mirror of
https://github.com/Azornes/Comfyui-LayerForge.git
synced 2026-03-21 20:52:12 -03:00
da37900b33
Removed duplicate code from CanvasIO.ts and replaced it with unified helpers from ImageUtils.ts. All tensor-to-image conversions and image creation now use centralized utility functions for consistency and maintainability.
416 lines
16 KiB
JavaScript
416 lines
16 KiB
JavaScript
import { createModuleLogger } from "./LoggerUtils.js";
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import { withErrorHandling, createValidationError } from "../ErrorHandler.js";
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import { createCanvas } from "./CommonUtils.js";
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const log = createModuleLogger('ImageUtils');
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export function validateImageData(data) {
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log.debug("Validating data structure:", {
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hasData: !!data,
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type: typeof data,
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isArray: Array.isArray(data),
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keys: data ? Object.keys(data) : null,
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shape: data?.shape,
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dataType: data?.data ? data.data.constructor.name : null,
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fullData: data
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});
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if (!data) {
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log.info("Data is null or undefined");
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return false;
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}
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if (Array.isArray(data)) {
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log.debug("Data is array, getting first element");
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data = data[0];
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}
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if (!data || typeof data !== 'object') {
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log.info("Invalid data type");
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return false;
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}
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if (!data.data) {
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log.info("Missing data property");
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return false;
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}
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if (!(data.data instanceof Float32Array)) {
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try {
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data.data = new Float32Array(data.data);
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}
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catch (e) {
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log.error("Failed to convert data to Float32Array:", e);
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return false;
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}
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}
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return true;
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}
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export function convertImageData(data) {
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log.info("Converting image data:", data);
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if (Array.isArray(data)) {
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data = data[0];
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}
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const shape = data.shape;
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const height = shape[1];
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const width = shape[2];
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const channels = shape[3];
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const floatData = new Float32Array(data.data);
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log.debug("Processing dimensions:", { height, width, channels });
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const rgbaData = new Uint8ClampedArray(width * height * 4);
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for (let h = 0; h < height; h++) {
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for (let w = 0; w < width; w++) {
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const pixelIndex = (h * width + w) * 4;
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const tensorIndex = (h * width + w) * channels;
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for (let c = 0; c < channels; c++) {
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const value = floatData[tensorIndex + c];
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rgbaData[pixelIndex + c] = Math.max(0, Math.min(255, Math.round(value * 255)));
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}
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rgbaData[pixelIndex + 3] = 255;
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}
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}
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return {
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data: rgbaData,
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width: width,
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height: height
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};
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}
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export function applyMaskToImageData(imageData, maskData) {
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log.info("Applying mask to image data");
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const rgbaData = new Uint8ClampedArray(imageData.data);
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const width = imageData.width;
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const height = imageData.height;
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const maskShape = maskData.shape;
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const maskFloatData = new Float32Array(maskData.data);
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log.debug(`Applying mask of shape: ${maskShape}`);
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for (let h = 0; h < height; h++) {
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for (let w = 0; w < width; w++) {
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const pixelIndex = (h * width + w) * 4;
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const maskIndex = h * width + w;
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const alpha = maskFloatData[maskIndex];
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rgbaData[pixelIndex + 3] = Math.max(0, Math.min(255, Math.round(alpha * 255)));
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}
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}
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log.info("Mask application completed");
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return {
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data: rgbaData,
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width: width,
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height: height
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};
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}
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export const prepareImageForCanvas = withErrorHandling(function (inputImage) {
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log.info("Preparing image for canvas:", inputImage);
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if (Array.isArray(inputImage)) {
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inputImage = inputImage[0];
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}
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if (!inputImage || !inputImage.shape || !inputImage.data) {
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throw createValidationError("Invalid input image format", { inputImage });
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}
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const shape = inputImage.shape;
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const height = shape[1];
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const width = shape[2];
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const channels = shape[3];
