import { Canvas, Image, loadImage } from 'skia-canvas';
import sha1 from 'sha1';
import { WeakLRUCache } from 'weak-lru-cache';
import * as starry from '../../src/starry';
import { LayoutResult, PyClients } from './predictors';
import { constructSystem, convertImage } from './util';
import { SemanticGraph } from '../../src/starry';

globalThis.OffscreenCanvas = (globalThis as any).OffscreenCanvas || Canvas;
globalThis.Image = globalThis.Image || Image;
globalThis.btoa = globalThis.btoa || ((str: string) => Buffer.from(str, 'binary').toString('base64'));

const STAFF_PADDING_LEFT = 32;

const MAX_PAGE_WIDTH = 1200;

const GAUGE_VISION_SPEC = {
	viewportHeight: 256,
	viewportUnit: 8,
};

const MASK_VISION_SPEC = {
	viewportHeight: 192,
	viewportUnit: 8,
};

const SEMANTIC_VISION_SPEC = {
	viewportHeight: 192,
	viewportUnit: 8,
};

interface OMRStat {
	cost: number; // in milliseconds
	pagesCost: number; // in milliseconds
	pages: number;
}

interface OMRSummary {
	costTotal: number; // in milliseconds
	costPerPage: number; // in milliseconds
	pagesTotal: number;
	scoreN: number;
}

/**
 * 为布局识别的图片标准化处理
 * @param image
 * @param width
 */
function scaleForLayout(image: Image, width: number): Canvas {
	let height = (image.height / image.width) * width;

	const canvas = new Canvas(width, height);
	const ctx = canvas.getContext('2d');

	ctx.drawImage(image, 0, 0, width, (width * image.height) / image.width);

	return canvas;
}

/**
 * 根据所有图像的检测结果设置合适的全局页面尺寸
 * @param score
 * @param detections
 * @param outputWidth
 */
function setGlobalPageSize(score: starry.Score, detections: LayoutResult[], outputWidth: number) {
	const sizeRatios = detections
		.filter((s) => s && s.detection)
		.map((v, k) => {
			const staffInterval = Math.min(...v.detection.areas.filter((area) => area.staves?.middleRhos?.length).map((x) => x.staves.interval));

			const sourceSize = v.sourceSize;
			return {
				...v,
				index: k,
				vw: sourceSize.width / staffInterval, // 页面宽度（逻辑单位）
				hwr: sourceSize.height / sourceSize.width, // 页面高宽比
			};
		});

	if (!sizeRatios.length) {
		throw new Error('empty result');
	}

	const maxVW = sizeRatios.sort((a, b) => b.vw - a.vw)[0];
	const maxAspect = Math.max(...sizeRatios.map((r) => r.hwr));

	score.unitSize = outputWidth / maxVW.vw;

	// 页面显示尺寸
	score.pageSize = {
		width: outputWidth,
		height: outputWidth * maxAspect,
	};
}

const batchTask = (fn: () => Promise<any>) => fn();
const concurrencyTask = (fns: (() => Promise<any>)[]) => Promise.all(fns.map((fn) => fn()));

const shootStaffImage = async (
	system: starry.System,
	staffIndex: number,
	{ paddingLeft = 0, scaling = 1, spec }: { paddingLeft?: number; scaling?: number; spec: { viewportHeight: number; viewportUnit: number } }
): Promise<Canvas> => {
	if (!system || !system.backgroundImage) return null;

	const staff = system.staves[staffIndex];
	if (!staff) return null;

	const middleUnits = spec.viewportHeight / spec.viewportUnit / 2;

	const width = system.imagePosition.width * spec.viewportUnit;
	const height = system.imagePosition.height * spec.viewportUnit;
	const x = system.imagePosition.x * spec.viewportUnit + paddingLeft;
	const y = (system.imagePosition.y - (staff.top + staff.staffY - middleUnits)) * spec.viewportUnit;

	const canvas = new Canvas(Math.round(width + x) * scaling, spec.viewportHeight * scaling);
	const context = canvas.getContext('2d');
	context.fillStyle = 'white';
	context.fillRect(0, 0, canvas.width, canvas.height);
	context.drawImage(await loadImage(system.backgroundImage), x * scaling, y * scaling, width * scaling, height * scaling);

	return canvas;
	// .substr(22);	// remove the prefix of 'data:image/png;base64,'
};

