[Feature]

[cheysokey7-pixel]

Problem

Read , lines 1 to 200
Completed with input: {
"path": "c:\Users\RAK\AppData\Roaming\Code\agentSessionData\502cfc85-5934-4ad5-a885-66be0dd3216a\attachments\bc639606-0d3c-47c9-8387-485d1055ccc0\Untitled-2.txt",
"view_range": [
1,
200
]
}

I don’t see a repository or a direct task description here, so I’m treating the pasted snippet as the target and giving you a corrected, safer drop-in version of the skin-smoothing function.

What was wrong in the original:

• It references originalImg and canvas without checking/initializing them.
• It reads from a canvas context but never guarantees the canvas exists.
• It adds detailPreserve directly to RGB values, which can overshoot 255 and cause washed-out results.
• It mixes smoothing and detail preservation in a way that can distort skin tones.
• It doesn’t clamp per-channel values consistently.

Here’s a cleaned-up version:

// SkinFiner Auto Skin Smoothing & Texture Preserving Algorithm
function applySkinFinerEffect(smoothAmount = 40, detailPreserve = 20) {
    if (!canvas || !canvas.getContext) return;

    const ctx = canvas.getContext('2d');
    if (!ctx) return;

    const width = canvas.width;
    const height = canvas.height;
    if (!width || !height) return;

    const imgData = ctx.getImageData(0, 0, width, height);
    const data = imgData.data;

    const radius = Math.max(1, Math.floor(smoothAmount / 10));
    const blendFactor = Math.min(0.9, smoothAmount / 100);
    const detailFactor = Math.min(0.35, detailPreserve / 100);

    for (let y = radius; y < height - radius; y++) {
        for (let x = radius; x < width - radius; x++) {
            const idx = (y * width + x) * 4;

            const r = data[idx];
            const g = data[idx + 1];
            const b = data[idx + 2];

            if (!isSkinColor(r, g, b)) continue;

            let sumR = 0, sumG = 0, sumB = 0, count = 0;

            for (let dy = -radius; dy <= radius; dy++) {
                for (let dx = -radius; dx <= radius; dx++) {
                    const nIdx = ((y + dy) * width + (x + dx)) * 4;
                    sumR += data[nIdx];
                    sumG += data[nIdx + 1];
                    sumB += data[nIdx + 2];
                    count++;
                }
            }

            const avgR = sumR / count;
            const avgG = sumG / count;
            const avgB = sumB / count;

            // Preserve original texture while smoothing the region
            const smoothR = r + (avgR - r) * blendFactor;
            const smoothG = g + (avgG - g) * blendFactor;
            const smoothB = b + (avgB - b) * blendFactor;

            const detailR = (r - avgR) * detailFactor;
            const detailG = (g - avgG) * detailFactor;
            const detailB = (b - avgB) * detailFactor;

            data[idx] = clampChannel(smoothR + detailR);
            data[idx + 1] = clampChannel(smoothG + detailG);
            data[idx + 2] = clampChannel(smoothB + detailB);
        }
    }

    ctx.putImageData(imgData, 0, 0);

    if (typeof saveHistory === 'function') saveHistory();
    if (typeof render === 'function') render();
}

function clampChannel(value) {
    return Math.max(0, Math.min(255, value));
}

// RGB Skin Detection
function isSkinColor(r, g, b) {
    return (
        r > 95 && g > 40 && b > 20 &&
        (Math.max(r, g, b) - Math.min(r, g, b) > 15) &&
        Math.abs(r - g) > 15 &&
        r > g &&
        r > b
    );
}

Why this version is better:

• Validates canvas/context before use.
• Keeps the smoothing effect within a controlled blur strength.
• Preserves texture via a proportional detail term rather than adding raw values.
• Prevents RGB overflow by clamping every channel.

If you want, I can also give you:

• a more advanced bilateral-smoothing version,
• a version that works from an offscreen image instead of the live canvas,
• or a version optimized for performance on large images.

