/**
 * @file Helper module for image processing.
 *
 * These functions and classes are only used internally,
 * meaning an end-user shouldn't need to access anything here.
 *
 * @module utils/image
 */
import { getFile } from './hub.js';
import { env } from '../env.js';
// Will be empty (or not used) if running in browser or web-worker
import sharp from 'sharp';
const BROWSER_ENV = typeof self !== 'undefined';
const WEBWORKER_ENV = BROWSER_ENV && self.constructor.name === 'DedicatedWorkerGlobalScope';
let createCanvasFunction;
let ImageDataClass;
let loadImageFunction;
if (BROWSER_ENV) {
    // Running in browser or web-worker
    createCanvasFunction = (/** @type {number} */ width, /** @type {number} */ height) => {
        if (!self.OffscreenCanvas) {
            throw new Error('OffscreenCanvas not supported by this browser.');
        }
        return new self.OffscreenCanvas(width, height);
    };
    loadImageFunction = self.createImageBitmap;
    ImageDataClass = self.ImageData;
}
else if (sharp) {
    // Running in Node.js, electron, or other non-browser environment
    loadImageFunction = async (/**@type {sharp.Sharp}*/ img) => {
        const metadata = await img.metadata();
        const rawChannels = metadata.channels;
        let { data, info } = await img.raw().toBuffer({ resolveWithObject: true });
        const newImage = new RawImage(new Uint8ClampedArray(data), info.width, info.height, info.channels);
        if (rawChannels !== undefined && rawChannels !== info.channels) {
            // Make sure the new image has the same number of channels as the input image.
            // This is necessary for grayscale images.
            newImage.convert(rawChannels);
        }
        return newImage;
    };
}
else {
    throw new Error('Unable to load image processing library.');
}
// Defined here: https://github.com/python-pillow/Pillow/blob/a405e8406b83f8bfb8916e93971edc7407b8b1ff/src/libImaging/Imaging.h#L262-L268
const RESAMPLING_MAPPING = {
    0: 'nearest',
    1: 'lanczos',
    2: 'bilinear',
    3: 'bicubic',
    4: 'box',
    5: 'hamming',
};
/**
 * Mapping from file extensions to MIME types.
 */
const CONTENT_TYPE_MAP = new Map([
    ['png', 'image/png'],
    ['jpg', 'image/jpeg'],
    ['jpeg', 'image/jpeg'],
    ['gif', 'image/gif'],
]);
export class RawImage {
    /**
     * Create a new `RawImage` object.
     * @param {Uint8ClampedArray|Uint8Array} data The pixel data.
     * @param {number} width The width of the image.
     * @param {number} height The height of the image.
     * @param {1|2|3|4} channels The number of channels.
     */
    constructor(data, width, height, channels) {
        this.data = data;
        this.width = width;
        this.height = height;
        this.channels = channels;
    }
    /**
     * Returns the size of the image (width, height).
     * @returns {[number, number]} The size of the image (width, height).
     */
    get size() {
        return [this.width, this.height];
    }
    /**
     * Helper method for reading an image from a variety of input types.
     * @param {RawImage|string|URL} input
     * @returns The image object.
     *
     * **Example:** Read image from a URL.
     * ```javascript
     * let image = await RawImage.read('https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/football-match.jpg');
     * // RawImage {
     * //   "data": Uint8ClampedArray [ 25, 25, 25, 19, 19, 19, ... ],
     * //   "width": 800,
     * //   "height": 533,
     * //   "channels": 3
     * // }
     * ```
     */
    static async read(input) {
        if (input instanceof RawImage) {
            return input;
        }
        else if (typeof input === 'string' || input instanceof URL) {
            return await this.fromURL(input);
        }
        else {
            throw new Error(`Unsupported input type: ${typeof input}`);
        }
    }
    /**
     * Read an image from a URL or file path.
     * @param {string|URL} url The URL or file path to read the image from.
     * @returns {Promise<RawImage>} The image object.
     */
    static async fromURL(url) {
        let response = await getFile(url);
        if (response.status !== 200) {
            throw new Error(`Unable to read image from "${url}" (${response.status} ${response.statusText})`);
        }
        let blob = await response.blob();
        return this.fromBlob(blob);
    }
    /**
     * Helper method to create a new Image from a blob.
