Merge remote-tracking branch 'upstream/3.4' into merge-3.4

cmake/OpenCVFindLibsGrfmt.cmake

@@ -6,6 +6,7 @@
 if(BUILD_ZLIB)
   ocv_clear_vars(ZLIB_FOUND)
 else()
+  ocv_clear_internal_cache_vars(ZLIB_LIBRARY ZLIB_INCLUDE_DIR)
   find_package(ZLIB "${MIN_VER_ZLIB}")
   if(ZLIB_FOUND AND ANDROID)
     if(ZLIB_LIBRARIES MATCHES "/usr/(lib|lib32|lib64)/libz.so$")
@@ -31,11 +32,12 @@ if(WITH_JPEG)
   if(BUILD_JPEG)
     ocv_clear_vars(JPEG_FOUND)
   else()
+    ocv_clear_internal_cache_vars(JPEG_LIBRARY JPEG_INCLUDE_DIR)
     include(FindJPEG)
   endif()
 
   if(NOT JPEG_FOUND)
-    ocv_clear_vars(JPEG_LIBRARY JPEG_LIBRARIES JPEG_INCLUDE_DIR)
+    ocv_clear_vars(JPEG_LIBRARY JPEG_INCLUDE_DIR)
 
     if(NOT BUILD_JPEG_TURBO_DISABLE)
       set(JPEG_LIBRARY libjpeg-turbo CACHE INTERNAL "")
@@ -76,6 +78,7 @@ if(WITH_TIFF)
   if(BUILD_TIFF)
     ocv_clear_vars(TIFF_FOUND)
   else()
+    ocv_clear_internal_cache_vars(TIFF_LIBRARY TIFF_INCLUDE_DIR)
     include(FindTIFF)
     if(TIFF_FOUND)
       ocv_parse_header("${TIFF_INCLUDE_DIR}/tiff.h" TIFF_VERSION_LINES TIFF_VERSION_CLASSIC TIFF_VERSION_BIG TIFF_VERSION TIFF_BIGTIFF_VERSION)
@@ -119,6 +122,7 @@ if(WITH_WEBP)
   if(BUILD_WEBP)
     ocv_clear_vars(WEBP_FOUND WEBP_LIBRARY WEBP_LIBRARIES WEBP_INCLUDE_DIR)
   else()
+    ocv_clear_internal_cache_vars(WEBP_LIBRARY WEBP_INCLUDE_DIR)
     include(cmake/OpenCVFindWebP.cmake)
     if(WEBP_FOUND)
       set(HAVE_WEBP 1)
@@ -212,6 +216,7 @@ if(WITH_PNG)
   if(BUILD_PNG)
     ocv_clear_vars(PNG_FOUND)
   else()
+    ocv_clear_internal_cache_vars(PNG_LIBRARY PNG_INCLUDE_DIR)
     include(FindPNG)
     if(PNG_FOUND)
       include(CheckIncludeFile)
@@ -243,6 +248,7 @@ endif()
 if(WITH_OPENEXR)
   ocv_clear_vars(HAVE_OPENEXR)
   if(NOT BUILD_OPENEXR)
+    ocv_clear_internal_cache_vars(OPENEXR_INCLUDE_PATHS OPENEXR_LIBRARIES OPENEXR_ILMIMF_LIBRARY OPENEXR_VERSION)
     include("${OpenCV_SOURCE_DIR}/cmake/OpenCVFindOpenEXR.cmake")
   endif()
 

cmake/OpenCVModule.cmake

@@ -98,15 +98,6 @@ macro(ocv_add_dependencies full_modname)
   endforeach()
   unset(__depsvar)
 
-  # hack for python
-  set(__python_idx)
-  list(FIND OPENCV_MODULE_${full_modname}_WRAPPERS "python" __python_idx)
-  if (NOT __python_idx EQUAL -1)
-    list(REMOVE_ITEM OPENCV_MODULE_${full_modname}_WRAPPERS "python")
-    list(APPEND OPENCV_MODULE_${full_modname}_WRAPPERS "python_bindings_generator" "python2" "python3")
-  endif()
-  unset(__python_idx)
-
   ocv_list_unique(OPENCV_MODULE_${full_modname}_REQ_DEPS)
   ocv_list_unique(OPENCV_MODULE_${full_modname}_OPT_DEPS)
   ocv_list_unique(OPENCV_MODULE_${full_modname}_PRIVATE_REQ_DEPS)
@@ -210,9 +201,17 @@ macro(ocv_add_module _name)
       set(OPENCV_MODULES_DISABLED_USER ${OPENCV_MODULES_DISABLED_USER} "${the_module}" CACHE INTERNAL "List of OpenCV modules explicitly disabled by user")
     endif()
 
-    # add reverse wrapper dependencies
+    # add reverse wrapper dependencies (BINDINDS)
     foreach (wrapper ${OPENCV_MODULE_${the_module}_WRAPPERS})
-      ocv_add_dependencies(opencv_${wrapper} OPTIONAL ${the_module})
+      if(wrapper STREQUAL "python")  # hack for python (BINDINDS)
+        ocv_add_dependencies(opencv_python2 OPTIONAL ${the_module})
+        ocv_add_dependencies(opencv_python3 OPTIONAL ${the_module})
+      else()
+        ocv_add_dependencies(opencv_${wrapper} OPTIONAL ${the_module})
+      endif()
+      if(DEFINED OPENCV_MODULE_opencv_${wrapper}_bindings_generator_CLASS)
+        ocv_add_dependencies(opencv_${wrapper}_bindings_generator OPTIONAL ${the_module})
+      endif()
     endforeach()
 
     # stop processing of current file

cmake/OpenCVUtils.cmake

@@ -400,6 +400,24 @@ macro(ocv_clear_vars)
   endforeach()
 endmacro()
 
+
+# Clears passed variables with INTERNAL type from CMake cache
+macro(ocv_clear_internal_cache_vars)
+  foreach(_var ${ARGN})
+    get_property(_propertySet CACHE ${_var} PROPERTY TYPE SET)
+    if(_propertySet)
+      get_property(_type CACHE ${_var} PROPERTY TYPE)
+      if(_type STREQUAL "INTERNAL")
+        message("Cleaning INTERNAL cached variable: ${_var}")
+        unset(${_var} CACHE)
+      endif()
+    endif()
+  endforeach()
+  unset(_propertySet)
+  unset(_type)
+endmacro()
+
+
 set(OCV_COMPILER_FAIL_REGEX
     "argument .* is not valid"                  # GCC 9+ (including support of unicode quotes)
     "command[- ]line option .* is valid for .* but not for C\\+\\+" # GNU

cmake/platforms/OpenCV-Emscripten.cmake

@@ -0,0 +1 @@
+set(OPENCV_SKIP_LINK_AS_NEEDED 1)

doc/js_tutorials/js_assets/js_dnn_example_helper.js

@@ -0,0 +1,119 @@
+getBlobFromImage = function(inputSize, mean, std, swapRB, image) {
+    let mat;
+    if (typeof(image) === 'string') {
+        mat = cv.imread(image);
+    } else {
+        mat = image;
+    }
+
+    let matC3 = new cv.Mat(mat.matSize[0], mat.matSize[1], cv.CV_8UC3);
+    cv.cvtColor(mat, matC3, cv.COLOR_RGBA2BGR);
+    let input = cv.blobFromImage(matC3, std, new cv.Size(inputSize[0], inputSize[1]),
+                                 new cv.Scalar(mean[0], mean[1], mean[2]), swapRB);
+
+    matC3.delete();
+    return input;
+}
+
+loadLables = async function(labelsUrl) {
+    let response = await fetch(labelsUrl);
+    let label = await response.text();
+    label = label.split('\n');
+    return label;
+}
+
+loadModel = async function(e) {
+    return new Promise((resolve) => {
+        let file = e.target.files[0];
+        let path = file.name;
+        let reader = new FileReader();
+        reader.readAsArrayBuffer(file);
+        reader.onload = function(ev) {
+            if (reader.readyState === 2) {
+                let buffer = reader.result;
+                let data = new Uint8Array(buffer);
+                cv.FS_createDataFile('/', path, data, true, false, false);
+                resolve(path);
+            }
+        }
+    });
+}
+
+getTopClasses = function(probs, labels, topK = 3) {
+    probs = Array.from(probs);
+    let indexes = probs.map((prob, index) => [prob, index]);
+    let sorted = indexes.sort((a, b) => {
+        if (a[0] === b[0]) {return 0;}
+        return a[0] < b[0] ? -1 : 1;
+    });
+    sorted.reverse();
+    let classes = [];
+    for (let i = 0; i < topK; ++i) {
+        let prob = sorted[i][0];
+        let index = sorted[i][1];
+        let c = {
+            label: labels[index],
+            prob: (prob * 100).toFixed(2)
+        }
+        classes.push(c);
+    }
+    return classes;
+}
+
+loadImageToCanvas = function(e, canvasId) {
+    let files = e.target.files;
+    let imgUrl = URL.createObjectURL(files[0]);
+    let canvas = document.getElementById(canvasId);
+    let ctx = canvas.getContext('2d');
+    let img = new Image();
+    img.crossOrigin = 'anonymous';
+    img.src = imgUrl;
+    img.onload = function() {
+        ctx.drawImage(img, 0, 0, canvas.width, canvas.height);
+    };
+}
+
+drawInfoTable = async function(jsonUrl, divId) {
+    let response = await fetch(jsonUrl);
+    let json = await response.json();
+
+    let appendix = document.getElementById(divId);
+    for (key of Object.keys(json)) {
+        let h3 = document.createElement('h3');
+        h3.textContent = key + " model";
+        appendix.appendChild(h3);
+
+        let table = document.createElement('table');
+        let head_tr = document.createElement('tr');
+        for (head of Object.keys(json[key][0])) {
+            let th = document.createElement('th');
+            th.textContent = head;
+            th.style.border = "1px solid black";
+            head_tr.appendChild(th);
+        }
+        table.appendChild(head_tr)
+
+        for (model of json[key]) {
+            let tr = document.createElement('tr');
+            for (params of Object.keys(model)) {
+                let td = document.createElement('td');
+                td.style.border = "1px solid black";
+                if (params !== "modelUrl" && params !== "configUrl" && params !== "labelsUrl") {
+                    td.textContent = model[params];
+                    tr.appendChild(td);
+                } else {
+                    let a = document.createElement('a');
+                    let link = document.createTextNode('link');
+                    a.append(link);
+                    a.href = model[params];
+                    td.appendChild(a);
+                    tr.appendChild(td);
+                }
+            }
+            table.appendChild(tr);
+        }
+        table.style.width = "800px";
+        table.style.borderCollapse = "collapse";
+        appendix.appendChild(table);
+    }
+}

doc/js_tutorials/js_assets/js_image_classification.html

@@ -0,0 +1,263 @@
+<!DOCTYPE html>
+<html>
+
+<head>
+    <meta charset="utf-8">
+    <title>Image Classification Example</title>
+    <link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+
+<body>
+<h2>Image Classification Example</h2>
+<p>
+    This tutorial shows you how to write an image classification example with OpenCV.js.<br>
+    To try the example you should click the <b>modelFile</b> button(and <b>configFile</b> button if needed) to upload inference model.
+    You can find the model URLs and parameters in the <a href="#appendix">model info</a> section.
+    Then You should change the parameters in the first code snippet according to the uploaded model.
+    Finally click <b>Try it</b> button to see the result. You can choose any other images.<br>
+</p>
+
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+        <tr>
+            <td>
+                <canvas id="canvasInput" width="400" height="400"></canvas>
+            </td>
+            <td>
+                <table style="visibility: hidden;" id="result">
+                    <thead>
+                        <tr>
+                            <th scope="col">#</th>
+                            <th scope="col" width=300>Label</th>
+                            <th scope="col">Probability</th>
+                        </tr>
+                    </thead>
+                    <tbody>
+                        <tr>
+                            <th scope="row">1</th>
+                            <td id="label0" align="center"></td>
+                            <td id="prob0" align="center"></td>
+                        </tr>
+                        <tr>
+                            <th scope="row">2</th>
+                            <td id="label1" align="center"></td>
+                            <td id="prob1" align="center"></td>
+                        </tr>
+                        <tr>
+                            <th scope="row">3</th>
+                            <td id="label2" align="center"></td>
+                            <td id="prob2" align="center"></td>
+                        </tr>
+                    </tbody>
+                </table>
+                <p id='status' align="left"></p>
+            </td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    canvasInput <input type="file" id="fileInput" name="file" accept="image/*">
+                </div>
+            </td>
+            <td></td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    modelFile <input type="file" id="modelFile">
+                </div>
+            </td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    configFile <input type="file" id="configFile">
+                </div>
+            </td>
+        </tr>
+    </table>
+</div>
+
+<div>
+    <p class="err" id="errorMessage"></p>
+</div>
+
+<div>
+    <h3>Help function</h3>
+    <p>1.The parameters for model inference which you can modify to investigate more models.</p>
+    <textarea class="code" rows="13" cols="100" id="codeEditor" spellcheck="false"></textarea>
+    <p>2.Main loop in which will read the image from canvas and do inference once.</p>
+    <textarea class="code" rows="17" cols="100" id="codeEditor1" spellcheck="false"></textarea>
+    <p>3.Load labels from txt file and process it into an array.</p>
+    <textarea class="code" rows="7" cols="100" id="codeEditor2" spellcheck="false"></textarea>
+    <p>4.Get blob from image as input for net, and standardize it with <b>mean</b> and <b>std</b>.</p>
+    <textarea class="code" rows="17" cols="100" id="codeEditor3" spellcheck="false"></textarea>
+    <p>5.Fetch model file and save to emscripten file system once click the input button.</p>
+    <textarea class="code" rows="17" cols="100" id="codeEditor4" spellcheck="false"></textarea>
+    <p>6.The post-processing, including softmax if needed and get the top classes from the output vector.</p>
+    <textarea class="code" rows="35" cols="100" id="codeEditor5" spellcheck="false"></textarea>
+</div>
+
+<div id="appendix">
+    <h2>Model Info:</h2>
+</div>
+
+<script src="utils.js" type="text/javascript"></script>
+<script src="js_dnn_example_helper.js" type="text/javascript"></script>
+
+<script id="codeSnippet" type="text/code-snippet">
+inputSize = [224,224];
+mean = [104, 117, 123];
+std = 1;
+swapRB = false;
+
+// record if need softmax function for post-processing
+needSoftmax = false;
+
+// url for label file, can from local or Internet
+labelsUrl = "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/classification_classes_ILSVRC2012.txt";
+</script>
+
+<script id="codeSnippet1" type="text/code-snippet">
+main = async function() {
+    const labels = await loadLables(labelsUrl);
+    const input = getBlobFromImage(inputSize, mean, std, swapRB, 'canvasInput');
+    let net = cv.readNet(configPath, modelPath);
+    net.setInput(input);
+    const start = performance.now();
+    const result = net.forward();
+    const time  = performance.now()-start;
+    const probs = softmax(result);
+    const classes = getTopClasses(probs, labels);
+
+    updateResult(classes, time);
+    input.delete();
+    net.delete();
+    result.delete();
+}
+</script>
+
+<script id="codeSnippet5" type="text/code-snippet">
+softmax = function(result) {
+    let arr = result.data32F;
+    if (needSoftmax) {
+        const maxNum = Math.max(...arr);
+        const expSum = arr.map((num) => Math.exp(num - maxNum)).reduce((a, b) => a + b);
+        return arr.map((value, index) => {
+            return Math.exp(value - maxNum) / expSum;
+        });
+    } else {
+        return arr;
+    }
+}
+</script>
+
+<script type="text/javascript">
+    let jsonUrl = "js_image_classification_model_info.json";
+    drawInfoTable(jsonUrl, 'appendix');
+
+    let utils = new Utils('errorMessage');
+    utils.loadCode('codeSnippet', 'codeEditor');
+    utils.loadCode('codeSnippet1', 'codeEditor1');
+
+    let loadLablesCode = 'loadLables = ' + loadLables.toString();
+    document.getElementById('codeEditor2').value = loadLablesCode;
+    let getBlobFromImageCode = 'getBlobFromImage = ' + getBlobFromImage.toString();
+    document.getElementById('codeEditor3').value = getBlobFromImageCode;
+    let loadModelCode = 'loadModel = ' + loadModel.toString();
+    document.getElementById('codeEditor4').value = loadModelCode;
+
+    utils.loadCode('codeSnippet5', 'codeEditor5');
+    let getTopClassesCode = 'getTopClasses = ' + getTopClasses.toString();
+    document.getElementById('codeEditor5').value  += '\n' + '\n' + getTopClassesCode;
+
+    let canvas = document.getElementById('canvasInput');
+    let ctx = canvas.getContext('2d');
+    let img = new Image();
+    img.crossOrigin = 'anonymous';
+    img.src = 'space_shuttle.jpg';
+    img.onload = function() {
+        ctx.drawImage(img, 0, 0, canvas.width, canvas.height);
+    };
+
+    let tryIt = document.getElementById('tryIt');
+    tryIt.addEventListener('click', () => {
+        initStatus();
+        document.getElementById('status').innerHTML = 'Running function main()...';
+        utils.executeCode('codeEditor');
+        utils.executeCode('codeEditor1');
+        if (modelPath === "") {
+            document.getElementById('status').innerHTML = 'Runing failed.';
+            utils.printError('Please upload model file by clicking the button first.');
+        } else {
+            setTimeout(main, 1);
+        }
+    });
+
+    let fileInput = document.getElementById('fileInput');
+    fileInput.addEventListener('change', (e) => {
+        initStatus();
+        loadImageToCanvas(e, 'canvasInput');
+    });
+
+    let configPath = "";
+    let configFile = document.getElementById('configFile');
+    configFile.addEventListener('change', async (e) => {
+        initStatus();
+        configPath = await loadModel(e);
+        document.getElementById('status').innerHTML = `The config file '${configPath}' is created successfully.`;
+    });
+
+    let modelPath = "";
+    let modelFile = document.getElementById('modelFile');
+    modelFile.addEventListener('change', async (e) => {
+        initStatus();
+        modelPath = await loadModel(e);
+        document.getElementById('status').innerHTML = `The model file '${modelPath}' is created successfully.`;
+        configPath = "";
+        configFile.value = "";
+    });
+
+    utils.loadOpenCv(() => {
+        tryIt.removeAttribute('disabled');
+    });
+
+    var main = async function() {};
+    var softmax = function(result){};
+    var getTopClasses = function(mat, labels, topK = 3){};
+
+    utils.executeCode('codeEditor1');
+    utils.executeCode('codeEditor2');
+    utils.executeCode('codeEditor3');
+    utils.executeCode('codeEditor4');
+    utils.executeCode('codeEditor5');
+
+    function updateResult(classes, time) {
+        try{
+            classes.forEach((c,i) => {
+                let labelElement = document.getElementById('label'+i);
+                let probElement = document.getElementById('prob'+i);
+                labelElement.innerHTML = c.label;
+                probElement.innerHTML = c.prob + '%';
+            });
+            let result = document.getElementById('result');
+            result.style.visibility = 'visible';
+            document.getElementById('status').innerHTML = `<b>Model:</b> ${modelPath}<br>
+                                                         <b>Inference time:</b> ${time.toFixed(2)} ms`;
+        } catch(e) {
+            console.log(e);
+        }
+    }
+
+    function initStatus() {
+        document.getElementById('status').innerHTML = '';
+        document.getElementById('result').style.visibility = 'hidden';
+        utils.clearError();
+    }
+
+</script>
+
+</body>
+
+</html>

