xcc.es / spatial-maker
[READ-ONLY] Mirror of https://github.com/mrgnw/spatial-maker.
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Restructure as publishable crate with CoreML-first depth estimation

Replace candle_onnx with CoreML Swift bridge (default) and ort ONNX
(optional fallback). Port Frame's full API surface: async video pipeline,
multi-format image loader, SBS/TAB/Separate output with MV-HEVC, proper
error types, and model management with correct download URLs.

Feature-gated: coreml (default), onnx (optional), avif/jxl/heic.
Video pipeline now caches the depth model across frames.
Frame can use this as a direct path dep with zero worker.rs changes.

author
Morgan Williams date (Feb 14, 2026, 1:09 PM +0100) commit 3723d79b parent a4d64d0d
+3122 -509
+1417 -61
Cargo.lock
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+62 -5
Cargo.toml
··· 5 5 license = "MIT" 6 6 description = "Convert 2D images and videos to stereoscopic 3D spatial content for Apple Vision Pro using AI depth estimation" 7 7 keywords = ["spatial-video", "3d", "stereoscopic", "apple-vision-pro", "depth-estimation"] 8 + categories = ["multimedia::images", "computer-vision"] 9 + repository = "https://github.com/mrgnw/spatial-maker" 10 + readme = "RUST_README.md" 11 + rust-version = "1.75" 12 + exclude = [ 13 + "spatial-maker-rust/", 14 + "test_*.jpg", 15 + "*.py", 16 + "benchmarking/", 17 + "samples/", 18 + "todos/", 19 + "output/", 20 + "checkpoints/", 21 + "__pycache__/", 22 + ".venv/", 23 + "scripts/", 24 + "research/", 25 + "pyproject.toml", 26 + "uv.lock", 27 + "pipeline.py", 28 + ] 29 + 30 + [lib] 31 + name = "spatial_maker" 32 + path = "src/lib.rs" 33 + 34 + [features] 35 + default = ["coreml"] 36 + coreml = [] 37 + onnx = ["dep:ort"] 38 + 39 + # Native format decoders (optional - falls back to ffmpeg if not enabled) 40 + avif = ["image/avif"] 41 + jxl = ["dep:jxl-oxide"] 42 + heic = ["dep:libheif-rs"] 43 + native-formats = ["avif", "jxl", "heic"] 8 44 9 45 [dependencies] 10 - ort = { version = "2.0.0-rc.11", features = ["coreml", "ndarray", "half", "download-binaries"] } 11 - image = "0.25" 12 - ndarray = "0.17" 13 - anyhow = "1.0" 46 + image = { version = "0.25", default-features = false, features = ["jpeg", "png", "gif", "bmp", "tiff", "webp"] } 47 + ndarray = "0.16" 48 + thiserror = "2" 49 + serde = { version = "1.0", features = ["derive"] } 50 + tokio = { version = "1.0", features = ["full"] } 51 + reqwest = { version = "0.12", features = ["stream"] } 52 + futures-util = "0.3" 14 53 dirs = "5.0" 54 + tracing = "0.1" 55 + 56 + # Optional: ONNX Runtime backend 57 + ort = { version = "2.0.0-rc.11", features = ["half", "download-binaries"], optional = true } 15 58 16 - [build-dependencies] 59 + # Optional native decoders 60 + jxl-oxide = { version = "0.9", optional = true } 61 + libheif-rs = { version = "2.1", optional = true } 17 62 18 63 [dev-dependencies] 19 64 clap = { version = "4", features = ["derive"] } 65 + tempfile = "3.8" 66 + tokio = { version = "1.0", features = ["full", "macros"] } 67 + 68 + [[example]] 69 + name = "photo_coreml" 70 + path = "examples/photo_coreml.rs" 71 + required-features = ["coreml"] 72 + 73 + [[example]] 74 + name = "photo" 75 + path = "examples/photo.rs" 76 + required-features = ["onnx"]
+41 -43
build.rs
··· 2 2 use std::process::Command; 3 3 4 4 fn main() { 5 - // Only build Swift bridge on macOS 6 - #[cfg(target_os = "macos")] 7 - { 8 - println!("cargo:rerun-if-changed=swift-bridge/CoreMLDepth.swift"); 5 + #[cfg(target_os = "macos")] 6 + { 7 + if env::var("CARGO_FEATURE_COREML").is_ok() { 8 + println!("cargo:rerun-if-changed=swift-bridge/CoreMLDepth.swift"); 9 9 10 - let out_dir = env::var("OUT_DIR").unwrap(); 11 - let lib_path = format!("{}/libCoreMLDepth.a", out_dir); 10 + let out_dir = env::var("OUT_DIR").unwrap(); 11 + let lib_path = format!("{}/libCoreMLDepth.a", out_dir); 12 12 13 - // Compile Swift to static library 14 - let status = Command::new("swiftc") 15 - .args(&[ 16 - "-emit-library", 17 - "-static", 18 - "-module-name", 19 - "CoreMLDepth", 20 - "-O", 21 - "swift-bridge/CoreMLDepth.swift", 22 - "-o", 23 - &lib_path, 24 - ]) 25 - .status() 26 - .expect("Failed to compile Swift code. Is Xcode Command Line Tools installed?"); 13 + let status = Command::new("swiftc") 14 + .args(&[ 15 + "-emit-library", 16 + "-static", 17 + "-module-name", 18 + "CoreMLDepth", 19 + "-O", 20 + "swift-bridge/CoreMLDepth.swift", 21 + "-o", 22 + &lib_path, 23 + ]) 24 + .status() 25 + .expect("Failed to compile Swift code. Is Xcode Command Line Tools installed?"); 27 26 28 - if !status.success() { 29 - panic!("swiftc compilation failed"); 30 - } 27 + if !status.success() { 28 + panic!("swiftc compilation failed"); 29 + } 31 30 32 - println!("cargo:rustc-link-search=native={}", out_dir); 33 - println!("cargo:rustc-link-lib=static=CoreMLDepth"); 31 + println!("cargo:rustc-link-search=native={}", out_dir); 32 + println!("cargo:rustc-link-lib=static=CoreMLDepth"); 34 33 35 - // Link Apple frameworks 36 - println!("cargo:rustc-link-lib=framework=CoreML"); 37 - println!("cargo:rustc-link-lib=framework=Foundation"); 38 - println!("cargo:rustc-link-lib=framework=CoreVideo"); 39 - println!("cargo:rustc-link-lib=framework=Accelerate"); 34 + println!("cargo:rustc-link-lib=framework=CoreML"); 35 + println!("cargo:rustc-link-lib=framework=Foundation"); 36 + println!("cargo:rustc-link-lib=framework=CoreVideo"); 37 + println!("cargo:rustc-link-lib=framework=Accelerate"); 40 38 41 - // Link Swift runtime 42 - let sdk_path = String::from_utf8( 43 - Command::new("xcrun") 44 - .args(&["--show-sdk-path"]) 45 - .output() 46 - .expect("Failed to find SDK path") 47 - .stdout, 48 - ) 49 - .unwrap(); 39 + let sdk_path = String::from_utf8( 40 + Command::new("xcrun") 41 + .args(&["--show-sdk-path"]) 42 + .output() 43 + .expect("Failed to find SDK path") 44 + .stdout, 45 + ) 46 + .unwrap(); 50 47 51 - let sdk_path = sdk_path.trim(); 52 - println!("cargo:rustc-link-search=native={}/usr/lib/swift", sdk_path); 53 - println!("cargo:rustc-link-search=native=/usr/lib/swift"); 54 - } 48 + let sdk_path = sdk_path.trim(); 49 + println!("cargo:rustc-link-search=native={}/usr/lib/swift", sdk_path); 50 + println!("cargo:rustc-link-search=native=/usr/lib/swift"); 51 + } 52 + } 55 53 }
+24 -34
examples/photo.rs
··· 1 - use anyhow::Result; 2 1 use clap::Parser; 3 - use spatial_maker::{create_stereo_pair, model, output, DepthEstimator}; 2 + use spatial_maker::{generate_stereo_pair, model, output, OnnxDepthEstimator}; 4 3 5 4 #[derive(Parser)] 6 - #[command(name = "spatial-maker-photo")] 7 - #[command(about = "Convert a 2D photo to side-by-side stereo", long_about = None)] 5 + #[command(name = "spatial-maker-onnx")] 6 + #[command(about = "Convert a 2D photo to side-by-side stereo using ONNX Runtime")] 8 7 struct Args { 9 - /// Input image file 10 - input: String, 8 + input: String, 11 9 12 - /// Output image file 13 - #[arg(short, long)] 14 - output: String, 10 + #[arg(short, long)] 11 + output: String, 15 12 16 - /// Maximum pixel disparity for 3D effect (default: 30) 17 - #[arg(long, default_value = "30.0")] 18 - max_disparity: f32, 13 + #[arg(long, default_value = "30")] 14 + max_disparity: u32, 19 15 20 - /// Model checkpoint name (default: depth_anything_v2_vits.onnx) 21 - #[arg(long, default_value = "depth_anything_v2_vits.onnx")] 22 - model: String, 16 + #[arg(long, default_value = "s")] 17 + encoder_size: String, 23 18 } 24 19 25 - fn main() -> Result<()> { 26 - let args = Args::parse(); 20 + fn main() -> Result<(), Box<dyn std::error::Error>> { 21 + let args = Args::parse(); 27 22 28 - println!("Loading image: {}", args.input); 29 - let input_image = image::open(&args.input)?; 23 + let input_image = image::open(&args.input)?; 30 24 31 - println!("Finding model checkpoint: {}", args.model); 32 - let model_path = model::find_checkpoint(&args.model)?; 33 - println!("Using model: {}", model_path.display()); 25 + let model_path = model::find_model(&args.encoder_size)?; 34 26 35 - println!("Initializing depth estimator..."); 36 - let mut estimator = DepthEstimator::new(model_path.to_str().unwrap())?; 27 + let estimator = OnnxDepthEstimator::new(model_path.to_str().unwrap())?; 37 28 38 - println!("Estimating depth..."); 39 - let depth_map = estimator.estimate(&input_image)?; 29 + let start = std::time::Instant::now(); 30 + let depth_map = estimator.estimate(&input_image)?; 31 + let depth_time = start.elapsed(); 32 + eprintln!("Depth estimation: {:?}", depth_time); 40 33 41 - println!("Creating stereo pair..."); 42 - let (left, right) = create_stereo_pair(&input_image, &depth_map, args.max_disparity)?; 34 + let (left, right) = generate_stereo_pair(&input_image, &depth_map, args.max_disparity)?; 43 35 44 - println!("Creating SBS image..."); 45 - let sbs = output::create_sbs_image(left, right); 36 + let sbs = output::create_sbs_image(&left, &right); 37 + sbs.save(&args.output)?; 46 38 47 - println!("Saving to: {}", args.output); 48 - sbs.save(&args.output)?; 39 + eprintln!("Total time: {:?}", start.elapsed()); 49 40 50 - println!("Done!"); 51 - Ok(()) 41 + Ok(()) 52 42 }
