
Similar to Conv2
Imagesc
Scale data and display as image. imagesc(...) is the same as IMAGE(...) except the data is scaled to use the full colormap.
SO Imagesc
Imfilter
N-D filtering of multidimensional images. B = imfilter(A,H) filters the multidimensional array A with the multidimensional filter H. A can be logical or it can be a nonsparse numeric array of any class and dimension. The result, B, has the same size and class as A.
SO Imfilter
Imregionalmax
Regional maxima. BW = imregionalmax(I) computes the regional maxima of I. imregionalmax returns a binary image, BW, the same size as I, that identifies the locations of the regional maxima in I. In BW, pixels that are set to 1 identify regional maxima; all other pixels are set to 0.
SO Imregionalmax
Ordfilt2
2-D order-statistic filtering. B=ordfilt2(A,ORDER,DOMAIN) replaces each element in A by the ORDER-th element in the sorted set of neighbors specified by the nonzero elements in DOMAIN.
SO Ordfilt2
Roipoly
Select polygonal region of interest. Use roipoly to select a polygonal region of interest within an image. roipoly returns a binary image that you can use as a mask for masked filtering.
SO Roipoly
Gradient
Approximate gradient. [FX,FY] = gradient(F) returns the numerical gradient of the matrix F. FX corresponds to dF/dx, the differences in x (horizontal) direction. FY corresponds to dF/dy, the differences in y (vertical) direction. The spacing between points in each direction is assumed to be one. When F is a vector, DF = gradient(F)is the 1-D gradient.
SO Gradient
Sub2Ind
Linear index from multiple subscripts. sub2ind is used to determine the equivalent single index corresponding to a given set of subscript values.
SO sub2ind
backslash operator or mldivide
solves the system of linear equations A*x = B. The matrices A and B must have the same number of rows.
cv::solve
I am having difficulty with reading an image, extracting features for training, and testing on new images in OpenCV using SVMs. can someone please point me to a great link? I have looked at the OpenCV Introduction to Support Vector Machines. But it doesn't help with reading in images, and I am not sure how to incorporate it.
My goals are to classify pixels in an image. These pixel would belong to a curves. I understand forming the training matrix (for instance,
image A
1,1 1,2 1,3 1,4 1,5
2,1 2,2 2,3 2,4 2,5
3,1 3,2 3,3 3,4 3,5
I would form my training matrix as a [3][2]={ {1,1} {1,2} {1,3} {1,4} {1,5} {2,1} ..{} }
However, I am a little confuse about the labels. From my understanding, I have to specify which row (image) in the training matrix corresponds, which corresponds to a curve or non-curve. But, how can I label a training matrix row (image) if there are some pixels belonging to the curve and some not belonging to a curve. For example, my training matrix is [3][2]={ {1,1} {1,2} {1,3} {1,4} {1,5} {2,1} ..{} }, pixels {1,1} and {1,4} belong to the curve but the rest does not.
I've had to deal with this recently, and here's what I ended up doing to get SVM to work for images.
To train your SVM on a set of images, first you have to construct the training matrix for the SVM. This matrix is specified as follows: each row of the matrix corresponds to one image, and each element in that row corresponds to one feature of the class -- in this case, the color of the pixel at a certain point. Since your images are 2D, you will need to convert them to a 1D matrix. The length of each row will be the area of the images (note that the images must be the same size).
Let's say you wanted to train the SVM on 5 different images, and each image was 4x3 pixels. First you would have to initialize the training matrix. The number of rows in the matrix would be 5, and the number of columns would be the area of the image, 4*3 = 12.
int num_files = 5;
int img_area = 4*3;
Mat training_mat(num_files,img_area,CV_32FC1);
Ideally, num_files and img_area wouldn't be hardcoded, but obtained from looping through a directory and counting the number of images and taking the actual area of an image.
The next step is to "fill in" the rows of training_mat with the data from each image. Below is an example of how this mapping would work for one row.
I've numbered each element of the image matrix with where it should go in the corresponding row in the training matrix. For example, if that were the third image, this would be the third row in the training matrix.
You would have to loop through each image and set the value in the output matrix accordingly. Here's an example for multiple images:
As for how you would do this in code, you could use reshape(), but I've had issues with that due to matrices not being continuous. In my experience I've done something like this:
Mat img_mat = imread(imgname,0); // I used 0 for greyscale
int ii = 0; // Current column in training_mat
for (int i = 0; i<img_mat.rows; i++) {
for (int j = 0; j < img_mat.cols; j++) {
training_mat.at<float>(file_num,ii++) = img_mat.at<uchar>(i,j);
}
}
Do this for every training image (remembering to increment file_num). After this, you should have your training matrix set up properly to pass into the SVM functions. The rest of the steps should be very similar to examples online.
Note that while doing this, you also have to set up labels for each training image. So for example if you were classifying eyes and non-eyes based on images, you would need to specify which row in the training matrix corresponds to an eye and a non-eye. This is specified as a 1D matrix, where each element in the 1D matrix corresponds to each row in the 2D matrix. Pick values for each class (e.g., -1 for non-eye and 1 for eye) and set them in the labels matrix.
Mat labels(num_files,1,CV_32FC1);
So if the 3rd element in this labels matrix were -1, it means the 3rd row in the training matrix is in the "non-eye" class. You can set these values in the loop where you evaluate each image. One thing you could do is to sort the training data into separate directories for each class, and loop through the images in each directory, and set the labels based on the directory.
The next thing to do is set up your SVM parameters. These values will vary based on your project, but basically you would declare a CvSVMParams object and set the values:
CvSVMParams params;
params.svm_type = CvSVM::C_SVC;
params.kernel_type = CvSVM::POLY;
params.gamma = 3;
// ...etc
There are several examples online on how to set these parameters, like in the link you posted in the question.
Next, you create a CvSVM object and train it based on your data!
CvSVM svm;
svm.train(training_mat, labels, Mat(), Mat(), params);
Depending on how much data you have, this could take a long time. After it's done training, however, you can save the trained SVM so you don't have to retrain it every time.
svm.save("svm_filename"); // saving
svm.load("svm_filename"); // loading
To test your images using the trained SVM, simply read an image, convert it to a 1D matrix, and pass that in to svm.predict():
svm.predict(img_mat_1d);
It will return a value based on what you set as your labels (e.g., -1 or 1, based on my eye/non-eye example above). Alternatively, if you want to test more than one image at a time, you can create a matrix that has the same format as the training matrix defined earlier and pass that in as the argument. The return value will be different, though.
Good luck!
I created a m-by-n matrix Mat and I would like to add a scalar to just one row of this matrix. In order to add a scalar to all elements of the matrix, you can use the following statement: A += b, where A is a Mat object and b in a scalar. But if I wanted to add a scalar to just one row of this matrix, how to perform this operation just as easily?
This is very easy:
image.row(i) += Scalar(...);
Taken from docs:
There are many different ways to create a Mat object. The most popular
options are listed below:
...
Construct a header for a part of another
array. It can be a single row, single column, several rows, several
columns, rectangular region in the array (called a minor in algebra)
or a diagonal. Such operations are also O(1) because the new header
references the same data. You can actually modify a part of the array
using this feature
...