     * @param {Blob} blob The blob to read the image from.
     * @returns {Promise<RawImage>} The image object.
     */
    static async fromBlob(blob) {
        if (BROWSER_ENV) {
            // Running in environment with canvas
            let img = await loadImageFunction(blob);
            const ctx = createCanvasFunction(img.width, img.height).getContext('2d');
            // Draw image to context
            ctx.drawImage(img, 0, 0);
            return new this(ctx.getImageData(0, 0, img.width, img.height).data, img.width, img.height, 4);
        }
        else {
            // Use sharp.js to read (and possible resize) the image.
            let img = sharp(await blob.arrayBuffer());
            return await loadImageFunction(img);
        }
    }
    /**
     * Helper method to create a new Image from a tensor
     * @param {import('./tensor.js').Tensor} tensor
     */
    static fromTensor(tensor, channel_format = 'CHW') {
        if (tensor.dims.length !== 3) {
            throw new Error(`Tensor should have 3 dimensions, but has ${tensor.dims.length} dimensions.`);
        }
        if (channel_format === 'CHW') {
            tensor = tensor.transpose(1, 2, 0);
        }
        else if (channel_format === 'HWC') {
            // Do nothing
        }
        else {
            throw new Error(`Unsupported channel format: ${channel_format}`);
        }
        if (!(tensor.data instanceof Uint8ClampedArray || tensor.data instanceof Uint8Array)) {
            throw new Error(`Unsupported tensor type: ${tensor.type}`);
        }
        switch (tensor.dims[2]) {
            case 1:
            case 2:
            case 3:
            case 4:
                return new RawImage(tensor.data, tensor.dims[1], tensor.dims[0], tensor.dims[2]);
            default:
                throw new Error(`Unsupported number of channels: ${tensor.dims[2]}`);
        }
    }
    /**
     * Convert the image to grayscale format.
     * @returns {RawImage} `this` to support chaining.
     */
    grayscale() {
        if (this.channels === 1) {
            return this;
        }
        let newData = new Uint8ClampedArray(this.width * this.height * 1);
        switch (this.channels) {
            case 3: // rgb to grayscale
            case 4: // rgba to grayscale
                for (let i = 0, offset = 0; i < this.data.length; i += this.channels) {
                    const red = this.data[i];
                    const green = this.data[i + 1];
                    const blue = this.data[i + 2];
                    newData[offset++] = Math.round(0.2989 * red + 0.5870 * green + 0.1140 * blue);
                }
                break;
            default:
                throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`);
        }
        return this._update(newData, this.width, this.height, 1);
    }
    /**
     * Convert the image to RGB format.
     * @returns {RawImage} `this` to support chaining.
     */
    rgb() {
        if (this.channels === 3) {
            return this;
        }
        let newData = new Uint8ClampedArray(this.width * this.height * 3);
        switch (this.channels) {
            case 1: // grayscale to rgb
                for (let i = 0, offset = 0; i < this.data.length; ++i) {
                    newData[offset++] = this.data[i];
                    newData[offset++] = this.data[i];
                    newData[offset++] = this.data[i];
                }
                break;
            case 4: // rgba to rgb
                for (let i = 0, offset = 0; i < this.data.length; i += 4) {
                    newData[offset++] = this.data[i];
                    newData[offset++] = this.data[i + 1];
                    newData[offset++] = this.data[i + 2];
                }
                break;
            default:
                throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`);
        }
        return this._update(newData, this.width, this.height, 3);
    }
    /**
     * Convert the image to RGBA format.
     * @returns {RawImage} `this` to support chaining.