doc/js_tutorials/js_assets/js_image_classification_model_info.json

@@ -0,0 +1,65 @@
+{
+    "caffe": [
+        {
+            "model": "alexnet",
+            "mean": "104, 117, 123",
+            "std": "1",
+            "swapRB": "false",
+            "needSoftmax": "false",
+            "labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/classification_classes_ILSVRC2012.txt",
+            "modelUrl": "http://dl.caffe.berkeleyvision.org/bvlc_alexnet.caffemodel",
+            "configUrl": "https://raw.githubusercontent.com/BVLC/caffe/master/models/bvlc_alexnet/deploy.prototxt"
+        },
+        {
+            "model": "densenet",
+            "mean": "127.5, 127.5, 127.5",
+            "std": "0.007843",
+            "swapRB": "false",
+            "needSoftmax": "true",
+            "labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/classification_classes_ILSVRC2012.txt",
+            "modelUrl": "https://drive.google.com/open?id=0B7ubpZO7HnlCcHlfNmJkU2VPelE",
+            "configUrl": "https://raw.githubusercontent.com/shicai/DenseNet-Caffe/master/DenseNet_121.prototxt"
+        },
+        {
+            "model": "googlenet",
+            "mean": "104, 117, 123",
+            "std": "1",
+            "swapRB": "false",
+            "needSoftmax": "false",
+            "labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/classification_classes_ILSVRC2012.txt",
+            "modelUrl": "http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel",
+            "configUrl": "https://raw.githubusercontent.com/BVLC/caffe/master/models/bvlc_googlenet/deploy.prototxt"
+        },
+        {
+            "model": "squeezenet",
+            "mean": "104, 117, 123",
+            "std": "1",
+            "swapRB": "false",
+            "needSoftmax": "false",
+            "labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/classification_classes_ILSVRC2012.txt",
+            "modelUrl": "https://raw.githubusercontent.com/forresti/SqueezeNet/master/SqueezeNet_v1.0/squeezenet_v1.0.caffemodel",
+            "configUrl": "https://raw.githubusercontent.com/forresti/SqueezeNet/master/SqueezeNet_v1.0/deploy.prototxt"
+        },
+        {
+            "model": "VGG",
+            "mean": "104, 117, 123",
+            "std": "1",
+            "swapRB": "false",
+            "needSoftmax": "false",
+            "labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/classification_classes_ILSVRC2012.txt",
+            "modelUrl": "http://www.robots.ox.ac.uk/~vgg/software/very_deep/caffe/VGG_ILSVRC_19_layers.caffemodel",
+            "configUrl": "https://gist.githubusercontent.com/ksimonyan/3785162f95cd2d5fee77/raw/f02f8769e64494bcd3d7e97d5d747ac275825721/VGG_ILSVRC_19_layers_deploy.prototxt"
+        }
+    ],
+    "tensorflow": [
+        {
+            "model": "inception",
+            "mean": "123, 117, 104",
+            "std": "1",
+            "swapRB": "true",
+            "needSoftmax": "false",
+            "labelsUrl": "https://raw.githubusercontent.com/petewarden/tf_ios_makefile_example/master/data/imagenet_comp_graph_label_strings.txt",
+            "modelUrl": "https://raw.githubusercontent.com/petewarden/tf_ios_makefile_example/master/data/tensorflow_inception_graph.pb"
+        }
+    ]
+}

doc/js_tutorials/js_assets/js_image_classification_with_camera.html

@@ -0,0 +1,281 @@
+<!DOCTYPE html>
+<html>
+
+<head>
+    <meta charset="utf-8">
+    <title>Image Classification Example with Camera</title>
+    <link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+
+<body>
+<h2>Image Classification Example with Camera</h2>
+<p>
+    This tutorial shows you how to write an image classification example with camera.<br>
+    To try the example you should click the <b>modelFile</b> button(and <b>configFile</b> button if needed) to upload inference model.
+    You can find the model URLs and parameters in the <a href="#appendix">model info</a> section.
+    Then You should change the parameters in the first code snippet according to the uploaded model.
+    Finally click <b>Start/Stop</b> button to start or stop the camera capture.<br>
+</p>
+
+<div class="control"><button id="startAndStop" disabled>Start</button></div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+        <tr>
+            <td>
+                <video id="videoInput" width="400" height="400"></video>
+            </td>
+            <td>
+                <table style="visibility: hidden;" id="result">
+                    <thead>
+                        <tr>
+                            <th scope="col">#</th>
+                            <th scope="col" width=300>Label</th>
+                            <th scope="col">Probability</th>
+                        </tr>
+                    </thead>
+                    <tbody>
+                        <tr>
+                            <th scope="row">1</th>
+                            <td id="label0" align="center"></td>
+                            <td id="prob0" align="center"></td>
+                        </tr>
+                        <tr>
+                            <th scope="row">2</th>
+                            <td id="label1" align="center"></td>
+                            <td id="prob1" align="center"></td>
+                        </tr>
+                        <tr>
+                            <th scope="row">3</th>
+                            <td id="label2" align="center"></td>
+                            <td id="prob2" align="center"></td>
+                        </tr>
+                    </tbody>
+                </table>
+                <p id='status' align="left"></p>
+            </td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    videoInput
+                </div>
+            </td>
+            <td></td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    modelFile <input type="file" id="modelFile">
+                </div>
+            </td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    configFile <input type="file" id="configFile">
+                </div>
+            </td>
+        </tr>
+    </table>
+</div>
+
+<div>
+    <p class="err" id="errorMessage"></p>
+</div>
+
+<div>
+    <h3>Help function</h3>
+    <p>1.The parameters for model inference which you can modify to investigate more models.</p>
+    <textarea class="code" rows="13" cols="100" id="codeEditor" spellcheck="false"></textarea>
+    <p>2.The function to capture video from camera, and the main loop in which will do inference once.</p>
+    <textarea class="code" rows="35" cols="100" id="codeEditor1" spellcheck="false"></textarea>
+    <p>3.Load labels from txt file and process it into an array.</p>
+    <textarea class="code" rows="7" cols="100" id="codeEditor2" spellcheck="false"></textarea>
+    <p>4.Get blob from image as input for net, and standardize it with <b>mean</b> and <b>std</b>.</p>
+    <textarea class="code" rows="17" cols="100" id="codeEditor3" spellcheck="false"></textarea>
+    <p>5.Fetch model file and save to emscripten file system once click the input button.</p>
+    <textarea class="code" rows="17" cols="100" id="codeEditor4" spellcheck="false"></textarea>
+    <p>6.The post-processing, including softmax if needed and get the top classes from the output vector.</p>
+    <textarea class="code" rows="35" cols="100" id="codeEditor5" spellcheck="false"></textarea>
+</div>
+
+<div id="appendix">
+    <h2>Model Info:</h2>
+</div>
+
+<script src="utils.js" type="text/javascript"></script>
+<script src="js_dnn_example_helper.js" type="text/javascript"></script>
+
+<script id="codeSnippet" type="text/code-snippet">
+inputSize = [224,224];
+mean = [104, 117, 123];
+std = 1;
+swapRB = false;
+
+// record if need softmax function for post-processing
+needSoftmax = false;
+
+// url for label file, can from local or Internet
+labelsUrl = "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/classification_classes_ILSVRC2012.txt";
+</script>
+
+<script id="codeSnippet1" type="text/code-snippet">
+let frame = new cv.Mat(video.height, video.width, cv.CV_8UC4);
+let cap = new cv.VideoCapture(video);
+
+main = async function(frame) {
+    const labels = await loadLables(labelsUrl);
+    const input = getBlobFromImage(inputSize, mean, std, swapRB, frame);
+    let net = cv.readNet(configPath, modelPath);
+    net.setInput(input);
+    const start = performance.now();
+    const result = net.forward();
+    const time  = performance.now()-start;
+    const probs = softmax(result);
+    const classes = getTopClasses(probs, labels);
+
+    updateResult(classes, time);
+    setTimeout(processVideo, 0);
+    input.delete();
+    net.delete();
+    result.delete();
+}
+
+function processVideo() {
+    try {
+        if (!streaming) {
+            return;
+        }
+        cap.read(frame);
+        main(frame);
+    } catch (err) {
+        utils.printError(err);
+    }
+}
+
+setTimeout(processVideo, 0);
+</script>
+
+<script id="codeSnippet5" type="text/code-snippet">
+softmax = function(result) {
+    let arr = result.data32F;
+    if (needSoftmax) {
+        const maxNum = Math.max(...arr);
+        const expSum = arr.map((num) => Math.exp(num - maxNum)).reduce((a, b) => a + b);
+        return arr.map((value, index) => {
+            return Math.exp(value - maxNum) / expSum;
+        });
+    } else {
+        return arr;
+    }
+}
+</script>
+
+<script type="text/javascript">
+    let jsonUrl = "js_image_classification_model_info.json";
+    drawInfoTable(jsonUrl, 'appendix');
+
+    let utils = new Utils('errorMessage');
+    utils.loadCode('codeSnippet', 'codeEditor');
+    utils.loadCode('codeSnippet1', 'codeEditor1');
+
+    let loadLablesCode = 'loadLables = ' + loadLables.toString();
+    document.getElementById('codeEditor2').value = loadLablesCode;
+    let getBlobFromImageCode = 'getBlobFromImage = ' + getBlobFromImage.toString();
+    document.getElementById('codeEditor3').value = getBlobFromImageCode;
+    let loadModelCode = 'loadModel = ' + loadModel.toString();
+    document.getElementById('codeEditor4').value = loadModelCode;
+
+    utils.loadCode('codeSnippet5', 'codeEditor5');
+    let getTopClassesCode = 'getTopClasses = ' + getTopClasses.toString();
+    document.getElementById('codeEditor5').value  += '\n' + '\n' + getTopClassesCode;
+
+    let video = document.getElementById('videoInput');
+    let streaming = false;
+    let startAndStop = document.getElementById('startAndStop');
+    startAndStop.addEventListener('click', () => {
+        if (!streaming) {
+            utils.clearError();
+            utils.startCamera('qvga', onVideoStarted, 'videoInput');
+        } else {
+            utils.stopCamera();
+            onVideoStopped();
+        }
+    });
+
+    let configPath = "";
+    let configFile = document.getElementById('configFile');
+    configFile.addEventListener('change', async (e) => {
+        initStatus();
+        configPath = await loadModel(e);
+        document.getElementById('status').innerHTML = `The config file '${configPath}' is created successfully.`;
+    });
+
+    let modelPath = "";
+    let modelFile = document.getElementById('modelFile');
+    modelFile.addEventListener('change', async (e) => {
+        initStatus();
+        modelPath = await loadModel(e);
+        document.getElementById('status').innerHTML = `The model file '${modelPath}' is created successfully.`;
+        configPath = "";
+        configFile.value = "";
+    });
+
+    utils.loadOpenCv(() => {
+        startAndStop.removeAttribute('disabled');
+
+    });
+
+    var main = async function(frame) {};
+    var softmax = function(result){};
+    var getTopClasses = function(mat, labels, topK = 3){};
+
+    utils.executeCode('codeEditor1');
+    utils.executeCode('codeEditor2');
+    utils.executeCode('codeEditor3');
+    utils.executeCode('codeEditor4');
+    utils.executeCode('codeEditor5');
+
+    function onVideoStarted() {
+        streaming = true;
+        startAndStop.innerText = 'Stop';
+        videoInput.width = videoInput.videoWidth;
+        videoInput.height = videoInput.videoHeight;
+        utils.executeCode('codeEditor');
+        utils.executeCode('codeEditor1');
+    }
+
+    function onVideoStopped() {
+        streaming = false;
+        startAndStop.innerText = 'Start';
+        initStatus();
+    }
+
+    function updateResult(classes, time) {
+        try{
+            classes.forEach((c,i) => {
+                let labelElement = document.getElementById('label'+i);
+                let probElement = document.getElementById('prob'+i);
+                labelElement.innerHTML = c.label;
+                probElement.innerHTML = c.prob + '%';
+            });
+            let result = document.getElementById('result');
+            result.style.visibility = 'visible';
+            document.getElementById('status').innerHTML = `<b>Model:</b> ${modelPath}<br>
+                                                         <b>Inference time:</b> ${time.toFixed(2)} ms`;
+        } catch(e) {
+            console.log(e);
+        }
+    }
+
+    function initStatus() {
+        document.getElementById('status').innerHTML = '';
+        document.getElementById('result').style.visibility = 'hidden';
+        utils.clearError();
+    }
+
+</script>
+
+</body>
+
+</html>

