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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" }, | |
| width: { ideal: 2000 }, | |
| height: { ideal: 2000 }, | |
| advanced: [{ focusMode: "continuous" }] | |
| } | |
| }) | |
| .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); | |
| // Preprocess the image | |
| const imageData = context.getImageData(0, 0, canvas.width, canvas.height); | |
| preprocess(imageData); | |
| // Draw the preprocessed image on the canvas | |
| context.putImageData(imageData, 0, 0); | |
| // Convert the canvas to data URL | |
| const dataURL = canvas.toDataURL('image/png'); | |
| // Recognize text using Tesseract | |
| Tesseract.recognize( | |
| dataURL, | |
| 'kor+eng', // Recognize both Korean and English | |
| { | |
| logger: m => console.log(m) | |
| } | |
| ).then(({ data: { text } }) => { | |
| console.log(text); | |
| analyzeNutrition(text); | |
| }).catch(err => { | |
| console.error("Tesseract error: ", err); | |
| }); | |
| }); | |
| function preprocess(imageData) { | |
| // Binarization | |
| binarization(imageData); | |
| // Noise reduction | |
| noiseReduction(imageData); | |
| // Border enhancement | |
| borderEnhancement(imageData); | |
| // Resize | |
| resize(imageData); | |
| } | |
| function binarization(imageData) { | |
| // Binarization code | |
| } | |
| function noiseReduction(imageData) { | |
| // Noise reduction code | |
| } | |
| function borderEnhancement(imageData) { | |
| // Border enhancement code | |
| } | |
| function resize(imageData) { | |
| // Resize code | |
| } | |
| function analyzeNutrition(text) { | |
| // Extract sugar content from the recognized text | |
| const sugarMatch = text.match(/(당[^\d]*)(\d+(\.\d+)?)(\s*(g|grams|그램))/i); | |
| if (sugarMatch) { | |
| const sugarContent = parseFloat(sugarMatch[2]); | |
| let message = `Sugar content: ${sugarContent}g - `; | |
| if (sugarContent <= 20) { | |
| message += 'Good'; | |
| output.className = 'green'; | |
| } else if (sugarContent <= 50) { | |
| message += 'Normal'; | |
| output.className = 'yellow'; | |
| } else { | |
| message += 'Dangerous'; | |
| output.className = 'red'; | |
| } | |
| output.textContent = message; | |
| } else { | |
| output.textContent = 'Sugar content not found'; | |
| output.className = ''; | |
| } | |
| } | |
| </script> | |
| </body> | |
| </html> |