ml5js · mop9047 · Apr 1, 2025 · Apr 5, 2025 · Apr 5, 2025 · May 7, 2025
diff --git a/examples/timeSeries-hand-gestures/index.html b/examples/timeSeries-hand-gestures/index.html
@@ -0,0 +1,22 @@
+<!--
+  👋 Hello! This is an ml5.js example made and shared with ❤️.
+  Learn more about the ml5.js project: https://ml5js.org/
+  ml5.js license and Code of Conduct: https://github.com/ml5js/ml5-next-gen/blob/main/LICENSE.md
+
+  This example demonstrates training a Sign Language classifier through ml5.TimeSeries.
+-->
+
+<html>
+  <head>
+    <meta charset="UTF-8" />
+    <meta http-equiv="X-UA-Compatible" content="IE=edge" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>ml5.js Time Series Hand Gesture Train and Save</title>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/1.9.4/p5.min.js"></script>
+    <script src="../../dist/ml5.js"></script>
+  </head>
+
+  <body>
+    <script src="sketch.js"></script>
+  </body>
+</html>
diff --git a/examples/timeSeries-hand-gestures/sketch.js b/examples/timeSeries-hand-gestures/sketch.js
@@ -0,0 +1,192 @@
+/*
+ * 👋 Hello! This is an ml5.js example made and shared with ❤️.
+ * Learn more about the ml5.js project: https://ml5js.org/
+ * ml5.js license and Code of Conduct: https://github.com/ml5js/ml5-next-gen/blob/main/LICENSE.md
+ *
+ * This example demonstrates training a Hand Gesture classifier through ml5.TimeSeries.
+ */
+
+let seqLength = 50;
+
+let handPose;
+let video;
+
+let hands = [];
+let sequence = [];
+
+let recordingFinished = false;
+let predictedWord = "";
+
+// UI variables
+let trainingWords = {};
+
+function preload() {
+  // Load the handPose model
+  handPose = ml5.handPose();
+
+  // setup the timeseries neural network
+  let options = {
+    outputs: ["label"],
+    task: "classification",
+    dataMode: "spatial",
+    debug: "true",
+    learningRate: 0.001,
+  };
+  model = ml5.timeSeries(options);
+}
+
+function setup() {
+  createCanvas(640, 480);
+
+  // setup video capture
+  video = createCapture(VIDEO);
+  video.size(640, 480);
+  video.hide();
+
+  // place UI elements
+  UI();
+
+  // use handpose model on video
+  handPose.detectStart(video, gotHands);
+}
+
+function draw() {
+  // draw video on frame
+  image(video, 0, 0, width, height);
+
+  drawPredictedWord();
+
+  // if hands are found then start recording
+  if (hands.length > 0 && recordingFinished == false) {
+    if (sequence.length <= seqLength) {
+      // get coordinates from hands (21 points)
+      handpoints = drawPoints();
+      sequence.push(handpoints);
+
+      // once sequence reaches the seqLength, add sequence as just one X value
+    } else if (sequence.length > 0) {
+      // get the training word from the input box
+      let train_word = nameField.value();
+
+      // if there is a word currently in the box then add data with that label
+      if (train_word.length > 0) {
+        // add data to the model
+        let target = { label: train_word };
+        model.addData(sequence, target);
+        trainingWordsUpdate();
+
+        // if there is no word in the box then classify instead
+      } else {
+        // classify the data
+        model.classify(sequence, gotResults);
+      }
+
+      // reset the sequence
+      sequence = [];
+      recordingFinished = true;
+    }
+
+    // can only record again when hand is out of frame
