cordova-plugin-facedetection-lite

Cordova Plugin Face Detection - LITE

This plug-in makes facial detection offline with a few lines of code, without the need to use TensorFlow. Was designed to run on old smartphones. This plugin implementation of the method described in 2013 by Markuš et al.

Old devices tested:

• Moto E - Android 5.1
• Moto J5 - Android 6.0
• iPhone 6 - iOS 11
• iPhone 6s - iOS 12

PS: For older smartphones, it is ideal that each frame analyzed has a maximum height and width of 60 pixels and the processing loop runs every 100 milliseconds or more. In the sample project this was implemented.

Installation

cordova plugins add cordova-plugin-facedetection-lite

Methods

initFaceDetection(sizeFrameMemory, faceFinderPath, resultCallback)

• sizeFrameMemory - Number of frames that will be used to reinforce the detection of all faces. Defaults to 5
• faceFinderPath - Facial training file location, being allowed offline. Defaults to cascades/facefinder
• resultCallback - Callback function

Code:

facedetection.initFaceDetection(5, "./facefinder", function (result) {
    /* Here you can create the loop to detect frames */
});

Warning: Until the current version, the parameters are being ignored on some platforms, being ixed default value in the code. {: .alert .alert-warning}

---

detections(rgba, width, height, minSizeFace, maxSizeFace, iouthreshold, resultCallback)

• rgba - Image in byte array
• width - Image width
• height - Image height
• minSizeFace - Minimum size of selected faces
• maxSizeFace - Minimum size of selected faces
• iouthreshold - Maximum size of selected faces
• resultCallback - Callback function

Warning: Until the current version, only the first 3 parameters are implemented and the rest of the parameters are being ignored on some of the platforms, being fixed default value in the code.

Code:

facedetection.detections(rgba, cameraWidth, cameraHeight, cameraWidth * 0.2, cameraWidth * 1.2, 0.1, function (dets) {
    for (i = 0; i < dets.length; ++i) {
        var box = dets[i];

        var canvasPreviewCtx = canvasPreview.getContext('2d');
        canvasPreviewCtx.beginPath();
        canvasPreviewCtx.arc(box[1], box[0], box[2] / 2, 0, 2 * Math.PI, false);
        canvasPreviewCtx.lineWidth = 1;
        canvasPreviewCtx.strokeStyle = 'red';
        canvasPreviewCtx.stroke();
    }
});

Sample App

TODO

iOS

Browser

Task List

• Basic structure of the plugin;
• Add PicoJS library to the Browser
• Compile Pico library in C for iOS
• Compile Pico library in C for Android
• Process dynamic path to training file
• Process dynamic parameters when calling each function

Development

If you intend to do some improvement in the project, follow some instructions, such as compiling library in the C language.

Recompiling libraries

If you modify the C source files, be sure to re-build the compiled libraries.

Android

You can re-build the libpicornt.so binaries using the ndk-build script.

To do so:

• Install Android NDK as instructed here
• Add the NDK install path to your path environment variable
  • By default it's installed under $ANDROID_SDK_HOME/ndk-bundle
  • e.g. export PATH=$PATH;$ANDROID_SDK_HOME/ndk-bundle
• Set the ANDROID_NDK_HOME environment variable to your NDK install path
  • e.g. export ANDROID_NDK_HOME=$ANDROID_SDK_HOME/ndk-bundle
• Open terminal in plugin root folder
• Run ./compile-android (compile-android.cmd on Windows)

iOS

If you modify the C source code in common/picornt/ you'll need to rebuild the static library and headers in src/ios/libs.

• Open terminal in plugin root folder
• Run ./compile-ios

Credits

Created by Luís De Marchi @luisdemarchi - Linkedin

Libraries used:

• Mobile (Language C) : nenadmarkus/pico
• Browser (Language JS): tehnokv/picojs