/*
 * Copyright 2021 Google LLC
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 *
 * ---------------------------------------------------------------------
 *
 * Portions of this file were modified and ported to Vala by Victor Vintorez in 2026.
 * These modifications are licensed under the European Union Public License (EUPL),
 * Version 1.2 or later. You may obtain a copy of the License at:
 * https://joinup.ec.europa.eu/collection/eupl/eupl-text-eupl-12
 */

using GLib;

namespace MaterialColorUtilities {
	/**
	 * An image quantizer that improves on the quality of a standard K-Means algorithm by setting the
	 * K-Means initial state to the output of a Wu quantizer, instead of random centroids. Improves on
	 * speed by several optimizations, as implemented in Wsmeans, or Weighted Square Means, K-Means with
	 * those optimizations.
	 *
	 * <p>This algorithm was designed by M. Emre Celebi, and was found in their 2011 paper, Improving
	 * the Performance of K-Means for Color Quantization. https://arxiv.org/abs/1101.0395
	 */
	public sealed class QuantizerCelebi : Object {
		private QuantizerCelebi() {}

		/**
		 * Reduce the number of colors needed to represented the input, minimizing the difference between
		 * the original image and the recolored image.
		 *
		 * @param pixels Colors in ARGB format.
		 * @param maxColors The number of colors to divide the image into. A lower number of colors may be
		 *     returned.
		 * @return Map with keys of colors in ARGB format, and values of number of pixels in the original
		 *     image that correspond to the color in the quantized image.
		 */
		public static HashTable<int, int> quantize(int[] pixels, int max_colors) {
			QuantizerWu wu = new QuantizerWu();
			QuantizerResult wu_result = wu.quantize(pixels, max_colors);

			int[] wu_clusters = new int[wu_result.color_to_count.size()];
			int index = 0;

			var iter = HashTableIter<int, int> (wu_result.color_to_count);
			int argb;
			int count;

			while (iter.next(out argb, out count)) {
				wu_clusters[index++] = argb;
			}

			return QuantizerWsmeans.quantize(pixels, wu_clusters, max_colors);
		}
	}
}
