mirror of
https://github.com/Azornes/Comfyui-LayerForge.git
synced 2026-03-21 20:52:12 -03:00
be4fae2964
Introduces a WebSocket-based mechanism for CanvasNode to send and receive canvas image and mask data in RAM, enabling fast, diskless data transfer between frontend and backend. Adds a new WebSocketManager utility, updates CanvasIO to support RAM output mode, and modifies CanvasView to send canvas data via WebSocket before prompt execution. The backend (canvas_node.py) is updated to handle WebSocket data storage and retrieval, with improved locking and cleanup logic. This change improves workflow speed and reliability by avoiding unnecessary disk I/O and ensuring up-to-date canvas data is available during node execution.
785 lines
33 KiB
JavaScript
785 lines
33 KiB
JavaScript
import {createCanvas} from "./utils/CommonUtils.js";
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import {createModuleLogger} from "./utils/LoggerUtils.js";
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import {webSocketManager} from "./utils/WebSocketManager.js";
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const log = createModuleLogger('CanvasIO');
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export class CanvasIO {
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constructor(canvas) {
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this.canvas = canvas;
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this._saveInProgress = null;
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}
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async saveToServer(fileName, outputMode = 'disk') {
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if (outputMode === 'disk') {
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if (!window.canvasSaveStates) {
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window.canvasSaveStates = new Map();
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}
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const nodeId = this.canvas.node.id;
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const saveKey = `${nodeId}_${fileName}`;
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if (this._saveInProgress || window.canvasSaveStates.get(saveKey)) {
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log.warn(`Save already in progress for node ${nodeId}, waiting...`);
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return this._saveInProgress || window.canvasSaveStates.get(saveKey);
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}
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log.info(`Starting saveToServer (disk) with fileName: ${fileName} for node: ${nodeId}`);
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this._saveInProgress = this._performSave(fileName, outputMode);
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window.canvasSaveStates.set(saveKey, this._saveInProgress);
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try {
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return await this._saveInProgress;
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} finally {
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this._saveInProgress = null;
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window.canvasSaveStates.delete(saveKey);
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log.debug(`Save completed for node ${nodeId}, lock released`);
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}
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} else {
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// For RAM mode, we don't need the lock/state management as it's synchronous
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log.info(`Starting saveToServer (RAM) for node: ${this.canvas.node.id}`);
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return this._performSave(fileName, outputMode);
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}
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}
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async _performSave(fileName, outputMode) {
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if (this.canvas.layers.length === 0) {
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log.warn(`Node ${this.canvas.node.id} has no layers, creating empty canvas`);
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return Promise.resolve(true);
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}
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await this.canvas.saveStateToDB(true);
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const nodeId = this.canvas.node.id;
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const delay = (nodeId % 10) * 50;
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if (delay > 0) {
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await new Promise(resolve => setTimeout(resolve, delay));
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}
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return new Promise((resolve) => {
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const { canvas: tempCanvas, ctx: tempCtx } = createCanvas(this.canvas.width, this.canvas.height);
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const { canvas: maskCanvas, ctx: maskCtx } = createCanvas(this.canvas.width, this.canvas.height);
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tempCtx.fillStyle = '#ffffff';
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tempCtx.fillRect(0, 0, this.canvas.width, this.canvas.height);
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const visibilityCanvas = document.createElement('canvas');
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visibilityCanvas.width = this.canvas.width;
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visibilityCanvas.height = this.canvas.height;
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const visibilityCtx = visibilityCanvas.getContext('2d', { alpha: true });
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maskCtx.fillStyle = '#ffffff';
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maskCtx.fillRect(0, 0, this.canvas.width, this.canvas.height);
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log.debug(`Canvas contexts created, starting layer rendering`);
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const sortedLayers = this.canvas.layers.sort((a, b) => a.zIndex - b.zIndex);
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log.debug(`Processing ${sortedLayers.length} layers in order`);
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sortedLayers.forEach((layer, index) => {
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log.debug(`Processing layer ${index}: zIndex=${layer.zIndex}, size=${layer.width}x${layer.height}, pos=(${layer.x},${layer.y})`);
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log.debug(`Layer ${index}: blendMode=${layer.blendMode || 'normal'}, opacity=${layer.opacity !== undefined ? layer.opacity : 1}`);
