ml.js Demo

{"name":"ml.js Demo","description":"","photo":"","view_count":2,"fork_count":0,"expires_at":null,"created_at":"2024-11-22T23:58:27.714829Z","updated_at":"2024-11-22T23:58:27.714832Z","user_id":60,"status":"PRIVATE","parent_id":null,"id":4756,"user":{"photo":null,"email":"jacob@jippity.pro","recess_id":"34481dd2-2bdf-14f9-99da-131de59dc90f","created_at":"2024-02-26T18:31:54.473216Z","reset_token":null,"stripe_customer_id":"cus_RPbovGjLueUufv","seats":100,"username":"2718281828459045","id":60,"password_hash":"$2b$12$ZpelZmCi5wEA/VuXSdOJXeylQ6vOUks0sfwQxIKdjwBAeamaWIMOW","updated_at":"2024-02-26T18:31:54.473218Z","reset_token_expiry":null,"subscription_status":"active"},"code":{"script.js":"let video;\n\nlet facemesh;\n\nlet predictions = [];\n\n// Setup function to initialize video and facemesh\nfunction setup() {\n createCanvas(windowWidth, windowHeight);\n background(135, 206, 235); // Sky blue background\n\n // Create a video capture\n video = createCapture(VIDEO);\n video.size(width, height);\n video.hide(); // Hide the video element, we will use the canvas to display it\n\n // Load the Facemesh model\n facemesh = ml5.facemesh(video, modelReady);\n}\n\n// Draw function to render the video and wireframe\nfunction draw() {\n image(video, 0, 0, width, height); // Draw the video on the canvas\n\n // Check if there are any predictions (detected faces)\n if (predictions.length > 0) {\n // Loop through each prediction\n predictions.forEach(prediction => {\n const keypoints = prediction.scaledMesh; // Get the keypoints of the face\n\n // Draw the wireframe using the keypoints\n stroke(0, 255, 0); // Green color for the wireframe\n strokeWeight(2); // Set the stroke weight\n\n // Draw lines between keypoints to create the wireframe\n for (let i = 0; i < keypoints.length; i++) {\n const [x, y] = keypoints[i]; // Destructure the x and y coordinates\n point(x, y); // Draw a point at the keypoint position\n }\n });\n }\n}\n\n// Function that gets called when the model is loaded\nfunction modelReady() {\n console.log('Facemesh model loaded!');\n // Start detecting faces\n facemesh.on('predict', results => {\n predictions = results;\n });\n}\n\n","style.css":"body { margin: 0; } canvas { display: block; }","index.html":"\n\n\n\n \n \n ml.js Demo\n \n \n \n\n\n\n \n\n\n"},"chat_history":[{"deleted":0,"code_id":185824,"input_tokens":null,"id":91788,"project_id":4756,"sender":"human","message":"ml.js demo","created_at":"2024-11-22T23:57:21.483974Z","output_tokens":null},{"deleted":0,"code_id":185824,"input_tokens":null,"id":91789,"project_id":4756,"sender":"human","message":"Let's get started!","created_at":"2024-11-22T23:57:25.922481Z","output_tokens":null},{"deleted":0,"code_id":185824,"input_tokens":null,"id":91790,"project_id":4756,"sender":"ai","message":"Great! I'd love to help you get started on your project. Could you tell me more about your idea? What kind of project are you looking to build with this ml.js demo? Is it going to be a game, a website, an animation, or something else?","created_at":"2024-11-22T23:57:25.947856Z","output_tokens":null},{"deleted":0,"code_id":185825,"input_tokens":null,"id":91791,"project_id":4756,"sender":"human","message":"import ml.js and let's explore its abilities","created_at":"2024-11-22T23:57:48.919200Z","output_tokens":null},{"deleted":0,"code_id":185825,"input_tokens":null,"id":91792,"project_id":4756,"sender":"ai","message":"To explore the abilities of ml.js, we first need to include it in your project. Let's start by adding the ml.js library to your HTML file. \n\nCould you confirm if you're interested in using any specific features or models from ml.js, such as image recognition, natural language processing, or something else? This will help me guide you better on how to use the library effectively in your project.","created_at":"2024-11-22T23:57:48.935923Z","output_tokens":null},{"deleted":0,"code_id":185825,"input_tokens":null,"id":91793,"project_id":4756,"sender":"human","message":"I want to create a wireframe on my face","created_at":"2024-11-22T23:58:05.711533Z","output_tokens":null},{"deleted":0,"code_id":185825,"input_tokens":null,"id":91794,"project_id":4756,"sender":"ai","message":"That sounds like a fun project! To create a wireframe on your face, we can use a machine learning model to detect the facial landmarks and then draw a wireframe overlay. \n\nFirst, let's add ml5.js, which is a friendly wrapper around TensorFlow.js and provides pre-trained models for tasks like face detection. You can use it to detect facial landmarks easily.