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Manav Rathi 2024-05-16 13:16:20 +05:30
parent 7160ae700f
commit 569808c291
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1 changed files with 120 additions and 126 deletions
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246
web/apps/photos/src/services/machineLearning/laplacianBlurDetectionService.ts
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@ -22,134 +22,11 @@ export const detectBlur = (
i,
);
const laplacian = applyLaplacian(faceImage, direction);
const variance = calculateVariance(laplacian);
blurValues.push(variance);
blurValues.push(matrixVariance(laplacian));
}
return blurValues;
};
const calculateVariance = (matrix: number[][]): number => {
const numRows = matrix.length;
const numCols = matrix[0].length;
const totalElements = numRows * numCols;
// Calculate the mean
let mean: number = 0;
matrix.forEach((row) => {
row.forEach((value) => {
mean += value;
});
});
mean /= totalElements;
// Calculate the variance
let variance: number = 0;
matrix.forEach((row) => {
row.forEach((value) => {
const diff: number = value - mean;
variance += diff * diff;
});
});
variance /= totalElements;
return variance;
};
const padImage = (
image: number[][],
removeSideColumns: number = 56,
direction: FaceDirection = "straight",
): number[][] => {
// Exception is removeSideColumns is not even
if (removeSideColumns % 2 != 0) {
throw new Error("removeSideColumns must be even");
}
const numRows = image.length;
const numCols = image[0].length;
const paddedNumCols = numCols + 2 - removeSideColumns;
const paddedNumRows = numRows + 2;
// Create a new matrix with extra padding
const paddedImage: number[][] = Array.from({ length: paddedNumRows }, () =>
new Array(paddedNumCols).fill(0),
);
// Copy original image into the center of the padded image
if (direction === "straight") {
for (let i = 0; i < numRows; i++) {
for (let j = 0; j < paddedNumCols - 2; j++) {
paddedImage[i + 1][j + 1] =
image[i][j + Math.round(removeSideColumns / 2)];
}
}
} // If the face is facing left, we only take the right side of the face image
else if (direction === "left") {
for (let i = 0; i < numRows; i++) {
for (let j = 0; j < paddedNumCols - 2; j++) {
paddedImage[i + 1][j + 1] = image[i][j + removeSideColumns];
}
}
} // If the face is facing right, we only take the left side of the face image
else if (direction === "right") {
for (let i = 0; i < numRows; i++) {
for (let j = 0; j < paddedNumCols - 2; j++) {
paddedImage[i + 1][j + 1] = image[i][j];
}
}
}
// Reflect padding
// Top and bottom rows
for (let j = 1; j <= paddedNumCols - 2; j++) {
paddedImage[0][j] = paddedImage[2][j]; // Top row
paddedImage[numRows + 1][j] = paddedImage[numRows - 1][j]; // Bottom row
}
// Left and right columns
for (let i = 0; i < numRows + 2; i++) {
paddedImage[i][0] = paddedImage[i][2]; // Left column
paddedImage[i][paddedNumCols - 1] = paddedImage[i][paddedNumCols - 3]; // Right column
}
return paddedImage;
};
const applyLaplacian = (
image: number[][],
direction: FaceDirection = "straight",
): number[][] => {
const paddedImage: number[][] = padImage(image, undefined, direction);
const numRows = paddedImage.length - 2;
const numCols = paddedImage[0].length - 2;
// Create an output image initialized to 0
const outputImage: number[][] = Array.from({ length: numRows }, () =>
new Array(numCols).fill(0),
);
// Define the Laplacian kernel
const kernel: number[][] = [
[0, 1, 0],
[1, -4, 1],
[0, 1, 0],
];
// Apply the kernel to each pixel
for (let i = 0; i < numRows; i++) {
for (let j = 0; j < numCols; j++) {
let sum = 0;
for (let ki = 0; ki < 3; ki++) {
for (let kj = 0; kj < 3; kj++) {
sum += paddedImage[i + ki][j + kj] * kernel[ki][kj];
}
}
// Adjust the output value if necessary (e.g., clipping)
outputImage[i][j] = sum;
}
}
return outputImage;
};
type FaceDirection = "left" | "right" | "straight";
const faceDirection = (face: Face): FaceDirection => {
@ -181,13 +58,130 @@ const faceDirection = (face: Face): FaceDirection => {
const noseCloseToRightEye =
Math.abs(nose.x - rightEye.x) < 0.2 * eyeDistanceX;
// if (faceIsUpright && (noseStickingOutLeft || noseCloseToLeftEye)) {
if (noseStickingOutLeft || (faceIsUpright && noseCloseToLeftEye)) {
return "left";
// } else if (faceIsUpright && (noseStickingOutRight || noseCloseToRightEye)) {
} else if (noseStickingOutRight || (faceIsUpright && noseCloseToRightEye)) {
return "right";
}
return "straight";
};
/**
* Return a new image by applying a Laplacian blur kernel to each pixel.