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const floatData = new Float32Array(inputImage.data);
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log.debug("Image dimensions:", { height, width, channels });
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const rgbaData = new Uint8ClampedArray(width * height * 4);
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for (let h = 0; h < height; h++) {
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for (let w = 0; w < width; w++) {
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const pixelIndex = (h * width + w) * 4;
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const tensorIndex = (h * width + w) * channels;
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for (let c = 0; c < channels; c++) {
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const value = floatData[tensorIndex + c];
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rgbaData[pixelIndex + c] = Math.max(0, Math.min(255, Math.round(value * 255)));
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}
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rgbaData[pixelIndex + 3] = 255;
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}
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}
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return {
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data: rgbaData,
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width: width,
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height: height
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};
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}, 'prepareImageForCanvas');
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export const imageToTensor = withErrorHandling(async function (image) {
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if (!image) {
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throw createValidationError("Image is required");
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}
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const { canvas, ctx } = createCanvas(image.width, image.height, '2d', { willReadFrequently: true });
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if (ctx) {
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ctx.drawImage(image, 0, 0);
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const imageData = ctx.getImageData(0, 0, canvas.width, canvas.height);
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const data = new Float32Array(canvas.width * canvas.height * 3);
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for (let i = 0; i < imageData.data.length; i += 4) {
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const pixelIndex = i / 4;
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data[pixelIndex * 3] = imageData.data[i] / 255;
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data[pixelIndex * 3 + 1] = imageData.data[i + 1] / 255;
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data[pixelIndex * 3 + 2] = imageData.data[i + 2] / 255;
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}
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return {
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data: data,
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shape: [1, canvas.height, canvas.width, 3],
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width: canvas.width,
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height: canvas.height
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};
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}
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throw new Error("Canvas context not available");
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}, 'imageToTensor');
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export const tensorToImage = withErrorHandling(async function (tensor) {
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if (!tensor || !tensor.data || !tensor.shape) {
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throw createValidationError("Invalid tensor format", { tensor });
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}
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const [, height, width, channels] = tensor.shape;
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const { canvas, ctx } = createCanvas(width, height, '2d', { willReadFrequently: true });
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if (ctx) {
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const imageData = ctx.createImageData(width, height);
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const data = tensor.data;
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for (let i = 0; i < width * height; i++) {
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const pixelIndex = i * 4;
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const tensorIndex = i * channels;
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imageData.data[pixelIndex] = Math.round(data[tensorIndex] * 255);
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imageData.data[pixelIndex + 1] = Math.round(data[tensorIndex + 1] * 255);
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imageData.data[pixelIndex + 2] = Math.round(data[tensorIndex + 2] * 255);
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imageData.data[pixelIndex + 3] = 255;
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}
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ctx.putImageData(imageData, 0, 0);
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return new Promise((resolve, reject) => {
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const img = new Image();
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img.onload = () => resolve(img);
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img.onerror = (err) => reject(err);
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img.src = canvas.toDataURL();
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});
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}
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throw new Error("Canvas context not available");
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}, 'tensorToImage');
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export const resizeImage = withErrorHandling(async function (image, maxWidth, maxHeight) {
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if (!image) {
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throw createValidationError("Image is required");
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}
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const originalWidth = image.width;
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const originalHeight = image.height;
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const scale = Math.min(maxWidth / originalWidth, maxHeight / originalHeight);
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const newWidth = Math.round(originalWidth * scale);
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const newHeight = Math.round(originalHeight * scale);
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const { canvas, ctx } = createCanvas(newWidth, newHeight, '2d', { willReadFrequently: true });
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if (ctx) {
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ctx.imageSmoothingEnabled = true;
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ctx.imageSmoothingQuality = 'high';
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ctx.drawImage(image, 0, 0, newWidth, newHeight);
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return new Promise((resolve, reject) => {
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const img = new Image();
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img.onload = () => resolve(img);
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img.onerror = (err) => reject(err);
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img.src = canvas.toDataURL();
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});
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}
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throw new Error("Canvas context not available");
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}, 'resizeImage');
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export const createThumbnail = withErrorHandling(async function (image, size = 128) {
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return resizeImage(image, size, size);
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}, 'createThumbnail');
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export const imageToBase64 = withErrorHandling(function (image, format = 'png', quality = 0.9) {
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if (!image) {
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throw createValidationError("Image is required");
