mirror of
https://github.com/Azornes/Comfyui-LayerForge.git
synced 2026-03-21 20:52:12 -03:00
207bacc1f8
Moved convertToImage, createMaskFromImageSrc, and canvasToMaskImage from MaskProcessingUtils and mask_utils to ImageUtils for better modularity and reuse. Updated imports in dependent modules to use the new locations. Removed duplicate implementations from mask_utils and MaskProcessingUtils.
389 lines
12 KiB
TypeScript
389 lines
12 KiB
TypeScript
import {createModuleLogger} from "./LoggerUtils.js";
|
|
import {withErrorHandling, createValidationError} from "../ErrorHandler.js";
|
|
import { createCanvas } from "./CommonUtils.js";
|
|
import type { Tensor, ImageDataPixel } from '../types';
|
|
|
|
const log = createModuleLogger('ImageUtils');
|
|
|
|
export function validateImageData(data: any): boolean {
|
|
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: any): ImageDataPixel {
|
|
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: ImageDataPixel, maskData: Tensor): ImageDataPixel {
|
|
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: any): ImageDataPixel {
|
|
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: HTMLImageElement | HTMLCanvasElement): Promise<Tensor> {
|
|
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: Tensor): Promise<HTMLImageElement> {
|
|
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: HTMLImageElement, maxWidth: number, maxHeight: number): Promise<HTMLImageElement> {
|
|
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: HTMLImageElement, size = 128): Promise<HTMLImageElement> {
|
|
return resizeImage(image, size, size);
|
|
}, 'createThumbnail');
|
|
|
|
export const imageToBase64 = withErrorHandling(function (image: HTMLImageElement | HTMLCanvasElement, format = 'png', quality = 0.9): string {
|
|
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: string): Promise<HTMLImageElement> {
|
|
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: any): image is HTMLImageElement | HTMLCanvasElement {
|
|
return image &&
|
|
(image instanceof HTMLImageElement || image instanceof HTMLCanvasElement) &&
|
|
image.width > 0 &&
|
|
image.height > 0;
|
|
}
|
|
|
|
export function getImageInfo(image: HTMLImageElement | HTMLCanvasElement): {width: number, height: number, aspectRatio: number, area: number} | null {
|
|
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: string): Promise<HTMLImageElement> {
|
|
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: number, height: number, color = 'transparent'): Promise<HTMLImageElement> {
|
|
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');
|
|
|
|
/**
|
|
* Converts a canvas or image to an Image element
|
|
* Consolidated from MaskProcessingUtils.convertToImage()
|
|
* @param source - Source canvas or image
|
|
* @returns Promise with Image element
|
|
*/
|
|
export async function convertToImage(source: HTMLCanvasElement | HTMLImageElement): Promise<HTMLImageElement> {
|
|
if (source instanceof HTMLImageElement) {
|
|
return source; // Already an image
|
|
}
|
|
|
|
const image = new Image();
|
|
image.src = source.toDataURL();
|
|
|
|
await new Promise<void>((resolve, reject) => {
|
|
image.onload = () => resolve();
|
|
image.onerror = reject;
|
|
});
|
|
|
|
return image;
|
|
}
|
|
|
|
/**
|
|
* Creates a mask from image source for use in mask editor
|
|
* Consolidated from mask_utils.create_mask_from_image_src()
|
|
* @param imageSrc - Image source (URL or data URL)
|
|
* @returns Promise returning Image object
|
|
*/
|
|
export function createMaskFromImageSrc(imageSrc: string): Promise<HTMLImageElement> {
|
|
return new Promise((resolve, reject) => {
|
|
const img = new Image();
|
|
img.onload = () => resolve(img);
|
|
img.onerror = (err) => reject(err);
|
|
img.src = imageSrc;
|
|
});
|
|
}
|
|
|
|
/**
|
|
* Converts canvas to Image for use as mask
|
|
* Consolidated from mask_utils.canvas_to_mask_image()
|
|
* @param canvas - Canvas to convert
|
|
* @returns Promise returning Image object
|
|
*/
|
|
export function canvasToMaskImage(canvas: HTMLCanvasElement): Promise<HTMLImageElement> {
|
|
return new Promise((resolve, reject) => {
|
|
const img = new Image();
|
|
img.onload = () => resolve(img);
|
|
img.onerror = (err) => reject(err);
|
|
img.src = canvas.toDataURL();
|
|
});
|
|
}
|