最小二乘法来求解矩阵
// 计算Homography矩阵
function calculateHomography(srcPoints, dstPoints) {
if (srcPoints.length !== dstPoints.length || srcPoints.length < 4) {
throw new Error('需要至少四个点进行计算');
}
// 设置矩阵方程 Ax = b
let A = [];
let b = [];
// 构建方程的A矩阵和b向量
for (let i = 0; i < srcPoints.length; i++) {
let x1 = srcPoints[i][0];
let y1 = srcPoints[i][1];
let x2 = dstPoints[i][0];
let y2 = dstPoints[i][1];
// 方程:x2 = H11 * x1 + H12 * y1 + H13
// 方程:y2 = H21 * x1 + H22 * y1 + H23
A.push([-x1, -y1, -1, 0, 0, 0, x1 * x2, y1 * x2]);
A.push([0, 0, 0, -x1, -y1, -1, x1 * y2, y1 * y2]);
b.push(x2);
b.push(y2);
}
// 使用高斯-约旦消元法求解A * h = b
let H = gaussJordan(A, b);
// 构造Homography矩阵
let homographyMatrix = [
[H[0], H[1], H[2]],
[H[3], H[4], H[5]],
[H[6], H[7], 1]
];
return homographyMatrix;
}
// 高斯-约旦消元法解线性方程组
function gaussJordan(A, b) {
let n = A.length;
let augmentedMatrix = A.map((row, i) => row.concat(b[i])); // A 与 b 合并为增广矩阵
for (let i = 0; i < n; i++) {
// 找到最大值所在的行进行行交换
let maxRow = i;
for (let j = i + 1; j < n; j++) {
if (Math.abs(augmentedMatrix[j][i]) > Math.abs(augmentedMatrix[maxRow][i])) {
maxRow = j;
}
}
[augmentedMatrix[i], augmentedMatrix[maxRow]] = [augmentedMatrix[maxRow], augmentedMatrix[i]];
// 将主对角线上的元素变为1
let scale = augmentedMatrix[i][i];
for (let j = i; j < n + 1; j++) {
augmentedMatrix[i][j] /= scale;
}
// 通过行操作消去其他列的元素
for (let j = 0; j < n; j++) {
if (j !== i) {
scale = augmentedMatrix[j][i];
for (let k = i; k < n + 1; k++) {
augmentedMatrix[j][k] -= augmentedMatrix[i][k] * scale;
}
}
}
}
// 提取解向量(即矩阵的最后一列)
let solution = augmentedMatrix.map(row => row[n]);
return solution;
}
// 示例:输入源点和目标点
let srcPoints = [
[7.606727752, 13.23349844],
[5.727826596, 13.23044123],
[4.788626754, 13.23106254],
[2.906026488, 13.23060659]
];
let dstPoints = [
[7.623758876, 13.24447489],
[5.744538875, 13.24339615],
[4.804678876, 13.24468678],
[2.923728876, 13.24552804]
];
// 计算Homography矩阵
let homographyMatrix = calculateHomography(srcPoints, dstPoints);
console.log("Homography Matrix:");
console.log(homographyMatrix);