+26 -41
examples/photo_coreml.rs
··· 1 - use anyhow::Result; 2 1 use clap::Parser; 3 - use spatial_maker::{create_stereo_pair, model, output, CoreMLDepthEstimator}; 2 + use spatial_maker::{generate_stereo_pair, model, output, CoreMLDepthEstimator}; 4 3 5 4 #[derive(Parser)] 6 5 #[command(name = "spatial-maker-coreml")] 7 - #[command(about = "Convert a 2D photo to side-by-side stereo using CoreML (macOS only)", long_about = None)] 6 + #[command(about = "Convert a 2D photo to side-by-side stereo using CoreML (macOS only)")] 8 7 struct Args { 9 - /// Input image file 10 - input: String, 8 + input: String, 11 9 12 - /// Output image file 13 - #[arg(short, long)] 14 - output: String, 10 + #[arg(short, long)] 11 + output: String, 15 12 16 - /// Maximum pixel disparity for 3D effect (default: 30) 17 - #[arg(long, default_value = "30.0")] 18 - max_disparity: f32, 13 + #[arg(long, default_value = "30")] 14 + max_disparity: u32, 19 15 20 - /// Model checkpoint name (default: DepthAnythingV2BaseF16.mlpackage) 21 - #[arg(long, default_value = "DepthAnythingV2BaseF16.mlpackage")] 22 - model: String, 16 + #[arg(long, default_value = "DepthAnythingV2BaseF16.mlpackage")] 17 + model: String, 23 18 } 24 19 25 - fn main() -> Result<()> { 26 - let args = Args::parse(); 20 + fn main() -> Result<(), Box<dyn std::error::Error>> { 21 + let args = Args::parse(); 27 22 28 - println!("🍎 Using CoreML backend (Apple Neural Engine + GPU + CPU)"); 29 - println!("Loading image: {}", args.input); 30 - let input_image = image::open(&args.input)?; 23 + let input_image = image::open(&args.input)?; 31 24 32 - println!("Finding model checkpoint: {}", args.model); 33 - let model_path = model::find_checkpoint(&args.model)?; 34 - println!("Using model: {}", model_path.display()); 25 + let model_path = model::find_model("b").unwrap_or_else(|_| { 26 + let checkpoint_dir = model::get_checkpoint_dir().expect("Could not determine checkpoint dir"); 27 + checkpoint_dir.join(&args.model) 28 + }); 35 29 36 - println!("Initializing CoreML depth estimator..."); 37 - let estimator = CoreMLDepthEstimator::new(model_path.to_str().unwrap())?; 30 + let estimator = CoreMLDepthEstimator::new(model_path.to_str().unwrap())?; 38 31 39 - println!("Estimating depth (this should be ~60-90ms on M4 Pro)..."); 40 - let start = std::time::Instant::now(); 41 - let depth_map = estimator.estimate(&input_image)?; 42 - let depth_time = start.elapsed(); 43 - println!("✓ Depth estimation: {:?}", depth_time); 32 + let start = std::time::Instant::now(); 33 + let depth_map = estimator.estimate(&input_image)?; 34 + let depth_time = start.elapsed(); 35 + eprintln!("Depth estimation: {:?}", depth_time); 44 36 45 - println!("Creating stereo pair..."); 46 - let (left, right) = create_stereo_pair(&input_image, &depth_map, args.max_disparity)?; 37 + let (left, right) = generate_stereo_pair(&input_image, &depth_map, args.max_disparity)?; 47 38 48 - println!("Creating SBS image..."); 49 - let sbs = output::create_sbs_image(left, right); 50 - 51 - println!("Saving to: {}", args.output); 52 - sbs.save(&args.output)?; 39 + let sbs = output::create_sbs_image(&left, &right); 40 + sbs.save(&args.output)?; 53 41 54 - let total_time = start.elapsed(); 55 - println!("\n✅ Done!"); 56 - println!("Total time: {:?}", total_time); 57 - println!("Depth time: {:?}", depth_time); 42 + eprintln!("Total time: {:?}", start.elapsed()); 58 43 59 - Ok(()) 44 + Ok(()) 60 45 }
+75 -72
src/depth.rs
··· 1 - use anyhow::{Context, Result}; 2 - use image::{DynamicImage, ImageBuffer, Luma}; 3 - use ndarray::{Array, Array4, Axis}; 4 - use ort::{ 5 - session::{builder::GraphOptimizationLevel, Session}, 6 - value::Value, 7 - }; 1 + #[cfg(feature = "onnx")] 2 + use crate::error::{SpatialError, SpatialResult}; 3 + #[cfg(feature = "onnx")] 4 + use image::DynamicImage; 5 + #[cfg(feature = "onnx")] 6 + use ndarray::Array2; 7 + #[cfg(feature = "onnx")] 8 + use ort::session::{builder::GraphOptimizationLevel, Session}; 8 9 10 + #[cfg(feature = "onnx")] 9 11 const INPUT_SIZE: u32 = 518; 12 + #[cfg(feature = "onnx")] 10 13 const IMAGENET_MEAN: [f32; 3] = [0.485, 0.456, 0.406]; 14 + #[cfg(feature = "onnx")] 11 15 const IMAGENET_STD: [f32; 3] = [0.229, 0.224, 0.225]; 12 16 13 - pub struct DepthEstimator { 14 - session: Session, 17 + #[cfg(feature = "onnx")] 18 + pub struct OnnxDepthEstimator { 19 + session: Session, 15 20 } 16 21 17 - impl DepthEstimator { 18 - pub fn new(model_path: &str) -> Result<Self> { 19 - // Note: CoreML EP tested but is slower than CPU on M4 Pro (9.5s vs 566ms) 20 - // Likely due to compilation overhead or unsupported ops. CPU is fast enough. 21 - let session = Session::builder()? 22 - .with_optimization_level(GraphOptimizationLevel::Level3)? 23 - .with_intra_threads(4)? 24 - .commit_from_file(model_path) 25 - .context("Failed to load ONNX model")?; 22 + #[cfg(feature = "onnx")] 23 + impl OnnxDepthEstimator { 24 + pub fn new(model_path: &str) -> SpatialResult<Self> { 25 + let session = Session::builder() 26 + .map_err(|e| SpatialError::ModelError(format!("Failed to create session: {}", e)))? 27 + .with_optimization_level(GraphOptimizationLevel::Level3) 28 + .map_err(|e| SpatialError::ModelError(format!("Failed to set opt level: {}", e)))? 29 + .with_intra_threads(4) 30 + .map_err(|e| SpatialError::ModelError(format!("Failed to set threads: {}", e)))? 31 + .commit_from_file(model_path) 32 + .map_err(|e| SpatialError::ModelError(format!("Failed to load ONNX model: {}", e)))?; 26 33 27 - Ok(Self { session }) 28 - } 34 + Ok(Self { session }) 35 + } 36 + 37 + pub fn estimate(&mut self, image: &DynamicImage) -> SpatialResult<Array2<f32>> { 38 + let (orig_width, orig_height) = (image.width(), image.height()); 39 + let size = INPUT_SIZE as usize; 29 40 30 - /// Estimate depth from an image, returning a normalized depth map 31 - pub fn estimate(&mut self, image: &DynamicImage) -> Result<ImageBuffer<Luma<f32>, Vec<f32>>> { 32 - let (orig_width, orig_height) = (image.width(), image.height()); 41 + let resized = image.resize_exact( 42 + INPUT_SIZE, 43 + INPUT_SIZE, 44 + image::imageops::FilterType::Lanczos3, 45 + ); 33 46 34 - // Preprocess: resize to 518x518 35 - let resized = image.resize_exact( 36 - INPUT_SIZE, 37 - INPUT_SIZE, 38 - image::imageops::FilterType::Lanczos3, 39 - ); 47 + let rgb = resized.to_rgb8(); 48 + let mut input_data = vec![0.0f32; 1 * 3 * size * size]; 40 49 41 - // Convert to RGB and create NCHW tensor 42 - let rgb = resized.to_rgb8(); 43 - let mut input_tensor = 44 - Array4::<f32>::zeros((1, 3, INPUT_SIZE as usize, INPUT_SIZE as usize)); 50 + for (i, pixel) in rgb.pixels().enumerate() { 51 + for c in 0..3 { 52 + let normalized = (pixel[c] as f32 / 255.0 - IMAGENET_MEAN[c]) / IMAGENET_STD[c]; 53 + input_data[c * size * size + i] = normalized; 54 + } 55 + } 45 56 46 - for (y, row) in rgb.enumerate_rows() { 47 - for (x, _, pixel) in row { 48 - for c in 0..3 { 49 - let normalized = (pixel[c] as f32 / 255.0 - IMAGENET_MEAN[c]) / IMAGENET_STD[c]; 50 - input_tensor[[0, c, y as usize, x as usize]] = normalized; 51 - } 52 - } 53 - } 57 + let input_value = ort::value::Value::from_array(([1usize, 3, size, size], input_data)) 58 + .map_err(|e| SpatialError::TensorError(format!("Failed to create input: {}", e)))?; 54 59 55 - // Run inference 56 - let input_value = Value::from_array(input_tensor)?; 57 - let outputs = self.session.run(ort::inputs![input_value])?; 60 + let outputs = self.session.run(ort::inputs![input_value]) 61 + .map_err(|e| SpatialError::ModelError(format!("Inference failed: {}", e)))?; 58 62 59 - // Extract the depth output tensor - returns (shape, data) 60 - let (shape, data) = outputs[0].try_extract_tensor::<f32>()?; 61 - let dims: Vec<usize> = shape.iter().map(|&d| d as usize).collect(); 63 + let (shape, data) = outputs[0].try_extract_tensor::<f32>() 64 + .map_err(|e| SpatialError::TensorError(format!("Failed to extract output: {}", e)))?; 62 65 63 - // Reconstruct as ndarray - shape should be [1, H, W] 64 - let depth_3d = Array::from_shape_vec((dims[0], dims[1], dims[2]), data.to_vec())?; 66 + let dims: Vec<usize> = shape.iter().map(|&d| d as usize).collect(); 67 + let h = dims[1]; 68 + let w = dims[2]; 65 69 66 - // Extract to [H, W] 67 - let depth_2d = depth_3d.index_axis(Axis(0), 0).to_owned(); 70 + let depth_data: Vec<f32> = data.to_vec(); 68 71 69 - // Normalize to 0-1 range 70 - let min_val = depth_2d.iter().copied().fold(f32::INFINITY, f32::min); 71 - let max_val = depth_2d.iter().copied().fold(f32::NEG_INFINITY, f32::max); 72 - let range = max_val - min_val; 72 + let min_val = depth_data.iter().copied().fold(f32::INFINITY, f32::min); 73 + let max_val = depth_data.iter().copied().fold(f32::NEG_INFINITY, f32::max); 74 + let range = max_val - min_val; 73 75 74 - let normalized = if range > 1e-6 { 75 - depth_2d.mapv(|v| (v - min_val) / range) 76 - } else { 77 - Array::zeros(depth_2d.dim()) 78 - }; 76 + let normalized: Vec<f32> = if range > 1e-6 { 77 + depth_data.iter().map(|&v| (v - min_val) / range).collect() 78 + } else { 79 + vec![0.5; depth_data.len()] 80 + }; 79 81 80 - // Resize back to original dimensions 81 - let depth_image = ImageBuffer::from_fn(INPUT_SIZE, INPUT_SIZE, |x, y| { 82 - Luma([normalized[[y as usize, x as usize]]]) 83 - }); 82 + let depth_image = image::ImageBuffer::from_fn(w as u32, h as u32, |x, y| { 83 + image::Luma([normalized[y as usize * w + x as usize]]) 84 + }); 84 85 85 - let resized_depth = image::imageops::resize( 86 - &depth_image, 87 - orig_width, 88 - orig_height, 89 - image::imageops::FilterType::Lanczos3, 90 - ); 86 + let resized_depth = image::imageops::resize( 87 + &depth_image, 88 + orig_width, 89 + orig_height, 90 + image::imageops::FilterType::Lanczos3, 91 + ); 91 92 92 - Ok(resized_depth) 93 - } 93 + let data: Vec<f32> = resized_depth.pixels().map(|p| p[0]).collect(); 94 + Array2::from_shape_vec((orig_height as usize, orig_width as usize), data) 95 + .map_err(|e| SpatialError::TensorError(format!("Failed to reshape depth: {}", e))) 96 + } 94 97 }