\n\nLet's start by including the ml5.js library in your HTML file. Here's how you can do it:\n\n```html\n\n\n\n \n \n Interactive ml.js Demo\n \n \n \n\n\n \n\n\n```\n\nNext, we'll need to update your JavaScript code to use the face landmark detection model. Do you have any specific preferences on how you'd like to visualize the wireframe on your face, or should we proceed with a basic implementation?","created_at":"2024-11-22T23:58:05.727309Z","output_tokens":null},{"deleted":0,"code_id":185827,"input_tokens":null,"id":91795,"project_id":4756,"sender":"human","message":"do a standard basic wifre frame onto which we add components later","created_at":"2024-11-22T23:58:46.725363Z","output_tokens":null},{"deleted":0,"code_id":185827,"input_tokens":null,"id":91796,"project_id":4756,"sender":"ai","message":"Let's start by implementing a basic wireframe overlay on your face using the ml5.js library. We will set up the video capture, use the face detection model, and draw a wireframe on detected facial landmarks.\n\nFirst, we need to modify your `script.js` file to include the necessary code for video capture and face landmark detection. Here's how you can do it:\n\n### Update the JavaScript Code\n\n```javascript\nlet video;\nlet facemesh;\nlet predictions = [];\n\n// Setup function to initialize video and facemesh\nfunction setup() {\n createCanvas(windowWidth, windowHeight);\n background(135, 206, 235); // Sky blue background\n\n // Create a video capture\n video = createCapture(VIDEO);\n video.size(width, height);\n video.hide(); // Hide the video element, we will use the canvas to display it\n\n // Load the Facemesh model\n facemesh = ml5.facemesh(video, modelReady);\n}\n\n// Function that gets called when the model is loaded\nfunction modelReady() {\n console.log('Facemesh model loaded!');\n // Start detecting faces\n facemesh.on('predict', results => {\n predictions = results;\n });\n}\n\n// Draw function to render the video and wireframe\nfunction draw() {\n image(video, 0, 0, width, height); // Draw the video on the canvas\n\n // Check if there are any predictions (detected faces)\n if (predictions.length > 0) {\n // Loop through each prediction\n predictions.forEach(prediction => {\n const keypoints = prediction.scaledMesh; // Get the keypoints of the face\n\n // Draw the wireframe using the keypoints\n stroke(0, 255, 0); // Green color for the wireframe\n strokeWeight(2); // Set the stroke weight\n\n // Draw lines between keypoints to create the wireframe\n for (let i = 0; i < keypoints.length; i++) {\n const [x, y] = keypoints[i]; // Destructure the x and y coordinates\n point(x, y); // Draw a point at the keypoint position\n }\n });\n }\n}\n```\n\n### Explanation of the Code\n- **Video Capture**: We create a video capture with `createCapture(VIDEO)` and hide it to only display on the canvas.\n- **Facemesh Model**: The facemesh model is loaded and set to predict facial landmarks.\n- **Drawing**: We draw the video on the canvas and overlay points at the facial landmarks detected by the model, creating a wireframe effect.\n\n### Next Steps\n1. **Update your `script.js` with the above code.**\n2. **Run your project to see the results!** You should see a wireframe overlay on your face when you allow camera access.\n\nWould you like to add any specific features or components to this wireframe later on?","created_at":"2024-11-22T23:58:46.734537Z","output_tokens":null},{"deleted":0,"code_id":185828,"input_tokens":null,"id":91797,"project_id":4756,"sender":"human","message":"LOG: \n🌸 p5.js says: It seems that you may have accidentally written \"ml5\" (on line 18 in d051e85b-fbb1-4516-a42e-237743c08574 [blob:https://jippity.pro/d051e85b-fbb1-4516-a42e-237743c08574:18:19]).