doc/js_tutorials/js_assets/js_object_detection.html

@@ -0,0 +1,387 @@
+<!DOCTYPE html>
+<html>
+
+<head>
+    <meta charset="utf-8">
+    <title>Object Detection Example</title>
+    <link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+
+<body>
+<h2>Object Detection Example</h2>
+<p>
+    This tutorial shows you how to write an object detection example with OpenCV.js.<br>
+    To try the example you should click the <b>modelFile</b> button(and <b>configFile</b> button if needed) to upload inference model.
+    You can find the model URLs and parameters in the <a href="#appendix">model info</a> section.
+    Then You should change the parameters in the first code snippet according to the uploaded model.
+    Finally click <b>Try it</b> button to see the result. You can choose any other images.<br>
+</p>
+
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+        <tr>
+            <td>
+                <canvas id="canvasInput" width="400" height="400"></canvas>
+            </td>
+            <td>
+                <canvas id="canvasOutput" style="visibility: hidden;" width="400" height="400"></canvas>
+            </td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    canvasInput <input type="file" id="fileInput" name="file" accept="image/*">
+                </div>
+            </td>
+            <td>
+                <p id='status' align="left"></p>
+            </td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    modelFile <input type="file" id="modelFile" name="file">
+                </div>
+            </td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    configFile <input type="file" id="configFile">
+                </div>
+            </td>
+        </tr>
+    </table>
+</div>
+
+<div>
+    <p class="err" id="errorMessage"></p>
+</div>
+
+<div>
+    <h3>Help function</h3>
+    <p>1.The parameters for model inference which you can modify to investigate more models.</p>
+    <textarea class="code" rows="15" cols="100" id="codeEditor" spellcheck="false"></textarea>
+    <p>2.Main loop in which will read the image from canvas and do inference once.</p>
+    <textarea class="code" rows="16" cols="100" id="codeEditor1" spellcheck="false"></textarea>
+    <p>3.Load labels from txt file and process it into an array.</p>
+    <textarea class="code" rows="7" cols="100" id="codeEditor2" spellcheck="false"></textarea>
+    <p>4.Get blob from image as input for net, and standardize it with <b>mean</b> and <b>std</b>.</p>
+    <textarea class="code" rows="17" cols="100" id="codeEditor3" spellcheck="false"></textarea>
+    <p>5.Fetch model file and save to emscripten file system once click the input button.</p>
+    <textarea class="code" rows="17" cols="100" id="codeEditor4" spellcheck="false"></textarea>
+    <p>6.The post-processing, including get boxes from output and draw boxes into the image.</p>
+    <textarea class="code" rows="35" cols="100" id="codeEditor5" spellcheck="false"></textarea>
+</div>
+
+<div id="appendix">
+    <h2>Model Info:</h2>
+</div>
+
+<script src="utils.js" type="text/javascript"></script>
+<script src="js_dnn_example_helper.js" type="text/javascript"></script>
+
+<script id="codeSnippet" type="text/code-snippet">
+inputSize = [300, 300];
+mean = [127.5, 127.5, 127.5];
+std = 0.007843;
+swapRB = false;
+confThreshold = 0.5;
+nmsThreshold = 0.4;
+
+// The type of output, can be YOLO or SSD
+outType = "SSD";
+
+// url for label file, can from local or Internet
+labelsUrl = "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/object_detection_classes_pascal_voc.txt";
+</script>
+
+<script id="codeSnippet1" type="text/code-snippet">
+main = async function() {
+    const labels = await loadLables(labelsUrl);
+    const input = getBlobFromImage(inputSize, mean, std, swapRB, 'canvasInput');
+    let net = cv.readNet(configPath, modelPath);
+    net.setInput(input);
+    const start = performance.now();
+    const result = net.forward();
+    const time  = performance.now()-start;
+    const output = postProcess(result, labels);
+
+    updateResult(output, time);
+    input.delete();
+    net.delete();
+    result.delete();
+}
+</script>
+
+<script id="codeSnippet5" type="text/code-snippet">
+postProcess = function(result, labels) {
+    let canvasOutput = document.getElementById('canvasOutput');
+    const outputWidth = canvasOutput.width;
+    const outputHeight = canvasOutput.height;
+    const resultData = result.data32F;
+
+    // Get the boxes(with class and confidence) from the output
+    let boxes = [];
+    switch(outType) {
+        case "YOLO": {
+            const vecNum = result.matSize[0];
+            const vecLength = result.matSize[1];
+            const classNum = vecLength - 5;
+
+            for (let i = 0; i < vecNum; ++i) {
+                let vector = resultData.slice(i*vecLength, (i+1)*vecLength);
+                let scores = vector.slice(5, vecLength);
+                let classId = scores.indexOf(Math.max(...scores));
+                let confidence = scores[classId];
+                if (confidence > confThreshold) {
+                    let center_x = Math.round(vector[0] * outputWidth);
+                    let center_y = Math.round(vector[1] * outputHeight);
+                    let width = Math.round(vector[2] * outputWidth);
+                    let height = Math.round(vector[3] * outputHeight);
+                    let left = Math.round(center_x - width / 2);
+                    let top = Math.round(center_y - height / 2);
+
+                    let box = {
+                        scores: scores,
+                        classId: classId,
+                        confidence: confidence,
+                        bounding: [left, top, width, height],
+                        toDraw: true
+                    }
+                    boxes.push(box);
+                }
+            }
+
+            // NMS(Non Maximum Suppression) algorithm
+            let boxNum = boxes.length;
+            let tmp_boxes = [];
+            let sorted_boxes = [];
+            for (let c = 0; c < classNum; ++c) {
+                for (let i = 0; i < boxes.length; ++i) {
+                    tmp_boxes[i] = [boxes[i], i];
+                }
+                sorted_boxes = tmp_boxes.sort((a, b) => { return (b[0].scores[c] - a[0].scores[c]); });
+                for (let i = 0; i < boxNum; ++i) {
+                    if (sorted_boxes[i][0].scores[c] === 0) continue;
+                    else {
+                        for (let j = i + 1; j < boxNum; ++j) {
+                            if (IOU(sorted_boxes[i][0], sorted_boxes[j][0]) >= nmsThreshold) {
+                                boxes[sorted_boxes[j][1]].toDraw = false;
+                            }
+                        }
+                    }
+                }
+            }
+        } break;
+        case "SSD": {
+            const vecNum = result.matSize[2];
+            const vecLength = 7;
+
+            for (let i = 0; i < vecNum; ++i) {
+                let vector = resultData.slice(i*vecLength, (i+1)*vecLength);
+                let confidence = vector[2];
+                if (confidence > confThreshold) {
+                    let left, top, right, bottom, width, height;
+                    left = Math.round(vector[3]);
+                    top = Math.round(vector[4]);
+                    right = Math.round(vector[5]);
+                    bottom = Math.round(vector[6]);
+                    width = right - left + 1;
+                    height = bottom - top + 1;
+                    if (width <= 2 || height <= 2) {
+                        left = Math.round(vector[3] * outputWidth);
+                        top = Math.round(vector[4] * outputHeight);
+                        right = Math.round(vector[5] * outputWidth);
+                        bottom = Math.round(vector[6] * outputHeight);
+                        width = right - left + 1;
+                        height = bottom - top + 1;
+                    }
+                    let box = {
+                        classId: vector[1] - 1,
+                        confidence: confidence,
+                        bounding: [left, top, width, height],
+                        toDraw: true
+                    }
+                    boxes.push(box);
+                }
+            }
+        } break;
+        default:
+            console.error(`Unsupported output type ${outType}`)
+    }
+
+    // Draw the saved box into the image
+    let image = cv.imread("canvasInput");
+    let output = new cv.Mat(outputWidth, outputHeight, cv.CV_8UC3);
+    cv.cvtColor(image, output, cv.COLOR_RGBA2RGB);
+    let boxNum = boxes.length;
+    for (let i = 0; i < boxNum; ++i) {
+        if (boxes[i].toDraw) {
+            drawBox(boxes[i]);
+        }
+    }
+
+    return output;
+
+
+    // Calculate the IOU(Intersection over Union) of two boxes
+    function IOU(box1, box2) {
+        let bounding1 = box1.bounding;
+        let bounding2 = box2.bounding;
+        let s1 = bounding1[2] * bounding1[3];
+        let s2 = bounding2[2] * bounding2[3];
+
+        let left1 = bounding1[0];
+        let right1 = left1 + bounding1[2];
+        let left2 = bounding2[0];
+        let right2 = left2 + bounding2[2];
+        let overlapW = calOverlap([left1, right1], [left2, right2]);
+
+        let top1 = bounding2[1];
+        let bottom1 = top1 + bounding1[3];
+        let top2 = bounding2[1];
+        let bottom2 = top2 + bounding2[3];
+        let overlapH = calOverlap([top1, bottom1], [top2, bottom2]);
+
+        let overlapS = overlapW * overlapH;
+        return overlapS / (s1 + s2 + overlapS);
+    }
+
+    // Calculate the overlap range of two vector
+    function calOverlap(range1, range2) {
+        let min1 = range1[0];
+        let max1 = range1[1];
+        let min2 = range2[0];
+        let max2 = range2[1];
+
+        if (min2 > min1 && min2 < max1) {
+            return max1 - min2;
+        } else if (max2 > min1 && max2 < max1) {
+            return max2 - min1;
+        } else {
+            return 0;
+        }
+    }
+
+    // Draw one predict box into the origin image
+    function drawBox(box) {
+        let bounding = box.bounding;
+        let left = bounding[0];
+        let top = bounding[1];
+        let width = bounding[2];
+        let height = bounding[3];
+
+        cv.rectangle(output, new cv.Point(left, top), new cv.Point(left + width, top + height),
+                             new cv.Scalar(0, 255, 0));
+        cv.rectangle(output, new cv.Point(left, top), new cv.Point(left + width, top + 15),
+                             new cv.Scalar(255, 255, 255), cv.FILLED);
+        let text = `${labels[box.classId]}: ${box.confidence.toFixed(4)}`;
+        cv.putText(output, text, new cv.Point(left, top + 10), cv.FONT_HERSHEY_SIMPLEX, 0.3,
+                                 new cv.Scalar(0, 0, 0));
+    }
+}
+</script>
+
+<script type="text/javascript">
+    let jsonUrl = "js_object_detection_model_info.json";
+    drawInfoTable(jsonUrl, 'appendix');
+
+    let utils = new Utils('errorMessage');
+    utils.loadCode('codeSnippet', 'codeEditor');
+    utils.loadCode('codeSnippet1', 'codeEditor1');
+
+    let loadLablesCode = 'loadLables = ' + loadLables.toString();
+    document.getElementById('codeEditor2').value = loadLablesCode;
+    let getBlobFromImageCode = 'getBlobFromImage = ' + getBlobFromImage.toString();
+    document.getElementById('codeEditor3').value = getBlobFromImageCode;
+    let loadModelCode = 'loadModel = ' + loadModel.toString();
+    document.getElementById('codeEditor4').value = loadModelCode;
+
+    utils.loadCode('codeSnippet5', 'codeEditor5');
+
+    let canvas = document.getElementById('canvasInput');
+    let ctx = canvas.getContext('2d');
+    let img = new Image();
+    img.crossOrigin = 'anonymous';
+    img.src = 'lena.png';
+    img.onload = function() {
+        ctx.drawImage(img, 0, 0, canvas.width, canvas.height);
+    };
+
+    let tryIt = document.getElementById('tryIt');
+    tryIt.addEventListener('click', () => {
+        initStatus();
+        document.getElementById('status').innerHTML = 'Running function main()...';
+        utils.executeCode('codeEditor');
+        utils.executeCode('codeEditor1');
+        if (modelPath === "") {
+            document.getElementById('status').innerHTML = 'Runing failed.';
+            utils.printError('Please upload model file by clicking the button first.');
+        } else {
+            setTimeout(main, 1);
+        }
+    });
+
+    let fileInput = document.getElementById('fileInput');
+    fileInput.addEventListener('change', (e) => {
+        initStatus();
+        loadImageToCanvas(e, 'canvasInput');
+    });
+
+    let configPath = "";
+    let configFile = document.getElementById('configFile');
+    configFile.addEventListener('change', async (e) => {
+        initStatus();
+        configPath = await loadModel(e);
+        document.getElementById('status').innerHTML = `The config file '${configPath}' is created successfully.`;
+    });
+
+    let modelPath = "";
+    let modelFile = document.getElementById('modelFile');
+    modelFile.addEventListener('change', async (e) => {
+        initStatus();
+        modelPath = await loadModel(e);
+        document.getElementById('status').innerHTML = `The model file '${modelPath}' is created successfully.`;
+        configPath = "";
+        configFile.value = "";
+    });
+
+    utils.loadOpenCv(() => {
+        tryIt.removeAttribute('disabled');
+    });
+
+    var main = async function() {};
+    var postProcess = function(result, labels) {};
+
+    utils.executeCode('codeEditor1');
+    utils.executeCode('codeEditor2');
+    utils.executeCode('codeEditor3');
+    utils.executeCode('codeEditor4');
+    utils.executeCode('codeEditor5');
+
+
+    function updateResult(output, time) {
+        try{
+            let canvasOutput = document.getElementById('canvasOutput');
+            canvasOutput.style.visibility = "visible";
+            cv.imshow('canvasOutput', output);
+            document.getElementById('status').innerHTML = `<b>Model:</b> ${modelPath}<br>
+                                                           <b>Inference time:</b> ${time.toFixed(2)} ms`;
+        } catch(e) {
+            console.log(e);
+        }
+    }
+
+    function initStatus() {
+        document.getElementById('status').innerHTML = '';
+        document.getElementById('canvasOutput').style.visibility = "hidden";
+        utils.clearError();
+    }
+
+</script>
+
+</body>
+
+</html>

doc/js_tutorials/js_assets/js_object_detection_model_info.json

@@ -0,0 +1,39 @@
+{
+    "caffe": [
+        {
+            "model": "mobilenet_SSD",
+            "inputSize": "300, 300",
+            "mean": "127.5, 127.5, 127.5",
+            "std": "0.007843",
+            "swapRB": "false",
+            "outType": "SSD",
+            "labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/object_detection_classes_pascal_voc.txt",
+            "modelUrl": "https://raw.githubusercontent.com/chuanqi305/MobileNet-SSD/master/mobilenet_iter_73000.caffemodel",
+            "configUrl": "https://raw.githubusercontent.com/chuanqi305/MobileNet-SSD/master/deploy.prototxt"
+        },
+        {
+            "model": "VGG_SSD",
+            "inputSize": "300, 300",
+            "mean": "104, 117, 123",
+            "std": "1",
+            "swapRB": "false",
+            "outType": "SSD",
+            "labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/object_detection_classes_pascal_voc.txt",
+            "modelUrl": "https://drive.google.com/uc?id=0BzKzrI_SkD1_WVVTSmQxU0dVRzA&export=download",
+            "configUrl": "https://drive.google.com/uc?id=0BzKzrI_SkD1_WVVTSmQxU0dVRzA&export=download"
+        }
+    ],
+    "darknet": [
+        {
+            "model": "yolov2_tiny",
+            "inputSize": "416, 416",
+            "mean": "0, 0, 0",
+            "std": "0.00392",
+            "swapRB": "false",
+            "outType": "YOLO",
+            "labelsUrl": "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/object_detection_classes_yolov3.txt",
+            "modelUrl": "https://pjreddie.com/media/files/yolov2-tiny.weights",
+            "configUrl": "https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov2-tiny.cfg"
+        }
+    ]
+}