+94 -88
src/depth_coreml.rs
··· 1 - use anyhow::Result; 1 + use crate::error::{SpatialError, SpatialResult}; 2 2 use image::{DynamicImage, ImageBuffer, Luma}; 3 + use ndarray::Array2; 3 4 use std::ffi::CString; 4 5 5 6 const INPUT_SIZE: u32 = 518; 6 7 const IMAGENET_MEAN: [f32; 3] = [0.485, 0.456, 0.406]; 7 8 const IMAGENET_STD: [f32; 3] = [0.229, 0.224, 0.225]; 8 9 9 - // FFI declarations for Swift CoreML bridge 10 10 extern "C" { 11 - fn coreml_load_model(path: *const std::os::raw::c_char) -> *mut std::os::raw::c_void; 12 - fn coreml_unload_model(model: *mut std::os::raw::c_void); 13 - fn coreml_infer_depth( 14 - model: *mut std::os::raw::c_void, 15 - rgb_data: *const f32, 16 - width: i32, 17 - height: i32, 18 - output: *mut f32, 19 - ) -> i32; 11 + fn coreml_load_model(path: *const std::os::raw::c_char) -> *mut std::os::raw::c_void; 12 + fn coreml_unload_model(model: *mut std::os::raw::c_void); 13 + fn coreml_infer_depth( 14 + model: *mut std::os::raw::c_void, 15 + rgb_data: *const f32, 16 + width: i32, 17 + height: i32, 18 + output: *mut f32, 19 + ) -> i32; 20 20 } 21 21 22 22 pub struct CoreMLDepthEstimator { 23 - model: *mut std::os::raw::c_void, 23 + model: *mut std::os::raw::c_void, 24 24 } 25 25 26 26 impl CoreMLDepthEstimator { 27 - pub fn new(model_path: &str) -> Result<Self> { 28 - let c_path = CString::new(model_path)?; 27 + pub fn new(model_path: &str) -> SpatialResult<Self> { 28 + let c_path = CString::new(model_path) 29 + .map_err(|e| SpatialError::ModelError(format!("Invalid model path: {}", e)))?; 29 30 30 - let model = unsafe { coreml_load_model(c_path.as_ptr()) }; 31 + let model = unsafe { coreml_load_model(c_path.as_ptr()) }; 31 32 32 - if model.is_null() { 33 - anyhow::bail!("Failed to load CoreML model: {}", model_path); 34 - } 33 + if model.is_null() { 34 + return Err(SpatialError::ModelError(format!( 35 + "Failed to load CoreML model: {}", 36 + model_path 37 + ))); 38 + } 35 39 36 - println!("✓ CoreML model loaded: {}", model_path); 40 + tracing::info!("CoreML model loaded: {}", model_path); 37 41 38 - Ok(Self { model }) 39 - } 42 + Ok(Self { model }) 43 + } 40 44 41 - /// Estimate depth from an image, returning a normalized depth map 42 - pub fn estimate(&self, image: &DynamicImage) -> Result<ImageBuffer<Luma<f32>, Vec<f32>>> { 43 - let (orig_width, orig_height) = (image.width(), image.height()); 45 + pub fn estimate_raw(&self, image: &DynamicImage) -> SpatialResult<ImageBuffer<Luma<f32>, Vec<f32>>> { 46 + let (orig_width, orig_height) = (image.width(), image.height()); 44 47 45 - // Preprocess: resize to 518x518 46 - let resized = image.resize_exact( 47 - INPUT_SIZE, 48 - INPUT_SIZE, 49 - image::imageops::FilterType::Lanczos3, 50 - ); 48 + let resized = image.resize_exact( 49 + INPUT_SIZE, 50 + INPUT_SIZE, 51 + image::imageops::FilterType::Lanczos3, 52 + ); 51 53 52 - // Convert to RGB and normalize to float32 NCHW layout 53 - let rgb = resized.to_rgb8(); 54 - let mut input_data = Vec::with_capacity(3 * INPUT_SIZE as usize * INPUT_SIZE as usize); 54 + let rgb = resized.to_rgb8(); 55 + let mut input_data = Vec::with_capacity(3 * INPUT_SIZE as usize * INPUT_SIZE as usize); 55 56 56 - // NCHW layout: all R channel, then G, then B 57 - for c in 0..3 { 58 - for pixel in rgb.pixels() { 59 - let normalized = (pixel[c] as f32 / 255.0 - IMAGENET_MEAN[c]) / IMAGENET_STD[c]; 60 - input_data.push(normalized); 61 - } 62 - } 57 + for c in 0..3 { 58 + for pixel in rgb.pixels() { 59 + let normalized = (pixel[c] as f32 / 255.0 - IMAGENET_MEAN[c]) / IMAGENET_STD[c]; 60 + input_data.push(normalized); 61 + } 62 + } 63 63 64 - // Allocate output buffer 65 - let output_size = (INPUT_SIZE * INPUT_SIZE) as usize; 66 - let mut output_data = vec![0.0f32; output_size]; 64 + let output_size = (INPUT_SIZE * INPUT_SIZE) as usize; 65 + let mut output_data = vec![0.0f32; output_size]; 67 66 68 - // Run inference via Swift CoreML 69 - let result = unsafe { 70 - coreml_infer_depth( 71 - self.model, 72 - input_data.as_ptr(), 73 - INPUT_SIZE as i32, 74 - INPUT_SIZE as i32, 75 - output_data.as_mut_ptr(), 76 - ) 77 - }; 67 + let result = unsafe { 68 + coreml_infer_depth( 69 + self.model, 70 + input_data.as_ptr(), 71 + INPUT_SIZE as i32, 72 + INPUT_SIZE as i32, 73 + output_data.as_mut_ptr(), 74 + ) 75 + }; 78 76 79 - if result != 0 { 80 - anyhow::bail!("CoreML inference failed with error code: {}", result); 81 - } 77 + if result != 0 { 78 + return Err(SpatialError::ModelError(format!( 79 + "CoreML inference failed with error code: {}", 80 + result 81 + ))); 82 + } 82 83 83 - // Normalize depth to 0-1 range 84 - let min_val = output_data.iter().copied().fold(f32::INFINITY, f32::min); 85 - let max_val = output_data 86 - .iter() 87 - .copied() 88 - .fold(f32::NEG_INFINITY, f32::max); 89 - let range = max_val - min_val; 84 + let min_val = output_data.iter().copied().fold(f32::INFINITY, f32::min); 85 + let max_val = output_data 86 + .iter() 87 + .copied() 88 + .fold(f32::NEG_INFINITY, f32::max); 89 + let range = max_val - min_val; 90 + 91 + if range > 1e-6 { 92 + for v in &mut output_data { 93 + *v = (*v - min_val) / range; 94 + } 95 + } 90 96 91 - if range > 1e-6 { 92 - for v in &mut output_data { 93 - *v = (*v - min_val) / range; 94 - } 95 - } 97 + let depth_image = ImageBuffer::from_fn(INPUT_SIZE, INPUT_SIZE, |x, y| { 98 + let idx = (y * INPUT_SIZE + x) as usize; 99 + Luma([output_data[idx]]) 100 + }); 96 101 97 - // Create depth image 98 - let depth_image = ImageBuffer::from_fn(INPUT_SIZE, INPUT_SIZE, |x, y| { 99 - let idx = (y * INPUT_SIZE + x) as usize; 100 - Luma([output_data[idx]]) 101 - }); 102 + let resized_depth = image::imageops::resize( 103 + &depth_image, 104 + orig_width, 105 + orig_height, 106 + image::imageops::FilterType::Lanczos3, 107 + ); 102 108 103 - // Resize back to original dimensions 104 - let resized_depth = image::imageops::resize( 105 - &depth_image, 106 - orig_width, 107 - orig_height, 108 - image::imageops::FilterType::Lanczos3, 109 - ); 109 + Ok(resized_depth) 110 + } 110 111 111 - Ok(resized_depth) 112 - } 112 + pub fn estimate(&self, image: &DynamicImage) -> SpatialResult<Array2<f32>> { 113 + let depth_image = self.estimate_raw(image)?; 114 + let (width, height) = depth_image.dimensions(); 115 + let data: Vec<f32> = depth_image.pixels().map(|p| p[0]).collect(); 116 + let depth_2d = Array2::from_shape_vec((height as usize, width as usize), data) 117 + .map_err(|e| SpatialError::TensorError(format!("Failed to reshape depth: {}", e)))?; 118 + Ok(depth_2d) 119 + } 113 120 } 114 121 115 122 impl Drop for CoreMLDepthEstimator { 116 - fn drop(&mut self) { 117 - unsafe { 118 - coreml_unload_model(self.model); 119 - } 120 - } 123 + fn drop(&mut self) { 124 + unsafe { 125 + coreml_unload_model(self.model); 126 + } 127 + } 121 128 } 122 129 123 - // Make it Send + Sync for multithreading (CoreML models are thread-safe) 124 130 unsafe impl Send for CoreMLDepthEstimator {} 125 131 unsafe impl Sync for CoreMLDepthEstimator {}
+40
src/error.rs
··· 1 + use std::fmt; 2 + 3 + pub type SpatialResult<T> = Result<T, SpatialError>; 4 + 5 + #[derive(Debug)] 6 + pub enum SpatialError { 7 + ModelError(String), 8 + ImageError(String), 9 + TensorError(String), 10 + IoError(String), 11 + ConfigError(String), 12 + Other(String), 13 + } 14 + 15 + impl fmt::Display for SpatialError { 16 + fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result { 17 + match self { 18 + SpatialError::ModelError(msg) => write!(f, "Model error: {}", msg), 19 + SpatialError::ImageError(msg) => write!(f, "Image error: {}", msg), 20 + SpatialError::TensorError(msg) => write!(f, "Tensor error: {}", msg), 21 + SpatialError::IoError(msg) => write!(f, "I/O error: {}", msg), 22 + SpatialError::ConfigError(msg) => write!(f, "Configuration error: {}", msg), 23 + SpatialError::Other(msg) => write!(f, "Error: {}", msg), 24 + } 25 + } 26 + } 27 + 28 + impl std::error::Error for SpatialError {} 29 + 30 + impl From<std::io::Error> for SpatialError { 31 + fn from(e: std::io::Error) -> Self { 32 + SpatialError::IoError(e.to_string()) 33 + } 34 + } 35 + 36 + impl From<image::ImageError> for SpatialError { 37 + fn from(e: image::ImageError) -> Self { 38 + SpatialError::ImageError(e.to_string()) 39 + } 40 + }
