BOSL2/scripts/img2scad.html

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<!DOCTYPE html>
<html lang="en">
<head>
<!--
Standalone web app to convert an image file to an OpenSCAD array, for use with BOSL2 textures.
Versions 1-5: 22 April 2025 - by Alex Matulich
2025-04-24 05:22:02 +00:00
Version 6: 23 April 2025 - added cropping UI
-->
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<title>Image to OpenSCAD array, v6</title>
<meta charset="UTF-8">
<style>
body { font-family: sans-serif; padding-left:1em; padding-right:1em;}
h1,h2,h3,h4 { font-family: serif; }
.uiContainer {
display: flex;
flex-wrap: wrap;
align-items: center;
gap: 20px;
margin: 10px 0 10px 0;
}
#inputArea {
background-color: #FFFFBB;
border: 6px outset #DDDD99;
padding: 1em;
}
#outputArea {
background-color: #EEFFEE;
border: 6px outset #BBDDBB;
padding: 1em;
}
.canvasWrapper {
display: flex;
flex-direction: column;
align-items: center;
}
canvas {
border: 1px solid #ccc;
}
.tooltip {
position: relative;
display: inline-block;
border-bottom: 1px dotted black;
}
.tooltip .tooltiptext {
visibility: hidden;
white-space: nowrap;
display: block;
font-size: small;
background-color: black;
color: #fff;
text-align: left;
border-radius: 6px;
padding: 5px;
/* Position the tooltip */
position: absolute;
z-index: 1;
}
.tooltip:hover .tooltiptext {
visibility: visible;
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}
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/* cropping control panel stuff */
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.crop-container {
display: grid;
grid-template-areas:
"top top top"
"left center right"
"bottom bottom bottom";
grid-template-columns: auto 60px auto;
grid-template-rows: auto 60px auto;
gap: 4px;
padding: 2px;
box-sizing: border-box;
width: fit-content;
height: fit-content;
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margin-top: 8px;
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}
.crop-center {
grid-area: center;
display: flex;
align-items: center;
justify-content: center;
width: 60px;
height: 60px;
border: 2px dashed #ccc;
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background-color: #eee;
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font-weight: bold;
font-size: 1rem;
box-sizing: border-box;
}
.crop-control {
display: flex;
flex-direction: column;
align-items: center;
justify-content: center;
font-size: 0.9rem;
}
.crop-control input[type="number"] {
width: 6ch;
padding: 2px;
font-size: 1rem;
text-align: right;
border: 1px solid #ccc;
border-radius: 3px;
}
.crop-top { grid-area: top; }
.crop-left { grid-area: left; }
.crop-right { grid-area: right; }
.crop-bottom {
grid-area: bottom;
display: flex;
flex-direction: column;
align-items: center;
justify-content: center;
}
</style>
</head>
<body>
<h1>Convert image to OpenSCAD array</h1>
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<p>This utility accepts any image that can be displayed in your browser, and converts it to grayscale expanded to use the maximum
possible luminance range. Alpha channel is ignored. After processing the image as desired, you may save it as an OpenSCAD array.</p>
<hr>
<div id="content">
<div class="uiContainer" id="inputArea" tabindex="0">
<div>
<h3>Select an image</h2>
<input type="file" id="imageInput" accept="image/*">
<p><em>You can also paste an image (Ctrl+V) into this section from your clipboard.</em></p>
</div>
<!-- Original image canvas -->
<div class="canvasWrapper">
<p id="originalSize"></p>
<canvas id="originalCanvas" width="200"></canvas>
</div>
</div>
<div class="uiContainer" id="outputArea">
<div>
<h3>Transformations</h3>
<label for="resizeWidth">Rescale original width (px):</label>
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<input type="number" id="resizeWidth" size="5" min="1" max="9000" value="100"><br>
<button id="rotateLeft">⟲ Rotate left</button>
<button id="rotateRight">⟳ Rotate right</button><br>
<button id="flipHorizontal">⇋ Flip horizontal</button>
<button id="flipVertical">⇵ Flip vertical</button>
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<div class="crop-container">