doc/js_tutorials/js_assets/js_object_detection_with_camera.html

@@ -0,0 +1,402 @@
+<!DOCTYPE html>
+<html>
+
+<head>
+    <meta charset="utf-8">
+    <title>Object Detection Example with Camera</title>
+    <link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+
+<body>
+<h2>Object Detection Example with Camera </h2>
+<p>
+    This tutorial shows you how to write an object detection example with camera.<br>
+    To try the example you should click the <b>modelFile</b> button(and <b>configInput</b> button if needed) to upload inference model.
+    You can find the model URLs and parameters in the <a href="#appendix">model info</a> section.
+    Then You should change the parameters in the first code snippet according to the uploaded model.
+    Finally click <b>Start/Stop</b> button to start or stop the camera capture.<br>
+</p>
+
+<div class="control"><button id="startAndStop" disabled>Start</button></div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+        <tr>
+            <td>
+                <video id="videoInput" width="400" height="400"></video>
+            </td>
+            <td>
+                <canvas id="canvasOutput" style="visibility: hidden;" width="400" height="400"></canvas>
+            </td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    videoInput
+                </div>
+            </td>
+            <td>
+                <p id='status' align="left"></p>
+            </td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    modelFile <input type="file" id="modelFile" name="file">
+                </div>
+            </td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    configFile <input type="file" id="configFile">
+                </div>
+            </td>
+        </tr>
+    </table>
+</div>
+
+<div>
+    <p class="err" id="errorMessage"></p>
+</div>
+
+<div>
+    <h3>Help function</h3>
+    <p>1.The parameters for model inference which you can modify to investigate more models.</p>
+    <textarea class="code" rows="15" cols="100" id="codeEditor" spellcheck="false"></textarea>
+    <p>2.The function to capture video from camera, and the main loop in which will do inference once.</p>
+    <textarea class="code" rows="34" cols="100" id="codeEditor1" spellcheck="false"></textarea>
+    <p>3.Load labels from txt file and process it into an array.</p>
+    <textarea class="code" rows="7" cols="100" id="codeEditor2" spellcheck="false"></textarea>
+    <p>4.Get blob from image as input for net, and standardize it with <b>mean</b> and <b>std</b>.</p>
+    <textarea class="code" rows="17" cols="100" id="codeEditor3" spellcheck="false"></textarea>
+    <p>5.Fetch model file and save to emscripten file system once click the input button.</p>
+    <textarea class="code" rows="17" cols="100" id="codeEditor4" spellcheck="false"></textarea>
+    <p>6.The post-processing, including get boxes from output and draw boxes into the image.</p>
+    <textarea class="code" rows="35" cols="100" id="codeEditor5" spellcheck="false"></textarea>
+</div>
+
+<div id="appendix">
+    <h2>Model Info:</h2>
+</div>
+
+<script src="utils.js" type="text/javascript"></script>
+<script src="js_dnn_example_helper.js" type="text/javascript"></script>
+
+<script id="codeSnippet" type="text/code-snippet">
+inputSize = [300, 300];
+mean = [127.5, 127.5, 127.5];
+std = 0.007843;
+swapRB = false;
+confThreshold = 0.5;
+nmsThreshold = 0.4;
+
+// the type of output, can be YOLO or SSD
+outType = "SSD";
+
+// url for label file, can from local or Internet
+labelsUrl = "https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/object_detection_classes_pascal_voc.txt";
+</script>
+
+<script id="codeSnippet1" type="text/code-snippet">
+let frame = new cv.Mat(videoInput.height, videoInput.width, cv.CV_8UC4);
+let cap = new cv.VideoCapture(videoInput);
+
+main = async function(frame) {
+    const labels = await loadLables(labelsUrl);
+    const input = getBlobFromImage(inputSize, mean, std, swapRB, frame);
+    let net = cv.readNet(configPath, modelPath);
+    net.setInput(input);
+    const start = performance.now();
+    const result = net.forward();
+    const time  = performance.now()-start;
+    const output = postProcess(result, labels, frame);
+
+    updateResult(output, time);
+    setTimeout(processVideo, 0);
+    input.delete();
+    net.delete();
+    result.delete();
+}
+
+function processVideo() {
+    try {
+        if (!streaming) {
+            return;
+        }
+        cap.read(frame);
+        main(frame);
+    } catch (err) {
+        utils.printError(err);
+    }
+}
+
+setTimeout(processVideo, 0);
+</script>
+
+<script id="codeSnippet5" type="text/code-snippet">
+postProcess = function(result, labels, frame) {
+    let canvasOutput = document.getElementById('canvasOutput');
+    const outputWidth = canvasOutput.width;
+    const outputHeight = canvasOutput.height;
+    const resultData = result.data32F;
+
+    // Get the boxes(with class and confidence) from the output
+    let boxes = [];
+    switch(outType) {
+        case "YOLO": {
+            const vecNum = result.matSize[0];
+            const vecLength = result.matSize[1];
+            const classNum = vecLength - 5;
+
+            for (let i = 0; i < vecNum; ++i) {
+                let vector = resultData.slice(i*vecLength, (i+1)*vecLength);
+                let scores = vector.slice(5, vecLength);
+                let classId = scores.indexOf(Math.max(...scores));
+                let confidence = scores[classId];
+                if (confidence > confThreshold) {
+                    let center_x = Math.round(vector[0] * outputWidth);
+                    let center_y = Math.round(vector[1] * outputHeight);
+                    let width = Math.round(vector[2] * outputWidth);
+                    let height = Math.round(vector[3] * outputHeight);
+                    let left = Math.round(center_x - width / 2);
+                    let top = Math.round(center_y - height / 2);
+
+                    let box = {
+                        scores: scores,
+                        classId: classId,
+                        confidence: confidence,
+                        bounding: [left, top, width, height],
+                        toDraw: true
+                    }
+                    boxes.push(box);
+                }
+            }
+
+            // NMS(Non Maximum Suppression) algorithm
+            let boxNum = boxes.length;
+            let tmp_boxes = [];
+            let sorted_boxes = [];
+            for (let c = 0; c < classNum; ++c) {
+                for (let i = 0; i < boxes.length; ++i) {
+                    tmp_boxes[i] = [boxes[i], i];
+                }
+                sorted_boxes = tmp_boxes.sort((a, b) => { return (b[0].scores[c] - a[0].scores[c]); });
+                for (let i = 0; i < boxNum; ++i) {
+                    if (sorted_boxes[i][0].scores[c] === 0) continue;
+                    else {
+                        for (let j = i + 1; j < boxNum; ++j) {
+                            if (IOU(sorted_boxes[i][0], sorted_boxes[j][0]) >= nmsThreshold) {
+                                boxes[sorted_boxes[j][1]].toDraw = false;
+                            }
+                        }
+                    }
+                }
+            }
+        } break;
+        case "SSD": {
+            const vecNum = result.matSize[2];
+            const vecLength = 7;
+
+            for (let i = 0; i < vecNum; ++i) {
+                let vector = resultData.slice(i*vecLength, (i+1)*vecLength);
+                let confidence = vector[2];
+                if (confidence > confThreshold) {
+                    let left, top, right, bottom, width, height;
+                    left = Math.round(vector[3]);
+                    top = Math.round(vector[4]);
+                    right = Math.round(vector[5]);
+                    bottom = Math.round(vector[6]);
+                    width = right - left + 1;
+                    height = bottom - top + 1;
+                    if (width <= 2 || height <= 2) {
+                        left = Math.round(vector[3] * outputWidth);
+                        top = Math.round(vector[4] * outputHeight);
+                        right = Math.round(vector[5] * outputWidth);
+                        bottom = Math.round(vector[6] * outputHeight);
+                        width = right - left + 1;
+                        height = bottom - top + 1;
+                    }
+                    let box = {
+                        classId: vector[1] - 1,
+                        confidence: confidence,
+                        bounding: [left, top, width, height],
+                        toDraw: true
+                    }
+                    boxes.push(box);
+                }
+            }
+        } break;
+        default:
+            console.error(`Unsupported output type ${outType}`)
+    }
+
+    // Draw the saved box into the image
+    let output = new cv.Mat(outputWidth, outputHeight, cv.CV_8UC3);
+    cv.cvtColor(frame, output, cv.COLOR_RGBA2RGB);
+    let boxNum = boxes.length;
+    for (let i = 0; i < boxNum; ++i) {
+        if (boxes[i].toDraw) {
+            drawBox(boxes[i]);
+        }
+    }
+
+    return output;
+
+
+    // Calculate the IOU(Intersection over Union) of two boxes
+    function IOU(box1, box2) {
+        let bounding1 = box1.bounding;
+        let bounding2 = box2.bounding;
+        let s1 = bounding1[2] * bounding1[3];
+        let s2 = bounding2[2] * bounding2[3];
+
+        let left1 = bounding1[0];
+        let right1 = left1 + bounding1[2];
+        let left2 = bounding2[0];
+        let right2 = left2 + bounding2[2];
+        let overlapW = calOverlap([left1, right1], [left2, right2]);
+
+        let top1 = bounding2[1];
+        let bottom1 = top1 + bounding1[3];
+        let top2 = bounding2[1];
+        let bottom2 = top2 + bounding2[3];
+        let overlapH = calOverlap([top1, bottom1], [top2, bottom2]);
+
+        let overlapS = overlapW * overlapH;
+        return overlapS / (s1 + s2 + overlapS);
+    }
+
+    // Calculate the overlap range of two vector
+    function calOverlap(range1, range2) {
+        let min1 = range1[0];
+        let max1 = range1[1];
+        let min2 = range2[0];
+        let max2 = range2[1];
+
+        if (min2 > min1 && min2 < max1) {
+            return max1 - min2;
+        } else if (max2 > min1 && max2 < max1) {
+            return max2 - min1;
+        } else {
+            return 0;
+        }
+    }
+
+    // Draw one predict box into the origin image
+    function drawBox(box) {
+        let bounding = box.bounding;
+        let left = bounding[0];
+        let top = bounding[1];
+        let width = bounding[2];
+        let height = bounding[3];
+
+        cv.rectangle(output, new cv.Point(left, top), new cv.Point(left + width, top + height),
+                             new cv.Scalar(0, 255, 0));
+        cv.rectangle(output, new cv.Point(left, top), new cv.Point(left + width, top + 15),
+                             new cv.Scalar(255, 255, 255), cv.FILLED);
+        let text = `${labels[box.classId]}: ${box.confidence.toFixed(4)}`;
+        cv.putText(output, text, new cv.Point(left, top + 10), cv.FONT_HERSHEY_SIMPLEX, 0.3,
+                                 new cv.Scalar(0, 0, 0));
+    }
+}
+</script>
+
+<script type="text/javascript">
+    let jsonUrl = "js_object_detection_model_info.json";
+    drawInfoTable(jsonUrl, 'appendix');
+
+    let utils = new Utils('errorMessage');
+    utils.loadCode('codeSnippet', 'codeEditor');
+    utils.loadCode('codeSnippet1', 'codeEditor1');
+
+    let loadLablesCode = 'loadLables = ' + loadLables.toString();
+    document.getElementById('codeEditor2').value = loadLablesCode;
+    let getBlobFromImageCode = 'getBlobFromImage = ' + getBlobFromImage.toString();
+    document.getElementById('codeEditor3').value = getBlobFromImageCode;
+    let loadModelCode = 'loadModel = ' + loadModel.toString();
+    document.getElementById('codeEditor4').value = loadModelCode;
+
+    utils.loadCode('codeSnippet5', 'codeEditor5');
+
+    let videoInput = document.getElementById('videoInput');
+    let streaming = false;
+    let startAndStop = document.getElementById('startAndStop');
+    startAndStop.addEventListener('click', () => {
+        if (!streaming) {
+            utils.clearError();
+            utils.startCamera('qvga', onVideoStarted, 'videoInput');
+        } else {
+            utils.stopCamera();
+            onVideoStopped();
+        }
+    });
+
+    let configPath = "";
+    let configFile = document.getElementById('configFile');
+    configFile.addEventListener('change', async (e) => {
+        initStatus();
+        configPath = await loadModel(e);
+        document.getElementById('status').innerHTML = `The config file '${configPath}' is created successfully.`;
+    });
+
+    let modelPath = "";
+    let modelFile = document.getElementById('modelFile');
+    modelFile.addEventListener('change', async (e) => {
+        initStatus();
+        modelPath = await loadModel(e);
+        document.getElementById('status').innerHTML = `The model file '${modelPath}' is created successfully.`;
+        configPath = "";
+        configFile.value = "";
+    });
+
+    utils.loadOpenCv(() => {
+        startAndStop.removeAttribute('disabled');
+    });
+
+    var main = async function(frame) {};
+    var postProcess = function(result, labels, frame) {};
+
+    utils.executeCode('codeEditor1');
+    utils.executeCode('codeEditor2');
+    utils.executeCode('codeEditor3');
+    utils.executeCode('codeEditor4');
+    utils.executeCode('codeEditor5');
+
+    function onVideoStarted() {
+        streaming = true;
+        startAndStop.innerText = 'Stop';
+        videoInput.width = videoInput.videoWidth;
+        videoInput.height = videoInput.videoHeight;
+        utils.executeCode('codeEditor');
+        utils.executeCode('codeEditor1');
+    }
+
+    function onVideoStopped() {
+        streaming = false;
+        startAndStop.innerText = 'Start';
+        initStatus();
+    }
+
+    function updateResult(output, time) {
+        try{
+            let canvasOutput = document.getElementById('canvasOutput');
+            canvasOutput.style.visibility = "visible";
+            cv.imshow('canvasOutput', output);
+            document.getElementById('status').innerHTML = `<b>Model:</b> ${modelPath}<br>
+                                                           <b>Inference time:</b> ${time.toFixed(2)} ms`;
+        } catch(e) {
+            console.log(e);
+        }
+    }
+
+    function initStatus() {
+        document.getElementById('status').innerHTML = '';
+        document.getElementById('canvasOutput').style.visibility = "hidden";
+        utils.clearError();
+    }
+
+</script>
+
+</body>
+
+</html>