+230
src/image_loader.rs
··· 1 + use crate::error::{SpatialError, SpatialResult}; 2 + use image::DynamicImage; 3 + use std::path::Path; 4 + use std::process::Command; 5 + 6 + pub async fn load_image(path: impl AsRef<Path>) -> SpatialResult<DynamicImage> { 7 + let path = path.as_ref(); 8 + 9 + if !path.exists() { 10 + return Err(SpatialError::ImageError(format!( 11 + "Image file not found: {:?}", 12 + path 13 + ))); 14 + } 15 + 16 + let extension = path 17 + .extension() 18 + .and_then(|ext| ext.to_str()) 19 + .map(|s| s.to_lowercase()) 20 + .ok_or_else(|| SpatialError::ImageError(format!("File has no extension: {:?}", path)))?; 21 + 22 + match extension.as_str() { 23 + "avif" => load_avif(path).await, 24 + "jxl" => load_jxl(path).await, 25 + "heic" | "heif" => load_heic(path).await, 26 + "jpg" | "jpeg" | "png" | "gif" | "bmp" | "tiff" | "tif" | "webp" => load_standard(path), 27 + _ => Err(SpatialError::ImageError(format!( 28 + "Unsupported image format: .{}", 29 + extension 30 + ))), 31 + } 32 + } 33 + 34 + fn load_standard(path: impl AsRef<Path>) -> SpatialResult<DynamicImage> { 35 + let path = path.as_ref(); 36 + let img = image::open(path) 37 + .map_err(|e| SpatialError::ImageError(format!("Failed to load image {:?}: {}", path, e)))?; 38 + Ok(img) 39 + } 40 + 41 + async fn load_avif(path: &Path) -> SpatialResult<DynamicImage> { 42 + #[cfg(feature = "avif")] 43 + { 44 + match image::open(path) { 45 + Ok(img) => return Ok(img), 46 + Err(e) => { 47 + tracing::warn!("Native AVIF decoder failed: {}, falling back to ffmpeg", e); 48 + } 49 + } 50 + } 51 + load_with_ffmpeg(path, "avif").await 52 + } 53 + 54 + async fn load_jxl(path: &Path) -> SpatialResult<DynamicImage> { 55 + #[cfg(feature = "jxl")] 56 + { 57 + match load_jxl_native(path) { 58 + Ok(img) => return Ok(img), 59 + Err(e) => { 60 + tracing::warn!("Native JXL decoder failed: {}, falling back to ffmpeg", e); 61 + } 62 + } 63 + } 64 + load_with_ffmpeg(path, "jxl").await 65 + } 66 + 67 + async fn load_heic(path: &Path) -> SpatialResult<DynamicImage> { 68 + #[cfg(feature = "heic")] 69 + { 70 + match load_heic_native(path) { 71 + Ok(img) => return Ok(img), 72 + Err(e) => { 73 + tracing::warn!("Native HEIC decoder failed: {}, falling back to ffmpeg", e); 74 + } 75 + } 76 + } 77 + load_with_ffmpeg(path, "heic").await 78 + } 79 + 80 + #[cfg(feature = "jxl")] 81 + fn load_jxl_native(path: &Path) -> SpatialResult<DynamicImage> { 82 + use jxl_oxide::JxlImage; 83 + 84 + let data = std::fs::read(path) 85 + .map_err(|e| SpatialError::IoError(format!("Failed to read JXL file: {}", e)))?; 86 + 87 + let jxl_image = JxlImage::builder() 88 + .read(&data[..]) 89 + .map_err(|e| SpatialError::ImageError(format!("JXL decode failed: {:?}", e)))?; 90 + 91 + let width = jxl_image.width(); 92 + let height = jxl_image.height(); 93 + 94 + let render = jxl_image 95 + .render_frame(0) 96 + .map_err(|e| SpatialError::ImageError(format!("JXL render failed: {:?}", e)))?; 97 + 98 + let planar = render.image_planar(); 99 + if planar.is_empty() { 100 + return Err(SpatialError::ImageError( 101 + "JXL image has no color channels".to_string(), 102 + )); 103 + } 104 + 105 + let mut rgb_data = Vec::with_capacity((width * height * 3) as usize); 106 + for y in 0..height { 107 + for x in 0..width { 108 + let idx = (y * width + x) as usize; 109 + let r = (planar[0].buf()[idx] * 255.0).clamp(0.0, 255.0) as u8; 110 + let g = if planar.len() > 1 { 111 + (planar[1].buf()[idx] * 255.0).clamp(0.0, 255.0) as u8 112 + } else { 113 + r 114 + }; 115 + let b = if planar.len() > 2 { 116 + (planar[2].buf()[idx] * 255.0).clamp(0.0, 255.0) as u8 117 + } else { 118 + r 119 + }; 120 + rgb_data.push(r); 121 + rgb_data.push(g); 122 + rgb_data.push(b); 123 + } 124 + } 125 + 126 + let img_buffer = image::RgbImage::from_raw(width, height, rgb_data).ok_or_else(|| { 127 + SpatialError::ImageError("Failed to create image buffer from JXL data".to_string()) 128 + })?; 129 + 130 + Ok(DynamicImage::ImageRgb8(img_buffer)) 131 + } 132 + 133 + #[cfg(feature = "heic")] 134 + fn load_heic_native(path: &Path) -> SpatialResult<DynamicImage> { 135 + use libheif_rs::{ColorSpace, HeifContext, LibHeif, RgbChroma}; 136 + 137 + let lib_heif = LibHeif::new(); 138 + let ctx = HeifContext::read_from_file( 139 + path.to_str() 140 + .ok_or_else(|| SpatialError::IoError("Invalid path encoding".to_string()))?, 141 + ) 142 + .map_err(|e| SpatialError::ImageError(format!("Failed to load HEIC file: {:?}", e)))?; 143 + 144 + let handle = ctx.primary_image_handle().map_err(|e| { 145 + SpatialError::ImageError(format!("Failed to get HEIC image handle: {:?}", e)) 146 + })?; 147 + 148 + let width = handle.width(); 149 + let height = handle.height(); 150 + 151 + let image = lib_heif 152 + .decode(&handle, ColorSpace::Rgb(RgbChroma::Rgb), None) 153 + .map_err(|e| SpatialError::ImageError(format!("HEIC decode failed: {:?}", e)))?; 154 + 155 + let planes = image.planes(); 156 + let interleaved = planes.interleaved.ok_or_else(|| { 157 + SpatialError::ImageError("No interleaved plane in HEIC image".to_string()) 158 + })?; 159 + 160 + let mut rgb_data = Vec::with_capacity((width * height * 3) as usize); 161 + for y in 0..height { 162 + let row_start = (y * interleaved.stride as u32) as usize; 163 + let row_end = row_start + (width * 3) as usize; 164 + rgb_data.extend_from_slice(&interleaved.data[row_start..row_end]); 165 + } 166 + 167 + let img_buffer = image::RgbImage::from_raw(width, height, rgb_data).ok_or_else(|| { 168 + SpatialError::ImageError("Failed to create image buffer from HEIC data".to_string()) 169 + })?; 170 + 171 + Ok(DynamicImage::ImageRgb8(img_buffer)) 172 + } 173 + 174 + async fn load_with_ffmpeg(path: &Path, format: &str) -> SpatialResult<DynamicImage> { 175 + if !is_ffmpeg_available() { 176 + return Err(SpatialError::ImageError(format!( 177 + "{} format requires ffmpeg for conversion (not installed or not in PATH)", 178 + format.to_uppercase() 179 + ))); 180 + } 181 + 182 + let temp_dir = std::env::temp_dir(); 183 + let temp_filename = format!( 184 + "spatial_maker_convert_{}_{}.jpg", 185 + format, 186 + std::time::SystemTime::now() 187 + .duration_since(std::time::UNIX_EPOCH) 188 + .unwrap_or_default() 189 + .as_millis() 190 + ); 191 + let temp_path = temp_dir.join(temp_filename); 192 + 193 + let input_str = path 194 + .to_str() 195 + .ok_or_else(|| SpatialError::IoError("Invalid input path".to_string()))?; 196 + let output_str = temp_path 197 + .to_str() 198 + .ok_or_else(|| SpatialError::IoError("Invalid output path".to_string()))?; 199 + 200 + let output = Command::new("ffmpeg") 201 + .args(&["-i", input_str, "-c:v", "libjpeg", "-q:v", "2", "-y", output_str]) 202 + .output() 203 + .map_err(|e| SpatialError::IoError(format!("Failed to run ffmpeg: {}", e)))?; 204 + 205 + if !output.status.success() { 206 + let stderr = String::from_utf8_lossy(&output.stderr); 207 + return Err(SpatialError::ImageError(format!( 208 + "ffmpeg conversion failed for {} format:\n{}", 209 + format.to_uppercase(), 210 + stderr 211 + ))); 212 + } 213 + 214 + let img = image::open(&temp_path).map_err(|e| { 215 + let _ = std::fs::remove_file(&temp_path); 216 + SpatialError::ImageError(format!("Failed to load converted image: {}", e)) 217 + })?; 218 + 219 + let _ = std::fs::remove_file(&temp_path); 220 + 221 + Ok(img) 222 + } 223 + 224 + fn is_ffmpeg_available() -> bool { 225 + Command::new("ffmpeg") 226 + .arg("-version") 227 + .output() 228 + .map(|output| output.status.success()) 229 + .unwrap_or(false) 230 + }
+82 -27
src/lib.rs