*/
const applyLaplacian = (
image: number[][],
direction: FaceDirection,
): number[][] => {
const paddedImage: number[][] = padImage(image, direction);
const numRows = paddedImage.length - 2;
const numCols = paddedImage[0].length - 2;
// Create an output image initialized to 0.
const outputImage: number[][] = Array.from({ length: numRows }, () =>
new Array(numCols).fill(0),
);
// Define the Laplacian kernel.
const kernel: number[][] = [
[0, 1, 0],
[1, -4, 1],
[0, 1, 0],
];
// Apply the kernel to each pixel
for (let i = 0; i < numRows; i++) {
for (let j = 0; j < numCols; j++) {
let sum = 0;
for (let ki = 0; ki < 3; ki++) {
for (let kj = 0; kj < 3; kj++) {
sum += paddedImage[i + ki][j + kj] * kernel[ki][kj];
}
}
// Adjust the output value if necessary (e.g., clipping).
outputImage[i][j] = sum;
}
}
return outputImage;
};
const padImage = (image: number[][], direction: FaceDirection): number[][] => {
const removeSideColumns = 56; /* must be even */
const numRows = image.length;
const numCols = image[0].length;
const paddedNumCols = numCols + 2 - removeSideColumns;
const paddedNumRows = numRows + 2;
// Create a new matrix with extra padding.
const paddedImage: number[][] = Array.from({ length: paddedNumRows }, () =>
new Array(paddedNumCols).fill(0),
);
if (direction === "straight") {
// Copy original image into the center of the padded image.
for (let i = 0; i < numRows; i++) {
for (let j = 0; j < paddedNumCols - 2; j++) {
paddedImage[i + 1][j + 1] =
image[i][j + Math.round(removeSideColumns / 2)];
}
}
} else if (direction === "left") {
// If the face is facing left, we only take the right side of the face image.
for (let i = 0; i < numRows; i++) {
for (let j = 0; j < paddedNumCols - 2; j++) {
paddedImage[i + 1][j + 1] = image[i][j + removeSideColumns];
}
}
} else if (direction === "right") {
// If the face is facing right, we only take the left side of the face image.
for (let i = 0; i < numRows; i++) {
for (let j = 0; j < paddedNumCols - 2; j++) {
paddedImage[i + 1][j + 1] = image[i][j];
}
}
}
// Reflect padding
// Top and bottom rows
for (let j = 1; j <= paddedNumCols - 2; j++) {
paddedImage[0][j] = paddedImage[2][j]; // Top row
paddedImage[numRows + 1][j] = paddedImage[numRows - 1][j]; // Bottom row
}
// Left and right columns
for (let i = 0; i < numRows + 2; i++) {
paddedImage[i][0] = paddedImage[i][2]; // Left column
paddedImage[i][paddedNumCols - 1] = paddedImage[i][paddedNumCols - 3]; // Right column
}
return paddedImage;
};
const matrixVariance = (matrix: number[][]): number => {
const numRows = matrix.length;
const numCols = matrix[0].length;
const totalElements = numRows * numCols;
// Calculate the mean.
let mean: number = 0;
matrix.forEach((row) => {
row.forEach((value) => {
mean += value;
});
});
mean /= totalElements;
// Calculate the variance.
let variance: number = 0;
matrix.forEach((row) => {
row.forEach((value) => {
const diff: number = value - mean;
variance += diff * diff;
});
});
variance /= totalElements;
return variance;
};