+  } else {
+    if (hands.length == 0) {
+      recordingFinished = false;
+    }
+  }
+}
+
+function drawPoints() {
+  let handpoints = [];
+  // iterate through both hands
+  for (let i = 0; i < hands.length; i++) {
+    let hand = hands[i];
+    for (let j = 0; j < hand.keypoints.length; j++) {
+      // access the keypoints in the hand
+      let keypoint = hand.keypoints[j];
+      handpoints.push(keypoint.x, keypoint.y);
+
+      fill(0, 255, 0);
+      noStroke();
+      circle(keypoint.x, keypoint.y, 5);
+    }
+  }
+  // assign to a different variable before clearing
+  let output = handpoints;
+  handpoints = [];
+
+  return output;
+}
+
+// Callback function for when handPose outputs data
+function gotHands(results) {
+  // save the output to the hands variable
+  hands = results;
+}
+
+function trainModelAndSave() {
+  model.normalizeData();
+  let options = {
+    epochs: 100,
+  };
+  model.train(options, whileTraining, finishedTraining);
+  nameField.value("");
+}
+
+function whileTraining(epoch) {
+  console.log(epoch);
+}
+
+function finishedTraining() {
+  console.log("finished training.");
+  model.save("model");
+}
+
+function gotResults(results) {
+  predictedWord = results[0].label;
+  console.log(predictedWord);
+  text(predictedWord, 200, 200);
+}
+
+function UI() {
+  nameField = createInput("");
+  nameField.attribute("placeholder", "Type the word to train");
+  nameField.position(110, 500);
+  nameField.size(250);
+
+  instructionP = createP(
+    'I want to train: <br><br> 1.) Type any word you want to pair with a gesture, e.g. "HELLO" <br> 2.) Do the gesture associated to the word, make sure to do it until the points disappear. <br> 3.) Move your hand out of the frame and repeat the gesture, do this multiple times <br> 4.) Do the same for other words e.g. "BYE" <br> 5.) Once all data is collected, press Train and Save<br><br> Tip: have at least 5 datasets for each word'
+  );
+  instructionP.style("width", "640px");
+  dataCountsP = createP("-> After the gesture a tally will appear here <-");
+
+  train_but = createButton("Train and Save");
+  train_but.mouseClicked(trainModelAndSave);
+  train_but.style("font-family", "Georgia");
+  train_but.style("font-size", "20px");
+  train_but.position(500, 490);
+}
+
+function drawPredictedWord() {
+  textSize(100);
+  fill(255);
+  text(predictedWord, 100, height / 2);
+}
+
+function trainingWordsUpdate() {
+  let tempWord = nameField.value();
+  console.log(Object.keys(trainingWords));
+  if (!(tempWord in trainingWords)) {
+    trainingWords[tempWord] = 1;
+  } else {
+    trainingWords[tempWord]++;
+  }
+
+  let counts = "";
+  let keys = Object.keys(trainingWords);
+  console.log("keys", keys);
+
+  for (let k of keys) {
+    counts += k + " : " + trainingWords[k] + "<br>";
+  }
+
+  dataCountsP.html(counts);
+}
diff --git a/examples/timeSeries-load-model-hand-gestures/index.html b/examples/timeSeries-load-model-hand-gestures/index.html
@@ -0,0 +1,45 @@
+<!--
+  👋 Hello! This is an ml5.js example made and shared with ❤️.
+  Learn more about the ml5.js project: https://ml5js.org/
+  ml5.js license and Code of Conduct: https://github.com/ml5js/ml5-next-gen/blob/main/LICENSE.md
+
+  This example demonstrates loading a Sign Language classifier through ml5.TimeSeries.