··· 1 1 pub mod depth; 2 + pub mod error; 3 + pub mod image_loader; 2 4 pub mod model; 3 5 pub mod output; 4 6 pub mod stereo; 7 + pub mod video; 5 8 6 - #[cfg(target_os = "macos")] 9 + #[cfg(all(target_os = "macos", feature = "coreml"))] 7 10 pub mod depth_coreml; 8 11 9 - pub use depth::DepthEstimator; 10 - pub use stereo::create_stereo_pair; 12 + pub use error::{SpatialError, SpatialResult}; 13 + pub use image_loader::load_image; 14 + pub use model::{find_model, get_checkpoint_dir, model_exists}; 15 + pub use output::{create_sbs_image, save_stereo_image, ImageEncoding, MVHEVCConfig, OutputFormat, OutputOptions}; 16 + pub use stereo::generate_stereo_pair; 17 + pub use video::{get_video_metadata, process_video, ProgressCallback, VideoMetadata, VideoProgress}; 11 18 12 - #[cfg(target_os = "macos")] 19 + #[cfg(all(target_os = "macos", feature = "coreml"))] 13 20 pub use depth_coreml::CoreMLDepthEstimator; 14 21 15 - use anyhow::Result; 16 - use image::DynamicImage; 22 + #[cfg(feature = "onnx")] 23 + pub use depth::OnnxDepthEstimator; 24 + 25 + use std::path::Path; 26 + 27 + #[derive(Clone, Debug, serde::Serialize, serde::Deserialize)] 28 + pub struct SpatialConfig { 29 + pub encoder_size: String, 30 + pub max_disparity: u32, 31 + pub target_depth_size: u32, 32 + } 33 + 34 + pub type StereoOutputFormat = OutputFormat; 35 + 36 + impl Default for SpatialConfig { 37 + fn default() -> Self { 38 + Self { 39 + encoder_size: "b".to_string(), 40 + max_disparity: 30, 41 + target_depth_size: 518, 42 + } 43 + } 44 + } 45 + 46 + pub async fn process_photo( 47 + input_path: &Path, 48 + output_path: &Path, 49 + config: SpatialConfig, 50 + output_options: OutputOptions, 51 + ) -> SpatialResult<()> { 52 + let input_image = load_image(input_path).await?; 53 + 54 + model::ensure_model_exists::<fn(u64, u64)>(&config.encoder_size, None).await?; 55 + 56 + #[cfg(all(target_os = "macos", feature = "coreml"))] 57 + let depth_map = { 58 + let model_path = model::find_model(&config.encoder_size)?; 59 + let model_str = model_path.to_str().ok_or_else(|| { 60 + SpatialError::ModelError("Invalid model path encoding".to_string()) 61 + })?; 62 + let estimator = CoreMLDepthEstimator::new(model_str)?; 63 + estimator.estimate(&input_image)? 64 + }; 17 65 18 - /// Process a photo to create a side-by-side stereo image (ONNX backend) 19 - pub fn process_photo( 20 - input_image: DynamicImage, 21 - model_path: &str, 22 - max_disparity: f32, 23 - ) -> Result<DynamicImage> { 24 - let mut estimator = DepthEstimator::new(model_path)?; 25 - let depth_map = estimator.estimate(&input_image)?; 26 - let (left, right) = create_stereo_pair(&input_image, &depth_map, max_disparity)?; 27 - Ok(output::create_sbs_image(left, right)) 66 + #[cfg(not(all(target_os = "macos", feature = "coreml")))] 67 + let depth_map = { 68 + #[cfg(feature = "onnx")] 69 + { 70 + let model_path = model::find_model(&config.encoder_size)?; 71 + let estimator = OnnxDepthEstimator::new(model_path.to_str().unwrap())?; 72 + estimator.estimate(&input_image)? 73 + } 74 + #[cfg(not(feature = "onnx"))] 75 + { 76 + return Err(SpatialError::ConfigError( 77 + "No depth backend enabled. Enable 'coreml' (macOS) or 'onnx' feature.".to_string(), 78 + )); 79 + } 80 + }; 81 + 82 + let (left, right) = generate_stereo_pair(&input_image, &depth_map, config.max_disparity)?; 83 + 84 + save_stereo_image(&left, &right, output_path, output_options)?; 85 + 86 + Ok(()) 28 87 } 29 88 30 - /// Process a photo to create a side-by-side stereo image (CoreML backend, macOS only) 31 - #[cfg(target_os = "macos")] 32 - pub fn process_photo_coreml( 33 - input_image: DynamicImage, 34 - model_path: &str, 35 - max_disparity: f32, 36 - ) -> Result<DynamicImage> { 37 - let estimator = CoreMLDepthEstimator::new(model_path)?; 38 - let depth_map = estimator.estimate(&input_image)?; 39 - let (left, right) = create_stereo_pair(&input_image, &depth_map, max_disparity)?; 40 - Ok(output::create_sbs_image(left, right)) 89 + pub async fn process_video_sbs( 90 + input_path: &Path, 91 + output_path: &Path, 92 + config: SpatialConfig, 93 + progress_cb: Option<ProgressCallback>, 94 + ) -> SpatialResult<()> { 95 + video::process_video(input_path, output_path, config, progress_cb).await 41 96 }
+263 -52
src/model.rs
··· 1 - use anyhow::Result; 2 - use std::path::PathBuf; 1 + use crate::error::{SpatialError, SpatialResult}; 2 + use std::path::{Path, PathBuf}; 3 + use tokio::io::AsyncWriteExt; 4 + 5 + pub fn get_checkpoint_dir() -> SpatialResult<PathBuf> { 6 + if let Ok(custom_dir) = std::env::var("SPATIAL_MAKER_CHECKPOINTS") { 7 + Ok(PathBuf::from(custom_dir)) 8 + } else { 9 + let home = dirs::home_dir().ok_or_else(|| { 10 + SpatialError::ConfigError("Could not determine home directory".to_string()) 11 + })?; 12 + Ok(home.join(".spatial-maker").join("checkpoints")) 13 + } 14 + } 15 + 16 + #[derive(Clone, Debug)] 17 + pub struct ModelMetadata { 18 + pub name: String, 19 + pub filename: String, 20 + pub url: String, 21 + pub size_mb: u32, 22 + } 23 + 24 + impl ModelMetadata { 25 + pub fn coreml(encoder_size: &str) -> SpatialResult<Self> { 26 + match encoder_size { 27 + "s" | "small" => Ok(ModelMetadata { 28 + name: "depth-anything-v2-small".to_string(), 29 + filename: "DepthAnythingV2SmallF16.mlpackage".to_string(), 30 + url: "https://huggingface.co/mrgnw/depth-anything-v2-coreml/resolve/main/DepthAnythingV2SmallF16.mlpackage.tar.gz".to_string(), 31 + size_mb: 48, 32 + }), 33 + "b" | "base" => Ok(ModelMetadata { 34 + name: "depth-anything-v2-base".to_string(), 35 + filename: "DepthAnythingV2BaseF16.mlpackage".to_string(), 36 + url: "https://huggingface.co/mrgnw/depth-anything-v2-coreml/resolve/main/DepthAnythingV2BaseF16.mlpackage.tar.gz".to_string(), 37 + size_mb: 186, 38 + }), 39 + "l" | "large" => Ok(ModelMetadata { 40 + name: "depth-anything-v2-large".to_string(), 41 + filename: "DepthAnythingV2LargeF16.mlpackage".to_string(), 42 + url: "https://huggingface.co/mrgnw/depth-anything-v2-coreml/resolve/main/DepthAnythingV2LargeF16.mlpackage.tar.gz".to_string(), 43 + size_mb: 638, 44 + }), 45 + other => Err(SpatialError::ConfigError( 46 + format!("Unknown encoder size: '{}'. Use 's', 'b', or 'l'", other) 47 + )), 48 + } 49 + } 50 + 51 + #[cfg(feature = "onnx")] 52 + pub fn onnx(encoder_size: &str) -> SpatialResult<Self> { 53 + match encoder_size { 54 + "s" | "small" => Ok(ModelMetadata { 55 + name: "depth-anything-v2-small".to_string(), 56 + filename: "depth_anything_v2_small.onnx".to_string(), 57 + url: "https://huggingface.co/onnx-community/depth-anything-v2-small/resolve/main/onnx/model.onnx".to_string(), 58 + size_mb: 99, 59 + }), 60 + "b" | "base" => Ok(ModelMetadata { 61 + name: "depth-anything-v2-base".to_string(), 62 + filename: "depth_anything_v2_base.onnx".to_string(), 63 + url: "https://huggingface.co/onnx-community/depth-anything-v2-base/resolve/main/onnx/model.onnx".to_string(), 64 + size_mb: 380, 65 + }), 66 + "l" | "large" => Ok(ModelMetadata { 67 + name: "depth-anything-v2-large".to_string(), 68 + filename: "depth_anything_v2_large.onnx".to_string(), 69 + url: "https://huggingface.co/onnx-community/depth-anything-v2-large/resolve/main/onnx/model.onnx".to_string(), 70 + size_mb: 1300, 71 + }), 72 + other => Err(SpatialError::ConfigError( 73 + format!("Unknown encoder size: '{}'. Use 's', 'b', or 'l'", other) 74 + )), 75 + } 76 + } 77 + } 78 + 79 + pub fn find_model(encoder_size: &str) -> SpatialResult<PathBuf> { 80 + let checkpoint_dir = get_checkpoint_dir()?; 81 + 82 + #[cfg(all(target_os = "macos", feature = "coreml"))] 83 + { 84 + let meta = ModelMetadata::coreml(encoder_size)?; 85 + let model_path = checkpoint_dir.join(&meta.filename); 86 + if model_path.exists() { 87 + return Ok(model_path); 88 + } 89 + } 90 + 91 + #[cfg(feature = "onnx")] 92 + { 93 + let meta = ModelMetadata::onnx(encoder_size)?; 94 + let model_path = checkpoint_dir.join(&meta.filename); 95 + if model_path.exists() { 96 + return Ok(model_path); 97 + } 98 + } 99 + 100 + // Also check development paths 101 + let dev_paths = [ 102 + PathBuf::from("checkpoints"), 103 + dirs::home_dir() 104 + .unwrap_or_default() 105 + .join(".spatial-maker") 106 + .join("checkpoints"), 107 + ]; 108 + 109 + for dir in &dev_paths { 110 + if dir.exists() { 111 + if let Ok(entries) = std::fs::read_dir(dir) { 112 + for entry in entries.flatten() { 113 + let name = entry.file_name().to_string_lossy().to_string(); 114 + if name.contains("DepthAnything") || name.contains("depth_anything") { 115 + let lower_size = encoder_size.to_lowercase(); 116 + let name_lower = name.to_lowercase(); 117 + let matches = match lower_size.as_str() { 118 + "s" | "small" => name_lower.contains("small"), 119 + "b" | "base" => name_lower.contains("base"), 120 + "l" | "large" => name_lower.contains("large"), 121 + _ => false, 122 + }; 123 + if matches { 124 + return Ok(entry.path()); 125 + } 126 + } 127 + } 128 + } 129 + } 130 + } 131 + 132 + Err(SpatialError::ModelError(format!( 133 + "Model not found for encoder size '{}'. Run download first.", 134 + encoder_size 135 + ))) 136 + } 137 + 138 + pub fn model_exists(encoder_size: &str) -> bool { 139 + find_model(encoder_size).is_ok() 140 + } 141 + 142 + pub async fn ensure_model_exists<F>( 143 + encoder_size: &str, 144 + progress_fn: Option<F>, 145 + ) -> SpatialResult<PathBuf> 146 + where 147 + F: FnMut(u64, u64), 148 + { 149 + if let Ok(path) = find_model(encoder_size) { 150 + return Ok(path); 151 + } 152 + 153 + let checkpoint_dir = get_checkpoint_dir()?; 154 + tokio::fs::create_dir_all(&checkpoint_dir) 155 + .await 156 + .map_err(|e| { 157 + SpatialError::IoError(format!