JavaScript bindings for dnn module

doc/tutorials/dnn/dnn_javascript/dnn_javascript.markdown

@@ -0,0 +1,44 @@
+# How to run deep networks in browser {#tutorial_dnn_javascript}
+
+## Introduction
+This tutorial will show us how to run deep learning models using OpenCV.js right
+in a browser. Tutorial refers a sample of face detection and face recognition
+models pipeline.
+
+## Face detection
+Face detection network gets BGR image as input and produces set of bounding boxes
+that might contain faces. All that we need is just select the boxes with a strong
+confidence.
+
+## Face recognition
+Network is called OpenFace (project https://github.com/cmusatyalab/openface).
+Face recognition model receives RGB face image of size `96x96`. Then it returns
+`128`-dimensional unit vector that represents input face as a point on the unit
+multidimensional sphere. So difference between two faces is an angle between two
+output vectors.
+
+## Sample
+All the sample is an HTML page that has JavaScript code to use OpenCV.js functionality.
+You may see an insertion of this page below. Press `Start` button to begin a demo.
+Press `Add a person` to name a person that is recognized as an unknown one.
+Next we'll discuss main parts of the code.
+
+@htmlinclude js_face_recognition.html
+
+-# Run face detection network to detect faces on input image.
+@snippet dnn/js_face_recognition.html Run face detection model
+You may play with input blob sizes to balance detection quality and efficiency.
+The bigger input blob the smaller faces may be detected.
+
+-# Run face recognition network to receive `128`-dimensional unit feature vector by input face image.
+@snippet dnn/js_face_recognition.html Get 128 floating points feature vector
+
+-# Perform a recognition.
+@snippet dnn/js_face_recognition.html Recognize
+Match a new feature vector with registered ones. Return a name of the best matched person.
+
+-# The main loop.
+@snippet dnn/js_face_recognition.html Define frames processing
+A main loop of our application receives a frames from a camera and makes a recognition
+of an every detected face on the frame. We start this function ones when OpenCV.js was
+initialized and deep learning models were downloaded.

doc/tutorials/dnn/table_of_content_dnn.markdown

@@ -25,6 +25,14 @@ Deep Neural Networks (dnn module) {#tutorial_table_of_content_dnn}
 
     In this tutorial we describe the ways to schedule your networks using Halide backend in OpenCV deep learning module.
 
+-   @subpage tutorial_dnn_android
+
+    *Compatibility:* \> OpenCV 3.3
+
+    *Author:* Dmitry Kurtaev
+
+    This tutorial will show you how to run deep learning model using OpenCV on Android device.
+
 -   @subpage tutorial_dnn_yolo
 
     *Compatibility:* \> OpenCV 3.3.1
@@ -32,3 +40,11 @@ Deep Neural Networks (dnn module) {#tutorial_table_of_content_dnn}
     *Author:* Alessandro de Oliveira Faria
 
     In this tutorial you will learn how to use opencv_dnn module using yolo_object_detection with device capture, video file or image.
+
+-   @subpage tutorial_dnn_javascript
+
+    *Compatibility:* \> OpenCV 3.3.1
+
+    *Author:* Dmitry Kurtaev
+
+    In this tutorial we'll run deep learning models in browser using OpenCV.js.