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Top 10 Examples of "image-q in functional component" in JavaScript

Dive into secure and efficient coding practices with our curated list of the top 10 examples showcasing 'image-q' in functional components in JavaScript. Our advanced machine learning engine meticulously scans each line of code, cross-referencing millions of open source libraries to ensure your implementation is not just functional, but also robust and secure. Elevate your React applications to new heights by mastering the art of handling side effects, API calls, and asynchronous operations with confidence and precision.

// create chosen palette quantizer (see classes implementing `iq.palette.IPaletteQuantizer`) 
		var paletteQuantizer = new iq.palette[opts.quantizer](distanceCalculator, targetColors);
		
		// feed out pointContainer filled with image to paletteQuantizer
		paletteQuantizer.sample(pointContainer);

		// generate palette
		var palette = paletteQuantizer.quantize();

		// get color array
		var iq_colors = palette.getPointContainer().getPointArray();
		
		// create image quantizer (see classes implementing `iq.image.IImageDitherer`)
		var imageDitherer = opts.dither ? 
			(new iq.image.ErrorDiffusionArray(distanceCalculator, iq.image.ErrorDiffusionArrayKernel[opts.ditherType])) : 
			(new iq.image.NearestColor(distanceCalculator));
		
		// apply palette to image
		var resultPointContainer = imageDitherer.quantize(pointContainer, palette);

		// get pixel array
		var iq_pixels = resultPointContainer.getPointArray();
		
		// convert pixels to array of palette indexes
		var palette_offset = 0;
		var palette_data = new Uint8Array( iq_colors.length * 3 );
		var color_hash = {};
		var iq_color = null;
		
		// build palette structure and color hash table
		for (var idx = 0, len = iq_colors.length; idx < len; idx++) {
			iq_color = iq_colors[idx];
// create chosen palette quantizer (see classes implementing `iq.palette.IPaletteQuantizer`) 
		var paletteQuantizer = new iq.palette[opts.quantizer](distanceCalculator, targetColors);
		
		// feed out pointContainer filled with image to paletteQuantizer
		paletteQuantizer.sample(pointContainer);

		// generate palette
		var palette = paletteQuantizer.quantize();

		// get color array
		var iq_colors = palette.getPointContainer().getPointArray();
		
		// create image quantizer (see classes implementing `iq.image.IImageDitherer`)
		var imageDitherer = opts.dither ? 
			(new iq.image.ErrorDiffusionArray(distanceCalculator, iq.image.ErrorDiffusionArrayKernel[opts.ditherType])) : 
			(new iq.image.NearestColor(distanceCalculator));
		
		// apply palette to image
		var resultPointContainer = imageDitherer.quantize(pointContainer, palette);

		// get pixel array
		var iq_pixels = resultPointContainer.getPointArray();
		
		// convert pixels to array of palette indexes
		var palette_offset = 0;
		var palette_data = new Uint8Array( iq_colors.length * 3 );
		var color_hash = {};
		var iq_color = null;
		
		// build palette structure and color hash table
		for (var idx = 0, len = iq_colors.length; idx < len; idx++) {
var imgData = this.context.getImageData(0, 0, width, height);
		
		// desired colors number
		var targetColors = parseInt( opts.colors || 0 );
		
		if (!targetColors || isNaN(targetColors) || (targetColors < 2) || (targetColors > 256)) {
			return this.doError('quantize', "Invalid palette size: " + targetColors);
		}
		
		// create pointContainer and fill it with image
		var pointContainer = iq.utils.PointContainer.fromImageData( imgData );
		
		// create chosen distance calculator (see classes inherited from `iq.distance.AbstractDistanceCalculator`)
		var distanceCalculator = opts.alpha ? 
			(new iq.distance.EuclideanRgbQuantWithAlpha()) : 
			(new iq.distance.EuclideanRgbQuantWOAlpha());
		
		// create chosen palette quantizer (see classes implementing `iq.palette.IPaletteQuantizer`) 
		var paletteQuantizer = new iq.palette[opts.quantizer](distanceCalculator, targetColors);
		
		// feed out pointContainer filled with image to paletteQuantizer
		paletteQuantizer.sample(pointContainer);

		// generate palette
		var palette = paletteQuantizer.quantize();

