Explorations using OpenCV in node.js.
Clone the repo, cd into the directory, and install the required packages. opencv4nodejs can take a while to download, so be patient.
$ git clone https://github.com/RobertMcReed/node-opencv.git
$ cd node-opencv
$ npm install
Pass it an OpenCV image and it will try to detect a face. If a face is found, a cropped image showing only the face is returned. gray is a switch determining whether or not the image should be converted to grayscale.
If no face is found, null is returned.
Pass it an OpenCV image and it will try to detect a face. If a face is found, an OpenCV Rect object is returned. The Rect object has the shape { width, height, x, y }.
If no face is found, null is returned.
Returns a function that can be passed as handleKeypress to startVideo to terminate the video after a given number of frames.
Take all images from the dirPath directory that match the regext string passed (if passed) and return an array of OpenCV Image Mats.
Same as getImgsFromDir, but returns faces found in the images (filtering out images that don't have a face). options must contain a key dirPath. options can include regex, gray, and resize. gray is a boolean, resize is an object { x, y }
Same as getFacesFromDir, but grayscales the image and resizes it to 80x80.
Takes an array of images, splitting it at the provided percentage [0, 1] and returning the first as train and the second as test.
const trainFaceClassifiers = imageSets => {
Takes an array of metadata/data and returns the Eigen, Fisher, and LBPH face detectors trained on the images referenced in the argument imageSets.
imageSets should be an array containing objects with the following shape:
{
label: 'someLabel',
dirPath: './some/path',
regex: 'some-pattern',
faces: [<array of mats>]
}
label is required and should describe the collection, ie the name of the person in the images. If you already have an array of faces formatted for detection (for example, from calling saveFacesFromVideo or getFacesFromDir and resizing to a square) then you can pass the array in directly. Otherwise, you must describe a dirPath with the optional regex string to read in and detect images from a directory.
The return is an object containing three different predictor functions. The object has this shape:
{ eigenPredict, fisherPredict, lbphPredict }
Pass an OpenCV Image Mat into any of the predictors and get an object containing the corresponding label and confidence of the prediction.
Takes an array and randomly shuffles its contents, returning a new array.
Takes an array and splits it at the provided percentage [0, 1]. Returns an array of arrays containing the contents.
Start an OpenCV video stream. By default, the stream will be displayed and can be closed by pressing q while the stream is focused.
handlers and options can be passed to modify the default behavior.
Supports the following methods:
handleFrame(frame, options)handleShow(frame, options)handleKeypress(frame, options)handleExit(frame, options)
A callback that will be called before the frame is displayed. Must return an OpenCV image.
The logic that controls how the video is shown. Runs after handleFrame. Does not return anything.
Run custom logic in response to user keypresses. Must return a boolean indicating whether the stream should continue or not.
A function to run on program exit. Returns result from startVideo.
An object to tweak behavior. Passed as the second argument to all handlers.
By default it has the shape { devicePort, qKey }.
deviceShape can be used to override the default webcam. qKey can be used to override the default exit key.
Starts the webcam and captures a given number of faces, returning them in an array.
- Write the faces to disk by providing a
folderPath. If the folder doesn't exist, it is created. - Capture 10 faces by default or provide your own number as
numFaces. - Resize the faces by providing
resize: { x: int, y: int }in options.
options = { resize, folderPath, numFaces=10, }