feat: 完成 tvai 库图片处理功能 (阶段四)
图片超分辨率处理 - 实现 upscale_image() 单图片超分辨率功能 - 支持所有 16 种 Topaz AI 模型 - 完整的参数验证和模型约束检查 - 多种输出格式支持 (PNG, JPG, TIFF, BMP) - GPU 加速和质量优化 批量图片处理 - 实现 batch_upscale_images() 批量处理功能 - 实现 upscale_directory() 目录批量处理 - 支持递归子目录扫描 - 智能文件格式过滤和识别 - 批量进度跟踪和状态报告 图片格式转换 - 实现 convert_image_format() 格式转换功能 - 实现 batch_convert_images() 批量格式转换 - 支持质量参数控制 - 多种图片格式互转 - 高效的批量处理流水线 图片增强功能 - 实现 resize_image() 传统几何缩放 - 支持宽高比保持选项 - 多种缩放算法支持 - 格式转换集成 - 高质量输出控制 便捷处理函数 - quick_upscale_image() 一键图片放大 - auto_enhance_image() 智能自动增强 - batch_upscale_directory() 批量目录处理 - convert_image() 简单格式转换 - 自动参数选择和优化 智能参数预设 - ImageUpscaleParams::for_photo() 照片增强 - ImageUpscaleParams::for_artwork() 艺术作品 - ImageUpscaleParams::for_screenshot() 截图增强 - ImageUpscaleParams::for_portrait() 人像优化 - 基于图片特征的自动参数选择 文件系统集成 - 智能图片文件发现和过滤 - 支持常见图片格式 (JPG, PNG, TIFF, BMP) - 递归目录遍历功能 - 自动输出文件命名 - 批量操作进度跟踪 完整示例和演示 - 创建 image_processing.rs 综合示例 - 展示所有图片处理场景 - 参数配置和模型选择演示 - 批量处理和格式转换演示 - 便捷函数使用演示 技术特性 - 完整的 Topaz Video AI 集成 - 智能参数验证和错误处理 - 批量处理优化和进度跟踪 - 多格式支持和质量控制 - 异步处理和资源管理 代码质量 - 所有测试通过 (6/6 单元测试 + 1 文档测试) - 完整的错误处理和验证 - 内存安全的资源管理 - 清晰的 API 设计和文档 功能覆盖 - 单图片超分辨率 (16 种模型) - 批量图片处理 (目录/文件列表) - 图片格式转换 (4 种格式) - 传统图片缩放 (几何变换) - 便捷处理函数 (一键操作) - 智能参数预设 (场景优化) 下一步: 开始阶段五 - 便捷接口和优化
This commit is contained in:
imeepos
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cargos/tvai/examples/image_processing.rs
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cargos/tvai/examples/image_processing.rs
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//! Image processing examples demonstrating all image enhancement features
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use std::path::Path;
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use tvai::*;
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#[tokio::main]
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async fn main() -> std::result::Result<(), Box<dyn std::error::Error>> {
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println!("Topaz Video AI Library - Image Processing Examples");
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// Detect Topaz installation
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if let Some(topaz_path) = detect_topaz_installation() {
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println!("Found Topaz Video AI at: {}", topaz_path.display());
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// Create configuration
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let config = TvaiConfig::builder()
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.topaz_path(topaz_path)
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.use_gpu(true)
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.build()?;
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// Create processor
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let mut processor = TvaiProcessor::new(config)?;
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println!("Processor created successfully");
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// Demonstrate image upscaling
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demonstrate_image_upscaling(&mut processor).await?;
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// Demonstrate batch processing
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demonstrate_batch_processing(&mut processor).await?;
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// Demonstrate format conversion
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demonstrate_format_conversion(&mut processor).await?;
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// Demonstrate image enhancement
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demonstrate_image_enhancement(&mut processor).await?;
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// Demonstrate quick functions
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demonstrate_quick_functions().await?;
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println!("All image processing examples completed successfully!");
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} else {
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println!("Topaz Video AI not found. Please install it first.");
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}
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Ok(())
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}
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async fn demonstrate_image_upscaling(processor: &mut TvaiProcessor) -> std::result::Result<(), Box<dyn std::error::Error>> {
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println!("\n=== Image Upscaling Demo ===");
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// Create upscaling parameters for different scenarios
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let scenarios = vec![
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("Photo Enhancement", ImageUpscaleParams::for_photo()),
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("Artwork Upscaling", ImageUpscaleParams::for_artwork()),
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("Screenshot Enhancement", ImageUpscaleParams::for_screenshot()),
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("Portrait Enhancement", ImageUpscaleParams::for_portrait()),
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("Custom Parameters", ImageUpscaleParams {
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scale_factor: 3.0,
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model: UpscaleModel::Ghq5,
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compression: -0.2,
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blend: 0.0,
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output_format: ImageFormat::Tiff,
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}),
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];
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for (name, params) in scenarios {
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println!("Scenario: {}", name);
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println!(" Model: {} ({})", params.model.as_str(), params.model.description());
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println!(" Scale: {}x", params.scale_factor);
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println!(" Compression: {}", params.compression);
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println!(" Blend: {}", params.blend);
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println!(" Output Format: {}", params.output_format.extension().to_uppercase());
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// In a real scenario, you would call:
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// let result = processor.upscale_image(input, output, params, Some(&progress_callback)).await?;
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// println!(" Processing time: {:?}", result.processing_time);
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}
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Ok(())
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}
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async fn demonstrate_batch_processing(processor: &mut TvaiProcessor) -> std::result::Result<(), Box<dyn std::error::Error>> {
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println!("\n=== Batch Processing Demo ===");
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println!("Batch Image Upscaling:");
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println!(" - Process multiple images at once");
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println!(" - Consistent parameters across all images");
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println!(" - Progress tracking for entire batch");
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println!(" - Automatic output filename generation");
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// In a real scenario:
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// let input_paths = vec![Path::new("image1.jpg"), Path::new("image2.png")];
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// let params = ImageUpscaleParams::for_photo();
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// let results = processor.batch_upscale_images(&input_paths, output_dir, params, Some(&progress_callback)).await?;
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// println!(" Processed {} images", results.len());
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println!("\nDirectory Processing:");
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println!(" - Auto-discover images in directory");
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println!(" - Support for recursive subdirectory scanning");
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println!(" - Filter by supported image formats");
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println!(" - Batch process all discovered images");
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// In a real scenario:
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// let results = processor.upscale_directory(input_dir, output_dir, params, true, Some(&progress_callback)).await?;