\n\nYou may have meant one of the following:\n▶️ ALT (http://p5js.org/reference/#/p5/ALT)\n▶️ mag() (http://p5js.org/reference/#/p5/mag)\n▶️ map() (http://p5js.org/reference/#/p5/map)\n▶️ max() (http://p5js.org/reference/#/p5/max)\n▶️ min() (http://p5js.org/reference/#/p5/min)","created_at":"2024-11-23T00:00:08.532287Z","output_tokens":null},{"deleted":0,"code_id":185828,"input_tokens":null,"id":91798,"project_id":4756,"sender":"ai","message":"It looks like there might be an issue with the way we're trying to access the `ml5` library in your code. The error message indicates that `ml5` is not recognized, which usually happens if the library isn't loaded correctly or is referenced incorrectly.\n\nLet's ensure that the `facemesh` model is correctly initialized by checking your existing code for any potential typos. The code in your `script.js` looks correct based on what you've shared, but to troubleshoot, let's make sure the model is initialized properly.\n\n### Update the JavaScript Code\n\n1. Make sure you have the following lines in your `script.js` for loading the facemesh model:\n\n```javascript\n// Load the Facemesh model\nfacemesh = ml5.facemesh(video, modelReady);\n```\n\n2. Ensure that the model is loaded by checking the console log in your browser for the message \"Facemesh model loaded!\" after running your project.\n\n### Full Script for Reference\nHere’s the complete `script.js` for clarity:\n\n```javascript\nlet video;\nlet facemesh;\nlet predictions = [];\n\n// Setup function to initialize video and facemesh\nfunction setup() {\n createCanvas(windowWidth, windowHeight);\n background(135, 206, 235); // Sky blue background\n\n // Create a video capture\n video = createCapture(VIDEO);\n video.size(width, height);\n video.hide(); // Hide the video element, we will use the canvas to display it\n\n // Load the Facemesh model\n facemesh = ml5.facemesh(video, modelReady);\n}\n\n// Function that gets called when the model is loaded\nfunction modelReady() {\n console.log('Facemesh model loaded!');\n // Start detecting faces\n facemesh.on('predict', results => {\n predictions = results;\n });\n}\n\n// Draw function to render the video and wireframe\nfunction draw() {\n image(video, 0, 0, width, height); // Draw the video on the canvas\n\n // Check if there are any predictions (detected faces)\n if (predictions.length > 0) {\n // Loop through each prediction\n predictions.forEach(prediction => {\n const keypoints = prediction.scaledMesh; // Get the keypoints of the face\n\n // Draw the wireframe using the keypoints\n stroke(0, 255, 0); // Green color for the wireframe\n strokeWeight(2); // Set the stroke weight\n\n // Draw lines between keypoints to create the wireframe\n for (let i = 0; i < keypoints.length; i++) {\n const [x, y] = keypoints[i]; // Destructure the x and y coordinates\n point(x, y); // Draw a point at the keypoint position\n }\n });\n }\n}\n```\n\n### Next Steps\n1. **Double-check your `script.js` for typos.**\n2. **Ensure you are correctly loading the ml5.js library.**\n3. **Run your project again and check the console for any logs.**\n\nIf you still encounter issues, let me know, and we can troubleshoot further! Would you like to add any additional features to your wireframe project once it's working correctly?","created_at":"2024-11-23T00:00:08.544519Z","output_tokens":null},{"deleted":0,"code_id":185831,"input_tokens":null,"id":91799,"project_id":4756,"sender":"human","message":"not recognizing ml5, is it loaded","created_at":"2024-11-23T00:04:35.017221Z","output_tokens":null},{"deleted":0,"code_id":185831,"input_tokens":null,"id":91800,"project_id":4756,"sender":"ai","message":"From the details you've provided, it looks like your `index.html` correctly includes the ml5.js library:\n\n```html\n\n```\n\nSince you've confirmed that the library is there, let's troubleshoot a few common issues that might cause ml5 to not be recognized:\n\n1. **Check Console for Errors**: Open your browser's Developer Tools (usually F12 or right-click -> Inspect) and check the Console tab for any errors. If there's an error loading the ml5.js library, it will be displayed there.