/**
 * 根据布局检测结果进行截图
 * @param score
 * @param pageCanvas
 * @param page
 * @param detection
 */
async function shootImageByDetection({
	page,
	score,
	pageCanvas,
}: {
	score: starry.Score;
	page: starry.Page;
	pageCanvas: Canvas; // 原始图片绘制好的canvas
}) {
	if (!page?.layout?.areas?.length) {
		return null;
	}

	page.width = score.pageSize.width / score.unitSize;
	page.height = score.pageSize.height / score.unitSize;

	const correctCanvas = new Canvas(pageCanvas.width, pageCanvas.height);
	const ctx = correctCanvas.getContext('2d');

	ctx.save();

	const { width, height } = correctCanvas;
	const [a, b, c, d] = page.source.matrix;

	ctx.setTransform(a, b, c, d, (-1 / 2) * width + (1 / 2) * a * width + (1 / 2) * b * height, (-1 / 2) * height + (1 / 2) * c * width + (1 / 2) * d * height);

	ctx.drawImage(pageCanvas, 0, 0);

	ctx.restore();

	const interval = page.source.interval;

	page.layout.areas.map((area, systemIndex) => {
		console.assert(area.staves?.middleRhos?.length, '[shootImageByDetection] empty area:', area);

		const data = ctx.getImageData(area.x, area.y, area.width, area.height);

		const canvas = new Canvas(area.width, area.height);

		const context = canvas.getContext('2d');
		// context.rotate(-area.staves.theta);
		context.putImageData(data, 0, 0);

		const detection = area.staves;
		const size = { width: area.width, height: area.height };

		const sourceCenter = {
			x: pageCanvas.width / 2 / interval,
			y: pageCanvas.height / 2 / interval,
		};

		const position = {
			x: (area.x + area.staves.phi1) / interval - sourceCenter.x + page.width / 2,
			y: area.y / interval - sourceCenter.y + page.height / 2,
		};

		page.systems[systemIndex] = constructSystem({
			page,
			backgroundImage: canvas.toBufferSync('png'),
			detection,
			imageSize: size,
			position,
		});
	});

	return correctCanvas;
}

async function shootStaffBackgroundImage({ system, staff, staffIndex }: { system: starry.System; staff: starry.Staff; staffIndex: number }) {
	const sourceCanvas = await shootStaffImage(system, staffIndex, {
		paddingLeft: STAFF_PADDING_LEFT,
		spec: SEMANTIC_VISION_SPEC,
	});

	staff.backgroundImage = sourceCanvas.toBufferSync('png');

	staff.imagePosition = {
		x: -STAFF_PADDING_LEFT / SEMANTIC_VISION_SPEC.viewportUnit,
		y: staff.staffY - SEMANTIC_VISION_SPEC.viewportHeight / 2 / SEMANTIC_VISION_SPEC.viewportUnit,
		width: sourceCanvas.width / SEMANTIC_VISION_SPEC.viewportUnit,
		height: sourceCanvas.height / SEMANTIC_VISION_SPEC.viewportUnit,
	};
}

/**
 * 单个staff的变形矫正
 * @param system
 * @param staff
 * @param staffIndex
 * @param gaugeImage
 * @param pyClients
 */
async function gaugeStaff({
	system,
	staff,
	staffIndex,
	gaugeImage,
	pyClients,
}: {
	system: starry.System;
	staff: starry.Staff;
	staffIndex: number;
	gaugeImage: Buffer;
	pyClients: PyClients;
}) {
	const sourceCanvas = await shootStaffImage(system, staffIndex, {
		paddingLeft: STAFF_PADDING_LEFT,
		spec: GAUGE_VISION_SPEC,
		scaling: 2,
	});

	const sourceBuffer = sourceCanvas.toBufferSync('png');

	const baseY = (system.middleY - (staff.top + staff.staffY)) * GAUGE_VISION_SPEC.viewportUnit + GAUGE_VISION_SPEC.viewportHeight / 2;

	const { buffer, size } = await pyClients.predictScoreImages('gaugeRenderer', [sourceBuffer, gaugeImage, baseY]);

	staff.backgroundImage = buffer;

	staff.imagePosition = {
		x: -STAFF_PADDING_LEFT / GAUGE_VISION_SPEC.viewportUnit,
		y: staff.staffY - GAUGE_VISION_SPEC.viewportHeight / 2 / GAUGE_VISION_SPEC.viewportUnit,
		width: size.width / GAUGE_VISION_SPEC.viewportUnit,
		height: size.height / GAUGE_VISION_SPEC.viewportUnit,
	};

	staff.maskImage = null;
}

/**
 * 单个staff的降噪
 * @param staff
 * @param staffIndex
 * @param maskImage
 */
async function maskStaff({ staff, staffIndex, maskImage }: { staff: starry.Staff; staffIndex: number; maskImage: Buffer }) {
	const img = await loadImage(maskImage);

	staff.maskImage = maskImage;
	staff.imagePosition = {
		x: -STAFF_PADDING_LEFT / MASK_VISION_SPEC.viewportUnit,
		y: staff.staffY - MASK_VISION_SPEC.viewportHeight / 2 / MASK_VISION_SPEC.viewportUnit,
		width: img.width / MASK_VISION_SPEC.viewportUnit,
		height: img.height / MASK_VISION_SPEC.viewportUnit,
	};
}