<div class="crop-control crop-top">
<label for="crop-top">Top</label>
<input type="number" id="cropTop" min="0" max="9999" value="0">
</div>
<div class="crop-control crop-left">
<label for="crop-left">Left</label>
<input type="number" id="cropLeft" min="0" max="9999" value="0">
</div>
<div class="crop-center">Crop</div>
<div class="crop-control crop-right">
<label for="crop-right">Right</label>
<input type="number" id="cropRight" min="0" max="9999" value="0">
</div>
<div class="crop-control crop-bottom">
<input type="number" id="cropBottom" min="0" max="9999" value="0">
<label for="crop-bottom">Bottom</label>
</div>
</div>
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<h3>Appearance</h3>
<input type="radio" name="grayModel" value="ntsc" checked><label for "grayModel" class="tooltip"> NTSC grayscale formula
<span class="tooltiptext">0.299R + 0.587G + 0.114B<br>Based on average human perception of color luminance</span></label><br>
<input type="radio" name="grayModel" value="linear"><label for="grayModel" class="tooltip"> Linear luminance
<span class="tooltiptext">0.2126R + 0.7152G + 0.0722B<br>Used by OpenSCAD surface()</span></label>
<div style="margin-top:8px;">
<label><input type="checkbox" id="invertBrightness"> Invert brightness</label>
</div>
<div style="margin-top:8px;">
<label for="blurRadius">Blur radius (pixels):</label>
<input type="number" id="blurRadius" size="5" min="0" max="20" value="0">
</div>
<h3>Output</h3>
<label><input type="checkbox" id="normalizeToUnit" checked> Normalize to [0,1] range &mdash; [0,255] if unset</label>
<div style="margin-top:8px;">
<label for="arrayName">Name of array:</label>
<input type="text" id="arrayName" value="image_array" onkeypress="return event.charCode != 32">
<div style="margin-top:8px;">
<button id="downloadButton">Save as OpenSCAD array</button>
</div>
</div>
</div>
<!-- Grayscale output image canvas -->
<div class="canvasWrapper">
<p id="grayscaleSize"></p>
<div id="outcontainer">
<canvas id="grayscaleCanvas"></canvas>
</div>
</div>
</div>
</div>
<hr>
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<script>
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// get page element handles
const imageInput = document.getElementById('imageInput');
const downloadButton = document.getElementById('downloadButton');
const resizeWidthInput = document.getElementById('resizeWidth');
const originalSizeText = document.getElementById('originalSize');
const grayscaleSizeText = document.getElementById('grayscaleSize');
const invertBrightnessCheckbox = document.getElementById('invertBrightness');
const normalizeToUnitCheckbox = document.getElementById('normalizeToUnit');
const rotateLeftBtn = document.getElementById('rotateLeft');
const rotateRightBtn = document.getElementById('rotateRight');
const flipHorizontalBtn = document.getElementById('flipHorizontal');
const flipVerticalBtn = document.getElementById('flipVertical');
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const cropTop = document.getElementById('cropTop');
const cropLeft = document.getElementById('cropLeft');
const cropRight = document.getElementById('cropRight');
const cropBottom = document.getElementById('cropBottom');
const blurRadiusInput = document.getElementById('blurRadius');
const arrayName = document.getElementById('arrayName');
const inputArea = document.getElementById('inputArea');
const originalCanvas = document.getElementById('originalCanvas');
const grayscaleCanvas = document.getElementById('grayscaleCanvas');
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// other initializations
const originalCtx = originalCanvas.getContext('2d');
const grayscaleCtx = grayscaleCanvas.getContext('2d');
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const cropID = [ cropRight, cropTop, cropLeft, cropBottom ]; // counterclockwise from right
let edgeID = [ 0, 1, 2 ,3 ]; // counterclockwise from right: right, top, left, bottom
const edgeconfig = [
// IDs of crop gadgets corresponding to image edges, from right edge counterclockwise,
// in all combinations of rotations and flips.