doc/js_tutorials/js_assets/js_pose_estimation.html

@@ -0,0 +1,327 @@
+<!DOCTYPE html>
+<html>
+
+<head>
+    <meta charset="utf-8">
+    <title>Pose Estimation Example</title>
+    <link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+
+<body>
+<h2>Pose Estimation Example</h2>
+<p>
+    This tutorial shows you how to write an pose estimation example with OpenCV.js.<br>
+    To try the example you should click the <b>modelFile</b> button(and <b>configInput</b> button if needed) to upload inference model.
+    You can find the model URLs and parameters in the <a href="#appendix">model info</a> section.
+    Then You should change the parameters in the first code snippet according to the uploaded model.
+    Finally click <b>Try it</b> button to see the result. You can choose any other images.<br>
+</p>
+
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+        <tr>
+            <td>
+                <canvas id="canvasInput" width="400" height="250"></canvas>
+            </td>
+            <td>
+                <canvas id="canvasOutput" style="visibility: hidden;" width="400" height="250"></canvas>
+            </td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    canvasInput <input type="file" id="fileInput" name="file" accept="image/*">
+                </div>
+            </td>
+            <td>
+                <p id='status' align="left"></p>
+            </td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    modelFile <input type="file" id="modelFile" name="file">
+                </div>
+            </td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    configFile <input type="file" id="configFile">
+                </div>
+            </td>
+        </tr>
+    </table>
+</div>
+
+<div>
+    <p class="err" id="errorMessage"></p>
+</div>
+
+<div>
+    <h3>Help function</h3>
+    <p>1.The parameters for model inference which you can modify to investigate more models.</p>
+    <textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false"></textarea>
+    <p>2.Main loop in which will read the image from canvas and do inference once.</p>
+    <textarea class="code" rows="15" cols="100" id="codeEditor1" spellcheck="false"></textarea>
+    <p>3.Get blob from image as input for net, and standardize it with <b>mean</b> and <b>std</b>.</p>
+    <textarea class="code" rows="17" cols="100" id="codeEditor2" spellcheck="false"></textarea>
+    <p>4.Fetch model file and save to emscripten file system once click the input button.</p>
+    <textarea class="code" rows="17" cols="100" id="codeEditor3" spellcheck="false"></textarea>
+    <p>5.The pairs of keypoints of different dataset.</p>
+    <textarea class="code" rows="30" cols="100" id="codeEditor4" spellcheck="false"></textarea>
+    <p>6.The post-processing, including get the predicted points and draw lines into the image.</p>
+    <textarea class="code" rows="30" cols="100" id="codeEditor5" spellcheck="false"></textarea>
+</div>
+
+<div id="appendix">
+    <h2>Model Info:</h2>
+</div>
+
+<script src="utils.js" type="text/javascript"></script>
+<script src="js_dnn_example_helper.js" type="text/javascript"></script>
+
+<script id="codeSnippet" type="text/code-snippet">
+inputSize = [368, 368];
+mean = [0, 0, 0];
+std = 0.00392;
+swapRB = false;
+threshold = 0.1;
+
+// the pairs of keypoint, can be "COCO", "MPI" and "BODY_25"
+dataset = "COCO";
+</script>
+
+<script id="codeSnippet1" type="text/code-snippet">
+main = async function() {
+    const input = getBlobFromImage(inputSize, mean, std, swapRB, 'canvasInput');
+    let net = cv.readNet(configPath, modelPath);
+    net.setInput(input);
+    const start = performance.now();
+    const result = net.forward();
+    const time  = performance.now()-start;
+    const output = postProcess(result);
+
+    updateResult(output, time);
+    input.delete();
+    net.delete();
+    result.delete();
+}
+</script>
+
+<script id="codeSnippet4" type="text/code-snippet">
+BODY_PARTS = {};
+POSE_PAIRS = [];
+
+if (dataset === 'COCO') {
+    BODY_PARTS = { "Nose": 0, "Neck": 1, "RShoulder": 2, "RElbow": 3, "RWrist": 4,
+                   "LShoulder": 5, "LElbow": 6, "LWrist": 7, "RHip": 8, "RKnee": 9,
+                   "RAnkle": 10, "LHip": 11, "LKnee": 12, "LAnkle": 13, "REye": 14,
+                   "LEye": 15, "REar": 16, "LEar": 17, "Background": 18 };
+
+    POSE_PAIRS = [ ["Neck", "RShoulder"], ["Neck", "LShoulder"], ["RShoulder", "RElbow"],
+                   ["RElbow", "RWrist"], ["LShoulder", "LElbow"], ["LElbow", "LWrist"],
+                   ["Neck", "RHip"], ["RHip", "RKnee"], ["RKnee", "RAnkle"], ["Neck", "LHip"],
+                   ["LHip", "LKnee"], ["LKnee", "LAnkle"], ["Neck", "Nose"], ["Nose", "REye"],
+                   ["REye", "REar"], ["Nose", "LEye"], ["LEye", "LEar"] ]
+} else if (dataset === 'MPI') {
+    BODY_PARTS = { "Head": 0, "Neck": 1, "RShoulder": 2, "RElbow": 3, "RWrist": 4,
+                   "LShoulder": 5, "LElbow": 6, "LWrist": 7, "RHip": 8, "RKnee": 9,
+                   "RAnkle": 10, "LHip": 11, "LKnee": 12, "LAnkle": 13, "Chest": 14,
+                   "Background": 15 }
+
+    POSE_PAIRS = [ ["Head", "Neck"], ["Neck", "RShoulder"], ["RShoulder", "RElbow"],
+                   ["RElbow", "RWrist"], ["Neck", "LShoulder"], ["LShoulder", "LElbow"],
+                   ["LElbow", "LWrist"], ["Neck", "Chest"], ["Chest", "RHip"], ["RHip", "RKnee"],
+                   ["RKnee", "RAnkle"], ["Chest", "LHip"], ["LHip", "LKnee"], ["LKnee", "LAnkle"] ]
+} else if (dataset === 'BODY_25') {
+    BODY_PARTS = { "Nose": 0, "Neck": 1, "RShoulder": 2, "RElbow": 3, "RWrist": 4,
+                   "LShoulder": 5, "LElbow": 6, "LWrist": 7, "MidHip": 8, "RHip": 9,
+                   "RKnee": 10, "RAnkle": 11, "LHip": 12, "LKnee": 13, "LAnkle": 14,
+                   "REye": 15, "LEye": 16, "REar": 17, "LEar": 18, "LBigToe": 19,
+                   "LSmallToe": 20, "LHeel": 21, "RBigToe": 22, "RSmallToe": 23,
+                   "RHeel": 24, "Background": 25 }
+
+    POSE_PAIRS = [ ["Neck", "Nose"], ["Neck", "RShoulder"],
+                   ["Neck", "LShoulder"], ["RShoulder", "RElbow"],
+                   ["RElbow", "RWrist"], ["LShoulder", "LElbow"],
+                   ["LElbow", "LWrist"], ["Nose", "REye"],
+                   ["REye", "REar"], ["Neck", "LEye"],
+                   ["LEye", "LEar"], ["Neck", "MidHip"],
+                   ["MidHip", "RHip"], ["RHip", "RKnee"],
+                   ["RKnee", "RAnkle"], ["RAnkle", "RBigToe"],
+                   ["RBigToe", "RSmallToe"], ["RAnkle", "RHeel"],
+                   ["MidHip", "LHip"], ["LHip", "LKnee"],
+                   ["LKnee", "LAnkle"], ["LAnkle", "LBigToe"],
+                   ["LBigToe", "LSmallToe"], ["LAnkle", "LHeel"] ]
+}
+</script>
+
+<script id="codeSnippet5" type="text/code-snippet">
+postProcess = function(result) {
+    const resultData = result.data32F;
+    const matSize = result.matSize;
+    const size1 = matSize[1];
+    const size2 = matSize[2];
+    const size3 = matSize[3];
+    const mapSize = size2 * size3;
+
+    let canvasOutput = document.getElementById('canvasOutput');
+    const outputWidth = canvasOutput.width;
+    const outputHeight = canvasOutput.height;
+
+    let image = cv.imread("canvasInput");
+    let output = new cv.Mat(outputWidth, outputHeight, cv.CV_8UC3);
+    cv.cvtColor(image, output, cv.COLOR_RGBA2RGB);
+
+    // get position of keypoints from output
+    let points = [];
+    for (let i = 0; i < Object.keys(BODY_PARTS).length; ++i) {
+        heatMap = resultData.slice(i*mapSize, (i+1)*mapSize);
+
+        let maxIndex = 0;
+        let maxConf = heatMap[0];
+        for (index in heatMap) {
+            if (heatMap[index] > heatMap[maxIndex]) {
+                maxIndex = index;
+                maxConf = heatMap[index];
+            }
+        }
+
+        if (maxConf > threshold) {
+            indexX = maxIndex % size3;
+            indexY = maxIndex / size3;
+
+            x = outputWidth * indexX / size3;
+            y = outputHeight * indexY / size2;
+
+            points[i] = [Math.round(x), Math.round(y)];
+        }
+    }
+
+    // draw the points and lines into the image
+    for (pair of POSE_PAIRS) {
+        partFrom = pair[0];
+        partTo = pair[1];
+        idFrom = BODY_PARTS[partFrom];
+        idTo = BODY_PARTS[partTo];
+        pointFrom = points[idFrom];
+        pointTo = points[idTo];
+
+        if (points[idFrom] && points[idTo]) {
+            cv.line(output, new cv.Point(pointFrom[0], pointFrom[1]),
+                            new cv.Point(pointTo[0], pointTo[1]), new cv.Scalar(0, 255, 0), 3);
+            cv.ellipse(output, new cv.Point(pointFrom[0], pointFrom[1]), new cv.Size(3, 3), 0, 0, 360,
+                               new cv.Scalar(0, 0, 255), cv.FILLED);
+            cv.ellipse(output, new cv.Point(pointTo[0], pointTo[1]), new cv.Size(3, 3), 0, 0, 360,
+                               new cv.Scalar(0, 0, 255), cv.FILLED);
+        }
+    }
+
+    return output;
+}
+</script>
+
+<script type="text/javascript">
+    let jsonUrl = "js_pose_estimation_model_info.json";
+    drawInfoTable(jsonUrl, 'appendix');
+
+    let utils = new Utils('errorMessage');
+    utils.loadCode('codeSnippet', 'codeEditor');
+    utils.loadCode('codeSnippet1', 'codeEditor1');
+
+    let getBlobFromImageCode = 'getBlobFromImage = ' + getBlobFromImage.toString();
+    document.getElementById('codeEditor2').value = getBlobFromImageCode;
+    let loadModelCode = 'loadModel = ' + loadModel.toString();
+    document.getElementById('codeEditor3').value = loadModelCode;
+
+    utils.loadCode('codeSnippet4', 'codeEditor4');
+    utils.loadCode('codeSnippet5', 'codeEditor5');
+
+    let canvas = document.getElementById('canvasInput');
+    let ctx = canvas.getContext('2d');
+    let img = new Image();
+    img.crossOrigin = 'anonymous';
+    img.src = 'roi.jpg';
+    img.onload = function() {
+        ctx.drawImage(img, 0, 0, canvas.width, canvas.height);
+    };
+
+    let tryIt = document.getElementById('tryIt');
+    tryIt.addEventListener('click', () => {
+        initStatus();
+        document.getElementById('status').innerHTML = 'Running function main()...';
+        utils.executeCode('codeEditor');
+        utils.executeCode('codeEditor1');
+        if (modelPath === "") {
+            document.getElementById('status').innerHTML = 'Runing failed.';
+            utils.printError('Please upload model file by clicking the button first.');
+        } else {
+            setTimeout(main, 1);
+        }
+    });
+
+    let fileInput = document.getElementById('fileInput');
+    fileInput.addEventListener('change', (e) => {
+        initStatus();
+        loadImageToCanvas(e, 'canvasInput');
+    });
+
+    let configPath = "";
+    let configFile = document.getElementById('configFile');
+    configFile.addEventListener('change', async (e) => {
+        initStatus();
+        configPath = await loadModel(e);
+        document.getElementById('status').innerHTML = `The config file '${configPath}' is created successfully.`;
+    });
+
+    let modelPath = "";
+    let modelFile = document.getElementById('modelFile');
+    modelFile.addEventListener('change', async (e) => {
+        initStatus();
+        modelPath = await loadModel(e);
+        document.getElementById('status').innerHTML = `The model file '${modelPath}' is created successfully.`;
+        configPath = "";
+        configFile.value = "";
+    });
+
+    utils.loadOpenCv(() => {
+        tryIt.removeAttribute('disabled');
+    });
+
+    var main = async function() {};
+    var postProcess = function(result) {};
+
+    utils.executeCode('codeEditor');
+    utils.executeCode('codeEditor1');
+    utils.executeCode('codeEditor2');
+    utils.executeCode('codeEditor3');
+    utils.executeCode('codeEditor4');
+    utils.executeCode('codeEditor5');
+
+    function updateResult(output, time) {
+        try{
+            let canvasOutput = document.getElementById('canvasOutput');
+            canvasOutput.style.visibility = "visible";
+            let resized = new cv.Mat(canvasOutput.width, canvasOutput.height, cv.CV_8UC4);
+            cv.resize(output, resized, new cv.Size(canvasOutput.width, canvasOutput.height));
+            cv.imshow('canvasOutput', resized);
+            document.getElementById('status').innerHTML = `<b>Model:</b> ${modelPath}<br>
+                                                           <b>Inference time:</b> ${time.toFixed(2)} ms`;
+        } catch(e) {
+            console.log(e);
+        }
+    }
+
+    function initStatus() {
+        document.getElementById('status').innerHTML = '';
+        document.getElementById('canvasOutput').style.visibility = "hidden";
+        utils.clearError();
+    }
+
+</script>
+
+</body>
+
+</html>

doc/js_tutorials/js_assets/js_pose_estimation_model_info.json

@@ -0,0 +1,34 @@
+{
+    "caffe": [
+        {
+            "model": "body_25",
+            "inputSize": "368, 368",
+            "mean": "0, 0, 0",
+            "std": "0.00392",
+            "swapRB": "false",
+            "dataset": "BODY_25",
+            "modelUrl": "http://posefs1.perception.cs.cmu.edu/OpenPose/models/pose/body_25/pose_iter_584000.caffemodel",
+            "configUrl": "https://raw.githubusercontent.com/CMU-Perceptual-Computing-Lab/openpose/master/models/pose/body_25/pose_deploy.prototxt"
+        },
+        {
+            "model": "coco",
+            "inputSize": "368, 368",
+            "mean": "0, 0, 0",
+            "std": "0.00392",
+            "swapRB": "false",
+            "dataset": "COCO",
+            "modelUrl": "http://posefs1.perception.cs.cmu.edu/OpenPose/models/pose/coco/pose_iter_440000.caffemodel",
+            "configUrl": "https://raw.githubusercontent.com/CMU-Perceptual-Computing-Lab/openpose/master/models/pose/coco/pose_deploy_linevec.prototxt"
+        },
+        {
+            "model": "mpi",
+            "inputSize": "368, 368",
+            "mean": "0, 0, 0",
+            "std": "0.00392",
+            "swapRB": "false",
+            "dataset": "MPI",
+            "modelUrl": "http://posefs1.perception.cs.cmu.edu/OpenPose/models/pose/mpi/pose_iter_160000.caffemodel",
+            "configUrl": "https://raw.githubusercontent.com/CMU-Perceptual-Computing-Lab/openpose/master/models/pose/mpi/pose_deploy_linevec.prototxt"
+        }
+    ]
+}

doc/js_tutorials/js_assets/js_semantic_segmentation.html

@@ -0,0 +1,243 @@
+<!DOCTYPE html>
+<html>
+
+<head>
+    <meta charset="utf-8">
+    <title>Semantic Segmentation Example</title>
+    <link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+
+<body>
+<h2>Semantic Segmentation Example</h2>
+<p>
+    This tutorial shows you how to write an semantic segmentation example with OpenCV.js.<br>
+    To try the example you should click the <b>modelFile</b> button(and <b>configInput</b> button if needed) to upload inference model.
+    You can find the model URLs and parameters in the <a href="#appendix">model info</a> section.
+    Then You should change the parameters in the first code snippet according to the uploaded model.
+    Finally click <b>Try it</b> button to see the result. You can choose any other images.<br>
+</p>
+
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+        <tr>
+            <td>
+                <canvas id="canvasInput" width="400" height="400"></canvas>
+            </td>
+            <td>
+                <canvas id="canvasOutput" style="visibility: hidden;" width="400" height="400"></canvas>
+            </td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    canvasInput <input type="file" id="fileInput" name="file" accept="image/*">
+                </div>
+            </td>
+            <td>
+                <p id='status' align="left"></p>
+            </td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    modelFile <input type="file" id="modelFile" name="file">
+                </div>
+            </td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    configFile <input type="file" id="configFile">
+                </div>
+            </td>
+        </tr>
+    </table>
+</div>
+
+<div>
+    <p class="err" id="errorMessage"></p>
+</div>
+
+<div>
+    <h3>Help function</h3>
+    <p>1.The parameters for model inference which you can modify to investigate more models.</p>
+    <textarea class="code" rows="5" cols="100" id="codeEditor" spellcheck="false"></textarea>
+    <p>2.Main loop in which will read the image from canvas and do inference once.</p>
+    <textarea class="code" rows="16" cols="100" id="codeEditor1" spellcheck="false"></textarea>
+    <p>3.Get blob from image as input for net, and standardize it with <b>mean</b> and <b>std</b>.</p>
+    <textarea class="code" rows="17" cols="100" id="codeEditor2" spellcheck="false"></textarea>
+    <p>4.Fetch model file and save to emscripten file system once click the input button.</p>
+    <textarea class="code" rows="17" cols="100" id="codeEditor3" spellcheck="false"></textarea>
+    <p>5.The post-processing, including gengerate colors for different classes and argmax to get the classes for each pixel.</p>
+    <textarea class="code" rows="34" cols="100" id="codeEditor4" spellcheck="false"></textarea>
+</div>
+
+<div id="appendix">
+    <h2>Model Info:</h2>
+</div>
+
+<script src="utils.js" type="text/javascript"></script>
+<script src="js_dnn_example_helper.js" type="text/javascript"></script>
+
+<script id="codeSnippet" type="text/code-snippet">
+inputSize = [513, 513];
+mean = [127.5, 127.5, 127.5];
+std = 0.007843;
+swapRB = false;
+</script>
+
+<script id="codeSnippet1" type="text/code-snippet">
+main = async function() {
+    const input = getBlobFromImage(inputSize, mean, std, swapRB, 'canvasInput');
+    let net = cv.readNet(configPath, modelPath);
+    net.setInput(input);
+    const start = performance.now();
+    const result = net.forward();
+    const time  = performance.now()-start;
+    const colors = generateColors(result);
+    const output = argmax(result, colors);
+
+    updateResult(output, time);
+    input.delete();
+    net.delete();
+    result.delete();
+}
+</script>
+
+<script id="codeSnippet4" type="text/code-snippet">
+generateColors = function(result) {
+    const numClasses = result.matSize[1];
+    let colors = [0,0,0];
+    while(colors.length < numClasses*3){
+        colors.push(Math.round((Math.random()*255 + colors[colors.length-3]) / 2));
+    }
+    return colors;
+}
+
+argmax = function(result, colors) {
+    const C = result.matSize[1];
+    const H = result.matSize[2];
+    const W = result.matSize[3];
+    const resultData = result.data32F;
+    const imgSize = H*W;
+
+    let classId = [];
+    for (i = 0; i<imgSize; ++i) {
+        let id = 0;
+        for (j = 0; j < C; ++j) {
+            if (resultData[j*imgSize+i] > resultData[id*imgSize+i]) {
+                id = j;
+            }
+        }
+        classId.push(colors[id*3]);
+        classId.push(colors[id*3+1]);
+        classId.push(colors[id*3+2]);
+        classId.push(255);
+    }
+
+    output = cv.matFromArray(H,W,cv.CV_8UC4,classId);
+    return output;
+}
+</script>
+
+<script type="text/javascript">
+    let jsonUrl = "js_semantic_segmentation_model_info.json";
+    drawInfoTable(jsonUrl, 'appendix');
+
+    let utils = new Utils('errorMessage');
+    utils.loadCode('codeSnippet', 'codeEditor');
+    utils.loadCode('codeSnippet1', 'codeEditor1');
+
+    let getBlobFromImageCode = 'getBlobFromImage = ' + getBlobFromImage.toString();
+    document.getElementById('codeEditor2').value = getBlobFromImageCode;
+    let loadModelCode = 'loadModel = ' + loadModel.toString();
+    document.getElementById('codeEditor3').value = loadModelCode;
+
+    utils.loadCode('codeSnippet4', 'codeEditor4');
+
+    let canvas = document.getElementById('canvasInput');
+    let ctx = canvas.getContext('2d');
+    let img = new Image();
+    img.crossOrigin = 'anonymous';
+    img.src = 'roi.jpg';
+    img.onload = function() {
+        ctx.drawImage(img, 0, 0, canvas.width, canvas.height);
+    };
+
+    let tryIt = document.getElementById('tryIt');
+    tryIt.addEventListener('click', () => {
+        initStatus();
+        document.getElementById('status').innerHTML = 'Running function main()...';
+        utils.executeCode('codeEditor');
+        utils.executeCode('codeEditor1');
+        if (modelPath === "") {
+            document.getElementById('status').innerHTML = 'Runing failed.';
+            utils.printError('Please upload model file by clicking the button first.');
+        } else {
+            setTimeout(main, 1);
+        }
+    });
+
+    let fileInput = document.getElementById('fileInput');
+    fileInput.addEventListener('change', (e) => {
+        initStatus();
+        loadImageToCanvas(e, 'canvasInput');
+    });
+
+    let configPath = "";
+    let configFile = document.getElementById('configFile');
+    configFile.addEventListener('change', async (e) => {
+        initStatus();
+        configPath = await loadModel(e);
+        document.getElementById('status').innerHTML = `The config file '${configPath}' is created successfully.`;
+    });
+
+    let modelPath = "";
+    let modelFile = document.getElementById('modelFile');
+    modelFile.addEventListener('change', async (e) => {
+        initStatus();
+        modelPath = await loadModel(e);
+        document.getElementById('status').innerHTML = `The model file '${modelPath}' is created successfully.`;
+        configPath = "";
+        configFile.value = "";
+    });
+
+    utils.loadOpenCv(() => {
+        tryIt.removeAttribute('disabled');
+    });
+
+    var main = async function() {};
+    var generateColors = function(result) {};
+    var argmax = function(result, colors) {};
+
+    utils.executeCode('codeEditor1');
+    utils.executeCode('codeEditor2');
+    utils.executeCode('codeEditor3');
+    utils.executeCode('codeEditor4');
+
+    function updateResult(output, time) {
+        try{
+            let canvasOutput = document.getElementById('canvasOutput');
+            canvasOutput.style.visibility = "visible";
+            let resized = new cv.Mat(canvasOutput.width, canvasOutput.height, cv.CV_8UC4);
+            cv.resize(output, resized, new cv.Size(canvasOutput.width, canvasOutput.height));
+            cv.imshow('canvasOutput', resized);
+            document.getElementById('status').innerHTML = `<b>Model:</b> ${modelPath}<br>
+                                                           <b>Inference time:</b> ${time.toFixed(2)} ms`;
+        } catch(e) {
+            console.log(e);
+        }
+    }
+
+    function initStatus() {
+        document.getElementById('status').innerHTML = '';
+        document.getElementById('canvasOutput').style.visibility = "hidden";
+        utils.clearError();
+    }
+
+</script>
+
+</body>
+
+</html>