		// get color array
		var iq_colors = palette.getPointContainer().getPointArray();
		
		// create image quantizer (see classes implementing `iq.image.IImageDitherer`)
		var imageDitherer = opts.dither ? 
			(new iq.image.ErrorDiffusionArray(distanceCalculator, iq.image.ErrorDiffusionArrayKernel[opts.ditherType])) :
var targetColors = parseInt( opts.colors || 0 );
		
		if (!targetColors || isNaN(targetColors) || (targetColors < 2) || (targetColors > 256)) {
			return this.doError('quantize', "Invalid palette size: " + targetColors);
		}
		
		// create pointContainer and fill it with image
		var pointContainer = iq.utils.PointContainer.fromImageData( imgData );
		
		// create chosen distance calculator (see classes inherited from `iq.distance.AbstractDistanceCalculator`)
		var distanceCalculator = opts.alpha ? 
			(new iq.distance.EuclideanRgbQuantWithAlpha()) : 
			(new iq.distance.EuclideanRgbQuantWOAlpha());
		
		// create chosen palette quantizer (see classes implementing `iq.palette.IPaletteQuantizer`) 
		var paletteQuantizer = new iq.palette[opts.quantizer](distanceCalculator, targetColors);
		
		// feed out pointContainer filled with image to paletteQuantizer
		paletteQuantizer.sample(pointContainer);

		// generate palette
		var palette = paletteQuantizer.quantize();

		// get color array
		var iq_colors = palette.getPointContainer().getPointArray();
		
		// create image quantizer (see classes implementing `iq.image.IImageDitherer`)
		var imageDitherer = opts.dither ? 
			(new iq.image.ErrorDiffusionArray(distanceCalculator, iq.image.ErrorDiffusionArrayKernel[opts.ditherType])) : 
			(new iq.image.NearestColor(distanceCalculator));
		
		// apply palette to image
var num_pixels = width * height;
		
		this.logDebug(6, "Quantizing image", { colors: opts.colors, dither: opts.dither, width: width, height: height, pixels: num_pixels } );
		
		// get rgba pixels from canvas
		var imgData = this.context.getImageData(0, 0, width, height);
		
		// desired colors number
		var targetColors = parseInt( opts.colors || 0 );
		
		if (!targetColors || isNaN(targetColors) || (targetColors < 2) || (targetColors > 256)) {
			return this.doError('quantize', "Invalid palette size: " + targetColors);
		}
		
		// create pointContainer and fill it with image
		var pointContainer = iq.utils.PointContainer.fromImageData( imgData );
		
		// create chosen distance calculator (see classes inherited from `iq.distance.AbstractDistanceCalculator`)
		var distanceCalculator = opts.alpha ? 
			(new iq.distance.EuclideanRgbQuantWithAlpha()) : 
			(new iq.distance.EuclideanRgbQuantWOAlpha());
		
		// create chosen palette quantizer (see classes implementing `iq.palette.IPaletteQuantizer`) 
		var paletteQuantizer = new iq.palette[opts.quantizer](distanceCalculator, targetColors);
		
		// feed out pointContainer filled with image to paletteQuantizer
		paletteQuantizer.sample(pointContainer);

		// generate palette
		var palette = paletteQuantizer.quantize();

		// get color array
}
        if (dither.minimumColorDistanceToDither === undefined) {
            dither.minimumColorDistanceToDither = 0;
        }
        if (dither.calculateErrorLikeGIMP === undefined) {
            dither.calculateErrorLikeGIMP = false;
        }
    }

    const distCalculator = new ImageQ.distance.Euclidean();
    const quantizer = new ImageQ.palette[method](distCalculator, maxColorIndexes, modifier);
    let imageMaker;
    if (dither) {
        imageMaker = new ImageQ.image.ErrorDiffusionArray(
            distCalculator,
            ImageQ.image.ErrorDiffusionArrayKernel[dither.ditherAlgorithm],
            dither.serpentine,
            dither.minimumColorDistanceToDither,
            dither.calculateErrorLikeGIMP
        );
    }
    else {
        imageMaker = new ImageQ.image.NearestColor(distCalculator);
    }