("Failed to create checkpoint directory: {}", e)) 158 + })?; 159 + 160 + #[cfg(all(target_os = "macos", feature = "coreml"))] 161 + { 162 + let meta = ModelMetadata::coreml(encoder_size)?; 163 + let model_path = checkpoint_dir.join(&meta.filename); 164 + download_model(&meta, &model_path, progress_fn).await?; 165 + return Ok(model_path); 166 + } 167 + 168 + #[cfg(all(feature = "onnx", not(all(target_os = "macos", feature = "coreml"))))] 169 + { 170 + let meta = ModelMetadata::onnx(encoder_size)?; 171 + let model_path = checkpoint_dir.join(&meta.filename); 172 + download_model(&meta, &model_path, progress_fn).await?; 173 + return Ok(model_path); 174 + } 175 + 176 + #[cfg(not(any(all(target_os = "macos", feature = "coreml"), feature = "onnx")))] 177 + { 178 + let _ = progress_fn; 179 + Err(SpatialError::ConfigError( 180 + "No depth backend enabled. Enable 'coreml' (macOS) or 'onnx' feature.".to_string(), 181 + )) 182 + } 183 + } 184 + 185 + async fn download_model<F>( 186 + metadata: &ModelMetadata, 187 + destination: &Path, 188 + mut progress_fn: Option<F>, 189 + ) -> SpatialResult<()> 190 + where 191 + F: FnMut(u64, u64), 192 + { 193 + tracing::info!("Downloading model: {} from {}", metadata.name, metadata.url); 194 + 195 + let response = reqwest::get(&metadata.url) 196 + .await 197 + .map_err(|e| SpatialError::Other(format!("Failed to download model: {}", e)))?; 198 + 199 + let total_bytes = response 200 + .content_length() 201 + .unwrap_or(metadata.size_mb as u64 * 1_000_000); 202 + 203 + let is_tar_gz = metadata.url.ends_with(".tar.gz"); 204 + 205 + if is_tar_gz { 206 + let temp_path = destination.with_extension("tar.gz"); 207 + let mut file = tokio::fs::File::create(&temp_path) 208 + .await 209 + .map_err(|e| SpatialError::IoError(format!("Failed to create file: {}", e)))?; 210 + 211 + let mut downloaded = 0u64; 212 + let mut stream = response.bytes_stream(); 213 + use futures_util::StreamExt; 214 + 215 + while let Some(chunk) = stream.next().await { 216 + let chunk = chunk.map_err(|e| SpatialError::Other(format!("Download interrupted: {}", e)))?; 217 + file.write_all(&chunk) 218 + .await 219 + .map_err(|e| SpatialError::IoError(format!("Failed to write to file: {}", e)))?; 220 + downloaded += chunk.len() as u64; 221 + if let Some(ref mut f) = progress_fn { 222 + f(downloaded, total_bytes); 223 + } 224 + } 225 + drop(file); 226 + 227 + let parent = destination 228 + .parent() 229 + .ok_or_else(|| SpatialError::IoError("Invalid destination path".to_string()))?; 230 + 231 + let output = std::process::Command::new("tar") 232 + .args(&["xzf"]) 233 + .arg(&temp_path) 234 + .arg("-C") 235 + .arg(parent) 236 + .output() 237 + .map_err(|e| SpatialError::IoError(format!("Failed to extract tar.gz: {}", e)))?; 3 238 4 - /// Find the checkpoint file for a given model name 5 - /// 6 - /// Searches in order: 7 - /// 1. Relative to source directory (for development) 8 - /// 2. User's home directory ~/.spatial-maker/checkpoints/ 9 - /// 3. XDG data directory 10 - /// 4. Current working directory ./checkpoints/ 11 - /// 5. SPATIAL_MAKER_CHECKPOINTS env var 12 - pub fn find_checkpoint(checkpoint_name: &str) -> Result<PathBuf> { 13 - let search_paths = vec![ 14 - // Development: relative to source file 15 - PathBuf::from(env!("CARGO_MANIFEST_DIR")) 16 - .join("checkpoints") 17 - .join(checkpoint_name), 18 - // User home directory 19 - dirs::home_dir() 20 - .unwrap_or_default() 21 - .join(".spatial-maker") 22 - .join("checkpoints") 23 - .join(checkpoint_name), 24 - // XDG data home 25 - dirs::data_dir() 26 - .unwrap_or_default() 27 - .join("spatial-maker") 28 - .join("checkpoints") 29 - .join(checkpoint_name), 30 - // Current working directory 31 - PathBuf::from("checkpoints").join(checkpoint_name), 32 - ]; 239 + if !output.status.success() { 240 + let stderr = String::from_utf8_lossy(&output.stderr); 241 + return Err(SpatialError::IoError(format!("tar extraction failed: {}", stderr))); 242 + } 33 243 34 - // Check env var first 35 - if let Ok(env_dir) = std::env::var("SPATIAL_MAKER_CHECKPOINTS") { 36 - let env_path = PathBuf::from(env_dir).join(checkpoint_name); 37 - if env_path.exists() { 38 - return Ok(env_path); 39 - } 40 - } 244 + let _ = tokio::fs::remove_file(&temp_path).await; 245 + } else { 246 + let mut file = tokio::fs::File::create(destination) 247 + .await 248 + .map_err(|e| SpatialError::IoError(format!("Failed to create file: {}", e)))?; 41 249 42 - for path in &search_paths { 43 - if path.exists() { 44 - return Ok(path.clone()); 45 - } 46 - } 250 + let mut downloaded = 0u64; 251 + let mut stream = response.bytes_stream(); 252 + use futures_util::StreamExt; 253 + 254 + while let Some(chunk) = stream.next().await { 255 + let chunk = chunk.map_err(|e| SpatialError::Other(format!("Download interrupted: {}", e)))?; 256 + file.write_all(&chunk) 257 + .await 258 + .map_err(|e| SpatialError::IoError(format!("Failed to write to file: {}", e)))?; 259 + downloaded += chunk.len() as u64; 260 + if let Some(ref mut f) = progress_fn { 261 + f(downloaded, total_bytes); 262 + } 263 + } 264 + } 47 265 48 - anyhow::bail!( 49 - "Checkpoint '{}' not found.\nSearched locations:\n{}", 50 - checkpoint_name, 51 - search_paths 52 - .iter() 53 - .map(|p| format!(" - {}", p.display())) 54 - .collect::<Vec<_>>() 55 - .join("\n") 56 - ); 266 + tracing::info!("Model downloaded: {:?}", destination); 267 + Ok(()) 57 268 }
+274 -18
src/output.rs
··· 1 - use image::{DynamicImage, ImageBuffer, RgbImage}; 1 + use crate::error::{SpatialError, SpatialResult}; 2 + use image::DynamicImage; 3 + use std::path::{Path, PathBuf}; 4 + use std::process::Command; 5 + 6 + #[derive(Clone, Copy, Debug, PartialEq, Eq)] 7 + pub enum OutputFormat { 8 + SideBySide, 9 + TopAndBottom, 10 + Separate, 11 + } 12 + 13 + impl OutputFormat { 14 + pub fn name(&self) -> &'static str { 15 + match self { 16 + OutputFormat::SideBySide => "side-by-side", 17 + OutputFormat::TopAndBottom => "top-and-bottom", 18 + OutputFormat::Separate => "separate", 19 + } 20 + } 21 + } 22 + 23 + #[derive(Clone, Copy, Debug, PartialEq, Eq)] 24 + pub enum ImageEncoding { 25 + Jpeg { quality: u8 }, 26 + Png, 27 + } 2 28 3 - /// Create a side-by-side stereo image by horizontally stacking left and right views 4 - pub fn create_sbs_image(left: RgbImage, right: RgbImage) -> DynamicImage { 5 - let (width, height) = left.dimensions(); 6 - let mut sbs = ImageBuffer::new(width * 2, height); 29 + impl ImageEncoding { 30 + pub fn extension(&self) -> &'static str { 31 + match self { 32 + ImageEncoding::Jpeg { .. } => "jpg", 33 + ImageEncoding::Png => "png", 34 + } 35 + } 36 + 37 + pub fn from_path<P: AsRef<Path>>(path: P) -> Self { 38 + let ext = path 39 + .as_ref() 40 + .extension() 41 + .and_then(|e| e.to_str()) 42 + .unwrap_or("") 43 + .to_lowercase(); 7 44 8 - // Copy left eye to left half 9 - for y in 0..height { 10 - for x in 0..width { 11 - sbs.put_pixel(x, y, *left.get_pixel(x, y)); 12 - } 13 - } 45 + match ext.as_str() { 46 + "png" => ImageEncoding::Png, 47 + _ => ImageEncoding::Jpeg { quality: 95 }, 48 + } 49 + } 50 + } 14 51 15 - // Copy right eye to right half 16 - for y in 0..height { 17 - for x in 0..width { 18 - sbs.put_pixel(width + x, y, *right.get_pixel(x, y)); 19 - } 20 - } 52 + #[derive(Clone, Debug)] 53 + pub struct MVHEVCConfig { 54 + pub spatial_cli_path: Option<PathBuf>, 55 + pub enabled: bool, 56 + pub quality: u8, 57 + pub keep_intermediate: bool, 58 + } 21 59 22 - DynamicImage::ImageRgb8(sbs) 60 + impl Default for MVHEVCConfig { 61 + fn default() -> Self { 62 + Self { 63 + spatial_cli_path: None, 64 + enabled: false, 65 + quality: 95, 66 + keep_intermediate: false, 67 + } 68 + } 69 + } 70 + 71 + #[derive(Clone, Debug)] 72 + pub struct OutputOptions { 73 + pub layout: OutputFormat, 74 + pub image_format: ImageEncoding, 75 + pub mvhevc: Option<MVHEVCConfig>, 76 + } 77 + 78 + impl Default for OutputOptions { 79 + fn default() -> Self { 80 + Self { 81 + layout: OutputFormat::SideBySide, 82 + image_format: ImageEncoding::Jpeg { quality: 95 }, 83 + mvhevc: None, 84 + } 85 + } 86 + } 87 + 88 + pub fn create_sbs_image(left: &DynamicImage, right: &DynamicImage) -> DynamicImage { 89 + let left_width = left.width(); 90 + let left_height = left.height(); 91 + 92 + let combined_width = left_width + right.width(); 93 + let mut combined = DynamicImage::new_rgb8(combined_width, left_height); 94 + 95 + image::imageops::overlay(&mut combined, left, 0, 0); 96 + image::imageops::overlay(&mut combined, right, left_width as i64, 0); 97 + 98 + combined 99 + } 100 + 101 + pub fn save_stereo_image( 102 + left: &DynamicImage, 103 + right: &DynamicImage, 104 + output_path: impl AsRef<Path>, 105 + options: OutputOptions, 106 + ) -> SpatialResult<()> { 107 + let output_path = output_path.as_ref(); 108 + 109 + if let Some(parent) = output_path.parent() { 110 + std::fs::create_dir_all(parent).map_err(|e| { 111 + SpatialError::ImageError(format!