\n\n2. **Network Issues**: Sometimes, network issues or ad blockers can prevent scripts from loading. Try refreshing your page or checking if the script URL is accessible directly in your browser.\n\n3. **Correct Script Order**: Ensure that you're including your `script.js` **after** both p5.js and ml5.js in your HTML file. Your current setup looks correct, so this is likely not the issue, but it's worth checking.\n\n4. **Cache Issues**: Clear your browser cache or try opening your project in Incognito mode to see if that resolves the issue.\n\n5. **Version Compatibility**: Check if you're using compatible versions of p5.js and ml5.js. It's a good practice to use the latest stable versions.\n\n### Next Steps\n- After checking these points, try running your project again.\n- If you still see the error, please share any specific error messages you find in the console, as that will help us diagnose the issue further.\n\nLet me know how it goes! Would you like to add any specific features once the wireframe is working?","created_at":"2024-11-23T00:04:35.036450Z","output_tokens":null},{"deleted":0,"code_id":185832,"input_tokens":null,"id":91801,"project_id":4756,"sender":"human","message":"nothing appearing on screen","created_at":"2024-11-23T00:06:59.811025Z","output_tokens":null},{"deleted":0,"code_id":185832,"input_tokens":null,"id":91802,"project_id":4756,"sender":"ai","message":"It seems like your code is almost set up correctly, but let's make sure everything is functioning properly. Here are a few steps to troubleshoot and fix the issue of nothing appearing on the screen:\n\n### 1. Ensure Predictions Are Being Made\nMake sure that the predictions are being updated when the model is ready. You can add a console log in the `draw` function to see if it detects any predictions:\n\n```javascript\nfunction draw() {\n image(video, 0, 0, width, height); // Draw the video on the canvas\n\n // Check if there are any predictions (detected faces)\n if (predictions.length > 0) {\n console.log(predictions); // Log predictions to check if faces are detected\n predictions.forEach(prediction => {\n const keypoints = prediction.scaledMesh; // Get the keypoints of the face\n\n // Draw the wireframe using the keypoints\n stroke(0, 255, 0); // Green color for the wireframe\n strokeWeight(2); // Set the stroke weight\n\n // Draw lines between keypoints to create the wireframe\n for (let i = 0; i < keypoints.length; i++) {\n const [x, y] = keypoints[i]; // Destructure the x and y coordinates\n point(x, y); // Draw a point at the keypoint position\n }\n });\n } else {\n console.log(\"No face detected\"); // Log if no faces are detected\n }\n}\n```\n\n### 2. Check for Camera Permissions\nMake sure that your browser has permission to access the camera. If the camera is not accessible, the video capture will not work, and you won't see anything on the screen.\n\n### 3. Verify Model Loading\nCheck your browser’s console for the message \"Facemesh model loaded!\" to confirm that the model is initialized correctly. If you don't see this message, there may be an issue with loading the model.\n\n### 4. Adjust Canvas Size\nSometimes the canvas might be too large or not visible. You can try setting a fixed size for the canvas initially, like so:\n\n```javascript\nfunction setup() {\n createCanvas(640, 480); // Set a fixed size for easier visibility\n ...\n}\n```\n\n### 5. Refresh and Test\nAfter making these changes, refresh your page and check the console for logs related to predictions or errors. \n\n### Summary\n- Add console logs to check predictions.\n- Ensure camera permissions are granted.\n- Confirm that the model is loaded.\n- Set a fixed canvas size for visibility.\n\nAfter trying these steps, let me know what you see in the console, and we can continue troubleshooting from there! Would you like to add any specific features later on?","created_at":"2024-11-23T00:06:59.820293Z","output_tokens":null}],"assets":[],"messages_sent":8,"comments":[],"steps":[]}