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tempCtx.save();
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tempCtx.globalCompositeOperation = layer.blendMode || 'normal';
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tempCtx.globalAlpha = layer.opacity !== undefined ? layer.opacity : 1;
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tempCtx.translate(layer.x + layer.width / 2, layer.y + layer.height / 2);
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tempCtx.rotate(layer.rotation * Math.PI / 180);
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tempCtx.drawImage(layer.image, -layer.width / 2, -layer.height / 2, layer.width, layer.height);
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tempCtx.restore();
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log.debug(`Layer ${index} rendered successfully`);
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visibilityCtx.save();
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visibilityCtx.translate(layer.x + layer.width / 2, layer.y + layer.height / 2);
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visibilityCtx.rotate(layer.rotation * Math.PI / 180);
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visibilityCtx.drawImage(layer.image, -layer.width / 2, -layer.height / 2, layer.width, layer.height);
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visibilityCtx.restore();
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});
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const visibilityData = visibilityCtx.getImageData(0, 0, this.canvas.width, this.canvas.height);
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const maskData = maskCtx.getImageData(0, 0, this.canvas.width, this.canvas.height);
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for (let i = 0; i < visibilityData.data.length; i += 4) {
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const alpha = visibilityData.data[i + 3];
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const maskValue = 255 - alpha;
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maskData.data[i] = maskData.data[i + 1] = maskData.data[i + 2] = maskValue;
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maskData.data[i + 3] = 255;
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}
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maskCtx.putImageData(maskData, 0, 0);
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const toolMaskCanvas = this.canvas.maskTool.getMask();
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if (toolMaskCanvas) {
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// Create a temp canvas for processing the mask
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const tempMaskCanvas = document.createElement('canvas');
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tempMaskCanvas.width = this.canvas.width;
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tempMaskCanvas.height = this.canvas.height;
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const tempMaskCtx = tempMaskCanvas.getContext('2d');
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// Clear the canvas
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tempMaskCtx.clearRect(0, 0, tempMaskCanvas.width, tempMaskCanvas.height);
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// Calculate the correct position to extract the mask
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// The mask's position in world space
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const maskX = this.canvas.maskTool.x;
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const maskY = this.canvas.maskTool.y;
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log.debug(`Extracting mask from world position (${maskX}, ${maskY}) for output area (0,0) to (${this.canvas.width}, ${this.canvas.height})`);
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// Calculate the source rectangle in the mask canvas that corresponds to the output area
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const sourceX = Math.max(0, -maskX); // Where in the mask canvas to start reading
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const sourceY = Math.max(0, -maskY);
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const destX = Math.max(0, maskX); // Where in the output canvas to start writing
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const destY = Math.max(0, maskY);
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// Calculate the dimensions of the area to copy
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const copyWidth = Math.min(
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toolMaskCanvas.width - sourceX, // Available width in source
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this.canvas.width - destX // Available width in destination
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);
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const copyHeight = Math.min(
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toolMaskCanvas.height - sourceY, // Available height in source
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this.canvas.height - destY // Available height in destination
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);
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// Only draw if there's an actual intersection
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if (copyWidth > 0 && copyHeight > 0) {
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log.debug(`Copying mask region: source(${sourceX}, ${sourceY}) to dest(${destX}, ${destY}) size(${copyWidth}, ${copyHeight})`);
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tempMaskCtx.drawImage(
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toolMaskCanvas,
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sourceX, sourceY, copyWidth, copyHeight, // Source rectangle
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destX, destY, copyWidth, copyHeight // Destination rectangle
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);
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}
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// Convert to proper mask format
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const tempMaskData = tempMaskCtx.getImageData(0, 0, this.canvas.width, this.canvas.height);
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for (let i = 0; i < tempMaskData.data.length; i += 4) {
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const alpha = tempMaskData.data[i + 3];
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tempMaskData.data[i] = tempMaskData.data[i + 1] = tempMaskData.data[i + 2] = 255;
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tempMaskData.data[i + 3] = alpha;
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}
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tempMaskCtx.putImageData(tempMaskData, 0, 0);