// no flip flipH flipV flipV+H
/* 0*/ [[0,1,2,3], [2,1,0,3], [0,3,2,1], [2,3,0,1]],
/* 90*/ [[3,0,1,2], [1,0,3,2], [3,2,1,0], [1,2,3,0]],
/*180*/ [[2,3,0,1], [0,3,2,1], [2,1,0,3], [0,1,2,3]],
/*270*/ [[1,2,3,0], [3,2,1,0], [1,0,3,2], [3,0,1,2]]
];
let grayscaleMatrix = [];
let currentImage = new Image();
let rotation = 0;
let flipH = false;
let flipV = false;
let fileSuffix = "";
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let origDim = { width:0, height:0 };
let uncropDim = { width:0, height:0 };
let cropDim = { width:0, height:0 };
// image processing functions
function getGrayscaleModel() {
return document.querySelector('input[name="grayModel"]:checked').value;
}
function applyGaussianBlur(matrix, radius) {
if (radius <= 0) return matrix;
const kernelSize = 2 * radius + 1;
const sigma = radius > 0 ? radius / 3 : 1;
const kernel = [];
let sum = 0;
for (let i = -radius; i <= radius; i++) {
const value = Math.exp(-(i * i) / (2 * sigma * sigma));
kernel.push(value);
sum += value;
}
kernel.forEach((v, i) => kernel[i] = v / sum);
const width = matrix[0].length;
const height = matrix.length;
const horizontalBlur = [];
for (let y = 0; y < height; y++) {
horizontalBlur[y] = [];
for (let x = 0; x < width; x++) {
let val = 0;
let weightSum = 0;
for (let k = -radius; k <= radius; k++) {
const nx = x + k;
if (nx >= 0 && nx < width) {
val += matrix[y][nx] * kernel[k + radius];
weightSum += kernel[k + radius];
}
}
horizontalBlur[y][x] = val / weightSum;
}
}
const output = [];
for (let y = 0; y < height; y++) {
output[y] = [];
for (let x = 0; x < width; x++) {
let val = 0;
let weightSum = 0;
for (let k = -radius; k <= radius; k++) {
const ny = y + k;
if (ny >= 0 && ny < height) {
val += horizontalBlur[ny][x] * kernel[k + radius];
weightSum += kernel[k + radius];
}
}
output[y][x] = val / weightSum;
}
}
return output;
}
function processImage() {
if (!currentImage.src) return;
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origDim.width = currentImage.naturalWidth;
origDim.height = currentImage.naturalHeight;
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// display thumbnail original image
const thumbWidth = 200;
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const thumbHeight = Math.round((origDim.height / origDim.width) * thumbWidth);
originalCanvas.width = thumbWidth;
originalCanvas.height = thumbHeight;
originalCtx.clearRect(0, 0, thumbWidth, thumbHeight);
originalCtx.drawImage(currentImage, 0, 0, thumbWidth, thumbHeight);
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originalSizeText.textContent = `Original size: ${origDim.width}×${origDim.height}`;
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// get output image dimensions
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uncropDim.width = origDim.width;
uncropDim.height = origDim.height;
const newWidth = parseInt(resizeWidthInput.value);
if (!isNaN(newWidth) && newWidth > 0) {
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uncropDim.width = newWidth;
uncropDim.height = Math.round(newWidth * origDim.height / origDim.width);
}
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// put original image in a temporary canvas with output dimensions and get image data
const tempCanvas = document.createElement('canvas');
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tempCanvas.width = uncropDim.width;
tempCanvas.height = uncropDim.height;
const tempCtx = tempCanvas.getContext('2d');
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tempCtx.drawImage(currentImage, 0, 0, uncropDim.width, uncropDim.height);
const imgData = tempCtx.getImageData(0, 0, uncropDim.width, uncropDim.height);
const data = imgData.data;
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// convert image data to grayscale
const brightnessMatrix = [];
const model = getGrayscaleModel();
const weights = model === 'linear' ? [0.2126, 0.7152, 0.0722] : [0.299, 0.587, 0.114];
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for (let y = 0; y < uncropDim.height; y++) {
const row = [];
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for (let x = 0; x < uncropDim.width; x++) {
const i = (y * uncropDim.width + x) * 4;
const r = data[i];
const g = data[i + 1];
const b = data[i + 2];
let brightness = weights[0] * r + weights[1] * g + weights[2] * b;
row.push(brightness);
}
brightnessMatrix.push(row);
}
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// apply blurring to the grayscale image
const blurRadius = parseInt(blurRadiusInput.value) || 0;
const blurredMatrix = applyGaussianBlur(brightnessMatrix, blurRadius);
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// crop the blurred matrix, gather min and max values in crop area
const cropMatrix = [];
