AI Color Palette Generator

"ai learning, keyvisual"

"feng shui, app, bazi"

"feng shui, app, bazi"

"feng shui, app, bazi"

"feng shui, fortune telling"

"feng shui, fortune telling"

"feng shui, fortune telling"

"<div>Teachable Machine Image Model</div> <button type="button" onclick="init()">Start</button> <div id="webcam-container"></div> <div id="label-container"></div> <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest/dist/tf.min.js"></script> <script src="https://cdn.jsdelivr.net/npm/@teachablemachine/image@latest/dist/teachablemachine-image.min.js"></script> <script type="text/javascript"> // More API functions here: // https://github.com/googlecreativelab/teachablemachine-community/tree/master/libraries/image // the link to your model provided by Teachable Machine export panel const URL = "./my_model/"; let model, webcam, labelContainer, maxPredictions; // Load the image model and setup the webcam async function init() { const modelURL = URL + "model.json"; const metadataURL = URL + "metadata.json"; // load the model and metadata // Refer to tmImage.loadFromFiles() in the API to support files from a file picker // or files from your local hard drive // Note: the pose library adds "tmImage" object to your window (window.tmImage) model = await tmImage.load(modelURL, metadataURL); maxPredictions = model.getTotalClasses(); // Convenience function to setup a webcam const flip = true; // whether to flip the webcam webcam = new tmImage.Webcam(200, 200, flip); // width, height, flip await webcam.setup(); // request access to the webcam await webcam.play(); window.requestAnimationFrame(loop); // append elements to the DOM document.getElementById("webcam-container").appendChild(webcam.canvas); labelContainer = document.getElementById("label-container"); for (let i = 0; i < maxPredictions; i++) { // and class labels labelContainer.appendChild(document.createElement("div")); } } async function loop() { webcam.update(); // update the webcam frame await predict(); window.requestAnimationFrame(loop); } // run the webcam image through the image model async function predict() { // predict can take in an image, video or canvas html element const prediction = await model.predict(webcam.canvas); for (let i = 0; i < maxPredictions; i++) { const classPrediction = prediction[i].className + ": " + prediction[i].probability.toFixed(2); labelContainer.childNodes[i].innerHTML = classPrediction; } } </script>"

"<div>Teachable Machine Image Model</div> <button type="button" onclick="init()">Start</button> <div id="webcam-container"></div> <div id="label-container"></div> <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest/dist/tf.min.js"></script> <script src="https://cdn.jsdelivr.net/npm/@teachablemachine/image@latest/dist/teachablemachine-image.min.js"></script> <script type="text/javascript"> // More API functions here: // https://github.com/googlecreativelab/teachablemachine-community/tree/master/libraries/image // the link to your model provided by Teachable Machine export panel const URL = "./my_model/"; let model, webcam, labelContainer, maxPredictions; // Load the image model and setup the webcam async function init() { const modelURL = URL + "model.json"; const metadataURL = URL + "metadata.json"; // load the model and metadata // Refer to tmImage.loadFromFiles() in the API to support files from a file picker // or files from your local hard drive // Note: the pose library adds "tmImage" object to your window (window.tmImage) model = await tmImage.load(modelURL, metadataURL); maxPredictions = model.getTotalClasses(); // Convenience function to setup a webcam const flip = true; // whether to flip the webcam webcam = new tmImage.Webcam(200, 200, flip); // width, height, flip await webcam.setup(); // request access to the webcam await webcam.play(); window.requestAnimationFrame(loop); // append elements to the DOM document.getElementById("webcam-container").appendChild(webcam.canvas); labelContainer = document.getElementById("label-container"); for (let i = 0; i < maxPredictions; i++) { // and class labels labelContainer.appendChild(document.createElement("div")); } } async function loop() { webcam.update(); // update the webcam frame await predict(); window.requestAnimationFrame(loop); } // run the webcam image through the image model async function predict() { // predict can take in an image, video or canvas html element const prediction = await model.predict(webcam.canvas); for (let i = 0; i < maxPredictions; i++) { const classPrediction = prediction[i].className + ": " + prediction[i].probability.toFixed(2); labelContainer.childNodes[i].innerHTML = classPrediction; } } </script>"

"Generate color palette for Chinese Crafts Training Center from the Chinese traditional colors"

"Generate color palette for Chinese Crafts Training Center from the Chinese traditional colors"

"Generate color palette for Chinese Crafts Training Center from the Chinese traditional colors"

"Generate me a color palette for a character named 007n7"

"Generate me a color palette for a character named 007n7"

"Generate a color palette for a character named Veeronica"