     */
    rgba() {
        if (this.channels === 4) {
            return this;
        }
        let newData = new Uint8ClampedArray(this.width * this.height * 4);
        switch (this.channels) {
            case 1: // grayscale to rgba
                for (let i = 0, offset = 0; i < this.data.length; ++i) {
                    newData[offset++] = this.data[i];
                    newData[offset++] = this.data[i];
                    newData[offset++] = this.data[i];
                    newData[offset++] = 255;
                }
                break;
            case 3: // rgb to rgba
                for (let i = 0, offset = 0; i < this.data.length; i += 3) {
                    newData[offset++] = this.data[i];
                    newData[offset++] = this.data[i + 1];
                    newData[offset++] = this.data[i + 2];
                    newData[offset++] = 255;
                }
                break;
            default:
                throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`);
        }
        return this._update(newData, this.width, this.height, 4);
    }
    /**
     * Resize the image to the given dimensions. This method uses the canvas API to perform the resizing.
     * @param {number} width The width of the new image.
     * @param {number} height The height of the new image.
     * @param {Object} options Additional options for resizing.
     * @param {0|1|2|3|4|5|string} [options.resample] The resampling method to use.
     * @returns {Promise<RawImage>} `this` to support chaining.
     */
    async resize(width, height, { resample = 2, } = {}) {
        // Ensure resample method is a string
        let resampleMethod = RESAMPLING_MAPPING[resample] ?? resample;
        if (BROWSER_ENV) {
            // TODO use `resample` in browser environment
            // Store number of channels before resizing
            let numChannels = this.channels;
            // Create canvas object for this image
            let canvas = this.toCanvas();
            // Actually perform resizing using the canvas API
            const ctx = createCanvasFunction(width, height).getContext('2d');
            // Draw image to context, resizing in the process
            ctx.drawImage(canvas, 0, 0, width, height);
            // Create image from the resized data
            let resizedImage = new RawImage(ctx.getImageData(0, 0, width, height).data, width, height, 4);
            // Convert back so that image has the same number of channels as before
            return resizedImage.convert(numChannels);
        }
        else {
            // Create sharp image from raw data, and resize
            let img = this.toSharp();
            switch (resampleMethod) {
                case 'box':
                case 'hamming':
                    if (resampleMethod === 'box' || resampleMethod === 'hamming') {
                        console.warn(`Resampling method ${resampleMethod} is not yet supported. Using bilinear instead.`);
                        resampleMethod = 'bilinear';
                    }
                case 'nearest':
                case 'bilinear':
                case 'bicubic':
                    // Perform resizing using affine transform. 
                    // This matches how the python Pillow library does it.
                    img = img.affine([width / this.width, 0, 0, height / this.height], {
                        interpolator: resampleMethod
                    });
                    break;
                case 'lanczos':
                    // https://github.com/python-pillow/Pillow/discussions/5519
                    // https://github.com/lovell/sharp/blob/main/docs/api-resize.md
                    img = img.resize({
                        width, height,
                        fit: 'fill',
                        kernel: 'lanczos3', // PIL Lanczos uses a kernel size of 3 
                    });
                    break;
                default:
                    throw new Error(`Resampling method ${resampleMethod} is not supported.`);
            }
            return await loadImageFunction(img);
        }
    }
    async pad([left, right, top, bottom]) {
        left = Math.max(left, 0);
        right = Math.max(right, 0);
        top = Math.max(top, 0);
        bottom = Math.max(bottom, 0);
        if (left === 0 && right === 0 && top === 0 && bottom === 0) {
            // No padding needed
            return this;
        }
        if (BROWSER_ENV) {
            // Store number of channels before padding
            let numChannels = this.channels;
            // Create canvas object for this image
            let canvas = this.toCanvas();
            let newWidth = this.width + left + right;
            let newHeight = this.height + top + bottom;
            // Create a new canvas of the desired size.