let cropx1 = parseInt(cropID[edgeID[2]].value) || 0;
let cropx2 = parseInt(cropID[edgeID[0]].value) || 0;
let cropy1 = parseInt(cropID[edgeID[1]].value) || 0;
let cropy2 = parseInt(cropID[edgeID[3]].value) || 0;
let min = 255;
let max = 0;
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for (let y=cropy1; y<uncropDim.height-cropy2; y++) {
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const row = [];
for(let x=cropx1; x<uncropDim.width-cropx2; x++) {
row.push(blurredMatrix[y][x]);
min = Math.min(min, blurredMatrix[y][x]);
max = Math.max(max, blurredMatrix[y][x]);
}
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cropMatrix.push(row);
}
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cropDim.width = uncropDim.width - cropx1 - cropx2;
cropDim.height = uncropDim.height - cropy1 - cropy2;
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// normalize cropped image brightness to 0-255 range, invert brightness if checkbox is selected
const range = max - min || 1;
grayscaleMatrix = [];
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const grayImgData = grayscaleCtx.createImageData(cropDim.width, cropDim.height);
const grayData = grayImgData.data;
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for (let y = 0; y < cropDim.height; y++) {
const row = [];
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for (let x = 0; x < cropDim.width; x++) {
let brightness = cropMatrix[y][x];
brightness = ((brightness - min) / range) * 255;
brightness = Math.round(brightness);
brightness = Math.max(0, Math.min(255, brightness));
if (invertBrightnessCheckbox.checked) {
brightness = 255 - brightness;
}
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const i = (y * cropDim.width + x) * 4;
grayData[i] = grayData[i+1] = grayData[i+2] = brightness;
grayData[i + 3] = 255;
row.push(brightness);
}
grayscaleMatrix.push(row);
}
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// rotate and flip image
const rotated = (rotation % 180 !== 0);
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const finalWidth = rotated ? cropDim.height : cropDim.width;
const finalHeight = rotated ? cropDim.width : cropDim.height;
grayscaleCanvas.width = finalWidth;
grayscaleCanvas.height = finalHeight;
const tempDrawCanvas = document.createElement('canvas');
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tempDrawCanvas.width = cropDim.width;
tempDrawCanvas.height = cropDim.height;
const tempDrawCtx = tempDrawCanvas.getContext('2d');
tempDrawCtx.putImageData(grayImgData, 0, 0);
grayscaleCtx.save();
grayscaleCtx.setTransform(1, 0, 0, 1, 0, 0);
grayscaleCtx.clearRect(0, 0, finalWidth, finalHeight);
grayscaleCtx.translate(finalWidth / 2, finalHeight / 2);
grayscaleCtx.rotate(rotation * Math.PI / 180);
grayscaleCtx.scale(flipH ? -1 : 1, flipV ? -1 : 1);
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grayscaleCtx.drawImage(tempDrawCanvas, -cropDim.width / 2, -cropDim.height / 2);
grayscaleCtx.restore();
grayscaleSizeText.textContent = `Output size: ${finalWidth}×${finalHeight}`;
fileSuffix = finalWidth.toString()+"x"+finalHeight.toString();
}
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// loading an image
function resetInputs() { // executed after an image loads
cropLeft.value="0";
cropRight.value="0";
cropTop.value="0";
cropBottom.value="0";
rotation = 0;
flipV = flipH = false;
resizeWidthInput.value = "100";
blurRadiusInput.value = "0";
invertBrightnessCheckbox.checked = false;
}
// user pressed button to load image from disk
imageInput.addEventListener('change', function () {
const file = this.files[0];
if (file && file.type.startsWith('image/')) {
const reader = new FileReader();
reader.onload = function (e) {
currentImage.onload = function () {
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resetInputs();
processImage();
};
currentImage.src = e.target.result;
};
reader.readAsDataURL(file);
}
});
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// user pasted an image from the clipboard into the input area
inputArea.addEventListener('paste', function (event) {
const items = (event.clipboardData || event.originalEvent.clipboardData).items;
for (const item of items) {
if (item.type.indexOf('image') !== -1) {
const blob = item.getAsFile();
const reader = new FileReader();
reader.onload = function (e) {
currentImage.onload = function () {
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resetInputs();
processImage();
};
currentImage.src = e.target.result;
};
reader.readAsDataURL(blob);
}
}
});
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// set up events for all the input gadgets
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[resizeWidthInput, invertBrightnessCheckbox, normalizeToUnitCheckbox, blurRadiusInput,