+-->
+
+<html>
+  <head>
+    <meta charset="UTF-8" />
+    <meta http-equiv="X-UA-Compatible" content="IE=edge" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>ml5.js Time Series Hand Gesture load model</title>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/1.9.4/p5.min.js"></script>
+    <script src="../../dist/ml5.js"></script>
+  </head>
+
+  <body>
+    <script src="sketch.js"></script>
+    <div id="canvasDiv"></div>
+    <p>
+      This example loads a model that is trained with ASL hand gestures for
+      Hello and Goodbye. <br />
+      <br />
+
+      Instructions: <br />
+      1.) Use one hand to do a gesture in front of the camera <br />
+      2.) Wait for the points to disappear or the prediction appears on
+      screen<br />
+      3.) To predict again, remove your hands in the frame and do the gesture
+      again<br /><br />
+
+      How to do gestures for Hello and Goodbye in ASL: <br />
+      Hello:
+      <a href="https://babysignlanguage.com/dictionary/hello/"
+        >https://babysignlanguage.com/dictionary/hello/ </a
+      ><br />
+      Goodbye:
+      <a href="https://babysignlanguage.com/dictionary/goodbye/"
+        >https://babysignlanguage.com/dictionary/goodbye/ </a
+      ><br />
+    </p>
+  </body>
+</html>
diff --git a/examples/timeSeries-load-model-hand-gestures/model/model.json b/examples/timeSeries-load-model-hand-gestures/model/model.json
@@ -0,0 +1 @@
+{"modelTopology":{"class_name":"Sequential","config":{"name":"sequential_1","layers":[{"class_name":"Conv1D","config":{"filters":8,"kernel_initializer":{"class_name":"VarianceScaling","config":{"scale":1,"mode":"fan_avg","distribution":"normal","seed":null}},"kernel_regularizer":null,"kernel_constraint":null,"kernel_size":[3],"strides":[1],"padding":"valid","dilation_rate":[1],"activation":"relu","use_bias":true,"bias_initializer":{"class_name":"Zeros","config":{}},"bias_regularizer":null,"activity_regularizer":null,"bias_constraint":null,"name":"conv1d_Conv1D1","trainable":true,"batch_input_shape":[null,51,42],"dtype":"float32"}},{"class_name":"MaxPooling1D","config":{"pool_size":[2],"padding":"valid","strides":[2],"name":"max_pooling1d_MaxPooling1D1","trainable":true}},{"class_name":"Conv1D","config":{"filters":16,"kernel_initializer":{"class_name":"VarianceScaling","config":{"scale":1,"mode":"fan_avg","distribution":"normal","seed":null}},"kernel_regularizer":null,"kernel_constraint":null,"kernel_size":[3],"strides":[1],"padding":"valid","dilation_rate":[1],"activation":"relu","use_bias":true,"bias_initializer":{"class_name":"Zeros","config":{}},"bias_regularizer":null,"activity_regularizer":null,"bias_constraint":null,"name":"conv1d_Conv1D2","trainable":true,"batch_input_shape":[null,51,42],"dtype":"float32"}},{"class_name":"MaxPooling1D","config":{"pool_size":[2],"padding":"valid","strides":[2],"name":"max_pooling1d_MaxPooling1D2","trainable":true}},{"class_name":"Flatten","config":{"name":"flatten_Flatten1","trainable":true}},{"class_name":"Dense","config":{"units":16,"activation":"relu","use_bias":true,"kernel_initializer":{"class_name":"VarianceScaling","config":{"scale":1,"mode":"fan_avg","distribution":"normal","seed":null}},"bias_initializer":{"class_name":"Zeros","config":{}},"kernel_regularizer":null,"bias_regularizer":null,"activity_regularizer":null,"kernel_constraint":null,"bias_constraint":null,"name":"dense_Dense1","trainable":true}},{"class_name":"Dense","config":{"units":2,"activation":"softmax","use_bias":true,"kernel_initializer":{"class_name":"VarianceScaling","config":{"scale":1,"mode":"fan_avg","distribution":"normal","seed":null}},"bias_initializer":{"class_name":"Zeros","config":{}},"kernel_regularizer":null,"bias_regularizer":null,"activity_regularizer":null,"kernel_constraint":null,"bias_constraint":null,"name":"dense_Dense2","trainable":true}}]},"keras_version":"tfjs-layers 4.8.0","backend":"tensor_flow.js"},"weightsManifest":[{"paths":["./hello.weights.bin"],"weights":[{"name":"conv1d_Conv1D1/kernel","shape":[3,42,8],"dtype":"float32"},{"name":"conv1d_Conv1D1/bias","shape":[8],"dtype":"float32"},{"name":"conv1d_Conv1D2/kernel","shape":[3,8,16],"dtype":"float32"},{"name":"conv1d_Conv1D2/bias","shape":[16],"dtype":"float32"},{"name":"dense_Dense1/kernel","shape":[176,16],"dtype":"float32"},{"name":"dense_Dense1/bias","shape":[16],"dtype":"float32"},{"name":"dense_Dense2/kernel","shape":[16,2],"dtype":"float32"},{"name":"dense_Dense2/bias","shape":[2],"dtype":"float32"}]}]}
