OpenCV  4.1.2-pre
Open Source Computer Vision
Basic Operations on Images

Goal

Accessing Image Properties

Image properties include number of rows, columns and size, depth, channels, type of image data.

let src = cv.imread("canvasInput");
console.log('image width: ' + src.cols + '\n' +
'image height: ' + src.rows + '\n' +
'image size: ' + src.size().width + '*' + src.size().height + '\n' +
'image depth: ' + src.depth() + '\n' +
'image channels ' + src.channels() + '\n' +
'image type: ' + src.type() + '\n');
Note
src.type() is very important while debugging because a large number of errors in OpenCV.js code are caused by invalid data type.

How to construct Mat

There are 4 basic constructors:

// 1. default constructor
let mat = new cv.Mat();
// 2. two-dimensional arrays by size and type
let mat = new cv.Mat(size, type);
// 3. two-dimensional arrays by rows, cols, and type
let mat = new cv.Mat(rows, cols, type);
// 4. two-dimensional arrays by rows, cols, and type with initialization value
let mat = new cv.Mat(rows, cols, type, new cv.Scalar());

There are 3 static functions:

// 1. Create a Mat which is full of zeros
let mat = cv.Mat.zeros(rows, cols, type);
// 2. Create a Mat which is full of ones
let mat = cv.Mat.ones(rows, cols, type);
// 3. Create a Mat which is an identity matrix
let mat = cv.Mat.eye(rows, cols, type);

There are 2 factory functions:

// 1. Use JS array to construct a mat.
// For example: let mat = cv.matFromArray(2, 2, cv.CV_8UC1, [1, 2, 3, 4]);
let mat = cv.matFromArray(rows, cols, type, array);
// 2. Use imgData to construct a mat
let ctx = canvas.getContext("2d");
let imgData = ctx.getImageData(0, 0, canvas.width, canvas.height);
let mat = cv.matFromImageData(imgData);
Note
Don't forget to delete cv.Mat when you don't want to use it any more.

How to copy Mat

There are 2 ways to copy a Mat:

// 1. Clone
let dst = src.clone();
// 2. CopyTo(only entries indicated in the mask are copied)
src.copyTo(dst, mask);

How to convert the type of Mat

We use the function: convertTo(m, rtype, alpha = 1, beta = 0)

Parameters
moutput matrix; if it does not have a proper size or type before the operation, it is reallocated.
rtypedesired output matrix type or, rather, the depth since the number of channels are the same as the input has; if rtype is negative, the output matrix will have the same type as the input.
alphaoptional scale factor.
betaoptional delta added to the scaled values.
src.convertTo(dst, rtype);

How use MatVector

let mat = new cv.Mat();
// Initialise a MatVector
let matVec = new cv.MatVector();
// Push a Mat back into MatVector
matVec.push_back(mat);
// Get a Mat fom MatVector
let cnt = matVec.get(0);
mat.delete(); matVec.delete(); cnt.delete();
Note
Don't forget to delete cv.Mat, cv.MatVector and cnt(the Mat you get from MatVector) when you don't want to use them any more.

Accessing and Modifying pixel values

Firstly, you should know the following type relationship:

Data Properties C++ Type JavaScript Typed Array Mat Type
data uchar Uint8Array CV_8U
data8S char Int8Array CV_8S
data16U ushort Uint16Array CV_16U
data16S short Int16Array CV_16S
data32S int Int32Array CV_32S
data32F float Float32Array CV_32F
data64F double Float64Array CV_64F

1. data

let row = 3, col = 4;
let src = cv.imread("canvasInput");
if (src.isContinuous()) {
let R = src.data[row * src.cols * src.channels() + col * src.channels()];
let G = src.data[row * src.cols * src.channels() + col * src.channels() + 1];
let B = src.data[row * src.cols * src.channels() + col * src.channels() + 2];
let A = src.data[row * src.cols * src.channels() + col * src.channels() + 3];
}
Note
Data manipulation is only valid for continuous Mat. You should use isContinuous() to check first.

2. at

Mat Type At Manipulation
CV_8U ucharAt
CV_8S charAt
CV_16U ushortAt
CV_16S shortAt
CV_32S intAt
CV_32F floatAt
CV_64F doubleAt
let row = 3, col = 4;
let src = cv.imread("canvasInput");
let R = src.ucharAt(row, col * src.channels());
let G = src.ucharAt(row, col * src.channels() + 1);
let B = src.ucharAt(row, col * src.channels() + 2);
let A = src.ucharAt(row, col * src.channels() + 3);
Note
At manipulation is only for single channel access and the value can't be modified.

3. ptr

Mat Type Ptr Manipulation JavaScript Typed Array
CV_8U ucharPtr Uint8Array
CV_8S charPtr Int8Array
CV_16U ushortPtr Uint16Array
CV_16S shortPtr Int16Array
CV_32S intPtr Int32Array
CV_32F floatPtr Float32Array
CV_64F doublePtr Float64Array
let row = 3, col = 4;
let src = cv.imread("canvasInput");
let pixel = src.ucharPtr(row, col);
let R = pixel[0];
let G = pixel[1];
let B = pixel[2];
let A = pixel[3];

mat.ucharPtr(k) get the k th row of the mat. mat.ucharPtr(i, j) get the i th row and the j th column of the mat.

Image ROI

Sometimes, you will have to play with certain region of images. For eye detection in images, first face detection is done all over the image and when face is obtained, we select the face region alone and search for eyes inside it instead of searching whole image. It improves accuracy (because eyes are always on faces) and performance (because we search for a small area)

We use the function: roi (rect)

Parameters
rectrectangle Region of Interest.

Try it

Splitting and Merging Image Channels

Sometimes you will need to work separately on R,G,B channels of image. Then you need to split the RGB images to single planes. Or another time, you may need to join these individual channels to RGB image.

let src = cv.imread("canvasInput");
let rgbaPlanes = new cv.MatVector();
// Split the Mat
cv.split(src, rgbaPlanes);
// Get R channel
let R = rgbaPlanes.get(0);
// Merge all channels
cv.merge(rgbaPlanes, src);
src.delete(); rgbaPlanes.delete(); R.delete();
Note
Don't forget to delete cv.Mat, cv.MatVector and R(the Mat you get from MatVector) when you don't want to use them any more.

Making Borders for Images (Padding)

If you want to create a border around the image, something like a photo frame, you can use cv.copyMakeBorder() function. But it has more applications for convolution operation, zero padding etc. This function takes following arguments:

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