doc/js_tutorials/js_assets/js_semantic_segmentation_model_info.json

@@ -0,0 +1,12 @@
+{
+    "tensorflow": [
+        {
+            "model": "deeplabv3",
+            "inputSize": "513, 513",
+            "mean": "127.5, 127.5, 127.5",
+            "std": "0.007843",
+            "swapRB": "false",
+            "modelUrl": "https://drive.google.com/uc?id=1v-hfGenaE9tiGOzo5qdgMNG_gqQ5-Xn4&export=download"
+        }
+    ]
+}

doc/js_tutorials/js_assets/js_style_transfer.html

@@ -0,0 +1,228 @@
+<!DOCTYPE html>
+<html>
+
+<head>
+    <meta charset="utf-8">
+    <title>Style Transfer Example</title>
+    <link href="js_example_style.css" rel="stylesheet" type="text/css" />
+</head>
+
+<body>
+<h2>Style Transfer Example</h2>
+<p>
+    This tutorial shows you how to write an style transfer example with OpenCV.js.<br>
+    To try the example you should click the <b>modelFile</b> button(and <b>configFile</b> button if needed) to upload inference model.
+    You can find the model URLs and parameters in the <a href="#appendix">model info</a> section.
+    Then You should change the parameters in the first code snippet according to the uploaded model.
+    Finally click <b>Try it</b> button to see the result. You can choose any other images.<br>
+</p>
+
+<div class="control"><button id="tryIt" disabled>Try it</button></div>
+<div>
+    <table cellpadding="0" cellspacing="0" width="0" border="0">
+        <tr>
+            <td>
+                <canvas id="canvasInput" width="400" height="400"></canvas>
+            </td>
+            <td>
+                <canvas id="canvasOutput" style="visibility: hidden;" width="400" height="400"></canvas>
+            </td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    canvasInput <input type="file" id="fileInput" name="file" accept="image/*">
+                </div>
+            </td>
+            <td>
+                <p id='status' align="left"></p>
+            </td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    modelFile <input type="file" id="modelFile" name="file">
+                </div>
+            </td>
+        </tr>
+        <tr>
+            <td>
+                <div class="caption">
+                    configFile <input type="file" id="configFile">
+                </div>
+            </td>
+        </tr>
+    </table>
+</div>
+
+<div>
+    <p class="err" id="errorMessage"></p>
+</div>
+
+<div>
+    <h3>Help function</h3>
+    <p>1.The parameters for model inference which you can modify to investigate more models.</p>
+    <textarea class="code" rows="5" cols="100" id="codeEditor" spellcheck="false"></textarea>
+    <p>2.Main loop in which will read the image from canvas and do inference once.</p>
+    <textarea class="code" rows="15" cols="100" id="codeEditor1" spellcheck="false"></textarea>
+    <p>3.Get blob from image as input for net, and standardize it with <b>mean</b> and <b>std</b>.</p>
+    <textarea class="code" rows="17" cols="100" id="codeEditor2" spellcheck="false"></textarea>
+    <p>4.Fetch model file and save to emscripten file system once click the input button.</p>
+    <textarea class="code" rows="17" cols="100" id="codeEditor3" spellcheck="false"></textarea>
+    <p>5.The post-processing, including scaling and reordering.</p>
+    <textarea class="code" rows="21" cols="100" id="codeEditor4" spellcheck="false"></textarea>
+</div>
+
+<div id="appendix">
+    <h2>Model Info:</h2>
+</div>
+
+<script src="utils.js" type="text/javascript"></script>
+<script src="js_dnn_example_helper.js" type="text/javascript"></script>
+
+<script id="codeSnippet" type="text/code-snippet">
+inputSize = [224, 224];
+mean = [104, 117, 123];
+std = 1;
+swapRB = false;
+</script>
+
+<script id="codeSnippet1" type="text/code-snippet">
+main = async function() {
+    const input = getBlobFromImage(inputSize, mean, std, swapRB, 'canvasInput');
+    let net = cv.readNet(configPath, modelPath);
+    net.setInput(input);
+    const start = performance.now();
+    const result = net.forward();
+    const time  = performance.now()-start;
+    const output = postProcess(result);
+
+    updateResult(output, time);
+    input.delete();
+    net.delete();
+    result.delete();
+}
+</script>
+
+<script id="codeSnippet4" type="text/code-snippet">
+postProcess = function(result) {
+    const resultData = result.data32F;
+    const C = result.matSize[1];
+    const H = result.matSize[2];
+    const W = result.matSize[3];
+    const mean = [104, 117, 123];
+
+    let normData = [];
+    for (let h = 0; h < H; ++h) {
+        for (let w = 0; w < W; ++w) {
+            for (let c = 0; c < C; ++c) {
+                normData.push(resultData[c*H*W + h*W + w] + mean[c]);
+            }
+            normData.push(255);
+        }
+    }
+
+    let output = new cv.matFromArray(H, W, cv.CV_8UC4, normData);
+    return output;
+}
+</script>
+
+<script type="text/javascript">
+    let jsonUrl = "js_style_transfer_model_info.json";
+    drawInfoTable(jsonUrl, 'appendix');
+
+    let utils = new Utils('errorMessage');
+    utils.loadCode('codeSnippet', 'codeEditor');
+    utils.loadCode('codeSnippet1', 'codeEditor1');
+
+    let getBlobFromImageCode = 'getBlobFromImage = ' + getBlobFromImage.toString();
+    document.getElementById('codeEditor2').value = getBlobFromImageCode;
+    let loadModelCode = 'loadModel = ' + loadModel.toString();
+    document.getElementById('codeEditor3').value = loadModelCode;
+
+    utils.loadCode('codeSnippet4', 'codeEditor4');
+
+    let canvas = document.getElementById('canvasInput');
+    let ctx = canvas.getContext('2d');
+    let img = new Image();
+    img.crossOrigin = 'anonymous';
+    img.src = 'lena.png';
+    img.onload = function() {
+        ctx.drawImage(img, 0, 0, canvas.width, canvas.height);
+    };
+
+    let tryIt = document.getElementById('tryIt');
+    tryIt.addEventListener('click', () => {
+        initStatus();
+        document.getElementById('status').innerHTML = 'Running function main()...';
+        utils.executeCode('codeEditor');
+        utils.executeCode('codeEditor1');
+        if (modelPath === "") {
+            document.getElementById('status').innerHTML = 'Runing failed.';
+            utils.printError('Please upload model file by clicking the button first.');
+        } else {
+            setTimeout(main, 1);
+        }
+    });
+
+    let fileInput = document.getElementById('fileInput');
+    fileInput.addEventListener('change', (e) => {
+        initStatus();
+        loadImageToCanvas(e, 'canvasInput');
+    });
+
+    let configPath = "";
+    let configFile = document.getElementById('configFile');
+    configFile.addEventListener('change', async (e) => {
+        initStatus();
+        configPath = await loadModel(e);
+        document.getElementById('status').innerHTML = `The config file '${configPath}' is created successfully.`;
+    });
+
+    let modelPath = "";
+    let modelFile = document.getElementById('modelFile');
+    modelFile.addEventListener('change', async (e) => {
+        initStatus();
+        modelPath = await loadModel(e);
+        document.getElementById('status').innerHTML = `The model file '${modelPath}' is created successfully.`;
+        configPath = "";
+        configFile.value = "";
+    });
+
+    utils.loadOpenCv(() => {
+        tryIt.removeAttribute('disabled');
+    });
+
+    var main = async function() {};
+    var postProcess = function(result) {};
+
+    utils.executeCode('codeEditor1');
+    utils.executeCode('codeEditor2');
+    utils.executeCode('codeEditor3');
+    utils.executeCode('codeEditor4');
+
+    function updateResult(output, time) {
+        try{
+            let canvasOutput = document.getElementById('canvasOutput');
+            canvasOutput.style.visibility = "visible";
+            let resized = new cv.Mat(canvasOutput.width, canvasOutput.height, cv.CV_8UC4);
+            cv.resize(output, resized, new cv.Size(canvasOutput.width, canvasOutput.height));
+            cv.imshow('canvasOutput', resized);
+            document.getElementById('status').innerHTML = `<b>Model:</b> ${modelPath}<br>
+                                                           <b>Inference time:</b> ${time.toFixed(2)} ms`;
+        } catch(e) {
+            console.log(e);
+        }
+    }
+
+    function initStatus() {
+        document.getElementById('status').innerHTML = '';
+        document.getElementById('canvasOutput').style.visibility = "hidden";
+        utils.clearError();
+    }
+
+</script>
+
+</body>
+
+</html>

doc/js_tutorials/js_assets/js_style_transfer_model_info.json

@@ -0,0 +1,76 @@
+{
+    "torch": [
+        {
+            "model": "candy.t7",
+            "inputSize": "224, 224",
+            "mean": "104, 117, 123",
+            "std": "1",
+            "swapRB": "false",
+            "modelUrl": "https://cs.stanford.edu/people/jcjohns/fast-neural-style/models//instance_norm/candy.t7"
+        },
+        {
+            "model": "composition_vii.t7",
+            "inputSize": "224, 224",
+            "mean": "104, 117, 123",
+            "std": "1",
+            "swapRB": "false",
+            "modelUrl": "https://cs.stanford.edu/people/jcjohns/fast-neural-style/models//eccv16/composition_vii.t7"
+        },
+        {
+            "model": "feathers.t7",
+            "inputSize": "224, 224",
+            "mean": "104, 117, 123",
+            "std": "1",
+            "swapRB": "false",
+            "modelUrl": "https://cs.stanford.edu/people/jcjohns/fast-neural-style/models//instance_norm/feathers.t7"
+        },
+        {
+            "model": "la_muse.t7",
+            "inputSize": "224, 224",
+            "mean": "104, 117, 123",
+            "std": "1",
+            "swapRB": "false",
+            "modelUrl": "https://cs.stanford.edu/people/jcjohns/fast-neural-style/models//instance_norm/la_muse.t7"
+        },
+        {
+            "model": "mosaic.t7",
+            "inputSize": "224, 224",
+            "mean": "104, 117, 123",
+            "std": "1",
+            "swapRB": "false",
+            "modelUrl": "https://cs.stanford.edu/people/jcjohns/fast-neural-style/models//instance_norm/mosaic.t7"
+        },
+        {
+            "model": "starry_night.t7",
+            "inputSize": "224, 224",
+            "mean": "104, 117, 123",
+            "std": "1",
+            "swapRB": "false",
+            "modelUrl": "https://cs.stanford.edu/people/jcjohns/fast-neural-style/models//eccv16/starry_night.t7"
+        },
+        {
+            "model": "the_scream.t7",
+            "inputSize": "224, 224",
+            "mean": "104, 117, 123",
+            "std": "1",
+            "swapRB": "false",
+            "modelUrl": "https://cs.stanford.edu/people/jcjohns/fast-neural-style/models//instance_norm/the_scream.t7"
+        },
+        {
+            "model": "the_wave.t7",
+            "inputSize": "224, 224",
+            "mean": "104, 117, 123",
+            "std": "1",
+            "swapRB": "false",
+            "modelUrl": "https://cs.stanford.edu/people/jcjohns/fast-neural-style/models//eccv16/the_wave.t7"
+        },
+        {
+            "model": "udnie.t7",
+            "inputSize": "224, 224",
+            "mean": "104, 117, 123",
+            "std": "1",
+            "swapRB": "false",
+            "modelUrl": "https://cs.stanford.edu/people/jcjohns/fast-neural-style/models//instance_norm/udnie.t7"
+        }
+    ]
+}

doc/js_tutorials/js_assets/utils.js

@@ -7,7 +7,7 @@ function Utils(errorOutputId) { // eslint-disable-line no-unused-vars
         let script = document.createElement('script');
         script.setAttribute('async', '');
         script.setAttribute('type', 'text/javascript');
-        script.addEventListener('load', () => {
+        script.addEventListener('load', async () => {
             if (cv.getBuildInformation)
             {
                 console.log(cv.getBuildInformation());
@@ -16,9 +16,15 @@ function Utils(errorOutputId) { // eslint-disable-line no-unused-vars
             else
             {
                 // WASM
-                cv['onRuntimeInitialized']=()=>{
+                if (cv instanceof Promise) {
+                    cv = await cv;
                     console.log(cv.getBuildInformation());
                     onloadCallback();
+                } else {
+                    cv['onRuntimeInitialized']=()=>{
+                        console.log(cv.getBuildInformation());
+                        onloadCallback();
+                    }
                 }
             }
         });

doc/js_tutorials/js_dnn/js_image_classification/js_image_classification.markdown

@@ -0,0 +1,13 @@
+Image Classification Example {#tutorial_js_image_classification}
+=======================================
+
+Goal
+----
+
+- In this tutorial you will learn how to use OpenCV.js dnn module for image classification.
+
+\htmlonly
+<iframe src="../../js_image_classification.html" width="100%"
+        onload="this.style.height=this.contentDocument.body.scrollHeight +'px';">
+</iframe>
+\endhtmlonly

doc/js_tutorials/js_dnn/js_image_classification/js_image_classification_with_camera.markdown

@@ -0,0 +1,15 @@
+Image Classification Example with Camera {#tutorial_js_image_classification_with_camera}
+=======================================
+
+Goal
+----
+
+- In this tutorial you will learn how to use OpenCV.js dnn module for image classification example with camera.
+
+@note  If you don't know how to capture video from camera, please review @ref tutorial_js_video_display.
+
+\htmlonly
+<iframe src="../../js_image_classification_with_camera.html" width="100%"
+        onload="this.style.height=this.contentDocument.body.scrollHeight +'px';">
+</iframe>
+\endhtmlonly

doc/js_tutorials/js_dnn/js_object_detection/js_object_detection.markdown

@@ -0,0 +1,13 @@
+Object Detection Example {#tutorial_js_object_detection}
+=======================================
+
+Goal
+----
+
+- In this tutorial you will learn how to use OpenCV.js dnn module for object detection.
+
+\htmlonly
+<iframe src="../../js_object_detection.html" width="100%"
+        onload="this.style.height=this.contentDocument.body.scrollHeight +'px';">
+</iframe>
+\endhtmlonly

doc/js_tutorials/js_dnn/js_object_detection/js_object_detection_with_camera.markdown

@@ -0,0 +1,13 @@
+Object Detection Example with Camera{#tutorial_js_object_detection_with_camera}
+=======================================
+
+Goal
+----
+
+- In this tutorial you will learn how to use OpenCV.js dnn module for object detection with camera.
+
+\htmlonly
+<iframe src="../../js_object_detection_with_camera.html" width="100%"
+        onload="this.style.height=this.contentDocument.body.scrollHeight +'px';">
+</iframe>
+\endhtmlonly

doc/js_tutorials/js_dnn/js_pose_estimation/js_pose_estimation.markdown

@@ -0,0 +1,13 @@
+Pose Estimation Example {#tutorial_js_pose_estimation}
+=======================================
+
+Goal
+----
+
+- In this tutorial you will learn how to use OpenCV.js dnn module for pose estimation.
+
+\htmlonly
+<iframe src="../../js_pose_estimation.html" width="100%"
+        onload="this.style.height=this.contentDocument.body.scrollHeight +'px';">
+</iframe>
+\endhtmlonly

doc/js_tutorials/js_dnn/js_semantic_segmentation/js_semantic_segmentation.markdown

@@ -0,0 +1,13 @@
+Semantic Segmentation Example {#tutorial_js_semantic_segmentation}
+=======================================
+
+Goal
+----
+
+- In this tutorial you will learn how to use OpenCV.js dnn module for semantic segmentation.
+
+\htmlonly
+<iframe src="../../js_semantic_segmentation.html" width="100%"
+        onload="this.style.height=this.contentDocument.body.scrollHeight +'px';">
+</iframe>
+\endhtmlonly

doc/js_tutorials/js_dnn/js_style_transfer/js_style_transfer.markdown

@@ -0,0 +1,13 @@
+Style Transfer Example {#tutorial_js_style_transfer}
+=======================================
+
+Goal
+----
+
+- In this tutorial you will learn how to use OpenCV.js dnn module for style transfer.
+
+\htmlonly
+<iframe src="../../js_style_transfer.html" width="100%"
+        onload="this.style.height=this.contentDocument.body.scrollHeight +'px';">
+</iframe>
+\endhtmlonly

doc/js_tutorials/js_dnn/js_table_of_contents_dnn.markdown

@@ -0,0 +1,30 @@
+Deep Neural Networks (dnn module) {#tutorial_js_table_of_contents_dnn}
+============
+
+-   @subpage tutorial_js_image_classification
+
+    Image classification example
+
+-   @subpage tutorial_js_image_classification_with_camera
+
+    Image classification example with camera
+
+-   @subpage tutorial_js_object_detection
+
+    Object detection example
+
+-   @subpage tutorial_js_object_detection_with_camera
+
+    Object detection example with camera
+
+-   @subpage tutorial_js_semantic_segmentation
+
+    Semantic segmentation example
+
+-   @subpage tutorial_js_style_transfer
+
+    Style transfer example
+
+-   @subpage tutorial_js_pose_estimation
+
+    Pose estimation example

doc/js_tutorials/js_tutorials.markdown

@@ -26,3 +26,7 @@ OpenCV.js Tutorials {#tutorial_js_root}
 
     In this section you
     will object detection techniques like face detection etc.
+
+-   @subpage tutorial_js_table_of_contents_dnn
+
+    These tutorials show how to use dnn module in JavaScript

doc/tutorials/videoio/video-write/video_write.markdown

@@ -109,7 +109,7 @@ const string NAME = source.substr(0, pAt) + argv[2][0] + ".avi";   // Form the n
     @code{.cpp}
     CV_FOURCC('P','I','M,'1') // this is an MPEG1 codec from the characters to integer
     @endcode
-    If you pass for this argument minus one than a window will pop up at runtime that contains all
+    If you pass for this argument minus one then a window will pop up at runtime that contains all
     the codec installed on your system and ask you to select the one to use:
 
     ![](images/videoCompressSelect.png)

modules/calib3d/include/opencv2/calib3d.hpp

@@ -91,7 +91,7 @@ respectively) by the same factor.
 