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}
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const width = image instanceof HTMLImageElement ? image.naturalWidth : image.width;
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const height = image instanceof HTMLImageElement ? image.naturalHeight : image.height;
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const { canvas, ctx } = createCanvas(width, height, '2d', { willReadFrequently: true });
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if (ctx) {
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ctx.drawImage(image, 0, 0);
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const mimeType = `image/${format}`;
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return canvas.toDataURL(mimeType, quality);
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}
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throw new Error("Canvas context not available");
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}, 'imageToBase64');
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export const base64ToImage = withErrorHandling(function (base64) {
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if (!base64) {
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throw createValidationError("Base64 string is required");
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}
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return new Promise((resolve, reject) => {
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const img = new Image();
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img.onload = () => resolve(img);
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img.onerror = () => reject(new Error("Failed to load image from base64"));
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img.src = base64;
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});
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}, 'base64ToImage');
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export function isValidImage(image) {
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return image &&
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(image instanceof HTMLImageElement || image instanceof HTMLCanvasElement) &&
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image.width > 0 &&
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image.height > 0;
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}
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export function getImageInfo(image) {
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if (!isValidImage(image)) {
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return null;
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}
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const width = image instanceof HTMLImageElement ? image.naturalWidth : image.width;
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const height = image instanceof HTMLImageElement ? image.naturalHeight : image.height;
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return {
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width,
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height,
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aspectRatio: width / height,
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area: width * height
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};
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}
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export function createImageFromSource(source) {
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return new Promise((resolve, reject) => {
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const img = new Image();
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img.onload = () => resolve(img);
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img.onerror = (err) => reject(err);
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img.src = source;
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});
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}
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export const createEmptyImage = withErrorHandling(function (width, height, color = 'transparent') {
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const { canvas, ctx } = createCanvas(width, height, '2d', { willReadFrequently: true });
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if (ctx) {
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if (color !== 'transparent') {
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ctx.fillStyle = color;
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ctx.fillRect(0, 0, width, height);
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}
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return new Promise((resolve, reject) => {
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const img = new Image();
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img.onload = () => resolve(img);
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img.onerror = (err) => reject(err);
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img.src = canvas.toDataURL();
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});
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}
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throw new Error("Canvas context not available");
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}, 'createEmptyImage');
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/**
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* Converts a canvas or image to an Image element
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* Consolidated from MaskProcessingUtils.convertToImage()
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* @param source - Source canvas or image
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* @returns Promise with Image element
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*/
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export async function convertToImage(source) {
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if (source instanceof HTMLImageElement) {
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return source; // Already an image
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}
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const image = new Image();
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image.src = source.toDataURL();
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await new Promise((resolve, reject) => {
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image.onload = () => resolve();
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image.onerror = reject;
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});
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return image;
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}
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/**
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* Creates a mask from image source for use in mask editor
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* Consolidated from mask_utils.create_mask_from_image_src()
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* @param imageSrc - Image source (URL or data URL)
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* @returns Promise returning Image object
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*/
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export function createMaskFromImageSrc(imageSrc) {
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return new Promise((resolve, reject) => {
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const img = new Image();
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img.onload = () => resolve(img);
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img.onerror = (err) => reject(err);
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img.src = imageSrc;
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});
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}
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/**
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* Converts canvas to Image for use as mask
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* Consolidated from mask_utils.canvas_to_mask_image()
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* @param canvas - Canvas to convert
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* @returns Promise returning Image object
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*/
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export function canvasToMaskImage(canvas) {
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return new Promise((resolve, reject) => {
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const img = new Image();