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// Draw the processed mask to the final mask canvas
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maskCtx.globalCompositeOperation = 'source-over';
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maskCtx.drawImage(tempMaskCanvas, 0, 0);
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}
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if (outputMode === 'ram') {
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const imageData = tempCanvas.toDataURL('image/png');
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const maskData = maskCanvas.toDataURL('image/png');
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log.info("Returning image and mask data as base64 for RAM mode.");
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resolve({ image: imageData, mask: maskData });
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return;
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}
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// --- Disk Mode (original logic) ---
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const fileNameWithoutMask = fileName.replace('.png', '_without_mask.png');
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log.info(`Saving image without mask as: ${fileNameWithoutMask}`);
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tempCanvas.toBlob(async (blobWithoutMask) => {
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log.debug(`Created blob for image without mask, size: ${blobWithoutMask.size} bytes`);
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const formDataWithoutMask = new FormData();
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formDataWithoutMask.append("image", blobWithoutMask, fileNameWithoutMask);
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formDataWithoutMask.append("overwrite", "true");
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try {
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const response = await fetch("/upload/image", {
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method: "POST",
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body: formDataWithoutMask,
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});
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log.debug(`Image without mask upload response: ${response.status}`);
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} catch (error) {
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log.error(`Error uploading image without mask:`, error);
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}
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}, "image/png");
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log.info(`Saving main image as: ${fileName}`);
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tempCanvas.toBlob(async (blob) => {
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log.debug(`Created blob for main image, size: ${blob.size} bytes`);
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const formData = new FormData();
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formData.append("image", blob, fileName);
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formData.append("overwrite", "true");
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try {
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const resp = await fetch("/upload/image", {
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method: "POST",
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body: formData,
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});
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log.debug(`Main image upload response: ${resp.status}`);
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if (resp.status === 200) {
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const maskFileName = fileName.replace('.png', '_mask.png');
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log.info(`Saving mask as: ${maskFileName}`);
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maskCanvas.toBlob(async (maskBlob) => {
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log.debug(`Created blob for mask, size: ${maskBlob.size} bytes`);
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const maskFormData = new FormData();
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maskFormData.append("image", maskBlob, maskFileName);
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maskFormData.append("overwrite", "true");
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try {
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const maskResp = await fetch("/upload/image", {
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method: "POST",
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body: maskFormData,
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});
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log.debug(`Mask upload response: ${maskResp.status}`);
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if (maskResp.status === 200) {
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const data = await resp.json();
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if (this.canvas.widget) {
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this.canvas.widget.value = fileName;
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}
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log.info(`All files saved successfully, widget value set to: ${fileName}`);
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resolve(true);
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} else {
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log.error(`Error saving mask: ${maskResp.status}`);
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resolve(false);
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}
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} catch (error) {
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log.error(`Error saving mask:`, error);
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resolve(false);
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}
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}, "image/png");
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} else {
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log.error(`Main image upload failed: ${resp.status} - ${resp.statusText}`);
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resolve(false);
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}
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} catch (error) {
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log.error(`Error uploading main image:`, error);
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resolve(false);
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}
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}, "image/png");
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});
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}