 The joint rotation-translation matrix \f$[R|t]\f$ is the matrix product of a projective
 transformation and a homogeneous transformation. The 3-by-4 projective transformation maps 3D points
-represented in camera coordinates to 2D poins in the image plane and represented in normalized
+represented in camera coordinates to 2D points in the image plane and represented in normalized
 camera coordinates \f$x' = X_c / Z_c\f$ and \f$y' = Y_c / Z_c\f$:
 
 \f[Z_c \begin{bmatrix}

modules/calib3d/test/test_cornerssubpix.cpp

@@ -153,9 +153,8 @@ void CV_ChessboardSubpixelTest::run( int )
 
         vector<Point2f> test_corners;
         bool result = findChessboardCorners(chessboard_image, pattern_size, test_corners, 15);
-        if(!result)
+        if (!result && cvtest::debugLevel > 0)
         {
-#if 0
             ts->printf(cvtest::TS::LOG, "Warning: chessboard was not detected! Writing image to test.png\n");
             ts->printf(cvtest::TS::LOG, "Size = %d, %d\n", pattern_size.width, pattern_size.height);
             ts->printf(cvtest::TS::LOG, "Intrinsic params: fx = %f, fy = %f, cx = %f, cy = %f\n",
@@ -167,7 +166,9 @@ void CV_ChessboardSubpixelTest::run( int )
                        distortion_coeffs_.at<double>(0, 4));
 
             imwrite("test.png", chessboard_image);
-#endif
+        }
+        if (!result)
+        {
             continue;
         }
 

modules/calib3d/test/test_fisheye.cpp

@@ -551,7 +551,10 @@ TEST_F(fisheyeTest, stereoRectify)
             << "Q =" << std::endl << Q << std::endl;
     }
 
-#if 1 // Debug code
+    if (cvtest::debugLevel == 0)
+        return;
+    // DEBUG code is below
+
     cv::Mat lmapx, lmapy, rmapx, rmapy;
     //rewrite for fisheye
     cv::fisheye::initUndistortRectifyMap(K1, D1, R1, P1, requested_size, CV_32F, lmapx, lmapy);
@@ -584,7 +587,6 @@ TEST_F(fisheyeTest, stereoRectify)
 
         cv::imwrite(cv::format("fisheye_rectification_AB_%03d.png", i), rectification);
     }
-#endif
 }
 
 TEST_F(fisheyeTest, stereoCalibrate)

modules/core/include/opencv2/core/mat.hpp

@@ -2397,20 +2397,11 @@ public:
     UMat(const UMat& m, const Rect& roi);
     UMat(const UMat& m, const Range* ranges);
     UMat(const UMat& m, const std::vector<Range>& ranges);
+
+    // FIXIT copyData=false is not implemented, drop this in favor of cv::Mat (OpenCV 5.0)
     //! builds matrix from std::vector with or without copying the data
     template<typename _Tp> explicit UMat(const std::vector<_Tp>& vec, bool copyData=false);
 
-    //! builds matrix from cv::Vec; the data is copied by default
-    template<typename _Tp, int n> explicit UMat(const Vec<_Tp, n>& vec, bool copyData=true);
-    //! builds matrix from cv::Matx; the data is copied by default
-    template<typename _Tp, int m, int n> explicit UMat(const Matx<_Tp, m, n>& mtx, bool copyData=true);
-    //! builds matrix from a 2D point
-    template<typename _Tp> explicit UMat(const Point_<_Tp>& pt, bool copyData=true);
-    //! builds matrix from a 3D point
-    template<typename _Tp> explicit UMat(const Point3_<_Tp>& pt, bool copyData=true);
-    //! builds matrix from comma initializer
-    template<typename _Tp> explicit UMat(const MatCommaInitializer_<_Tp>& commaInitializer);
-
     //! destructor - calls release()
     ~UMat();
     //! assignment operators

modules/core/src/count_non_zero.dispatch.cpp

@@ -62,11 +62,9 @@ static bool ipp_countNonZero( Mat &src, int &res )
 {
     CV_INSTRUMENT_REGION_IPP();
 
-#if defined __APPLE__ || (defined _MSC_VER && defined _M_IX86)
     // see https://github.com/opencv/opencv/issues/17453
-    if (src.dims <= 2 && src.step > 520000)
+    if (src.dims <= 2 && src.step > 520000 && cv::ipp::getIppTopFeatures() == ippCPUID_SSE42)
         return false;
-#endif
 
 #if IPP_VERSION_X100 < 201801
     // Poor performance of SSE42

modules/core/src/norm.cpp

@@ -152,10 +152,10 @@ float normL2Sqr_(const float* a, const float* b, int n)
     {
         v_float32 t0 = vx_load(a + j) - vx_load(b + j);
         v_float32 t1 = vx_load(a + j + v_float32::nlanes) - vx_load(b + j + v_float32::nlanes);
-        v_float32 t2 = vx_load(a + j + 2 * v_float32::nlanes) - vx_load(b + j + 2 * v_float32::nlanes);
-        v_float32 t3 = vx_load(a + j + 3 * v_float32::nlanes) - vx_load(b + j + 3 * v_float32::nlanes);
         v_d0 = v_muladd(t0, t0, v_d0);
+        v_float32 t2 = vx_load(a + j + 2 * v_float32::nlanes) - vx_load(b + j + 2 * v_float32::nlanes);
         v_d1 = v_muladd(t1, t1, v_d1);
+        v_float32 t3 = vx_load(a + j + 3 * v_float32::nlanes) - vx_load(b + j + 3 * v_float32::nlanes);
         v_d2 = v_muladd(t2, t2, v_d2);
         v_d3 = v_muladd(t3, t3, v_d3);
     }

modules/core/src/system.cpp

@@ -2365,6 +2365,13 @@ public:
             ippTopFeatures = ippCPUID_SSE42;
 
         pIppLibInfo = ippiGetLibVersion();
+
+        // workaround: https://github.com/opencv/opencv/issues/12959
+        std::string ippName(pIppLibInfo->Name ? pIppLibInfo->Name : "");
+        if (ippName.find("SSE4.2") != std::string::npos)
+        {
+            ippTopFeatures = ippCPUID_SSE42;
+        }
     }
 
 public:
@@ -2396,16 +2403,12 @@ unsigned long long getIppFeatures()
 #endif
 }
 
-unsigned long long getIppTopFeatures();
+#ifdef HAVE_IPP
-
 unsigned long long getIppTopFeatures()
 {
-#ifdef HAVE_IPP
     return getIPPSingleton().ippTopFeatures;
-#else
-    return 0;
-#endif
 }
+#endif
 
 void setIppStatus(int status, const char * const _funcname, const char * const _filename, int _line)
 {

modules/imgproc/test/test_drawing.cpp

@@ -535,7 +535,8 @@ protected:
             img->copyTo(sub);
             shift += img->size().height + 1;
         }
-        //imwrite("/tmp/all_fonts.png", result);
+        if (cvtest::debugLevel > 0)
+            imwrite("all_fonts.png", result);
     }
 };
 

modules/js/CMakeLists.txt

@@ -1,13 +1,19 @@
 # ----------------------------------------------------------------------------
 #  CMake file for js support
 # ----------------------------------------------------------------------------
-set(the_description "The js bindings")
+if(OPENCV_INITIAL_PASS)
+  # generator for Objective-C source code and documentation signatures
+  add_subdirectory(generator)
+endif()
 
 if(NOT BUILD_opencv_js)  # should be enabled explicitly (by build_js.py script)
-  ocv_module_disable(js)
+  return()
 endif()
 
+set(the_description "The JavaScript(JS) bindings")
+
 set(OPENCV_JS "opencv.js")
+set(JS_HELPER "${CMAKE_CURRENT_SOURCE_DIR}/src/helpers.js")
 
 find_path(EMSCRIPTEN_INCLUDE_DIR
           emscripten/bind.h
@@ -28,59 +34,18 @@ if(NOT EMSCRIPTEN_INCLUDE_DIR OR NOT PYTHON_DEFAULT_AVAILABLE)
   ocv_module_disable(js)
 endif()
 
-ocv_add_module(js BINDINGS)
+ocv_add_module(js BINDINGS PRIVATE_REQUIRED opencv_js_bindings_generator)
 
 ocv_module_include_directories(${EMSCRIPTEN_INCLUDE_DIR})
 
-# get list of modules to wrap
-# message(STATUS "Wrapped in js:")
-set(OPENCV_JS_MODULES)
-foreach(m ${OPENCV_MODULES_BUILD})
-  if(";${OPENCV_MODULE_${m}_WRAPPERS};" MATCHES ";js;" AND HAVE_${m})
-    list(APPEND OPENCV_JS_MODULES ${m})
-    # message(STATUS "\t${m}")
-  endif()
-endforeach()
-
-set(opencv_hdrs "")
-foreach(m ${OPENCV_JS_MODULES})
-  list(APPEND opencv_hdrs ${OPENCV_MODULE_${m}_HEADERS})
-endforeach(m)
-
-# header blacklist
-ocv_list_filterout(opencv_hdrs "modules/.*.h$")
-ocv_list_filterout(opencv_hdrs "modules/core/.*/cuda")
-ocv_list_filterout(opencv_hdrs "modules/core/.*/opencl")
-ocv_list_filterout(opencv_hdrs "modules/core/include/opencv2/core/opengl.hpp")
-ocv_list_filterout(opencv_hdrs "modules/core/include/opencv2/core/ocl.hpp")
-ocv_list_filterout(opencv_hdrs "modules/cuda.*")
-ocv_list_filterout(opencv_hdrs "modules/cudev")
-ocv_list_filterout(opencv_hdrs "modules/core/.*/hal/")
-ocv_list_filterout(opencv_hdrs "modules/.*/detection_based_tracker.hpp") # Conditional compilation
-ocv_list_filterout(opencv_hdrs "modules/core/include/opencv2/core/utils/.*")
-
-file(WRITE "${CMAKE_CURRENT_BINARY_DIR}/headers.txt" "${opencv_hdrs}")
-
-set(bindings_cpp "${CMAKE_CURRENT_BINARY_DIR}/bindings.cpp")
-
-set(scripts_hdr_parser "${CMAKE_CURRENT_SOURCE_DIR}/../python/src2/hdr_parser.py")
-
-set(JS_HELPER "${CMAKE_CURRENT_SOURCE_DIR}/src/helpers.js")
-
-add_custom_command(
-   OUTPUT ${bindings_cpp}
-   COMMAND ${PYTHON_DEFAULT_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/src/embindgen.py" ${scripts_hdr_parser} ${bindings_cpp} "${CMAKE_CURRENT_BINARY_DIR}/headers.txt" "${CMAKE_CURRENT_SOURCE_DIR}/src/core_bindings.cpp"
-   DEPENDS ${CMAKE_CURRENT_SOURCE_DIR}/src/core_bindings.cpp
-   DEPENDS ${CMAKE_CURRENT_SOURCE_DIR}/src/embindgen.py
-   DEPENDS ${CMAKE_CURRENT_SOURCE_DIR}/src/templates.py
-   DEPENDS ${scripts_hdr_parser}
-   #(not needed - generated by CMake) DEPENDS ${CMAKE_CURRENT_BINARY_DIR}/headers.txt
-   DEPENDS ${opencv_hdrs}
-   DEPENDS ${JS_HELPER})
-
 add_definitions("-std=c++11")
 
-link_libraries(${OPENCV_MODULE_${the_module}_DEPS})
+set(deps ${OPENCV_MODULE_${the_module}_DEPS})
+list(REMOVE_ITEM deps opencv_js_bindings_generator)  # don't add dummy module
+link_libraries(${deps})
+
+set(bindings_cpp "${OPENCV_JS_BINDINGS_DIR}/gen/bindings.cpp")
+set_source_files_properties(${bindings_cpp} PROPERTIES GENERATED TRUE)
 
 OCV_OPTION(BUILD_WASM_INTRIN_TESTS "Build WASM intrin tests" OFF )
 if(BUILD_WASM_INTRIN_TESTS)
@@ -94,6 +59,8 @@ else()
   ocv_add_executable(${the_module} ${bindings_cpp})
 endif()
 
+add_dependencies(${the_module} gen_opencv_js_source)
+
 set(COMPILE_FLAGS "")
 if(NOT CMAKE_CXX_COMPILER_ID MATCHES "MSVC")
     set(COMPILE_FLAGS "${COMPILE_FLAGS} -Wno-missing-prototypes")
@@ -101,6 +68,7 @@ endif()
 if(COMPILE_FLAGS)
     set_target_properties(${the_module} PROPERTIES COMPILE_FLAGS ${COMPILE_FLAGS})
 endif()
+
 set(EMSCRIPTEN_LINK_FLAGS "${EMSCRIPTEN_LINK_FLAGS} --memory-init-file 0 -s TOTAL_MEMORY=128MB -s WASM_MEM_MAX=1GB -s ALLOW_MEMORY_GROWTH=1")
 set(EMSCRIPTEN_LINK_FLAGS "${EMSCRIPTEN_LINK_FLAGS} -s MODULARIZE=1 -s SINGLE_FILE=1")
 set(EMSCRIPTEN_LINK_FLAGS "${EMSCRIPTEN_LINK_FLAGS} -s EXPORT_NAME=\"'cv'\" -s DEMANGLE_SUPPORT=1")
@@ -155,7 +123,7 @@ add_custom_command(OUTPUT "${opencv_test_js_bin_dir}/${test_data}"
                   )
 list(APPEND opencv_test_js_file_deps "${test_data_path}" "${opencv_test_js_bin_dir}/${test_data}")
 
-add_custom_target(${PROJECT_NAME}_test ALL
+add_custom_target(${PROJECT_NAME}_test
                   DEPENDS ${OCV_JS_PATH} ${opencv_test_js_file_deps})
 
 # perf
@@ -178,7 +146,7 @@ foreach(f ${perf_files})
   list(APPEND opencv_perf_js_file_deps "${perf_dir}/${f}" "${opencv_perf_js_bin_dir}/${f}")
 endforeach()
 
-add_custom_target(${PROJECT_NAME}_perf ALL
+add_custom_target(${PROJECT_NAME}_perf
                   DEPENDS ${OCV_JS_PATH} ${opencv_perf_js_file_deps})
 