/**
 * 单个staff的语义识别
 * @param score
 * @param staffIndex
 * @param system
 * @param staff
 * @param graph
 */
async function semanticStaff({
	score,
	staffIndex,
	system,
	staff,
	graph,
}: {
	score: starry.Score;
	staffIndex: number;
	system: starry.System;
	staff: starry.Staff;
	graph: SemanticGraph;
}) {
	graph.offset(-STAFF_PADDING_LEFT / SEMANTIC_VISION_SPEC.viewportUnit, 0);

	system.assignSemantics(staffIndex, graph);

	staff.assignSemantics(graph);
	staff.clearPredictedTokens();

	score.assembleSystem(system, score.settings?.semanticConfidenceThreshold || 1);
}

function replacePageImages(page: starry.Page, onReplaceImageKey: (src: string) => any) {
	const tasks = [
		[page.source, 'url'],
		...page.systems
			.map((system) => {
				return [
					[system, 'backgroundImage'],
					...system.staves
						.map((staff) => [
							[staff, 'backgroundImage'],
							[staff, 'maskImage'],
						])
						.flat(),
				];
			})
			.flat(),
	];

	tasks.map(([target, key]: [any, string]) => {
		target[key] = onReplaceImageKey(target[key]);
	});
}

export type TaskProgress = { total?: number; finished?: number };

export interface OMRPage {
	url: string | Buffer;
	key?: string;
	layout?: LayoutResult;
	renew?: boolean;
	enableGauge?: boolean;
}

export interface ProgressState {
	layout?: TaskProgress;
	text?: TaskProgress;
	gauge?: TaskProgress;
	mask?: TaskProgress;
	semantic?: TaskProgress;
	regulate?: TaskProgress;
	brackets?: TaskProgress;
}

class OMRProgress {
	state: ProgressState = {};

	onChange: (evt: ProgressState) => void;

	constructor(onChange: (evt: ProgressState) => void) {
		this.onChange = onChange;
	}

	setTotal(stage: keyof ProgressState, total: number) {
		this.state[stage] = this.state[stage] || {
			total,
			finished: 0,
		};
	}

	increase(stage: keyof ProgressState, step = 1) {
		const info: TaskProgress = this.state[stage] || {
			finished: 0,
		};
		info.finished += step;

		this.onChange(this.state);
	}
}

type SourceImage = string | Buffer;

export interface OMROption {
	outputWidth?: number;
	title?: string; // 曲谱标题
	pageStore?: {
		has?: (key: string) => Promise<Boolean>;
		get: (key: string) => Promise<string>;
		set: (key: string, val: string) => Promise<void>;
	};
	renew?: boolean;
	processes?: (keyof ProgressState)[]; // 选择流程
	onProgress?: (progress: ProgressState) => void;
	onReplaceImage?: (src: SourceImage) => Promise<string>; // 替换所有图片地址，用于上传或者格式转换
}

const lruCache = new WeakLRUCache();

// 默认store
const pageStore = {
	async get(key: string) {
		return lruCache.getValue(key) as string;
	},
	async set(key: string, val: string) {
		lruCache.setValue(key, val);
	},
};

/**
 * 默认将图片转换为webp格式的base64字符串
 * @param src
 */
const onReplaceImage = async (src: SourceImage) => {
	if (src instanceof Buffer || (typeof src === 'string' && (/^https?:\/\//.test(src) || /^data:image\//.test(src)))) {
		const webpBuffer = (await convertImage(src)).buffer;
		return `data:image/webp;base64,${webpBuffer.toString('base64')}`;
	}

	return src;
};

/**
 * 识别所有图片
 * @param pyClients
 * @param images
 * @param option
 */
export const predictPages = async (
	pyClients: PyClients,
	images: OMRPage[],
	option: OMROption = { outputWidth: 1200, pageStore, onReplaceImage }
): Promise<{ score: starry.Score; omitPages: number[]; stat: OMRStat }> => {
	// return {
	// 	score,
	// 	omitPages,
	// 	stat: {
	// 		cost: t3 - t0,
	// 		pagesCost: t2 - t1,
	// 		pages: n_page,
	// 	},
	// };
};