Proposed solution

Read , lines 1 to 200
Completed with input: {
"path": "c:\Users\RAK\AppData\Roaming\Code\agentSessionData\502cfc85-5934-4ad5-a885-66be0dd3216a\attachments\bc639606-0d3c-47c9-8387-485d1055ccc0\Untitled-2.txt",
"view_range": [
1,
200
]
}

I don’t see a repository or a direct task description here, so I’m treating the pasted snippet as the target and giving you a corrected, safer drop-in version of the skin-smoothing function.

What was wrong in the original:

• It references originalImg and canvas without checking/initializing them.
• It reads from a canvas context but never guarantees the canvas exists.
• It adds detailPreserve directly to RGB values, which can overshoot 255 and cause washed-out results.
• It mixes smoothing and detail preservation in a way that can distort skin tones.
• It doesn’t clamp per-channel values consistently.

Here’s a cleaned-up version:

// SkinFiner Auto Skin Smoothing & Texture Preserving Algorithm
function applySkinFinerEffect(smoothAmount = 40, detailPreserve = 20) {
    if (!canvas || !canvas.getContext) return;

    const ctx = canvas.getContext('2d');
    if (!ctx) return;

    const width = canvas.width;
    const height = canvas.height;
    if (!width || !height) return;

    const imgData = ctx.getImageData(0, 0, width, height);
    const data = imgData.data;

    const radius = Math.max(1, Math.floor(smoothAmount / 10));
    const blendFactor = Math.min(0.9, smoothAmount / 100);
    const detailFactor = Math.min(0.35, detailPreserve / 100);

    for (let y = radius; y < height - radius; y++) {
        for (let x = radius; x < width - radius; x++) {
            const idx = (y * width + x) * 4;

            const r = data[idx];
            const g = data[idx + 1];
            const b = data[idx + 2];

            if (!isSkinColor(r, g, b)) continue;

            let sumR = 0, sumG = 0, sumB = 0, count = 0;

            for (let dy = -radius; dy <= radius; dy++) {
                for (let dx = -radius; dx <= radius; dx++) {
                    const nIdx = ((y + dy) * width + (x + dx)) * 4;
                    sumR += data[nIdx];
                    sumG += data[nIdx + 1];
                    sumB += data[nIdx + 2];
                    count++;
                }
            }

            const avgR = sumR / count;
            const avgG = sumG / count;
            const avgB = sumB / count;

            // Preserve original texture while smoothing the region
            const smoothR = r + (avgR - r) * blendFactor;
            const smoothG = g + (avgG - g) * blendFactor;
            const smoothB = b + (avgB - b) * blendFactor;

            const detailR = (r - avgR) * detailFactor;
            const detailG = (g - avgG) * detailFactor;
            const detailB = (b - avgB) * detailFactor;

            data[idx] = clampChannel(smoothR + detailR);
            data[idx + 1] = clampChannel(smoothG + detailG);
            data[idx + 2] = clampChannel(smoothB + detailB);
        }
    }

    ctx.putImageData(imgData, 0, 0);

    if (typeof saveHistory === 'function') saveHistory();
    if (typeof render === 'function') render();
}

function clampChannel(value) {
    return Math.max(0, Math.min(255, value));
}

// RGB Skin Detection
function isSkinColor(r, g, b) {
    return (
        r > 95 && g > 40 && b > 20 &&
        (Math.max(r, g, b) - Math.min(r, g, b) > 15) &&
        Math.abs(r - g) > 15 &&
        r > g &&
        r > b
    );
}

Why this version is better:

• Validates canvas/context before use.
• Keeps the smoothing effect within a controlled blur strength.
• Preserves texture via a proportional detail term rather than adding raw values.
• Prevents RGB overflow by clamping every channel.

If you want, I can also give you:

• a more advanced bilateral-smoothing version,
• a version that works from an offscreen image instead of the live canvas,
• or a version optimized for performance on large images.