("Failed to create output directory: {}", e)) 112 + })?; 113 + } 114 + 115 + match options.layout { 116 + OutputFormat::SideBySide => { 117 + save_side_by_side(left, right, output_path, options.image_format)?; 118 + } 119 + OutputFormat::TopAndBottom => { 120 + save_top_and_bottom(left, right, output_path, options.image_format)?; 121 + } 122 + OutputFormat::Separate => { 123 + save_separate(left, right, output_path, options.image_format)?; 124 + } 125 + } 126 + 127 + if let Some(mvhevc_config) = options.mvhevc { 128 + if mvhevc_config.enabled { 129 + encode_mvhevc(output_path, &mvhevc_config)?; 130 + if !mvhevc_config.keep_intermediate { 131 + let _ = std::fs::remove_file(output_path); 132 + } 133 + } 134 + } 135 + 136 + Ok(()) 137 + } 138 + 139 + fn save_side_by_side( 140 + left: &DynamicImage, 141 + right: &DynamicImage, 142 + output_path: &Path, 143 + encoding: ImageEncoding, 144 + ) -> SpatialResult<()> { 145 + if left.height() != right.height() { 146 + return Err(SpatialError::ImageError(format!( 147 + "Left and right images must have the same height: {} != {}", 148 + left.height(), 149 + right.height() 150 + ))); 151 + } 152 + 153 + let combined = create_sbs_image(left, right); 154 + save_image(&combined, output_path, encoding) 155 + } 156 + 157 + fn save_top_and_bottom( 158 + left: &DynamicImage, 159 + right: &DynamicImage, 160 + output_path: &Path, 161 + encoding: ImageEncoding, 162 + ) -> SpatialResult<()> { 163 + if left.width() != right.width() { 164 + return Err(SpatialError::ImageError(format!( 165 + "Left and right images must have the same width: {} != {}", 166 + left.width(), 167 + right.width() 168 + ))); 169 + } 170 + 171 + let combined_height = left.height() + right.height(); 172 + let mut combined = DynamicImage::new_rgb8(left.width(), combined_height); 173 + 174 + image::imageops::overlay(&mut combined, left, 0, 0); 175 + image::imageops::overlay(&mut combined, right, 0, left.height() as i64); 176 + 177 + save_image(&combined, output_path, encoding) 178 + } 179 + 180 + fn save_separate( 181 + left: &DynamicImage, 182 + right: &DynamicImage, 183 + output_path: &Path, 184 + encoding: ImageEncoding, 185 + ) -> SpatialResult<()> { 186 + let stem = output_path 187 + .file_stem() 188 + .and_then(|s| s.to_str()) 189 + .ok_or_else(|| SpatialError::ImageError("Invalid output path".to_string()))?; 190 + 191 + let parent = output_path.parent().unwrap_or_else(|| Path::new(".")); 192 + let ext = encoding.extension(); 193 + 194 + let left_path = parent.join(format!("{}_L.{}", stem, ext)); 195 + let right_path = parent.join(format!("{}_R.{}", stem, ext)); 196 + 197 + save_image(left, &left_path, encoding)?; 198 + save_image(right, &right_path, encoding)?; 199 + 200 + Ok(()) 201 + } 202 + 203 + fn save_image(image: &DynamicImage, path: &Path, encoding: ImageEncoding) -> SpatialResult<()> { 204 + match encoding { 205 + ImageEncoding::Jpeg { quality } => { 206 + let rgb_image = image.to_rgb8(); 207 + let file = std::fs::File::create(path).map_err(|e| { 208 + SpatialError::ImageError(format!("Failed to create output file: {}", e)) 209 + })?; 210 + 211 + let mut jpeg_encoder = 212 + image::codecs::jpeg::JpegEncoder::new_with_quality(file, quality); 213 + jpeg_encoder 214 + .encode( 215 + rgb_image.as_ref(), 216 + rgb_image.width(), 217 + rgb_image.height(), 218 + image::ExtendedColorType::Rgb8, 219 + ) 220 + .map_err(|e| SpatialError::ImageError(format!("Failed to encode JPEG: {}", e)))?; 221 + } 222 + ImageEncoding::Png => { 223 + image 224 + .save(path) 225 + .map_err(|e| SpatialError::ImageError(format!("Failed to save PNG: {}", e)))?; 226 + } 227 + } 228 + 229 + Ok(()) 230 + } 231 + 232 + fn encode_mvhevc(stereo_path: &Path, config: &MVHEVCConfig) -> SpatialResult<()> { 233 + let spatial_path = config 234 + .spatial_cli_path 235 + .as_ref() 236 + .map(|p| p.as_path()) 237 + .unwrap_or_else(|| Path::new("spatial")); 238 + 239 + let hevc_path = stereo_path.with_extension("heic"); 240 + 241 + let format = if stereo_path.to_string_lossy().contains("top-bottom") 242 + || stereo_path.to_string_lossy().contains("_tb_") 243 + { 244 + "hou" 245 + } else { 246 + "sbs" 247 + }; 248 + 249 + let quality_normalized = (config.quality as f32 / 100.0).clamp(0.0, 1.0); 250 + 251 + let mut cmd = Command::new(spatial_path); 252 + cmd.arg("make") 253 + .arg("--input") 254 + .arg(stereo_path) 255 + .arg("--output") 256 + .arg(&hevc_path) 257 + .arg("--format") 258 + .arg(format) 259 + .arg("--quality") 260 + .arg(quality_normalized.to_string()) 261 + .arg("--overwrite"); 262 + 263 + let output = cmd.output().map_err(|e| { 264 + SpatialError::ImageError(format!( 265 + "Failed to run `spatial` CLI: {}. Ensure the `spatial` tool is installed and in PATH.", 266 + e 267 + )) 268 + })?; 269 + 270 + if !output.status.success() { 271 + let stderr = String::from_utf8_lossy(&output.stderr); 272 + return Err(SpatialError::ImageError(format!( 273 + "MV-HEVC encoding failed: {}", 274 + stderr 275 + ))); 276 + } 277 + 278 + Ok(()) 23 279 }
+96 -68
src/stereo.rs
··· 1 - use anyhow::Result; 2 - use image::{DynamicImage, ImageBuffer, Luma, Rgb, RgbImage}; 1 + use crate::error::SpatialResult; 2 + use image::{DynamicImage, ImageBuffer, Rgb}; 3 + use ndarray::Array2; 3 4 4 - /// Create left and right eye views from RGB frame and depth map using DIBR 5 - /// 6 - /// This ports the `create_stereo_pair` function from depth_to_stereo.py 7 - pub fn create_stereo_pair( 8 - rgb_frame: &DynamicImage, 9 - depth_frame: &ImageBuffer<Luma<f32>, Vec<f32>>, 10 - max_disparity: f32, 11 - ) -> Result<(RgbImage, RgbImage)> { 12 - let rgb = rgb_frame.to_rgb8(); 13 - let (width, height) = rgb.dimensions(); 5 + pub fn generate_stereo_pair( 6 + image: &DynamicImage, 7 + depth: &Array2<f32>, 8 + max_disparity: u32, 9 + ) -> SpatialResult<(DynamicImage, DynamicImage)> { 10 + let img_rgb = image.to_rgb8(); 11 + let width = img_rgb.width() as usize; 12 + let height = img_rgb.height() as usize; 14 13 15 - // Create disparity map (closer = higher value = more shift) 16 - let disparity: Vec<f32> = depth_frame.pixels().map(|p| p[0] * max_disparity).collect(); 14 + let mut right_rgb: ImageBuffer<Rgb<u8>, Vec<u8>> = ImageBuffer::new(width as u32, height as u32); 17 15 18 - let left_eye = remap_image(&rgb, &disparity, width, height, 0.5)?; 19 - let right_eye = remap_image(&rgb, &disparity, width, height, -0.5)?; 16 + let bg = Rgb([64u8, 64u8, 64u8]); 17 + for pixel in right_rgb.pixels_mut() { 18 + *pixel = bg; 19 + } 20 20 21 - Ok((left_eye, right_eye)) 22 - } 21 + for y in 0..height { 22 + for x in 0..width { 23 + let depth_val = get_depth_at(depth, x, y, width, height); 24 + let disparity = (depth_val * max_disparity as f32).round() as i32; 25 + let x_right = x as i32 - disparity; 23 26 24 - /// Remap an image based on disparity values 25 - /// 26 - /// This is equivalent to cv2.remap with INTER_LINEAR and BORDER_REPLICATE 27 - fn remap_image( 28 - source: &RgbImage, 29 - disparity: &[f32], 30 - width: u32, 31 - height: u32, 32 - shift_factor: f32, 33 - ) -> Result<RgbImage> { 34 - let mut output = ImageBuffer::new(width, height); 27 + if x_right >= 0 && x_right < width as i32 { 28 + if let Some(pixel) = img_rgb.get_pixel_checked(x as u32, y as u32) { 29 + right_rgb.put_pixel(x_right as u32, y as u32, *pixel); 30 + } 31 + } 32 + } 33 + } 35 34 36 - for y in 0..height { 37 - for x in 0..width { 38 - let idx = (y * width + x) as usize; 39 - let shift = disparity[idx] * shift_factor; 40 - let src_x = x as f32 + shift; 35 + fill_disocclusions(&mut right_rgb); 41 36 42 - // Bilinear interpolation 43 - let pixel = sample_bilinear(source, src_x, y as f32); 44 - output.put_pixel(x, y, pixel); 45 - } 46 - } 37 + let left_image = image.clone(); 38 + let right_image = DynamicImage::ImageRgb8(right_rgb); 47 39 48 - Ok(output) 40 + Ok((left_image, right_image)) 49 41 } 50 42 51 - /// Sample a pixel using bilinear interpolation 52 - fn sample_bilinear(image: &RgbImage, x: f32, y: f32) -> Rgb<u8> { 53 - let (width, height) = image.dimensions(); 43 + fn get_depth_at( 44 + depth: &Array2<f32>, 45 + x: usize, 46 + y: usize, 47 + img_width: usize, 48 + img_height: usize, 49 + ) -> f32 { 50 + let (depth_height, depth_width) = depth.dim(); 54 51 55 - // Clamp to image bounds (BORDER_REPLICATE) 56 - let x_clamped = x.clamp(0.0, (width - 1) as f32); 57 - let y_clamped = y.clamp(0.0, (height - 1) as f32); 52 + if depth_height == img_height && depth_width == img_width { 53 + depth[[y, x]] 54 + } else { 55 + let scaled_x = (x as f32 * depth_width as f32 / img_width as f32) 56 + .min(depth_width as f32 - 1.0) as usize; 57 + let scaled_y = (y as f32 * depth_height as f32 / img_height as f32) 58 + .min(depth_height as f32 - 1.0) as usize; 58 59 59 - let x0 = x_clamped.floor() as u32; 60 - let x1 = (x0 + 1).min(width - 1); 61 - let y0 = y_clamped.floor() as u32; 62 - let y1 = (y0 + 1).min(height - 1); 60 + if scaled_y < depth_height && scaled_x < depth_width { 61 + depth[[scaled_y, scaled_x]] 62 + } else { 63 + 0.5 64 + } 65 + } 66 + } 63 67 64 - let fx = x_clamped - x0 as f32; 65 - let fy = y_clamped - y0 as f32; 68 + fn fill_disocclusions(image: &mut ImageBuffer<Rgb<u8>, Vec<u8>>) { 69 + let width = image.width() as usize; 70 + let height = image.height() as usize; 71 + let bg = Rgb([64u8, 64u8, 64u8]); 66 72 67 - let p00 = image.get_pixel(x0, y0); 68 - let p10 = image.get_pixel(x1, y0); 69 - let p01 = image.get_pixel(x0, y1); 70 - let p11 = image.get_pixel(x1, y1); 73 + let original = image.clone(); 71 74 72 - let mut result = [0u8; 3]; 73 - for c in 0..3 { 74 - let v00 = p00[c] as f32; 75 - let v10 = p10[c] as f32; 76 - let v01 = p01[c] as f32; 77 - let v11 = p11[c] as f32; 75 + for y in 0..height { 76 + for x in 0..width { 77 + let pixel = original.get_pixel(x as u32, y as u32); 78 + if pixel[0] == bg[0] && pixel[1] == bg[1] && pixel[2] == bg[2] { 79 + if let Some(nearest) = find_nearest_valid(&original, x, y, bg) { 80 + image.put_pixel(x as u32, y as u32, nearest); 81 + } 82 + } 83 + } 84 + } 85 + } 78 86 79 - let v0 = v00 * (1.0 - fx) + v10 * fx; 80 - let v1 = v01 * (1.0 - fx) + v11 * fx; 81 - let v = v0 * (1.0 - fy) + v1 * fy; 87 + fn find_nearest_valid( 88 + image: &ImageBuffer<Rgb<u8>, Vec<u8>>, 89 + cx: usize, 90 + cy: usize, 91 + bg: Rgb<u8>, 92 + ) -> Option<Rgb<u8>> { 93 + let width = image.width() as usize; 94 + let height = image.height() as usize; 82 95 83 - result[c] = v.round().clamp(0.0, 255.0) as u8; 84 - } 96 + for radius in 1..