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| <html lang="en"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
| <title>Candy Label Scanner</title> | |
| <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs"></script> | |
| <script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/coco-ssd"></script> | |
| <script src="https://cdn.jsdelivr.net/npm/tesseract.js"></script> | |
| <style> | |
| #output { | |
| font-size: 20px; | |
| margin-top: 20px; | |
| } | |
| .red { | |
| color: red; | |
| } | |
| .yellow { | |
| color: yellow; | |
| } | |
| .green { | |
| color: green; | |
| } | |
| video { | |
| width: 100%; | |
| height: auto; | |
| } | |
| </style> | |
| </head> | |
| <body> | |
| <h1>Candy Label Scanner</h1> | |
| <video id="video" autoplay></video> | |
| <button id="capture">Capture</button> | |
| <canvas id="canvas" style="display: none;"></canvas> | |
| <div id="output"></div> | |
| <script> | |
| const video = document.getElementById('video'); | |
| const canvas = document.getElementById('canvas'); | |
| const output = document.getElementById('output'); | |
| const captureButton = document.getElementById('capture'); | |
| navigator.mediaDevices.getUserMedia({ | |
| video: { | |
| facingMode: { exact: "environment" } | |
| } | |
| }) | |
| .then(stream => { | |
| video.srcObject = stream; | |
| }) | |
| .catch(err => { | |
| console.error("Error accessing the camera: ", err); | |
| }); | |
| captureButton.addEventListener('click', () => { | |
| // Draw the video frame to the canvas | |
| canvas.width = video.videoWidth; | |
| canvas.height = video.videoHeight; | |
| const context = canvas.getContext('2d'); | |
| context.drawImage(video, 0, 0, canvas.width, canvas.height); | |
| // Convert canvas to image data | |
| const dataURL = canvas.toDataURL('image/png'); | |
| // Process the image with Tesseract | |
| Tesseract.recognize( | |
| dataURL, | |
| 'kor', | |
| "image-to-text", | |
| model="team-lucid/trocr-small-korean", | |
| { | |
| logger: m => console.log(m) | |
| } | |
| ).then(({ data: { text } }) => { | |
| console.log(text); | |
| analyzeNutrition(text); | |
| }); | |
| }); | |
| async function analyzeNutrition(text) { | |
| // Extract nutritional values (assuming sugar content is labeled as '당류' in Korean) | |
| const regex = /당류\s*:\s*(\d+(\.\d+)?)\s*g\s*/; // This regex might need adjustments based on label format | |
| const match = text.match(regex); | |
| let outputDiv = document.getElementById('output'); | |
| if (match) { | |
| const sugarContent = parseFloat(match[1]); | |
| let message = `Sugar content: ${sugarContent}g - `; | |
| if (sugarContent > 20) { | |
| message += 'Dangerous'; | |
| outputDiv.className = 'red'; | |
| } else if (sugarContent > 10) { | |
| message += 'Normal'; | |
| outputDiv.className = 'yellow'; | |
| } else { | |
| message += 'Good'; | |
| outputDiv.className = 'green'; | |
| } | |
| outputDiv.textContent = message; | |
| } else { | |
| outputDiv.textContent = 'Sugar content not found'; | |
| outputDiv.className = ''; | |
| } | |
| } | |
| </script> | |
| </body> | |
| </html> | |