    const inputContainers = [];
    images.forEach(image => {

        const imageBuf = image.bitmap.data;
        const inputBuf = new ArrayBuffer(imageBuf.length);
        const inputArray = new Uint32Array(inputBuf);
        for (let bi = 0, ai = 0; bi < imageBuf.length; bi += 4, ++ai) {
if (dither.serpentine === undefined) {
            dither.serpentine = true;
        }
        if (dither.minimumColorDistanceToDither === undefined) {
            dither.minimumColorDistanceToDither = 0;
        }
        if (dither.calculateErrorLikeGIMP === undefined) {
            dither.calculateErrorLikeGIMP = false;
        }
    }

    const distCalculator = new ImageQ.distance.Euclidean();
    const quantizer = new ImageQ.palette[method](distCalculator, maxColorIndexes, modifier);
    let imageMaker;
    if (dither) {
        imageMaker = new ImageQ.image.ErrorDiffusionArray(
            distCalculator,
            ImageQ.image.ErrorDiffusionArrayKernel[dither.ditherAlgorithm],
            dither.serpentine,
            dither.minimumColorDistanceToDither,
            dither.calculateErrorLikeGIMP
        );
    }
    else {
        imageMaker = new ImageQ.image.NearestColor(distCalculator);
    }

    const inputContainers = [];
    images.forEach(image => {

        const imageBuf = image.bitmap.data;
        const inputBuf = new ArrayBuffer(imageBuf.length);
if (dither) {
        if (ditherAlgs.indexOf(dither.ditherAlgorithm) < 0) {
            throw new Error(`Invalid ditherAlgorithm '${dither.ditherAlgorithm}'`);
        }
        if (dither.serpentine === undefined) {
            dither.serpentine = true;
        }
        if (dither.minimumColorDistanceToDither === undefined) {
            dither.minimumColorDistanceToDither = 0;
        }
        if (dither.calculateErrorLikeGIMP === undefined) {
            dither.calculateErrorLikeGIMP = false;
        }
    }

    const distCalculator = new ImageQ.distance.Euclidean();
    const quantizer = new ImageQ.palette[method](distCalculator, maxColorIndexes, modifier);
    let imageMaker;
    if (dither) {
        imageMaker = new ImageQ.image.ErrorDiffusionArray(
            distCalculator,
            ImageQ.image.ErrorDiffusionArrayKernel[dither.ditherAlgorithm],
            dither.serpentine,
            dither.minimumColorDistanceToDither,
            dither.calculateErrorLikeGIMP
        );
    }
    else {
        imageMaker = new ImageQ.image.NearestColor(distCalculator);
    }

    const inputContainers = [];
if (ditherAlgs.indexOf(dither.ditherAlgorithm) < 0) {
            throw new Error(`Invalid ditherAlgorithm '${dither.ditherAlgorithm}'`);
        }
        if (dither.serpentine === undefined) {
            dither.serpentine = true;
        }
        if (dither.minimumColorDistanceToDither === undefined) {
            dither.minimumColorDistanceToDither = 0;
        }
        if (dither.calculateErrorLikeGIMP === undefined) {
            dither.calculateErrorLikeGIMP = false;
        }
    }

    const distCalculator = new ImageQ.distance.Euclidean();
    const quantizer = new ImageQ.palette[method](distCalculator, maxColorIndexes, modifier);
    let imageMaker;
    if (dither) {
        imageMaker = new ImageQ.image.ErrorDiffusionArray(
            distCalculator,
            ImageQ.image.ErrorDiffusionArrayKernel[dither.ditherAlgorithm],
            dither.serpentine,
            dither.minimumColorDistanceToDither,
            dither.calculateErrorLikeGIMP
        );
    }
    else {
        imageMaker = new ImageQ.image.NearestColor(distCalculator);
    }

    const inputContainers = [];
    images.forEach(image => {
images.forEach(image => {

        const imageBuf = image.bitmap.data;
        const inputBuf = new ArrayBuffer(imageBuf.length);
        const inputArray = new Uint32Array(inputBuf);
        for (let bi = 0, ai = 0; bi < imageBuf.length; bi += 4, ++ai) {
            inputArray[ai] = imageBuf.readUInt32LE(bi, true);
        }
        const inputContainer = ImageQ.utils.PointContainer.fromUint32Array(
                inputArray, image.bitmap.width, image.bitmap.height);
        quantizer.sample(inputContainer);
        inputContainers.push(inputContainer);
    });

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