diff --git a/examples/timeSeries-load-model-hand-gestures/model/model.weights.bin b/examples/timeSeries-load-model-hand-gestures/model/model.weights.bin
diff --git a/examples/timeSeries-load-model-hand-gestures/model/model_meta.json b/examples/timeSeries-load-model-hand-gestures/model/model_meta.json
@@ -0,0 +1 @@
+{"inputUnits":[42],"outputUnits":2,"inputs":{"label_0":{"dtype":"number","min":4.151249399907168,"max":586.4725394909854},"label_1":{"dtype":"number","min":186.47223882383636,"max":496.34918695509003},"label_2":{"dtype":"number","min":12.818880217505907,"max":564.7860747522525},"label_3":{"dtype":"number","min":160.9460986889124,"max":478.89482602620234},"label_4":{"dtype":"number","min":20.681431005110262,"max":557.1173870582799},"label_5":{"dtype":"number","min":135.1274696802808,"max":454.0862355189599},"label_6":{"dtype":"number","min":29.375938053231934,"max":562.4826339023859},"label_7":{"dtype":"number","min":113.22511415628927,"max":455.15365538508894},"label_8":{"dtype":"number","min":37.27265551578051,"max":573.3838980891996},"label_9":{"dtype":"number","min":98.00531862273047,"max":473.4382341601794},"label_10":{"dtype":"number","min":2.706973037101564,"max":599.2858408346702},"label_11":{"dtype":"number","min":117.7350326456234,"max":453.76022921684716},"label_12":{"dtype":"number","min":11.635752695869659,"max":612.8243751678727},"label_13":{"dtype":"number","min":91.05094143918305,"max":481.6467136241304},"label_14":{"dtype":"number","min":22.9353041163117,"max":621.0127886598051},"label_15":{"dtype":"number","min":61.619264849841635,"max":499.63536096409143},"label_16":{"dtype":"number","min":33.53953084457643,"max":626.4181148091915},"label_17":{"dtype":"number","min":28.455718477478662,"max":512.7953875856006},"label_18":{"dtype":"number","min":-2.8065139589559984,"max":617.7828981986556},"label_19":{"dtype":"number","min":117.6886729722432,"max":459.5357193516273},"label_20":{"dtype":"number","min":3.7782929928570064,"max":633.7038985044576},"label_21":{"dtype":"number","min":86.77279076496669,"max":486.0751342925063},"label_22":{"dtype":"number","min":16.177018651157255,"max":642.8366376068107},"label_23":{"dtype":"number","min":51.687144639081325,"max":502.64037741142846},"label_24":{"dtype":"number","min":28.1461509145229,"max":650.2419536370577},"label_25":{"dtype":"number","min":15.922382743702723,"max":516.9301399988833},"label_26":{"dtype":"number","min":-6.382516546058305,"max":630.7077663350849},"label_27":{"dtype":"number","min":120.16376158664924,"max":461.0881814514869},"label_28":{"dtype":"number","min":-1.4074379536407533,"max":647.5041251714117},"label_29":{"dtype":"number","min":90.58035685591811,"max":485.04491883378125},"label_30":{"dtype":"number","min":10.174906800459325,"max":658.4893875478738},"label_31":{"dtype":"number","min":71.76407331703523,"max":500.55112323964187},"label_32":{"dtype":"number","min":21.11718120932074,"max":668.566957655395},"label_33":{"dtype":"number","min":39.557348432978586,"max":514.4287318106208},"label_34":{"dtype":"number","min":-7.9534800405596595,"max":641.3232619371444},"label_35":{"dtype":"number","min":126.31599791044414,"max":465.6320514399833},"label_36":{"dtype":"number","min":-3.8369034650104927,"max":658.2044139172733},"label_37":{"dtype":"number","min":103.73604938021917,"max":481.03793223993495},"label_38":{"dtype":"number","min":3.7075645592075435,"max":668.8017566330357},"label_39":{"dtype":"number","min":88.76136006394765,"max":494.63688258092407},"label_40":{"dtype":"number","min":6.9609311353376135,"max":676.9525074586147},"label_41":{"dtype":"number","min":75.97401514052241,"max":506.7948506427954}},"outputs":{"label":{"dtype":"string","min":0,"max":1,"uniqueValues":["hello","bye"],"legend":{"hello":[1,0],"bye":[0,1]}}},"isNormalized":true,"seriesShape":[51,42]}
diff --git a/examples/timeSeries-load-model-hand-gestures/sketch.js b/examples/timeSeries-load-model-hand-gestures/sketch.js
@@ -0,0 +1,131 @@
+/*
+ * 👋 Hello! This is an ml5.js example made and shared with ❤️.