 #loader
@@ -198,4 +166,6 @@ add_custom_command(
 list(APPEND opencv_loader_js_file_deps "${loader_dir}/loader.js" "${opencv_loader_js_bin_dir}/loader.js")
 
 add_custom_target(${PROJECT_NAME}_loader ALL
-                  DEPENDS ${OCV_JS_PATH} ${opencv_loader_js_file_deps})
+                  DEPENDS ${OCV_JS_PATH} ${opencv_loader_js_file_deps})
+
+add_custom_target(opencv_test_js ALL DEPENDS opencv_js_test opencv_js_perf opencv_js_loader)

modules/js/common.cmake

@@ -0,0 +1,13 @@
+# get list of modules to wrap
+if(HAVE_opencv_js)
+  message(STATUS "Wrapped in JavaScript(js):")
+endif()
+set(OPENCV_JS_MODULES "")
+foreach(m ${OPENCV_MODULES_BUILD})
+  if(";${OPENCV_MODULE_${m}_WRAPPERS};" MATCHES ";js;" AND HAVE_${m})
+    list(APPEND OPENCV_JS_MODULES ${m})
+    if(HAVE_opencv_js)
+      message(STATUS "    ${m}")
+    endif()
+  endif()
+endforeach()

modules/js/generator/CMakeLists.txt

@@ -0,0 +1,74 @@
+set(MODULE_NAME "js_bindings_generator")
+set(OPENCV_MODULE_IS_PART_OF_WORLD FALSE)
+ocv_add_module(${MODULE_NAME} INTERNAL)
+
+set(OPENCV_JS_BINDINGS_DIR "${CMAKE_CURRENT_BINARY_DIR}" CACHE INTERNAL "")
+file(REMOVE_RECURSE "${OPENCV_JS_BINDINGS_DIR}/gen")
+file(MAKE_DIRECTORY "${OPENCV_JS_BINDINGS_DIR}/gen")
+file(REMOVE "${OPENCV_DEPHELPER}/gen_opencv_js_source")  # force re-run after CMake
+
+# This file is included from a subdirectory
+set(JS_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/..")
+include(${JS_SOURCE_DIR}/common.cmake)  # fill OPENCV_JS_MODULES
+
+set(opencv_hdrs "")
+foreach(m ${OPENCV_JS_MODULES})
+  list(APPEND opencv_hdrs ${OPENCV_MODULE_${m}_HEADERS})
+endforeach(m)
+
+# header blacklist
+ocv_list_filterout(opencv_hdrs "modules/.*.h$")
+ocv_list_filterout(opencv_hdrs "modules/core/.*/cuda")
+ocv_list_filterout(opencv_hdrs "modules/core/.*/opencl")
+ocv_list_filterout(opencv_hdrs "modules/core/include/opencv2/core/opengl.hpp")
+ocv_list_filterout(opencv_hdrs "modules/core/include/opencv2/core/ocl.hpp")
+ocv_list_filterout(opencv_hdrs "modules/cuda.*")
+ocv_list_filterout(opencv_hdrs "modules/cudev")
+ocv_list_filterout(opencv_hdrs "modules/core/.*/hal/")
+ocv_list_filterout(opencv_hdrs "modules/.*/detection_based_tracker.hpp") # Conditional compilation
+ocv_list_filterout(opencv_hdrs "modules/core/include/opencv2/core/utils/.*")
+
+ocv_update_file("${CMAKE_CURRENT_BINARY_DIR}/headers.txt" "${opencv_hdrs}")
+
+set(bindings_cpp "${OPENCV_JS_BINDINGS_DIR}/gen/bindings.cpp")
+
+set(scripts_hdr_parser "${JS_SOURCE_DIR}/../python/src2/hdr_parser.py")
+
+if(DEFINED ENV{OPENCV_JS_WHITELIST})
+  set(OPENCV_JS_WHITELIST_FILE "$ENV{OPENCV_JS_WHITELIST}")
+else()
+  set(OPENCV_JS_WHITELIST_FILE "${OpenCV_SOURCE_DIR}/platforms/js/opencv_js.config.py")
+endif()
+
+add_custom_command(
+  OUTPUT ${bindings_cpp} "${OPENCV_DEPHELPER}/gen_opencv_js_source"
+  COMMAND
+      ${PYTHON_DEFAULT_EXECUTABLE}
+      "${CMAKE_CURRENT_SOURCE_DIR}/embindgen.py"
+      "${scripts_hdr_parser}"
+      "${bindings_cpp}"
+      "${CMAKE_CURRENT_BINARY_DIR}/headers.txt"
+      "${JS_SOURCE_DIR}/src/core_bindings.cpp"
+      "${OPENCV_JS_WHITELIST_FILE}"
+  COMMAND
+      ${CMAKE_COMMAND} -E touch "${OPENCV_DEPHELPER}/gen_opencv_js_source"
+  WORKING_DIRECTORY
+      "${CMAKE_CURRENT_BINARY_DIR}/gen"
+  DEPENDS
+      ${JS_SOURCE_DIR}/src/core_bindings.cpp
+      ${CMAKE_CURRENT_SOURCE_DIR}/embindgen.py
+      ${CMAKE_CURRENT_SOURCE_DIR}/templates.py
+      ${scripts_hdr_parser}
+      #(not needed - generated by CMake) ${CMAKE_CURRENT_BINARY_DIR}/headers.txt
+      ${opencv_hdrs}
+  COMMENT "Generate source files for JavaScript bindings"
+)
+
+add_custom_target(gen_opencv_js_source
+  # excluded from all: ALL
+  DEPENDS ${bindings_cpp} "${OPENCV_DEPHELPER}/gen_opencv_js_source"
+  SOURCES
+      ${JS_SOURCE_DIR}/src/core_bindings.cpp
+      ${CMAKE_CURRENT_SOURCE_DIR}/embindgen.py
+      ${CMAKE_CURRENT_SOURCE_DIR}/templates.py
+)

modules/js/generator/embindgen.py

@@ -104,8 +104,6 @@ def makeWhiteList(module_list):
     return wl
 
 white_list = None
-exec(open(os.environ["OPENCV_JS_WHITELIST"]).read())
-assert(white_list)
 
 # Features to be exported
 export_enums = False
@@ -891,10 +889,10 @@ class JSWrapperGenerator(object):
 
 
 if __name__ == "__main__":
-    if len(sys.argv) < 4:
+    if len(sys.argv) < 5:
         print("Usage:\n", \
             os.path.basename(sys.argv[0]), \
-            "<full path to hdr_parser.py> <bindings.cpp> <headers.txt> <core_bindings.cpp>")
+            "<full path to hdr_parser.py> <bindings.cpp> <headers.txt> <core_bindings.cpp> <opencv_js.config.py>")
         print("Current args are: ", ", ".join(["'"+a+"'" for a in sys.argv]))
         exit(0)
 
@@ -908,5 +906,9 @@ if __name__ == "__main__":
     bindingsCpp = sys.argv[2]
     headers = open(sys.argv[3], 'r').read().split(';')
     coreBindings = sys.argv[4]
+    whiteListFile = sys.argv[5]
+    exec(open(whiteListFile).read())
+    assert(white_list)
+
     generator = JSWrapperGenerator()
     generator.gen(bindingsCpp, headers, coreBindings)

modules/js/src/core_bindings.cpp

@@ -88,7 +88,7 @@ using namespace emscripten;
 using namespace cv;
 
 #ifdef HAVE_OPENCV_DNN
-using namespace dnn;
+using namespace cv::dnn;
 #endif
 
 #ifdef HAVE_OPENCV_ARUCO

modules/python/bindings/CMakeLists.txt

@@ -11,7 +11,7 @@ set(PYTHON_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../")
 # get list of modules to wrap
 set(OPENCV_PYTHON_MODULES)
 foreach(m ${OPENCV_MODULES_BUILD})
-  if (";${OPENCV_MODULE_${m}_WRAPPERS};" MATCHES ";${MODULE_NAME};" AND HAVE_${m})
+  if (";${OPENCV_MODULE_${m}_WRAPPERS};" MATCHES ";python;" AND HAVE_${m})
     list(APPEND OPENCV_PYTHON_MODULES ${m})
     #message(STATUS "\t${m}")
   endif()

modules/python/package/cv2/__init__.py

@@ -18,6 +18,10 @@ except ImportError:
 
 def bootstrap():
     import sys
+
+    import copy
+    save_sys_path = copy.copy(sys.path)
+
     if hasattr(sys, 'OpenCV_LOADER'):
         print(sys.path)
         raise ImportError('ERROR: recursion is detected during loading of "cv2" binary extensions. Check OpenCV installation.')
@@ -85,6 +89,8 @@ def bootstrap():
     del sys.modules['cv2']
     import cv2
 
+    sys.path = save_sys_path  # multiprocessing should start from bootstrap code (https://github.com/opencv/opencv/issues/18502)
+
     try:
         import sys
         del sys.OpenCV_LOADER

modules/ts/include/opencv2/ts/ts_ext.hpp

@@ -13,6 +13,7 @@ void checkIppStatus();
 extern bool skipUnstableTests;
 extern bool runBigDataTests;
 extern int testThreads;
+extern int debugLevel;  //< 0 - no debug, 1 - basic test debug information, >1 - extra debug information
 
 void testSetUp();
 void testTearDown();

modules/ts/src/ts.cpp

@@ -774,6 +774,7 @@ static bool checkTestData = cv::utils::getConfigurationParameterBool("OPENCV_TES
 bool skipUnstableTests = false;
 bool runBigDataTests = false;
 int testThreads = 0;
+int debugLevel = (int)cv::utils::getConfigurationParameterSizeT("OPENCV_TEST_DEBUG", 0);
 
 
 static size_t memory_usage_base = 0;
@@ -883,6 +884,7 @@ void parseCustomOptions(int argc, char **argv)
         "{ test_threads       |-1       |the number of worker threads, if parallel execution is enabled}"
         "{ skip_unstable      |false    |skip unstable tests }"
         "{ test_bigdata       |false    |run BigData tests (>=2Gb) }"
+        "{ test_debug         |         |0 - no debug (default), 1 - basic test debug information, >1 - extra debug information }"
         "{ test_require_data  |") + (checkTestData ? "true" : "false") + string("|fail on missing non-required test data instead of skip (env:OPENCV_TEST_REQUIRE_DATA)}"
         CV_TEST_TAGS_PARAMS
         "{ h   help           |false    |print help info                          }"
@@ -909,6 +911,14 @@ void parseCustomOptions(int argc, char **argv)
 
     skipUnstableTests = parser.get<bool>("skip_unstable");
     runBigDataTests = parser.get<bool>("test_bigdata");
+    if (parser.has("test_debug"))
+    {
+        cv::String s = parser.get<cv::String>("test_debug");
+        if (s.empty() || s == "true")
+            debugLevel = 1;
+        else
+            debugLevel = parser.get<int>("test_debug");
+    }
     if (parser.has("test_require_data"))
         checkTestData = parser.get<bool>("test_require_data");
 
@@ -1122,7 +1132,9 @@ void SystemInfoCollector::OnTestProgramStart(const testing::UnitTest&)
     }
     recordPropertyVerbose("cv_cpu_features", "CPU features", cv::getCPUFeaturesLine());
 #ifdef HAVE_IPP
-    recordPropertyVerbose("cv_ipp_version", "Intel(R) IPP version", cv::ipp::useIPP() ? cv::ipp::getIppVersion() :  "disabled");
+    recordPropertyVerbose("cv_ipp_version", "Intel(R) IPP version", cv::ipp::useIPP() ? cv::ipp::getIppVersion() : "disabled");
+    if (cv::ipp::useIPP())
+        recordPropertyVerbose("cv_ipp_features", "Intel(R) IPP features code", cv::format("0x%llx", cv::ipp::getIppTopFeatures()));
 #endif
 #ifdef HAVE_OPENCL
     cv::dumpOpenCLInformation();

modules/videoio/src/cap_mjpeg_decoder.cpp

@@ -116,6 +116,8 @@ double MotionJpegCapture::getProperty(int property) const
     {
         case CAP_PROP_POS_FRAMES:
             return (double)getFramePos();
+        case CAP_PROP_POS_MSEC:
+            return (double)getFramePos() * (1000. / m_fps);
         case CAP_PROP_POS_AVI_RATIO:
             return double(getFramePos())/m_mjpeg_frames.size();
         case CAP_PROP_FRAME_WIDTH:

modules/videoio/src/cap_msmf.cpp

@@ -351,8 +351,6 @@ public:
 
     STDMETHODIMP OnReadSample(HRESULT hrStatus, DWORD dwStreamIndex, DWORD dwStreamFlags, LONGLONG llTimestamp, IMFSample *pSample) CV_OVERRIDE
     {
-        CV_UNUSED(llTimestamp);
-
         HRESULT hr = 0;
         cv::AutoLock lock(m_mutex);
 
@@ -365,6 +363,7 @@ public:
                 {
                     CV_LOG_DEBUG(NULL, "videoio(MSMF): drop frame (not processed)");
                 }
+                m_lastSampleTimestamp = llTimestamp;
                 m_lastSample = pSample;
             }
         }
@@ -444,6 +443,7 @@ public:
 
     IMFSourceReader *m_reader;
     DWORD m_dwStreamIndex;
+    LONGLONG m_lastSampleTimestamp;
     _ComPtr<IMFSample>  m_lastSample;
 };
 
@@ -917,6 +917,7 @@ bool CvCapture_MSMF::grabFrame()
             CV_LOG_WARNING(NULL, "videoio(MSMF): EOS signal. Capture stream is lost");
             return false;
         }
+        sampleTime = reader->m_lastSampleTimestamp;
         return true;
     }
     else if (isOpen)

modules/videoio/test/test_camera.cpp

@@ -12,21 +12,51 @@
 
 namespace opencv_test { namespace {
 
-static void test_readFrames(/*const*/ VideoCapture& capture, const int N = 100, Mat* lastFrame = NULL)
+static void test_readFrames(/*const*/ VideoCapture& capture, const int N = 100, Mat* lastFrame = NULL, bool testTimestamps = true)
 {
     Mat frame;
     int64 time0 = cv::getTickCount();
+    int64 sysTimePrev = time0;
+    const double cvTickFreq = cv::getTickFrequency();
+
+    double camTimePrev = 0.0;
+    const double fps = capture.get(cv::CAP_PROP_FPS);
+    const double framePeriod = fps == 0.0 ? 1. : 1.0 / fps;
+
+    const bool validTickAndFps = cvTickFreq != 0 && fps != 0.;
+    testTimestamps &= validTickAndFps;
+
     for (int i = 0; i < N; i++)
     {
         SCOPED_TRACE(cv::format("frame=%d", i));
 
         capture >> frame;
+        const int64 sysTimeCurr = cv::getTickCount();
+        const double camTimeCurr = capture.get(cv::CAP_PROP_POS_MSEC);
         ASSERT_FALSE(frame.empty());
 
+        // Do we have a previous frame?
+        if (i > 0 && testTimestamps)
+        {
+            const double sysTimeElapsedSecs = (sysTimeCurr - sysTimePrev) / cvTickFreq;
+            const double camTimeElapsedSecs = (camTimeCurr - camTimePrev) / 1000.;
+
+            // Check that the time between two camera frames and two system time calls
+            // are within 1.5 frame periods of one another.
+            //
+            // 1.5x is chosen to accomodate for a dropped frame, and an additional 50%
+            // to account for drift in the scale of the camera and system time domains.
+            EXPECT_NEAR(sysTimeElapsedSecs, camTimeElapsedSecs, framePeriod * 1.5);
+        }
+
         EXPECT_GT(cvtest::norm(frame, NORM_INF), 0) << "Complete black image has been received";
+
+        sysTimePrev = sysTimeCurr;
+        camTimePrev = camTimeCurr;
     }
+
     int64 time1 = cv::getTickCount();
-    printf("Processed %d frames on %.2f FPS\n", N, (N * cv::getTickFrequency()) / (time1 - time0 + 1));
+    printf("Processed %d frames on %.2f FPS\n", N, (N * cvTickFreq) / (time1 - time0 + 1));
     if (lastFrame) *lastFrame = frame.clone();
 }
 

modules/videoio/test/test_video_io.cpp

@@ -233,6 +233,34 @@ public:
         else
             std::cout << "Frames counter is not available. Actual frames: " << count_actual << ". SKIP check." << std::endl;
     }
+
+    void doTimestampTest()
+    {
+        if (!isBackendAvailable(apiPref, cv::videoio_registry::getStreamBackends()))
+            throw SkipTestException(cv::String("Backend is not available/disabled: ") + cv::videoio_registry::getBackendName(apiPref));
+
+        if (((apiPref == CAP_FFMPEG) && ((ext == "h264") || (ext == "h265"))))
+            throw SkipTestException(cv::String("Backend ") +  cv::videoio_registry::getBackendName(apiPref) +
+                    cv::String(" does not support CAP_PROP_POS_MSEC option"));
+
+        VideoCapture cap;
+        EXPECT_NO_THROW(cap.open(video_file, apiPref));
+        if (!cap.isOpened())
+            throw SkipTestException(cv::String("Backend ") +  cv::videoio_registry::getBackendName(apiPref) +
+                    cv::String(" can't open the video: ")  + video_file);
+
+        Mat img;
+        for(int i = 0; i < 10; i++)
+        {
+            double timestamp = 0;
+            ASSERT_NO_THROW(cap >> img);
+            EXPECT_NO_THROW(timestamp = cap.get(CAP_PROP_POS_MSEC));
+            const double frame_period = 1000.f/bunny_param.getFps();
+            // NOTE: eps == frame_period, because videoCapture returns frame begining timestamp or frame end
+            // timestamp depending on codec and back-end. So the first frame has timestamp 0 or frame_period.
+            EXPECT_NEAR(timestamp, i*frame_period, frame_period);
+        }
+    }
 };
 
 //==================================================================================================
@@ -353,6 +381,8 @@ TEST_P(videoio_bunny, read_position) { doTest(); }
 
 TEST_P(videoio_bunny, frame_count) { doFrameCountTest(); }
 
+TEST_P(videoio_bunny, frame_timestamp) { doTimestampTest(); }
+
 INSTANTIATE_TEST_CASE_P(videoio, videoio_bunny,
                           testing::Combine(
                               testing::ValuesIn(bunny_params),

platforms/js/build_js.py

@@ -131,11 +131,9 @@ class Builder:
                "-DBUILD_opencv_superres=OFF",
                "-DBUILD_opencv_stitching=OFF",
                "-DBUILD_opencv_java=OFF",
-               "-DBUILD_opencv_java_bindings_generator=OFF",
                "-DBUILD_opencv_js=ON",
                "-DBUILD_opencv_python2=OFF",
                "-DBUILD_opencv_python3=OFF",
-               "-DBUILD_opencv_python_bindings_generator=OFF",
                "-DBUILD_EXAMPLES=OFF",
                "-DBUILD_PACKAGE=OFF",
                "-DBUILD_TESTS=OFF",