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...document.querySelectorAll('input[name="grayModel"]')].forEach(el => el.addEventListener('input', processImage));
cropLeft.addEventListener('input', () => {
if (!currentImage.src) { cropLeft.value="0"; return; }
const cl = parseInt(cropLeft.value) || 0;
const cr = parseInt(cropRight.value) || 0;
if(uncropDim.width - cl - cr < 2) cropLeft.value = (uncropDim.width - cr - 2).toString();
processImage();
});
cropTop.addEventListener('input', () => {
if (!currentImage.src) { cropTop.value="0"; return; }
const ct = parseInt(cropTop.value) || 0;
const cb = parseInt(cropBottom.value) || 0;
if(uncropDim.width - ct - cb < 2) cropTop.value = (uncropDim.height - cb - 2).toString();
processImage();
});
cropRight.addEventListener('input', () => {
if (!currentImage.src) { cropRight.value="0"; return; }
const cl = parseInt(cropLeft.value) || 0;
const cr = parseInt(cropRight.value) || 0;
if(uncropDim.width - cl - cr < 2) cropRight.value = (uncropDim.width - cl - 2).toString();
processImage();
});
cropBottom.addEventListener('input', () => {
if (!currentImage.src) { cropBottom.value="0"; return; }
const ct = parseInt(cropTop.value) || 0;
const cb = parseInt(cropBottom.value) || 0;
if(uncropDim.width - ct - cb < 2) cropBottom.value = (uncropDim.height - ct - 2).toString();
processImage();
});
function updateEdgeID(out="") {
const fi = (flipH ? 1 : 0) + (flipV ? 2 : 0);
const ri = Math.round(rotation/90);
edgeID = edgeconfig[ri][fi];
if (out.length>0) console.log(out, rotation, flipH, flipV, edgeID);
}
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rotateLeftBtn.addEventListener('click', () => {
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if (!currentImage.src) return;
rotation = (rotation - 90 + 360) % 360;
const tmp = cropTop.value;
cropTop.value = cropRight.value;
cropRight.value = cropBottom.value;
cropBottom.value = cropLeft.value;
cropLeft.value = tmp;
updateEdgeID();
processImage();
});
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rotateRightBtn.addEventListener('click', () => {
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if (!currentImage.src) return;
rotation = (rotation + 90) % 360;
const tmp = cropTop.value;
cropTop.value = cropLeft.value;
cropLeft.value = cropBottom.value;
cropBottom.value = cropRight.value;
cropRight.value = tmp;
updateEdgeID();
processImage();
});
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flipHorizontalBtn.addEventListener('click', () => {
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if (!currentImage.src) return;
flipH = !flipH;
let tmp = cropRight.value;
cropRight.value = cropLeft.value;
cropLeft.value = tmp;
updateEdgeID();
processImage();
});
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flipVerticalBtn.addEventListener('click', () => {
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if (!currentImage.src) return;
flipV = !flipV;
let tmp = cropTop.value;
cropTop.value = cropBottom.value;
cropBottom.value = tmp;
updateEdgeID();
processImage();
});
// saving the file - try to use "Save As" file picker,
// fall back to saving with a default name to browser's downloads directory.
downloadButton.addEventListener('click', () => {
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if (grayscaleMatrix.length === 0) return alert("No data to save.");
const useUnit = normalizeToUnitCheckbox.checked;
const arrayContent = grayscaleMatrix.map(row => {
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return " [" + row.map(val => useUnit ? parseFloat(val/255).toFixed(3) : val).join(",") + "]";
}).join(",\n");
const openscadArray = (arrayName.value.length>0 ? arrayName.value : 'image_array')+" = [\n" + arrayContent + "\n];";
const blob = new Blob([openscadArray], { type: "text/plain" });
let suffix = fileSuffix.length>0 ? (arrayName.value.length>0 ? fileSuffix : "image"+fileSuffix) : "image";
let filename = arrayName.value.length>0 ? arrayName.value+'_'+suffix+'.scad' : suffix+'.scad';
if (window.showSaveFilePicker) {
saveWithFilePicker(blob, filename);
} else {
fallbackSave(blob, filename);
}
});
async function saveWithFilePicker(blob, filename) {
try {
const handle = await window.showSaveFilePicker({
suggestedName: filename,
types: [{ description: 'OpenSCAD Data File', accept: { 'text/plain': ['.scad'] } }]
});
const writable = await handle.createWritable();
await writable.write(blob);
await writable.close();
} catch (err) {
alert('Save cancelled or failed: ' + err.message);
}
}
function fallbackSave(blob, filename) {
const url = URL.createObjectURL(blob);
const a = document.createElement("a");
a.href = url;
a.download = filename;
a.target = "_blank";
a.click();
URL.revokeObjectURL(url);
}
</script>
</body>
</html>