init - 初始化项目
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# How to run deep networks in browser {#tutorial_dnn_javascript}
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@tableofcontents
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@prev_tutorial{tutorial_dnn_yolo}
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@next_tutorial{tutorial_dnn_custom_layers}
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| | |
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| -: | :- |
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| Original author | Dmitry Kurtaev |
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| Compatibility | OpenCV >= 3.3.1 |
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## Introduction
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This tutorial will show us how to run deep learning models using OpenCV.js right
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in a browser. Tutorial refers a sample of face detection and face recognition
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models pipeline.
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## Face detection
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Face detection network gets BGR image as input and produces set of bounding boxes
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that might contain faces. All that we need is just select the boxes with a strong
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confidence.
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## Face recognition
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Network is called OpenFace (project https://github.com/cmusatyalab/openface).
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Face recognition model receives RGB face image of size `96x96`. Then it returns
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`128`-dimensional unit vector that represents input face as a point on the unit
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multidimensional sphere. So difference between two faces is an angle between two
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output vectors.
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## Sample
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All the sample is an HTML page that has JavaScript code to use OpenCV.js functionality.
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You may see an insertion of this page below. Press `Start` button to begin a demo.
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Press `Add a person` to name a person that is recognized as an unknown one.
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Next we'll discuss main parts of the code.
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@htmlinclude js_face_recognition.html
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-# Run face detection network to detect faces on input image.
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@snippet dnn/js_face_recognition.html Run face detection model
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You may play with input blob sizes to balance detection quality and efficiency.
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The bigger input blob the smaller faces may be detected.
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-# Run face recognition network to receive `128`-dimensional unit feature vector by input face image.
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@snippet dnn/js_face_recognition.html Get 128 floating points feature vector
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-# Perform a recognition.
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@snippet dnn/js_face_recognition.html Recognize
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Match a new feature vector with registered ones. Return a name of the best matched person.
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-# The main loop.
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@snippet dnn/js_face_recognition.html Define frames processing
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A main loop of our application receives a frames from a camera and makes a recognition
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of an every detected face on the frame. We start this function ones when OpenCV.js was
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initialized and deep learning models were downloaded.
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