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async _renderOutputData() {
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return new Promise((resolve) => {
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const { canvas: tempCanvas, ctx: tempCtx } = createCanvas(this.canvas.width, this.canvas.height);
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const { canvas: maskCanvas, ctx: maskCtx } = createCanvas(this.canvas.width, this.canvas.height);
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// This logic is mostly mirrored from _performSave to ensure consistency
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tempCtx.fillStyle = '#ffffff';
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tempCtx.fillRect(0, 0, this.canvas.width, this.canvas.height);
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const visibilityCanvas = document.createElement('canvas');
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visibilityCanvas.width = this.canvas.width;
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visibilityCanvas.height = this.canvas.height;
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const visibilityCtx = visibilityCanvas.getContext('2d', { alpha: true });
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maskCtx.fillStyle = '#ffffff'; // Start with a white mask (nothing masked)
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maskCtx.fillRect(0, 0, this.canvas.width, this.canvas.height);
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const sortedLayers = this.canvas.layers.sort((a, b) => a.zIndex - b.zIndex);
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sortedLayers.forEach((layer) => {
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// Render layer to main canvas
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tempCtx.save();
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tempCtx.globalCompositeOperation = layer.blendMode || 'normal';
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tempCtx.globalAlpha = layer.opacity !== undefined ? layer.opacity : 1;
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tempCtx.translate(layer.x + layer.width / 2, layer.y + layer.height / 2);
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tempCtx.rotate(layer.rotation * Math.PI / 180);
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tempCtx.drawImage(layer.image, -layer.width / 2, -layer.height / 2, layer.width, layer.height);
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tempCtx.restore();
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// Render layer to visibility canvas for the mask
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visibilityCtx.save();
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visibilityCtx.translate(layer.x + layer.width / 2, layer.y + layer.height / 2);
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visibilityCtx.rotate(layer.rotation * Math.PI / 180);
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visibilityCtx.drawImage(layer.image, -layer.width / 2, -layer.height / 2, layer.width, layer.height);
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visibilityCtx.restore();
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});
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// Create layer visibility mask
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const visibilityData = visibilityCtx.getImageData(0, 0, this.canvas.width, this.canvas.height);
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const maskData = maskCtx.getImageData(0, 0, this.canvas.width, this.canvas.height);
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for (let i = 0; i < visibilityData.data.length; i += 4) {
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const alpha = visibilityData.data[i + 3];
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const maskValue = 255 - alpha; // Invert alpha to create the mask
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maskData.data[i] = maskData.data[i + 1] = maskData.data[i + 2] = maskValue;
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maskData.data[i + 3] = 255; // Solid mask
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}
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maskCtx.putImageData(maskData, 0, 0);
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// Composite the tool mask on top
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const toolMaskCanvas = this.canvas.maskTool.getMask();
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if (toolMaskCanvas) {
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// Create a temp canvas for processing the mask
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const tempMaskCanvas = document.createElement('canvas');
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tempMaskCanvas.width = this.canvas.width;
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tempMaskCanvas.height = this.canvas.height;
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const tempMaskCtx = tempMaskCanvas.getContext('2d');
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// Clear the canvas
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tempMaskCtx.clearRect(0, 0, tempMaskCanvas.width, tempMaskCanvas.height);
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// Calculate the correct position to extract the mask
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const maskX = this.canvas.maskTool.x;
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const maskY = this.canvas.maskTool.y;
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log.debug(`[renderOutputData] Extracting mask from world position (${maskX}, ${maskY})`);
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const sourceX = Math.max(0, -maskX);
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const sourceY = Math.max(0, -maskY);
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const destX = Math.max(0, maskX);
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const destY = Math.max(0, maskY);
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const copyWidth = Math.min(toolMaskCanvas.width - sourceX, this.canvas.width - destX);
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const copyHeight = Math.min(toolMaskCanvas.height - sourceY, this.canvas.height - destY);
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if (copyWidth > 0 && copyHeight > 0) {
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tempMaskCtx.drawImage(
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toolMaskCanvas,
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sourceX, sourceY, copyWidth, copyHeight,
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destX, destY, copyWidth, copyHeight
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);
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}
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// Convert the brush mask (white with alpha) to a solid white mask on black background.
