🎨 Gemini Image Editor

import { GoogleGenerativeAI } from '@google/generative-ai'; import { ImageProcessor } from './image-processor'; interface EnhancementSettings { brightness: number; contrast: number; saturation: number; sharpness: number; noiseReduction: number; colorBalance: 'auto' | 'warm' | 'cool' | 'neutral'; } class AIImageEnhancer { private geminiVision: GoogleGenerativeAI; private canvas: HTMLCanvasElement; private ctx: CanvasRenderingContext2D; // Professional enhancement presets for different image types private enhancementPresets = { portrait: { brightness: 1.05, contrast: 1.1, saturation: 1.15, sharpness: 0.8, noiseReduction: 0.6, skinToneOptimization: true }, landscape: { brightness: 1.02, contrast: 1.2, saturation: 1.25, sharpness: 1.1, noiseReduction: 0.4, skyEnhancement: true }, product: { brightness: 1.08, contrast: 1.15, saturation: 1.05, sharpness: 1.3, noiseReduction: 0.8, backgroundClean: true } }; constructor(apiKey: string) { this.geminiVision = new GoogleGenerativeAI(apiKey); this.canvas = document.createElement('canvas'); this.ctx = this.canvas.getContext('2d')!; } async enhanceImageIntelligently(imageFile: File): Promise<string> { """ Apply AI-powered intelligent enhancement to images. Analyzes image content and applies optimal enhancement settings automatically. Args: imageFile: The input image file to enhance Returns: Base64-encoded enhanced image data """ // Step 1: Analyze image content and characteristics const imageAnalysis = await this.analyzeImageContent(imageFile); // Step 2: Determine optimal enhancement strategy const enhancementStrategy = this.determineEnhancementStrategy(imageAnalysis); // Step 3: Apply intelligent enhancements const enhancedImage = await this.applyIntelligentEnhancements( imageFile, enhancementStrategy ); // Step 4: Fine-tune based on content-specific optimizations const finalImage = await this.applyContentSpecificOptimizations( enhancedImage, imageAnalysis ); return finalImage; } private async analyzeImageContent(imageFile: File): Promise<any> { """ Use Gemini Vision to analyze image content and characteristics. Identifies image type, subjects, lighting conditions, and quality issues. """ const model = this.geminiVision.getGenerativeModel({ model: 'gemini-pro-vision' }); const analysisPrompt = """ Analyze this image for professional photo enhancement. Identify: 1. IMAGE TYPE: Portrait, landscape, product, architecture, etc. 2. LIGHTING CONDITIONS: Natural, artificial, mixed, under/overexposed 3. SUBJECTS: People, objects, scenery (describe main elements) 4. QUALITY ISSUES: Noise, blur, color cast, compression artifacts 5. COMPOSITION: Rule of thirds, symmetry, leading lines 6. COLOR PALETTE: Dominant colors and overall tone 7. ENHANCEMENT NEEDS: Specific areas requiring improvement Provide specific technical recommendations for enhancement. """; const imageBase64 = await this.convertToBase64(imageFile); const result = await model.generateContent([ analysisPrompt, { inlineData: { data: imageBase64, mimeType: imageFile.type } } ]); return this.parseImageAnalysis(result.response.text()); } private applyIntelligentEnhancements( imageFile: File, strategy: EnhancementSettings ): Promise<ImageData> { """ Apply enhancement settings using advanced image processing algorithms. Maintains quality while improving visual appeal and technical characteristics. """ return new Promise((resolve) => { const img = new Image(); img.onload = () => { // Set canvas dimensions to match image this.canvas.width = img.width; this.canvas.height = img.height; // Draw original image this.ctx.drawImage(img, 0, 0); // Apply brightness and contrast adjustments this.applyBrightnessContrast(strategy.brightness, strategy.contrast); // Apply saturation enhancement this.applySaturationAdjustment(strategy.saturation); // Apply sharpening filter this.applySharpeningFilter(strategy.sharpness); // Apply noise reduction this.applyNoiseReduction(strategy.noiseReduction); // Apply color balance correction this.applyColorBalance(strategy.colorBalance); const enhancedImageData = this.ctx.getImageData(0, 0, this.canvas.width, this.canvas.height); resolve(enhancedImageData); }; img.src = URL.createObjectURL(imageFile); }); } private applySharpeningFilter(intensity: number): void { """ Apply unsharp mask sharpening filter for enhanced image clarity. Uses convolution matrix to enhance edge definition without artifacts. """ const imageData = this.ctx.getImageData(0, 0, this.canvas.width, this.canvas.height); const data = imageData.data; // Unsharp mask kernel for professional sharpening const sharpenKernel = [ [0, -intensity * 0.25, 0], [-intensity * 0.25, 1 + intensity, -intensity * 0.25], [0, -intensity * 0.25, 0] ]; const outputData = new Uint8ClampedArray(data.length); // Apply convolution filter for (let y = 1; y < this.canvas.height - 1; y++) { for (let x = 1; x < this.canvas.width - 1; x++) { const idx = (y * this.canvas.width + x) * 4; for (let c = 0; c < 3; c++) { // RGB channels let sum = 0; for (let ky = -1; ky <= 1; ky++) { for (let kx = -1; kx <= 1; kx++) { const pixelIdx = ((y + ky) * this.canvas.width + (x + kx)) * 4; sum += data[pixelIdx + c] * sharpenKernel[ky + 1][kx + 1]; } } outputData[idx + c] = Math.max(0, Math.min(255, sum)); } outputData[idx + 3] = data[idx + 3]; // Preserve alpha } } const newImageData = new ImageData(outputData, this.canvas.width, this.canvas.height); this.ctx.putImageData(newImageData, 0, 0); }

class AdvancedImageProcessor { async removeBackgroundIntelligently(imageData: ImageData): Promise<ImageData> { """ Intelligent background removal using edge detection and color analysis. Preserves fine details like hair and maintains natural edge transitions. """ // Step 1: Detect subject boundaries using edge detection const edges = this.detectSubjectEdges(imageData); // Step 2: Analyze color regions to identify background const colorRegions = this.analyzeColorRegions(imageData); // Step 3: Create precision mask for subject isolation const subjectMask = this.createSubjectMask(edges, colorRegions); // Step 4: Apply feathering for natural edge transitions const featheredMask = this.applyEdgeFeathering(subjectMask); // Step 5: Extract subject with transparent background return this.extractSubjectWithMask(imageData, featheredMask); } private detectSubjectEdges(imageData: ImageData): ImageData { """ Apply Sobel edge detection to identify subject boundaries. Enhanced for portrait and object photography edge preservation. """ const { width, height, data } = imageData; const edgeData = new Uint8ClampedArray(data.length); // Sobel operators for edge detection const sobelX = [[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]; const sobelY = [[-1, -2, -1], [0, 0, 0], [1, 2, 1]]; for (let y = 1; y < height - 1; y++) { for (let x = 1; x < width - 1; x++) { let gx = 0, gy = 0; // Apply Sobel operators for (let ky = -1; ky <= 1; ky++) { for (let kx = -1; kx <= 1; kx++) { const idx = ((y + ky) * width + (x + kx)) * 4; const intensity = (data[idx] + data[idx + 1] + data[idx + 2]) / 3; gx += intensity * sobelX[ky + 1][kx + 1]; gy += intensity * sobelY[ky + 1][kx + 1]; } } // Calculate edge magnitude const magnitude = Math.sqrt(gx * gx + gy * gy); const edgeValue = Math.min(255, magnitude); const currentIdx = (y * width + x) * 4; edgeData[currentIdx] = edgeValue; // R edgeData[currentIdx + 1] = edgeValue; // G edgeData[currentIdx + 2] = edgeValue; // B edgeData[currentIdx + 3] = 255; // A } } return new ImageData(edgeData, width, height); } async applyCreativeEffects(imageData: ImageData, effectType: string): Promise<ImageData> { """ Apply creative effects like vintage, HDR, or artistic filters. Uses professional-grade algorithms for high-quality results. """ switch (effectType) { case 'vintage': return this.applyVintageEffect(imageData); case 'hdr': return this.applyHDREffect(imageData); case 'artistic': return this.applyArtisticEffect(imageData); case 'black_white': return this.applyBlackWhiteConversion(imageData); default: return imageData; } } private applyVintageEffect(imageData: ImageData): ImageData { """ Apply authentic vintage film effect with color grading and vignetting. Simulates classic film photography characteristics. """ const { width, height, data } = imageData; const vintageData = new Uint8ClampedArray(data.length); const centerX = width / 2; const centerY = height / 2; const maxDistance = Math.sqrt(centerX * centerX + centerY * centerY); for (let i = 0; i < data.length; i += 4) { const pixelIndex = i / 4; const x = pixelIndex % width; const y = Math.floor(pixelIndex / width); // Original RGB values let r = data[i]; let g = data[i + 1]; let b = data[i + 2]; // Apply sepia tone transformation const sepiaR = (r * 0.393) + (g * 0.769) + (b * 0.189); const sepiaG = (r * 0.349) + (g * 0.686) + (b * 0.168); const sepiaB = (r * 0.272) + (g * 0.534) + (b * 0.131); // Apply vignetting effect const distance = Math.sqrt((x - centerX) ** 2 + (y - centerY) ** 2); const vignette = 1 - (distance / maxDistance) * 0.6; // Combine effects and clamp values vintageData[i] = Math.min(255, sepiaR * vignette); vintageData[i + 1] = Math.min(255, sepiaG * vignette); vintageData[i + 2] = Math.min(255, sepiaB * vignette); vintageData[i + 3] = data[i + 3]; // Preserve alpha } return new ImageData(vintageData, width, height); }