CS909/harris.html at master · foxtrotmike/CS909 · GitHub

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<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <title>Image Gradient and Eigenvalue Calculator</title>
    <style>
        body {
            font-family: Arial, sans-serif;
            text-align: center;
        }
        canvas {
            border: 1px solid black;
            margin-top: 10px;
        }
        table {
            margin: 0 auto;
            margin-top: 20px;
            border-collapse: collapse;
        }
        th, td {
            border: 1px solid black;
            padding: 5px 10px;
        }
    </style>
</head>
<body>
    <h1>Image Gradient and Eigenvalue Calculator</h1>
    <label for="imageSelect">Select Image:</label>
    <select id="imageSelect">
        <option value="flat">Flat Image</option>
        <option value="edge">Edge Image</option>
        <option value="edgeT">Transposed Edge Image</option>
        <option value="combined">Combined Edges Image</option>
    </select>
    <button onclick="processImage()">Process Image</button>
    <canvas id="imageCanvas" width="100" height="100"></canvas>
    <div id="results"></div>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjs/11.8.0/math.min.js"></script>
    <script>
        // Function to create a 5x5 flat image
        function createFlatImage() {
            return Array(5).fill().map(() => Array(5).fill(1));
        }

        // Function to create a 5x5 image with an edge
        function createEdgeImage() {
            return Array(5).fill().map((_, i) => Array(5).fill(i < 3 ? 1 : 0));
        }

        // Function to transpose a matrix
        function transposeMatrix(matrix) {
            return matrix[0].map((_, colIndex) => matrix.map(row => row[colIndex]));
        }

        // Function to add two matrices
        function addMatrices(matrixA, matrixB) {
            return matrixA.map((row, i) => row.map((val, j) => val + matrixB[i][j]));
        }

        // Function to calculate the gradient matrix M
        function calculateMatrixM(image) {
            const I_x = image.map((row, i) => row.map((_, j) => {
                const left = j > 0 ? image[i][j - 1] : image[i][j];
                const right = j < 4 ? image[i][j + 1] : image[i][j];
                return (right - left) / 2;
            }));

            const I_y = image.map((row, i) => row.map((_, j) => {
                const top = i > 0 ? image[i - 1][j] : image[i][j];
                const bottom = i < 4 ? image[i + 1][j] : image[i][j];
                return (bottom - top) / 2;
            }));

            const sum = (matrix) => matrix.flat().reduce((acc, val) => acc + val, 0);

            const M = [
                [sum(I_x.map(row => row.map(val => val ** 2))), sum(I_x.flat().map((val, idx) => val * I_y.flat()[idx]))],
                [sum(I_x.flat().map((val, idx) => val * I_y.flat()[idx])), sum(I_y.map(row => row.map(val => val ** 2)))]
            ];

            return M;
        }

        // Function to display the image on canvas
        function displayImage(image) {
            const canvas = document.getElementById('imageCanvas');
            const ctx = canvas.getContext('2d');
            const scale = 20; // Each pixel in the 5x5 image is scaled to a 20x20 block
            ctx.clearRect(0, 0, canvas.width, canvas.height); // Clear previous image

            image.forEach((row, i) => {
                row.forEach((val, j) => {
                    const color = val * 255;
                    ctx.fillStyle = `rgb(${color}, ${color}, ${color})`;
                    ctx.fillRect(j * scale, i * scale, scale, scale);
                });
            });
        }

        // Function to process the selected image
        function processImage() {
            const selection = document.getElementById('imageSelect').value;
            let image;

            switch (selection) {
                case 'flat':
                    image = createFlatImage();
                    break;
                case 'edge':
                    image = createEdgeImage();
                    break;
                case 'edgeT':
                    image = transposeMatrix(createEdgeImage());
                    break;
                case 'combined':
                    image = addMatrices(createEdgeImage(), transposeMatrix(createEdgeImage()));
                    break;
                default:
                    alert('Invalid selection');
                    return;
            }

            displayImage(image);

            const M = calculateMatrixM(image);
            const eigen = math.eigs(M);
            const energy = Math.max(...eigen.values);

            const resultsDiv = document.getElementById('results');
            resultsDiv.innerHTML = `
                <h2>Results</h2>
                <table>
                    <tr>
                        <th>Matrix M</th>
                        <td>
                            <table>
                                <tr>
                                    <td>${M[0][0].toFixed(2)}</td>
                                    <td>${M[0][1].toFixed(2)}</td>
                                </tr>
                                <tr>
                                    <td>${M[1][0].toFixed(2)}</td>
                                    <td>${M[1][1].toFixed(2)}</td>
                                </tr>
                            </table>
                        </td>
                    </tr>
                    <tr>
                        <th>Eigenvalues</th>
                        <td>${eigen.values.map(val => val.toFixed(2)).join(', ')}</td>
                    </tr>
                    <tr>
                        <th>Energy (max eigenvalue)</th>
                        <td>${energy.toFixed(2)}</td>
                    </tr>
                </table>
            `;
        }
    </script>
</body>
</html>