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1eb1890
Initial Commit, added files and tests
mop9047 b8685d2
Created methods based on circle or square mousexy
mop9047 46c7dd1
Added new files for different experiments (instantiating classes vs c…
mop9047 212e3a2
added custom method for addData
mop9047 2d7ad72
modified normalizeData()
mop9047 55a4558
fixed normalized, added hands example, implemented load data, removed…
mop9047 11c6220
changed folder name to TimeSeries, added new examples, code cleanup
mop9047 658290c
added comments to example code
mop9047 bbbcc92
code changes based on gottfried's comments
mop9047 0747f51
weather prediction example
mop9047 6dc456a
RDP algorithm Util now available, bug fixes for compatibility for dif…
mop9047 a51ec76
changed spatialData to Data Mode
mop9047 261a8e8
Bug fixes and example UI changes
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| <!-- | ||
| 👋 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 | ||
|
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| This example demonstrates training a Sign Language classifier through ml5.TimeSeries. | ||
| --> | ||
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| <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> | ||
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| <body> | ||
| <script src="sketch.js"></script> | ||
| </body> | ||
| </html> | ||
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| @@ -0,0 +1,192 @@ | ||
| /* | ||
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| * 👋 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. | ||
| */ | ||
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| let seqLength = 50; | ||
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| let handPose; | ||
| let video; | ||
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| let hands = []; | ||
| let sequence = []; | ||
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| let recordingFinished = false; | ||
| let predictedWord = ""; | ||
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| // UI variables | ||
| let trainingWords = {}; | ||
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| function preload() { | ||
| // Load the handPose model | ||
| handPose = ml5.handPose(); | ||
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| // setup the timeseries neural network | ||
| let options = { | ||
| outputs: ["label"], | ||
| task: "classification", | ||
| dataMode: "spatial", | ||
| debug: "true", | ||
| learningRate: 0.001, | ||
| }; | ||
| model = ml5.timeSeries(options); | ||
| } | ||
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| function setup() { | ||
| createCanvas(640, 480); | ||
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| // setup video capture | ||
| video = createCapture(VIDEO); | ||
| video.size(640, 480); | ||
| video.hide(); | ||
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| // place UI elements | ||
| UI(); | ||
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| // use handpose model on video | ||
| handPose.detectStart(video, gotHands); | ||
| } | ||
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| function draw() { | ||
| // draw video on frame | ||
| image(video, 0, 0, width, height); | ||
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| drawPredictedWord(); | ||
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| // 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); | ||
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| // 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(); | ||
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| // 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(); | ||
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| // if there is no word in the box then classify instead | ||
| } else { | ||
| // classify the data | ||
| model.classify(sequence, gotResults); | ||
| } | ||
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| // reset the sequence | ||
| sequence = []; | ||
| recordingFinished = true; | ||
| } | ||
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| // can only record again when hand is out of frame | ||
| } else { | ||
| if (hands.length == 0) { | ||
| recordingFinished = false; | ||
| } | ||
| } | ||
| } | ||
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| 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); | ||
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| fill(0, 255, 0); | ||
| noStroke(); | ||
| circle(keypoint.x, keypoint.y, 5); | ||
| } | ||
| } | ||
| // assign to a different variable before clearing | ||
| let output = handpoints; | ||
| handpoints = []; | ||
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| return output; | ||
| } | ||
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| // Callback function for when handPose outputs data | ||
| function gotHands(results) { | ||
| // save the output to the hands variable | ||
| hands = results; | ||
| } | ||
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| function trainModelAndSave() { | ||
| model.normalizeData(); | ||
| let options = { | ||
| epochs: 100, | ||
| }; | ||
| model.train(options, whileTraining, finishedTraining); | ||
| nameField.value(""); | ||
| } | ||
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| function whileTraining(epoch) { | ||
| console.log(epoch); | ||
| } | ||
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| function finishedTraining() { | ||
| console.log("finished training."); | ||
| model.save("model"); | ||
| } | ||
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| function gotResults(results) { | ||
| predictedWord = results[0].label; | ||
| console.log(predictedWord); | ||
| text(predictedWord, 200, 200); | ||
| } | ||
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| function UI() { | ||
| nameField = createInput(""); | ||
| nameField.attribute("placeholder", "Type the word to train"); | ||
| nameField.position(110, 500); | ||
| nameField.size(250); | ||
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| 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 <-"); | ||
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| train_but = createButton("Train and Save"); | ||
| train_but.mouseClicked(trainModelAndSave); | ||
| train_but.style("font-family", "Georgia"); | ||
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| train_but.style("font-size", "20px"); | ||
| train_but.position(500, 490); | ||
| } | ||
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| function drawPredictedWord() { | ||
| textSize(100); | ||
| fill(255); | ||
| text(predictedWord, 100, height / 2); | ||
| } | ||
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| function trainingWordsUpdate() { | ||
| let tempWord = nameField.value(); | ||
| console.log(Object.keys(trainingWords)); | ||
| if (!(tempWord in trainingWords)) { | ||
| trainingWords[tempWord] = 1; | ||
| } else { | ||
| trainingWords[tempWord]++; | ||
| } | ||
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| let counts = ""; | ||
| let keys = Object.keys(trainingWords); | ||
| console.log("keys", keys); | ||
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| for (let k of keys) { | ||
| counts += k + " : " + trainingWords[k] + "<br>"; | ||
| } | ||
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| dataCountsP.html(counts); | ||
| } | ||
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| Original file line number | Diff line number | Diff line change |
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| @@ -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. | ||
| --> | ||
|
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| <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> | ||
|
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| <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 /> | ||
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| 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 /> | ||
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| 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> |
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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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examples/timeSeries-load-model-hand-gestures/model/model_meta.json
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| {"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]} |
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