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const tempMaskData = tempMaskCtx.getImageData(0, 0, this.canvas.width, this.canvas.height);
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for (let i = 0; i < tempMaskData.data.length; i += 4) {
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const alpha = tempMaskData.data[i + 3];
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// The painted area (alpha > 0) should become white (255).
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tempMaskData.data[i] = tempMaskData.data[i+1] = tempMaskData.data[i+2] = alpha;
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tempMaskData.data[i + 3] = 255; // Solid alpha
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}
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tempMaskCtx.putImageData(tempMaskData, 0, 0);
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// Use 'screen' blending mode. This correctly adds the white brush mask
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// to the existing layer visibility mask. (white + anything = white)
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maskCtx.globalCompositeOperation = 'screen';
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maskCtx.drawImage(tempMaskCanvas, 0, 0);
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}
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const imageDataUrl = tempCanvas.toDataURL('image/png');
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const maskDataUrl = maskCanvas.toDataURL('image/png');
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resolve({ image: imageDataUrl, mask: maskDataUrl });
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});
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}
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async sendDataViaWebSocket(nodeId) {
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log.info(`Preparing to send data for node ${nodeId} via WebSocket.`);
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const { image, mask } = await this._renderOutputData();
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try {
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log.info(`Sending data for node ${nodeId}...`);
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await webSocketManager.sendMessage({
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type: 'canvas_data',
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nodeId: String(nodeId),
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image: image,
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mask: mask,
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}, true); // `true` requires an acknowledgment
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log.info(`Data for node ${nodeId} has been sent and acknowledged by the server.`);
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return true;
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} catch (error) {
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log.error(`Failed to send data for node ${nodeId}:`, error);
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// We can alert the user here or handle it silently.
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// For now, let's throw to make it clear the process failed.
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throw new Error(`Failed to get confirmation from server for node ${nodeId}. The workflow might not have the latest canvas data.`);
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}
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}
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async addInputToCanvas(inputImage, inputMask) {
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try {
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log.debug("Adding input to canvas:", {inputImage});
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const { canvas: tempCanvas, ctx: tempCtx } = createCanvas(inputImage.width, inputImage.height);
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const imgData = new ImageData(
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inputImage.data,
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inputImage.width,
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inputImage.height
