Comfyui-LayerForge/js/utils/ImageUtils.js

import { createModuleLogger } from "./LoggerUtils.js";
import { withErrorHandling, createValidationError } from "../ErrorHandler.js";
import { createCanvas } from "./CommonUtils.js";
const log = createModuleLogger('ImageUtils');
export function validateImageData(data) {
    log.debug("Validating data structure:", {
        hasData: !!data,
        type: typeof data,
        isArray: Array.isArray(data),
        keys: data ? Object.keys(data) : null,
        shape: data?.shape,
        dataType: data?.data ? data.data.constructor.name : null,
        fullData: data
    });
    if (!data) {
        log.info("Data is null or undefined");
        return false;
    }
    if (Array.isArray(data)) {
        log.debug("Data is array, getting first element");
        data = data[0];
    }
    if (!data || typeof data !== 'object') {
        log.info("Invalid data type");
        return false;
    }
    if (!data.data) {
        log.info("Missing data property");
        return false;
    }
    if (!(data.data instanceof Float32Array)) {
        try {
            data.data = new Float32Array(data.data);
        }
        catch (e) {
            log.error("Failed to convert data to Float32Array:", e);
            return false;
        }
    }
    return true;
}
export function convertImageData(data) {
    log.info("Converting image data:", data);
    if (Array.isArray(data)) {
        data = data[0];
    }
    const shape = data.shape;
    const height = shape[1];
    const width = shape[2];
    const channels = shape[3];
    const floatData = new Float32Array(data.data);
    log.debug("Processing dimensions:", { height, width, channels });
    const rgbaData = new Uint8ClampedArray(width * height * 4);
    for (let h = 0; h < height; h++) {
        for (let w = 0; w < width; w++) {
            const pixelIndex = (h * width + w) * 4;
            const tensorIndex = (h * width + w) * channels;
            for (let c = 0; c < channels; c++) {
                const value = floatData[tensorIndex + c];
                rgbaData[pixelIndex + c] = Math.max(0, Math.min(255, Math.round(value * 255)));
            }
            rgbaData[pixelIndex + 3] = 255;
        }
    }
    return {
        data: rgbaData,
        width: width,
        height: height
    };
}
export function applyMaskToImageData(imageData, maskData) {
    log.info("Applying mask to image data");
    const rgbaData = new Uint8ClampedArray(imageData.data);
    const width = imageData.width;
    const height = imageData.height;
    const maskShape = maskData.shape;
    const maskFloatData = new Float32Array(maskData.data);
    log.debug(`Applying mask of shape: ${maskShape}`);
    for (let h = 0; h < height; h++) {
        for (let w = 0; w < width; w++) {
            const pixelIndex = (h * width + w) * 4;
            const maskIndex = h * width + w;
            const alpha = maskFloatData[maskIndex];
            rgbaData[pixelIndex + 3] = Math.max(0, Math.min(255, Math.round(alpha * 255)));
        }
    }
    log.info("Mask application completed");
    return {
        data: rgbaData,
        width: width,
        height: height
    };
}
export const prepareImageForCanvas = withErrorHandling(function (inputImage) {
    log.info("Preparing image for canvas:", inputImage);
    if (Array.isArray(inputImage)) {
        inputImage = inputImage[0];
    }
    if (!inputImage || !inputImage.shape || !inputImage.data) {
        throw createValidationError("Invalid input image format", { inputImage });
    }
    const shape = inputImage.shape;
    const height = shape[1];
    const width = shape[2];
    const channels = shape[3];
    const floatData = new Float32Array(inputImage.data);
    log.debug("Image dimensions:", { height, width, channels });
    const rgbaData = new Uint8ClampedArray(width * height * 4);
    for (let h = 0; h < height; h++) {
        for (let w = 0; w < width; w++) {
            const pixelIndex = (h * width + w) * 4;
            const tensorIndex = (h * width + w) * channels;
            for (let c = 0; c < channels; c++) {
                const value = floatData[tensorIndex + c];
                rgbaData[pixelIndex + c] = Math.max(0, Math.min(255, Math.round(value * 255)));
            }
            rgbaData[pixelIndex + 3] = 255;
        }
    }
    return {
        data: rgbaData,
        width: width,
        height: height
    };
}, 'prepareImageForCanvas');
export const imageToTensor = withErrorHandling(async function (image) {
    if (!image) {
        throw createValidationError("Image is required");
    }
    const { canvas, ctx } = createCanvas(image.width, image.height, '2d', { willReadFrequently: true });
    if (ctx) {
        ctx.drawImage(image, 0, 0);
        const imageData = ctx.getImageData(0, 0, canvas.width, canvas.height);
        const data = new Float32Array(canvas.width * canvas.height * 3);
        for (let i = 0; i < imageData.data.length; i += 4) {
            const pixelIndex = i / 4;
            data[pixelIndex * 3] = imageData.data[i] / 255;
            data[pixelIndex * 3 + 1] = imageData.data[i + 1] / 255;
            data[pixelIndex * 3 + 2] = imageData.data[i + 2] / 255;
        }
        return {
            data: data,