            const ctx = createCanvasFunction(newWidth, newHeight).getContext('2d');
            // Draw image to context, padding in the process
            ctx.drawImage(canvas, 0, 0, this.width, this.height, left, top, newWidth, newHeight);
            // Create image from the padded data
            let paddedImage = new RawImage(ctx.getImageData(0, 0, newWidth, newHeight).data, newWidth, newHeight, 4);
            // Convert back so that image has the same number of channels as before
            return paddedImage.convert(numChannels);
        }
        else {
            let img = this.toSharp().extend({ left, right, top, bottom });
            return await loadImageFunction(img);
        }
    }
    async crop([x_min, y_min, x_max, y_max]) {
        // Ensure crop bounds are within the image
        x_min = Math.max(x_min, 0);
        y_min = Math.max(y_min, 0);
        x_max = Math.min(x_max, this.width - 1);
        y_max = Math.min(y_max, this.height - 1);
        // Do nothing if the crop is the entire image
        if (x_min === 0 && y_min === 0 && x_max === this.width - 1 && y_max === this.height - 1) {
            return this;
        }
        const crop_width = x_max - x_min + 1;
        const crop_height = y_max - y_min + 1;
        if (BROWSER_ENV) {
            // Store number of channels before resizing
            const numChannels = this.channels;
            // Create canvas object for this image
            const canvas = this.toCanvas();
            // Create a new canvas of the desired size. This is needed since if the 
            // image is too small, we need to pad it with black pixels.
            const ctx = createCanvasFunction(crop_width, crop_height).getContext('2d');
            // Draw image to context, cropping in the process
            ctx.drawImage(canvas, x_min, y_min, crop_width, crop_height, 0, 0, crop_width, crop_height);
            // Create image from the resized data
            const resizedImage = new RawImage(ctx.getImageData(0, 0, crop_width, crop_height).data, crop_width, crop_height, 4);
            // Convert back so that image has the same number of channels as before
            return resizedImage.convert(numChannels);
        }
        else {
            // Create sharp image from raw data
            const img = this.toSharp().extract({
                left: x_min,
                top: y_min,
                width: crop_width,
                height: crop_height,
            });
            return await loadImageFunction(img);
        }
    }
    async center_crop(crop_width, crop_height) {
        // If the image is already the desired size, return it
        if (this.width === crop_width && this.height === crop_height) {
            return this;
        }
        // Determine bounds of the image in the new canvas
        let width_offset = (this.width - crop_width) / 2;
        let height_offset = (this.height - crop_height) / 2;
        if (BROWSER_ENV) {
            // Store number of channels before resizing
            let numChannels = this.channels;
            // Create canvas object for this image
            let canvas = this.toCanvas();
            // Create a new canvas of the desired size. This is needed since if the 
            // image is too small, we need to pad it with black pixels.
            const ctx = createCanvasFunction(crop_width, crop_height).getContext('2d');
            let sourceX = 0;
            let sourceY = 0;
            let destX = 0;
            let destY = 0;
            if (width_offset >= 0) {
                sourceX = width_offset;
            }
            else {
                destX = -width_offset;
            }
            if (height_offset >= 0) {
                sourceY = height_offset;
            }
            else {
                destY = -height_offset;
            }
            // Draw image to context, cropping in the process
            ctx.drawImage(canvas, sourceX, sourceY, crop_width, crop_height, destX, destY, crop_width, crop_height);
            // Create image from the resized data
            let resizedImage = new RawImage(ctx.getImageData(0, 0, crop_width, crop_height).data, crop_width, crop_height, 4);
            // Convert back so that image has the same number of channels as before
            return resizedImage.convert(numChannels);
        }
        else {
            // Create sharp image from raw data
            let img = this.toSharp();
            if (width_offset >= 0 && height_offset >= 0) {
                // Cropped image lies entirely within the original image
                img = img.extract({
                    left: Math.floor(width_offset),
                    top: Math.floor(height_offset),
                    width: crop_width,
                    height: crop_height,
                });
            }
            else if (width_offset <= 0 && height_offset <= 0) {
                // Cropped image lies entirely outside the original image,
                // so we add padding
                let top = Math.floor(-height_offset);
                let left = Math.floor(-width_offset);
                img = img.extend({
                    top: top,
                    left: left,
                    // Ensures the resulting image has the desired dimensions
                    right: crop_width - this.width - left,
                    bottom: crop_height - this.height - top,
                });
            }
            else {
                // Cropped image lies partially outside the original image.