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);
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tempCtx.putImageData(imgData, 0, 0);
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const image = new Image();
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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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image.src = tempCanvas.toDataURL();
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});
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const scale = Math.min(
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this.canvas.width / inputImage.width * 0.8,
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this.canvas.height / inputImage.height * 0.8
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);
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const layer = await this.canvas.addLayerWithImage(image, {
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x: (this.canvas.width - inputImage.width * scale) / 2,
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y: (this.canvas.height - inputImage.height * scale) / 2,
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width: inputImage.width * scale,
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height: inputImage.height * scale,
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});
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if (inputMask) {
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layer.mask = inputMask.data;
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}
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log.info("Layer added successfully");
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return true;
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} catch (error) {
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log.error("Error in addInputToCanvas:", error);
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throw error;
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}
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}
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async convertTensorToImage(tensor) {
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try {
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log.debug("Converting tensor to image:", tensor);
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if (!tensor || !tensor.data || !tensor.width || !tensor.height) {
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throw new Error("Invalid tensor data");
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}
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const canvas = document.createElement('canvas');
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const ctx = canvas.getContext('2d');
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canvas.width = tensor.width;
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canvas.height = tensor.height;
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const imageData = new ImageData(
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new Uint8ClampedArray(tensor.data),
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tensor.width,
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tensor.height
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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 = (e) => reject(new Error("Failed to load image: " + e));
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img.src = canvas.toDataURL();
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});
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} catch (error) {
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log.error("Error converting tensor to image:", error);
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throw error;
|
|
}
|
|
}
|
|
|
|
async convertTensorToMask(tensor) {
|
|
if (!tensor || !tensor.data) {
|
|
throw new Error("Invalid mask tensor");
|
|
}
|
|
|
|
try {
|
|
return new Float32Array(tensor.data);
|
|
} catch (error) {
|
|
throw new Error(`Mask conversion failed: ${error.message}`);
|
|
}
|
|
}
|
|
|
|
async initNodeData() {
|
|
try {
|
|
log.info("Starting node data initialization...");
|
|
|
|
if (!this.canvas.node || !this.canvas.node.inputs) {
|
|
log.debug("Node or inputs not ready");
|
|