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// println!(" Processed {} images from directory", results.len());
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Ok(())
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}
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async fn demonstrate_format_conversion(processor: &mut TvaiProcessor) -> std::result::Result<(), Box<dyn std::error::Error>> {
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println!("\n=== Format Conversion Demo ===");
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let formats = vec![
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(ImageFormat::Png, "Lossless compression, transparency support"),
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(ImageFormat::Jpg, "Lossy compression, smaller file size"),
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(ImageFormat::Tiff, "Professional format, lossless compression"),
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(ImageFormat::Bmp, "Uncompressed format, large file size"),
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];
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for (format, description) in formats {
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println!("Format: {} - {}", format.extension().to_uppercase(), description);
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println!(" FFmpeg format: {}", format.ffmpeg_format());
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// In a real scenario:
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// let result = processor.convert_image_format(input, output, format, 95, Some(&progress_callback)).await?;
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// println!(" Conversion time: {:?}", result.processing_time);
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}
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println!("\nBatch Format Conversion:");
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println!(" - Convert multiple images to same format");
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println!(" - Configurable quality settings");
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println!(" - Preserve original filenames");
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// In a real scenario:
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// let input_paths = vec![/* image paths */];
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// let results = processor.batch_convert_images(&input_paths, output_dir, ImageFormat::Png, 95, Some(&progress_callback)).await?;
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Ok(())
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}
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async fn demonstrate_image_enhancement(processor: &mut TvaiProcessor) -> std::result::Result<(), Box<dyn std::error::Error>> {
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println!("\n=== Image Enhancement Demo ===");
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println!("Traditional Resize (Non-AI):");
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println!(" - Fast geometric scaling");
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println!(" - Maintain aspect ratio option");
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println!(" - Multiple output formats");
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println!(" - Good for simple size adjustments");
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// In a real scenario:
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// let result = processor.resize_image(input, output, 1920, 1080, true, ImageFormat::Png, Some(&progress_callback)).await?;
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println!("\nAI-Powered Upscaling:");
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println!(" - Machine learning enhancement");
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println!(" - Detail reconstruction");
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println!(" - Artifact reduction");
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println!(" - Superior quality for enlargement");
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println!("\nModel Comparison:");
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let models = vec![
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(UpscaleModel::Iris3, "Best general purpose model"),
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(UpscaleModel::Nyx3, "Optimized for portraits"),
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(UpscaleModel::Thf4, "Old content restoration"),
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(UpscaleModel::Ghq5, "Game/CG content"),
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(UpscaleModel::Prob4, "Problem content repair"),
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];
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for (model, description) in models {
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println!(" {}: {}", model.as_str(), description);
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if let Some(scale) = model.forces_scale() {
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println!(" (Forces {}x scale)", scale);
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}
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}
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Ok(())
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}
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async fn demonstrate_quick_functions() -> std::result::Result<(), Box<dyn std::error::Error>> {
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println!("\n=== Quick Functions Demo ===");
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println!("Quick Image Upscale:");
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println!(" - One-line image upscaling");
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println!(" - Automatic Topaz detection");
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println!(" - Default high-quality settings");
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println!(" - Usage: quick_upscale_image(input, output, 2.0).await?");
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println!("\nAuto Enhance Image:");
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println!(" - Intelligent enhancement detection");
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println!(" - Automatic parameter selection");
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println!(" - Based on image characteristics");
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println!(" - Usage: auto_enhance_image(input, output).await?");
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println!("\nBatch Directory Processing:");
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println!(" - Process entire directories");
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println!(" - Recursive subdirectory support");
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println!(" - Automatic file discovery");
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println!(" - Usage: batch_upscale_directory(input_dir, output_dir, 2.0, true).await?");
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println!("\nFormat Conversion:");
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println!(" - Simple format conversion");
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println!(" - Quality control");
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println!(" - No AI processing needed");
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println!(" - Usage: convert_image(input, output, ImageFormat::Png, 95).await?");
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// In a real scenario, you would call these functions:
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// let result = quick_upscale_image(Path::new("input.jpg"), Path::new("output.png"), 2.0).await?;
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// let result = auto_enhance_image(Path::new("input.jpg"), Path::new("enhanced.png")).await?;
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// let results = batch_upscale_directory(Path::new("input_dir"), Path::new("output_dir"), 2.0, true).await?;
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// let result = convert_image(Path::new("input.jpg"), Path::new("output.png"), ImageFormat::Png, 95).await?;
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Ok(())
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}
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// Progress callback example
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fn create_progress_callback(operation_name: &str) -> ProgressCallback {
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let name = operation_name.to_string();
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Box::new(move |progress| {
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let percentage = (progress * 100.0) as u32;
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println!("{}: {}%", name, percentage);
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})
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}
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