                // We first pad, then crop.
                let y_padding = [0, 0];
                let y_extract = 0;
                if (height_offset < 0) {
                    y_padding[0] = Math.floor(-height_offset);
                    y_padding[1] = crop_height - this.height - y_padding[0];
                }
                else {
                    y_extract = Math.floor(height_offset);
                }
                let x_padding = [0, 0];
                let x_extract = 0;
                if (width_offset < 0) {
                    x_padding[0] = Math.floor(-width_offset);
                    x_padding[1] = crop_width - this.width - x_padding[0];
                }
                else {
                    x_extract = Math.floor(width_offset);
                }
                img = img.extend({
                    top: y_padding[0],
                    bottom: y_padding[1],
                    left: x_padding[0],
                    right: x_padding[1],
                }).extract({
                    left: x_extract,
                    top: y_extract,
                    width: crop_width,
                    height: crop_height,
                });
            }
            return await loadImageFunction(img);
        }
    }
    async toBlob(type = 'image/png', quality = 1) {
        if (!BROWSER_ENV) {
            throw new Error('toBlob() is only supported in browser environments.');
        }
        const canvas = this.toCanvas();
        return await canvas.convertToBlob({ type, quality });
    }
    toCanvas() {
        if (!BROWSER_ENV) {
            throw new Error('toCanvas() is only supported in browser environments.');
        }
        // Clone, and convert data to RGBA before drawing to canvas.
        // This is because the canvas API only supports RGBA
        let cloned = this.clone().rgba();
        // Create canvas object for the cloned image
        let clonedCanvas = createCanvasFunction(cloned.width, cloned.height);
        // Draw image to context
        let data = new ImageDataClass(cloned.data, cloned.width, cloned.height);
        clonedCanvas.getContext('2d').putImageData(data, 0, 0);
        return clonedCanvas;
    }
    /**
     * Helper method to update the image data.
     * @param {Uint8ClampedArray} data The new image data.
     * @param {number} width The new width of the image.
     * @param {number} height The new height of the image.
     * @param {1|2|3|4|null} [channels] The new number of channels of the image.
     * @private
     */
    _update(data, width, height, channels = null) {
        this.data = data;
        this.width = width;
        this.height = height;
        if (channels !== null) {
            this.channels = channels;
        }
        return this;
    }
    /**
     * Clone the image
     * @returns {RawImage} The cloned image
     */
    clone() {
        return new RawImage(this.data.slice(), this.width, this.height, this.channels);
    }
    /**
     * Helper method for converting image to have a certain number of channels
     * @param {number} numChannels The number of channels. Must be 1, 3, or 4.
     * @returns {RawImage} `this` to support chaining.
     */
    convert(numChannels) {
        if (this.channels === numChannels)
            return this; // Already correct number of channels
        switch (numChannels) {
            case 1:
                this.grayscale();
                break;
            case 3:
                this.rgb();
                break;
            case 4:
                this.rgba();
                break;
            default:
                throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`);
        }
        return this;
    }
    /**
     * Save the image to the given path.
     * @param {string} path The path to save the image to.
     */
    async save(path) {
        if (BROWSER_ENV) {
            if (WEBWORKER_ENV) {
                throw new Error('Unable to save an image from a Web Worker.');
            }
            const extension = path.split('.').pop().toLowerCase();
            const mime = CONTENT_TYPE_MAP.get(extension) ?? 'image/png';
            // Convert image to Blob
            const blob = await this.toBlob(mime);
            // Convert the canvas content to a data URL
            const dataURL = URL.createObjectURL(blob);
            // Create an anchor element with the data URL as the href attribute
            const downloadLink = document.createElement('a');
            downloadLink.href = dataURL;
            // Set the download attribute to specify the desired filename for the downloaded image
            downloadLink.download = path;
            // Trigger the download
            downloadLink.click();
            // Clean up: remove the anchor element from the DOM
            downloadLink.remove();
        }
        else if (!env.useFS) {
            throw new Error('Unable to save the image because filesystem is disabled in this environment.');
        }
        else {
            const img = this.toSharp();
            return await img.toFile(path);
        }
    }
    toSharp() {
        if (BROWSER_ENV) {
            throw new Error('toSharp() is only supported in server-side environments.');
        }
        return sharp(this.data, {
            raw: {
                width: this.width,
                height: this.height,
                channels: this.channels
            }
        });
    }
}