return this.scheduleDataCheck();
|
|
}
|
|
|
|
if (this.canvas.node.inputs[0] && this.canvas.node.inputs[0].link) {
|
|
const imageLinkId = this.canvas.node.inputs[0].link;
|
|
const imageData = app.nodeOutputs[imageLinkId];
|
|
|
|
if (imageData) {
|
|
log.debug("Found image data:", imageData);
|
|
await this.processImageData(imageData);
|
|
this.canvas.dataInitialized = true;
|
|
} else {
|
|
log.debug("Image data not available yet");
|
|
return this.scheduleDataCheck();
|
|
}
|
|
}
|
|
|
|
if (this.canvas.node.inputs[1] && this.canvas.node.inputs[1].link) {
|
|
const maskLinkId = this.canvas.node.inputs[1].link;
|
|
const maskData = app.nodeOutputs[maskLinkId];
|
|
|
|
if (maskData) {
|
|
log.debug("Found mask data:", maskData);
|
|
await this.processMaskData(maskData);
|
|
}
|
|
}
|
|
|
|
} catch (error) {
|
|
log.error("Error in initNodeData:", error);
|
|
return this.scheduleDataCheck();
|
|
}
|
|
}
|
|
|
|
scheduleDataCheck() {
|
|
if (this.canvas.pendingDataCheck) {
|
|
clearTimeout(this.canvas.pendingDataCheck);
|
|
}
|
|
|
|
this.canvas.pendingDataCheck = setTimeout(() => {
|
|
this.canvas.pendingDataCheck = null;
|
|
if (!this.canvas.dataInitialized) {
|
|
this.initNodeData();
|
|
}
|
|
}, 1000);
|
|
}
|
|
|
|
async processImageData(imageData) {
|
|
try {
|
|
if (!imageData) return;
|
|
|
|
log.debug("Processing image data:", {
|
|
type: typeof imageData,
|
|
isArray: Array.isArray(imageData),
|
|
shape: imageData.shape,
|
|
hasData: !!imageData.data
|
|
});
|
|
|
|
if (Array.isArray(imageData)) {
|
|
imageData = imageData[0];
|
|
}
|
|
|
|
if (!imageData.shape || !imageData.data) {
|
|
throw new Error("Invalid image data format");
|
|
}
|
|
|
|
const originalWidth = imageData.shape[2];
|
|
const originalHeight = imageData.shape[1];
|
|
|
|
const scale = Math.min(
|
|
this.canvas.width / originalWidth * 0.8,
|
|
this.canvas.height / originalHeight * 0.8
|
|
);
|
|
|
|
const convertedData = this.convertTensorToImageData(imageData);
|
|
if (convertedData) {
|
|
const image = await this.createImageFromData(convertedData);
|
|
|
|
this.addScaledLayer(image, scale);
|
|
log.info("Image layer added successfully with scale:", scale);
|
|
}
|
|
} catch (error) {
|
|
log.error("Error processing image data:", error);
|
|
throw error;
|
|
}
|
|
}
|
|
|
|
addScaledLayer(image, scale) {
|
|
try {
|
|
const scaledWidth = image.width * scale;
|
|
const scaledHeight = image.height * scale;
|
|
|
|
const layer = {
|
|
image: image,
|
|
x: (this.canvas.width - scaledWidth) / 2,
|
|
y: (this.canvas.height - scaledHeight) / 2,
|
|
width: scaledWidth,
|
|
height: scaledHeight,
|
|
rotation: 0,
|
|
zIndex: this.canvas.layers.length,
|
|
originalWidth: image.width,
|
|
originalHeight: image.height
|
|
};
|
|
|
|
this.canvas.layers.push(layer);
|
|
this.canvas.selectedLayer = layer;
|
|
this.canvas.render();
|
|
|
|
log.debug("Scaled layer added:", {
|
|
originalSize: `${image.width}x${image.height}`,
|
|
scaledSize: `${scaledWidth}x${scaledHeight}`,
|
|
scale: scale
|
|
});
|
|
} catch (error) {
|
|
log.error("Error adding scaled layer:", error);
|
|
throw error;
|
|
}
|
|
}
|
|
|
|
convertTensorToImageData(tensor) {
|
|
try {
|
|
const shape = tensor.shape;
|
|
const height = shape[1];
|
|
const width = shape[2];
|
|
const channels = shape[3];
|
|
|
|
log.debug("Converting tensor:", {
|
|
shape: shape,
|
|
dataRange: {
|
|
min: tensor.min_val,
|
|
max: tensor.max_val
|
|
}
|
|
});
|
|
|
|
const imageData = new ImageData(width, height);
|
|
const data = new Uint8ClampedArray(width * height * 4);
|
|
|
|
const flatData = tensor.data;
|
|
const pixelCount = width * height;
|
|
|
|
for (let i = 0; i < pixelCount; i++) {
|
|
const pixelIndex = i * 4;
|
|
const tensorIndex = i * channels;
|
|
|
|
for (let c = 0; c < channels; c++) {
|
|
const value = flatData[tensorIndex + c];
|
|
|
|
const normalizedValue = (value - tensor.min_val) / (tensor.max_val - tensor.min_val);
|
|
data[pixelIndex + c] = Math.round(normalizedValue * 255);
|
|
}
|
|
|
|
data[pixelIndex + 3] = 255;
|
|
}
|
|
|
|
imageData.data.set(data);
|
|
return imageData;
|
|
} catch (error) {
|
|
log.error("Error converting tensor:", error);
|
|
return null;
|
|
}
|
|
}
|
|
|
|
async createImageFromData(imageData) {
|
|
return new Promise((resolve, reject) => {
|
|
const canvas = document.createElement('canvas');