+ * Learn more about the ml5.js project: https://ml5js.org/
+ * ml5.js license and Code of Conduct: https://github.com/ml5js/ml5-next-gen/blob/main/LICENSE.md
+ *
+ * This example demonstrates loading a Hand Gesture classifier through ml5.TimeSeries.
+ * This example is trained with the ASL gestures for Hello and Goodbye
+ *
+ * Reference to sign hello and goodbye in ASL:
+ * Hello: https://babysignlanguage.com/dictionary/hello/
+ * Goodbye: https://babysignlanguage.com/dictionary/goodbye/
+ */
+
+// change this to make the recording longer
+let seqLength = 50;
+
+let handPose;
+let video;
+let hands = [];
+let sequence = [];
+let recordingFinished = false;
+let predictedWord = "";
+
+function preload() {
+  // Load the handPose model
+  handPose = ml5.handPose();
+
+  // setup the timeseries neural network
+  let options = {
+    task: "classification",
+    dataMode: "spatial",
+  };
+
+  model = ml5.timeSeries(options);
+}
+
+function setup() {
+  let canvas = createCanvas(640, 480);
+  canvas.parent("canvasDiv");
+
+  // create video capture
+  video = createCapture(VIDEO);
+  video.size(640, 480);
+  video.hide();
+
+  handPose.detectStart(video, gotHands);
+
+  // setup the model files to load
+  let modelDetails = {
+    model: "model/model.json",
+    metadata: "model/model_meta.json",
+    weights: "model/model.weights.bin",
+  };
+
+  // load the model and call modelLoaded once finished
+  model.load(modelDetails, modelLoaded);
+}
+// call back for load model
+function modelLoaded() {
+  console.log("model loaded!");
+}
+
+function draw() {
+  // draw video on the canvas
+  image(video, 0, 0, width, height);
+
+  // put the text on screen after a prediction
+  placePredictedText();
+
+  // if hands are found then start recording
+  if (hands.length > 0 && recordingFinished == false) {
+    if (sequence.length <= seqLength) {
+      // get coordinates from hands (21 points)
+      handpoints = drawPoints();
+      sequence.push(handpoints);
+
+      // once sequence reaches the seqLength, add sequence as just one X value
+    } else if (sequence.length > 0) {
+      // classify based on the collected data
+      model.classify(sequence, gotResults);
+
+      // reset the sequence
+      sequence = [];
+      recordingFinished = true;
+    }
+
+    // can only record again when hand is out of frame
+  } else {
+    if (hands.length == 0) {
+      recordingFinished = false;
+    }
+  }
+}
+
+// draw the points on the hands
+function drawPoints() {
+  let handpoints = [];
+  for (let i = 0; i < hands.length; i++) {
+    let hand = hands[i];
+    for (let j = 0; j < hand.keypoints.length; j++) {
+      let keypoint = hand.keypoints[j];
+      fill(0, 255, 0);
+      noStroke();
+      circle(keypoint.x, keypoint.y, 5);
+      handpoints.push(keypoint.x, keypoint.y);
+    }
+  }
+  let output = handpoints;
+  handpoints = [];
+  return output;
+}
+
+// Callback function for when handPose outputs data
+function gotHands(results) {
+  // save the output to the hands variable
+  hands = results;
+}
+
+// call back for accessing the results
+function gotResults(results) {
+  predictedWord = results[0].label;
+  console.log(predictedWord);
+  text(predictedWord, 100, 100);
+}
+
+// for drawing text on screen