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img.onload = () => resolve(img);
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img.onerror = (err) => reject(err);
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img.src = canvas.toDataURL();
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});
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}
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/**
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* Scales an image to fit within specified bounds while maintaining aspect ratio
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* @param image - Image to scale
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* @param targetWidth - Target width to fit within
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* @param targetHeight - Target height to fit within
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* @returns Promise with scaled Image element
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*/
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export async function scaleImageToFit(image, targetWidth, targetHeight) {
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const scale = Math.min(targetWidth / image.width, targetHeight / image.height);
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const scaledWidth = Math.max(1, Math.round(image.width * scale));
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const scaledHeight = Math.max(1, Math.round(image.height * scale));
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const { canvas, ctx } = createCanvas(scaledWidth, scaledHeight, '2d', { willReadFrequently: true });
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if (!ctx)
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throw new Error("Could not create scaled image context");
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ctx.drawImage(image, 0, 0, scaledWidth, scaledHeight);
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return new Promise((resolve, reject) => {
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const scaledImg = new Image();
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scaledImg.onload = () => resolve(scaledImg);
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scaledImg.onerror = reject;
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scaledImg.src = canvas.toDataURL();
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});
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}
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/**
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* Unified tensor to image data conversion
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* Handles both RGB images and grayscale masks
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* @param tensor - Input tensor data
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* @param mode - 'rgb' for images or 'grayscale' for masks
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* @returns ImageData object
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*/
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export function tensorToImageData(tensor, mode = 'rgb') {
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try {
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const shape = tensor.shape;
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const height = shape[1];
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const width = shape[2];
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const channels = shape[3] || 1; // Default to 1 for masks
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log.debug("Converting tensor:", { shape, channels, mode });
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const imageData = new ImageData(width, height);
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const data = new Uint8ClampedArray(width * height * 4);
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const flatData = tensor.data;
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const pixelCount = width * height;
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const min = tensor.min_val ?? 0;
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const max = tensor.max_val ?? 1;
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const denom = (max - min) || 1;
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for (let i = 0; i < pixelCount; i++) {
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const pixelIndex = i * 4;
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const tensorIndex = i * channels;
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let lum;
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if (mode === 'grayscale' || channels === 1) {
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lum = flatData[tensorIndex];
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}
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else {
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// Compute luminance for RGB
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const r = flatData[tensorIndex + 0] ?? 0;
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const g = flatData[tensorIndex + 1] ?? 0;
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const b = flatData[tensorIndex + 2] ?? 0;
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lum = 0.299 * r + 0.587 * g + 0.114 * b;
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}
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let norm = (lum - min) / denom;
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if (!isFinite(norm))
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norm = 0;
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norm = Math.max(0, Math.min(1, norm));
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const value = Math.round(norm * 255);
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if (mode === 'grayscale') {
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// For masks: RGB = value, A = 255 (MaskTool reads luminance)
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data[pixelIndex] = value;
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data[pixelIndex + 1] = value;
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data[pixelIndex + 2] = value;
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data[pixelIndex + 3] = 255;
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}
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else {
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// For images: RGB from channels, A = 255
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for (let c = 0; c < Math.min(3, channels); c++) {
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const channelValue = flatData[tensorIndex + c];
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const channelNorm = (channelValue - min) / denom;
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data[pixelIndex + c] = Math.round(channelNorm * 255);
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}
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data[pixelIndex + 3] = 255;
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}
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}
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imageData.data.set(data);
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return imageData;
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}
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catch (error) {
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log.error("Error converting tensor:", error);
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return null;
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}
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}
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/**
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* Creates an HTMLImageElement from ImageData
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* @param imageData - Input ImageData
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* @returns Promise with HTMLImageElement
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*/
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export async function createImageFromImageData(imageData) {
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const { canvas, ctx } = createCanvas(imageData.width, imageData.height, '2d', { willReadFrequently: true });
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if (!ctx)
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throw new Error("Could not create canvas context");
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ctx.putImageData(imageData, 0, 0);
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return await createImageFromSource(canvas.toDataURL());
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}
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