|
|
canvas.width = imageData.width;
|
|
canvas.height = imageData.height;
|
|
const ctx = canvas.getContext('2d');
|
|
ctx.putImageData(imageData, 0, 0);
|
|
|
|
const img = new Image();
|
|
img.onload = () => resolve(img);
|
|
img.onerror = reject;
|
|
img.src = canvas.toDataURL();
|
|
});
|
|
}
|
|
|
|
async retryDataLoad(maxRetries = 3, delay = 1000) {
|
|
for (let i = 0; i < maxRetries; i++) {
|
|
try {
|
|
await this.initNodeData();
|
|
return;
|
|
} catch (error) {
|
|
log.warn(`Retry ${i + 1}/${maxRetries} failed:`, error);
|
|
if (i < maxRetries - 1) {
|
|
await new Promise(resolve => setTimeout(resolve, delay));
|
|
}
|
|
}
|
|
}
|
|
log.error("Failed to load data after", maxRetries, "retries");
|
|
}
|
|
|
|
async processMaskData(maskData) {
|
|
try {
|
|
if (!maskData) return;
|
|
|
|
log.debug("Processing mask data:", maskData);
|
|
|
|
if (Array.isArray(maskData)) {
|
|
maskData = maskData[0];
|
|
}
|
|
|
|
if (!maskData.shape || !maskData.data) {
|
|
throw new Error("Invalid mask data format");
|
|
}
|
|
|
|
if (this.canvas.selectedLayer) {
|
|
const maskTensor = await this.convertTensorToMask(maskData);
|
|
this.canvas.selectedLayer.mask = maskTensor;
|
|
this.canvas.render();
|
|
log.info("Mask applied to selected layer");
|
|
}
|
|
} catch (error) {
|
|
log.error("Error processing mask data:", error);
|
|
}
|
|
}
|
|
|
|
async loadImageFromCache(base64Data) {
|
|
return new Promise((resolve, reject) => {
|
|
const img = new Image();
|
|
img.onload = () => resolve(img);
|
|
img.onerror = reject;
|
|
img.src = base64Data;
|
|
});
|
|
}
|
|
|
|
async importImage(cacheData) {
|
|
try {
|
|
log.info("Starting image import with cache data");
|
|
const img = await this.loadImageFromCache(cacheData.image);
|
|
const mask = cacheData.mask ? await this.loadImageFromCache(cacheData.mask) : null;
|
|
|
|
const scale = Math.min(
|
|
this.canvas.width / img.width * 0.8,
|
|
this.canvas.height / img.height * 0.8
|
|
);
|
|
|
|
const tempCanvas = document.createElement('canvas');
|
|
tempCanvas.width = img.width;
|
|
tempCanvas.height = img.height;
|
|
const tempCtx = tempCanvas.getContext('2d');
|
|
|
|
tempCtx.drawImage(img, 0, 0);
|
|
|
|
if (mask) {
|
|
const imageData = tempCtx.getImageData(0, 0, img.width, img.height);
|
|
const maskCanvas = document.createElement('canvas');
|
|
maskCanvas.width = img.width;
|
|
maskCanvas.height = img.height;
|
|
const maskCtx = maskCanvas.getContext('2d');
|
|
maskCtx.drawImage(mask, 0, 0);
|
|
const maskData = maskCtx.getImageData(0, 0, img.width, img.height);
|
|
|
|
for (let i = 0; i < imageData.data.length; i += 4) {
|
|
imageData.data[i + 3] = maskData.data[i];
|
|
}
|
|
|
|
tempCtx.putImageData(imageData, 0, 0);
|
|
}
|
|
|
|
const finalImage = new Image();
|
|
await new Promise((resolve) => {
|
|
finalImage.onload = resolve;
|
|
finalImage.src = tempCanvas.toDataURL();
|
|
});
|
|
|
|
const layer = {
|
|
image: finalImage,
|
|
x: (this.canvas.width - img.width * scale) / 2,
|
|
y: (this.canvas.height - img.height * scale) / 2,
|
|
width: img.width * scale,
|
|
height: img.height * scale,
|
|
rotation: 0,
|
|
zIndex: this.canvas.layers.length
|
|
};
|
|
|
|
this.canvas.layers.push(layer);
|
|
this.canvas.selectedLayer = layer;
|
|
this.canvas.render();
|
|
|
|
} catch (error) {
|
|
log.error('Error importing image:', error);
|
|
}
|
|
}
|
|
|
|
async importLatestImage() {
|
|
try {
|
|
log.info("Fetching latest image from server...");
|
|
const response = await fetch('/ycnode/get_latest_image');
|
|
const result = await response.json();
|
|
|
|
if (result.success && result.image_data) {
|
|
log.info("Latest image received, adding to canvas.");
|
|
const img = new Image();
|
|
await new Promise((resolve, reject) => {
|
|
img.onload = resolve;
|
|
img.onerror = reject;
|
|
img.src = result.image_data;
|
|
});
|
|
|
|
await this.canvas.addLayerWithImage(img, {
|
|
x: 0,
|
|
y: 0,
|
|
width: this.canvas.width,
|
|
height: this.canvas.height,
|
|
});
|
|
log.info("Latest image imported and placed on canvas successfully.");
|
|
return true;
|
|
} else {
|
|
throw new Error(result.error || "Failed to fetch the latest image.");
|
|
}
|
|
} catch (error) {
|
|
log.error("Error importing latest image:", error);
|
|
alert(`Failed to import latest image: ${error.message}`);
|
|
return false;
|
|
}
|
|
}
|
|
}
|