Alternatives considered

No response

Additional context

Read , lines 1 to 200
Completed with input: {
"path": "c:\Users\RAK\AppData\Roaming\Code\agentSessionData\502cfc85-5934-4ad5-a885-66be0dd3216a\attachments\bc639606-0d3c-47c9-8387-485d1055ccc0\Untitled-2.txt",
"view_range": [
1,
200
]
}

I don’t see a repository or a direct task description here, so I’m treating the pasted snippet as the target and giving you a corrected, safer drop-in version of the skin-smoothing function.

What was wrong in the original:

• It references originalImg and canvas without checking/initializing them.
• It reads from a canvas context but never guarantees the canvas exists.
• It adds detailPreserve directly to RGB values, which can overshoot 255 and cause washed-out results.
• It mixes smoothing and detail preservation in a way that can distort skin tones.
• It doesn’t clamp per-channel values consistently.

Here’s a cleaned-up version:

// SkinFiner Auto Skin Smoothing & Texture Preserving Algorithm
function applySkinFinerEffect(smoothAmount = 40, detailPreserve = 20) {
    if (!canvas || !canvas.getContext) return;

    const ctx = canvas.getContext('2d');
    if (!ctx) return;

    const width = canvas.width;
    const height = canvas.height;
    if (!width || !height) return;

    const imgData = ctx.getImageData(0, 0, width, height);
    const data = imgData.data;

    const radius = Math.max(1, Math.floor(smoothAmount / 10));
    const blendFactor = Math.min(0.9, smoothAmount / 100);
    const detailFactor = Math.min(0.35, detailPreserve / 100);

    for (let y = radius; y < height - radius; y++) {
        for (let x = radius; x < width - radius; x++) {
            const idx = (y * width + x) * 4;

            const r = data[idx];
            const g = data[idx + 1];
            const b = data[idx + 2];

            if (!isSkinColor(r, g, b)) continue;

            let sumR = 0, sumG = 0, sumB = 0, count = 0;

            for (let dy = -radius; dy <= radius; dy++) {
                for (let dx = -radius; dx <= radius; dx++) {
                    const nIdx = ((y + dy) * width + (x + dx)) * 4;
                    sumR += data[nIdx];
                    sumG += data[nIdx + 1];
                    sumB += data[nIdx + 2];
                    count++;
                }
            }

            const avgR = sumR / count;
            const avgG = sumG / count;
            const avgB = sumB / count;

            // Preserve original texture while smoothing the region
            const smoothR = r + (avgR - r) * blendFactor;
            const smoothG = g + (avgG - g) * blendFactor;
            const smoothB = b + (avgB - b) * blendFactor;

            const detailR = (r - avgR) * detailFactor;
            const detailG = (g - avgG) * detailFactor;
            const detailB = (b - avgB) * detailFactor;

            data[idx] = clampChannel(smoothR + detailR);
            data[idx + 1] = clampChannel(smoothG + detailG);
            data[idx + 2] = clampChannel(smoothB + detailB);
        }
    }

    ctx.putImageData(imgData, 0, 0);

    if (typeof saveHistory === 'function') saveHistory();
    if (typeof render === 'function') render();
}

function clampChannel(value) {
    return Math.max(0, Math.min(255, value));
}

// RGB Skin Detection
function isSkinColor(r, g, b) {
    return (
        r > 95 && g > 40 && b > 20 &&
        (Math.max(r, g, b) - Math.min(r, g, b) > 15) &&
        Math.abs(r - g) > 15 &&
        r > g &&
        r > b
    );
}

Why this version is better:

• Validates canvas/context before use.
• Keeps the smoothing effect within a controlled blur strength.
• Preserves texture via a proportional detail term rather than adding raw values.
• Prevents RGB overflow by clamping every channel.

If you want, I can also give you:

• a more advanced bilateral-smoothing version,
• a version that works from an offscreen image instead of the live canvas,
• or a version optimized for performance on large images.

Submission checks

• I searched existing issues and did not find a duplicate.
• This request is specifically about Microsoft Edge Tools for VS Code.