            shape: [1, canvas.height, canvas.width, 3],
            width: canvas.width,
            height: canvas.height
        };
    }
    throw new Error("Canvas context not available");
}, 'imageToTensor');
export const tensorToImage = withErrorHandling(async function (tensor) {
    if (!tensor || !tensor.data || !tensor.shape) {
        throw createValidationError("Invalid tensor format", { tensor });
    }
    const [, height, width, channels] = tensor.shape;
    const { canvas, ctx } = createCanvas(width, height, '2d', { willReadFrequently: true });
    if (ctx) {
        const imageData = ctx.createImageData(width, height);
        const data = tensor.data;
        for (let i = 0; i < width * height; i++) {
            const pixelIndex = i * 4;
            const tensorIndex = i * channels;
            imageData.data[pixelIndex] = Math.round(data[tensorIndex] * 255);
            imageData.data[pixelIndex + 1] = Math.round(data[tensorIndex + 1] * 255);
            imageData.data[pixelIndex + 2] = Math.round(data[tensorIndex + 2] * 255);
            imageData.data[pixelIndex + 3] = 255;
        }
        ctx.putImageData(imageData, 0, 0);
        return new Promise((resolve, reject) => {
            const img = new Image();
            img.onload = () => resolve(img);
            img.onerror = (err) => reject(err);
            img.src = canvas.toDataURL();
        });
    }
    throw new Error("Canvas context not available");
}, 'tensorToImage');
export const resizeImage = withErrorHandling(async function (image, maxWidth, maxHeight) {
    if (!image) {
        throw createValidationError("Image is required");
    }
    const originalWidth = image.width;
    const originalHeight = image.height;
    const scale = Math.min(maxWidth / originalWidth, maxHeight / originalHeight);
    const newWidth = Math.round(originalWidth * scale);
    const newHeight = Math.round(originalHeight * scale);
    const { canvas, ctx } = createCanvas(newWidth, newHeight, '2d', { willReadFrequently: true });
    if (ctx) {
        ctx.imageSmoothingEnabled = true;
        ctx.imageSmoothingQuality = 'high';
        ctx.drawImage(image, 0, 0, newWidth, newHeight);
        return new Promise((resolve, reject) => {
            const img = new Image();
            img.onload = () => resolve(img);
            img.onerror = (err) => reject(err);
            img.src = canvas.toDataURL();
        });
    }
    throw new Error("Canvas context not available");
}, 'resizeImage');
export const createThumbnail = withErrorHandling(async function (image, size = 128) {
    return resizeImage(image, size, size);
}, 'createThumbnail');
export const imageToBase64 = withErrorHandling(function (image, format = 'png', quality = 0.9) {
    if (!image) {
        throw createValidationError("Image is required");
    }
    const width = image instanceof HTMLImageElement ? image.naturalWidth : image.width;
    const height = image instanceof HTMLImageElement ? image.naturalHeight : image.height;
    const { canvas, ctx } = createCanvas(width, height, '2d', { willReadFrequently: true });
    if (ctx) {
        ctx.drawImage(image, 0, 0);
        const mimeType = `image/${format}`;
        return canvas.toDataURL(mimeType, quality);
    }
    throw new Error("Canvas context not available");
}, 'imageToBase64');
export const base64ToImage = withErrorHandling(function (base64) {
    if (!base64) {
        throw createValidationError("Base64 string is required");
    }
    return new Promise((resolve, reject) => {
        const img = new Image();
        img.onload = () => resolve(img);
        img.onerror = () => reject(new Error("Failed to load image from base64"));
        img.src = base64;
    });
}, 'base64ToImage');
export function isValidImage(image) {
    return image &&
        (image instanceof HTMLImageElement || image instanceof HTMLCanvasElement) &&
        image.width > 0 &&
        image.height > 0;
}
export function getImageInfo(image) {
    if (!isValidImage(image)) {
        return null;
    }
    const width = image instanceof HTMLImageElement ? image.naturalWidth : image.width;
    const height = image instanceof HTMLImageElement ? image.naturalHeight : image.height;
    return {
        width,
        height,
        aspectRatio: width / height,
        area: width * height
    };
}
export function createImageFromSource(source) {
    return new Promise((resolve, reject) => {
        const img = new Image();
        img.onload = () => resolve(img);
        img.onerror = (err) => reject(err);
        img.src = source;
    });
}
export const createEmptyImage = withErrorHandling(function (width, height, color = 'transparent') {
    const { canvas, ctx } = createCanvas(width, height, '2d', { willReadFrequently: true });
    if (ctx) {
        if (color !== 'transparent') {
            ctx.fillStyle = color;
            ctx.fillRect(0, 0, width, height);
        }
        return new Promise((resolve, reject) => {
            const img = new Image();
            img.onload = () => resolve(img);
            img.onerror = (err) => reject(err);
            img.src = canvas.toDataURL();
        });
    }
    throw new Error("Canvas context not available");
}, 'createEmptyImage');