+function placePredictedText() {
+  textSize(100);
+  fill(255);
+  text(predictedWord, 100, height / 2);
+}
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		{"modelTopology":{"class_name":"Sequential","config":{"name":"sequential_1","layers":[{"class_name":"Conv1D","config":{"filters":8,"kernel_initializer":{"class_name":"VarianceScaling","config":{"scale":1,"mode":"fan_avg","distribution":"normal","seed":null}},"kernel_regularizer":null,"kernel_constraint":null,"kernel_size":[3],"strides":[1],"padding":"valid","dilation_rate":[1],"activation":"relu","use_bias":true,"bias_initializer":{"class_name":"Zeros","config":{}},"bias_regularizer":null,"activity_regularizer":null,"bias_constraint":null,"name":"conv1d_Conv1D1","trainable":true,"batch_input_shape":[null,51,42],"dtype":"float32"}},{"class_name":"MaxPooling1D","config":{"pool_size":[2],"padding":"valid","strides":[2],"name":"max_pooling1d_MaxPooling1D1","trainable":true}},{"class_name":"Conv1D","config":{"filters":16,"kernel_initializer":{"class_name":"VarianceScaling","config":{"scale":1,"mode":"fan_avg","distribution":"normal","seed":null}},"kernel_regularizer":null,"kernel_constraint":null,"kernel_size":[3],"strides":[1],"padding":"valid","dilation_rate":[1],"activation":"relu","use_bias":true,"bias_initializer":{"class_name":"Zeros","config":{}},"bias_regularizer":null,"activity_regularizer":null,"bias_constraint":null,"name":"conv1d_Conv1D2","trainable":true,"batch_input_shape":[null,51,42],"dtype":"float32"}},{"class_name":"MaxPooling1D","config":{"pool_size":[2],"padding":"valid","strides":[2],"name":"max_pooling1d_MaxPooling1D2","trainable":true}},{"class_name":"Flatten","config":{"name":"flatten_Flatten1","trainable":true}},{"class_name":"Dense","config":{"units":16,"activation":"relu","use_bias":true,"kernel_initializer":{"class_name":"VarianceScaling","config":{"scale":1,"mode":"fan_avg","distribution":"normal","seed":null}},"bias_initializer":{"class_name":"Zeros","config":{}},"kernel_regularizer":null,"bias_regularizer":null,"activity_regularizer":null,"kernel_constraint":null,"bias_constraint":null,"name":"dense_Dense1","trainable":true}},{"class_name":"Dense","config":{"units":2,"activation":"softmax","use_bias":true,"kernel_initializer":{"class_name":"VarianceScaling","config":{"scale":1,"mode":"fan_avg","distribution":"normal","seed":null}},"bias_initializer":{"class_name":"Zeros","config":{}},"kernel_regularizer":null,"bias_regularizer":null,"activity_regularizer":null,"kernel_constraint":null,"bias_constraint":null,"name":"dense_Dense2","trainable":true}}]},"keras_version":"tfjs-layers 4.8.0","backend":"tensor_flow.js"},"weightsManifest":[{"paths":["./hello.weights.bin"],"weights":[{"name":"conv1d_Conv1D1/kernel","shape":[3,42,8],"dtype":"float32"},{"name":"conv1d_Conv1D1/bias","shape":[8],"dtype":"float32"},{"name":"conv1d_Conv1D2/kernel","shape":[3,8,16],"dtype":"float32"},{"name":"conv1d_Conv1D2/bias","shape":[16],"dtype":"float32"},{"name":"dense_Dense1/kernel","shape":[176,16],"dtype":"float32"},{"name":"dense_Dense1/bias","shape":[16],"dtype":"float32"},{"name":"dense_Dense2/kernel","shape":[16,2],"dtype":"float32"},{"name":"dense_Dense2/bias","shape":[2],"dtype":"float32"}]}]}
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