=20 { 97 + for dy in -(radius as i32)..=(radius as i32) { 98 + for dx in -(radius as i32)..=(radius as i32) { 99 + if dx.abs() != radius as i32 && dy.abs() != radius as i32 { 100 + continue; 101 + } 102 + let nx = (cx as i32 + dx) as usize; 103 + let ny = (cy as i32 + dy) as usize; 104 + if nx < width && ny < height { 105 + let pixel = image.get_pixel(nx as u32, ny as u32); 106 + if pixel[0] != bg[0] || pixel[1] != bg[1] || pixel[2] != bg[2] { 107 + return Some(*pixel); 108 + } 109 + } 110 + } 111 + } 112 + } 85 113 86 - Rgb(result) 114 + None 87 115 }
+398
src/video.rs
··· 1 + use crate::error::{SpatialError, SpatialResult}; 2 + use crate::stereo::generate_stereo_pair; 3 + use crate::SpatialConfig; 4 + use image::{DynamicImage, ImageBuffer, RgbImage}; 5 + use std::path::Path; 6 + use std::process::Stdio; 7 + use tokio::io::{AsyncReadExt, AsyncWriteExt}; 8 + use tokio::process::Command; 9 + use tokio::sync::mpsc; 10 + 11 + #[derive(Clone, Debug)] 12 + pub struct VideoProgress { 13 + pub current_frame: u32, 14 + pub total_frames: u32, 15 + pub stage: String, 16 + pub percent: f64, 17 + } 18 + 19 + impl VideoProgress { 20 + pub fn new(current_frame: u32, total_frames: u32, stage: String) -> Self { 21 + let percent = if total_frames > 0 { 22 + (current_frame as f64 / total_frames as f64 * 100.0).min(100.0) 23 + } else { 24 + 0.0 25 + }; 26 + Self { 27 + current_frame, 28 + total_frames, 29 + stage, 30 + percent, 31 + } 32 + } 33 + } 34 + 35 + #[derive(Clone, Debug)] 36 + pub struct VideoMetadata { 37 + pub width: u32, 38 + pub height: u32, 39 + pub fps: f64, 40 + pub total_frames: u32, 41 + pub duration: f64, 42 + pub has_audio: bool, 43 + } 44 + 45 + pub type ProgressCallback = Box<dyn Fn(VideoProgress) + Send + Sync>; 46 + 47 + pub async fn get_video_metadata(input_path: &Path) -> SpatialResult<VideoMetadata> { 48 + let output = Command::new("ffprobe") 49 + .args([ 50 + "-v", 51 + "error", 52 + "-select_streams", 53 + "v:0", 54 + "-count_frames", 55 + "-show_entries", 56 + "stream=width,height,r_frame_rate,nb_read_frames,duration", 57 + "-show_entries", 58 + "format=duration", 59 + "-of", 60 + "csv=p=0", 61 + input_path.to_str().unwrap(), 62 + ]) 63 + .output() 64 + .await 65 + .map_err(|e| { 66 + SpatialError::Other(format!( 67 + "Failed to run ffprobe (is ffmpeg installed?): {}", 68 + e 69 + )) 70 + })?; 71 + 72 + if !output.status.success() { 73 + let stderr = String::from_utf8_lossy(&output.stderr); 74 + return Err(SpatialError::Other(format!("ffprobe failed: {}", stderr))); 75 + } 76 + 77 + let stdout = String::from_utf8_lossy(&output.stdout); 78 + let parts: Vec<&str> = stdout.trim().split(',').collect(); 79 + 80 + if parts.len() < 4 { 81 + return Err(SpatialError::Other(format!( 82 + "Unexpected ffprobe output: {}", 83 + stdout 84 + ))); 85 + } 86 + 87 + let width = parts[0] 88 + .parse::<u32>() 89 + .map_err(|_| SpatialError::Other("Failed to parse width".to_string()))?; 90 + let height = parts[1] 91 + .parse::<u32>() 92 + .map_err(|_| SpatialError::Other("Failed to parse height".to_string()))?; 93 + 94 + let fps = if parts[2].contains('/') { 95 + let fps_parts: Vec<&str> = parts[2].split('/').collect(); 96 + let num: f64 = fps_parts[0].parse().unwrap_or(30.0); 97 + let den: f64 = fps_parts[1].parse().unwrap_or(1.0); 98 + num / den 99 + } else { 100 + parts[2].parse().unwrap_or(30.0) 101 + }; 102 + 103 + let total_frames = parts[3] 104 + .parse::<u32>() 105 + .map_err(|_| SpatialError::Other("Failed to parse frame count".to_string()))?; 106 + 107 + let duration = parts 108 + .get(4) 109 + .and_then(|s| s.parse::<f64>().ok()) 110 + .unwrap_or(total_frames as f64 / fps); 111 + 112 + let audio_output = Command::new("ffprobe") 113 + .args([ 114 + "-v", 115 + "error", 116 + "-select_streams", 117 + "a:0", 118 + "-show_entries", 119 + "stream=codec_type", 120 + "-of", 121 + "csv=p=0", 122 + input_path.to_str().unwrap(), 123 + ]) 124 + .output() 125 + .await 126 + .map_err(|e| SpatialError::Other(format!("Failed to check audio: {}", e)))?; 127 + 128 + let has_audio = String::from_utf8_lossy(&audio_output.stdout) 129 + .trim() 130 + .contains("audio"); 131 + 132 + Ok(VideoMetadata { 133 + width, 134 + height, 135 + fps, 136 + total_frames, 137 + duration, 138 + has_audio, 139 + }) 140 + } 141 + 142 + async fn extract_frames( 143 + input_path: &Path, 144 + metadata: &VideoMetadata, 145 + ) -> SpatialResult<mpsc::Receiver<Vec<u8>>> { 146 + let (tx, rx) = mpsc::channel::<Vec<u8>>(10); 147 + 148 + let width = metadata.width; 149 + let height = metadata.height; 150 + let frame_size = (width * height * 3) as usize; 151 + 152 + let input_path = input_path.to_path_buf(); 153 + 154 + tokio::spawn(async move { 155 + let mut child = Command::new("ffmpeg") 156 + .args([ 157 + "-i", 158 + input_path.to_str().unwrap(), 159 + "-f", 160 + "rawvideo", 161 + "-pix_fmt", 162 + "rgb24", 163 + "-vsync", 164 + "0", 165 + "-", 166 + ]) 167 + .stdout(Stdio::piped()) 168 + .stderr(Stdio::null()) 169 + .spawn() 170 + .expect("Failed to spawn ffmpeg"); 171 + 172 + let stdout = child.stdout.take().expect("Failed to capture stdout"); 173 + let mut reader = tokio::io::BufReader::new(stdout); 174 + let mut frame_buffer = vec![0u8; frame_size]; 175 + 176 + loop { 177 + match reader.read_exact(&mut frame_buffer).await { 178 + Ok(_) => { 179 + if tx.send(frame_buffer.clone()).await.is_err() { 180 + break; 181 + } 182 + } 183 + Err(e) if e.kind() == std::io::ErrorKind::UnexpectedEof => break, 184 + Err(_) => break, 185 + } 186 + } 187 + 188 + let _ = child.wait().await; 189 + }); 190 + 191 + Ok(rx) 192 + } 193 + 194 + fn frame_to_image(data: &[u8], width: u32, height: u32) -> SpatialResult<DynamicImage> { 195 + let rgb_image = RgbImage::from_raw(width, height, data.to_vec()).ok_or_else(|| { 196 + SpatialError::ImageError(format!( 197 + "Failed to create image from frame data ({}x{})", 198 + width, height 199 + )) 200 + })?; 201 + Ok(DynamicImage::ImageRgb8(rgb_image)) 202 + } 203 + 204 + async fn encode_stereo_video( 205 + output_path: std::path::PathBuf, 206 + metadata: VideoMetadata, 207 + mut rx: mpsc::Receiver<(DynamicImage, DynamicImage)>, 208 + ) -> SpatialResult<()> { 209 + let width = metadata.width; 210 + let height = metadata.height; 211 + let fps = metadata.fps; 212 + 213 + let output_width = width * 2; 214 + let output_height = height; 215 + 216 + let mut child = Command::new("ffmpeg") 217 + .args([ 218 + "-f", 219 + "rawvideo", 220 + "-pix_fmt", 221 + "rgb24", 222 + "-s", 223 + &format!("{}x{}", output_width, output_height), 224 + "-r", 225 + &format!("{}", fps), 226 + "-i", 227 + "-", 228 + "-c:v", 229 + "libx264", 230 + "-preset", 231 + "medium", 232 + "-crf", 233 + "23", 234 + "-pix_fmt", 235 + "yuv420p", 236 + "-y", 237 + output_path.to_str().unwrap(), 238 + ]) 239 + .stdin(Stdio::piped()) 240 + .stdout(Stdio::null()) 241 + .stderr(Stdio::null()) 242 + .spawn() 243 + .map_err(|e| SpatialError::Other(format!("Failed to spawn ffmpeg encoder: {}", e)))?; 244 + 245 + let mut stdin = child.stdin.take().expect("Failed to capture stdin"); 246 + 247 + while let Some((left, right)) = rx.recv().await { 248 + let mut sbs_image = ImageBuffer::new(output_width, output_height); 249 + 250 + let left_rgb = left.to_rgb8(); 251 + for y in 0..height { 252 + for x in 0..width { 253 + let pixel = left_rgb.get_pixel(x, y); 254 + sbs_image.put_pixel(x, y, *pixel); 255 + } 256 + } 257 + 258 + let right_rgb = right.to_rgb8(); 259 + for y in 0..height { 260 + for x in 0..width { 261 + let pixel = right_rgb.get_pixel(x, y); 262 + sbs_image.put_pixel(width + x, y, *pixel); 263 + } 264 + } 265 + 266 + stdin 267 + .write_all(&sbs_image.into_raw()) 268 + .await 269 + .map_err(|e| SpatialError::IoError(format!("Failed to write frame: {}", e)))?; 270 + } 271 + 272 + drop(stdin); 273 + 274 + let status = child 275 + .wait() 276 + .await 277 + .map_err(|e| SpatialError::Other(format!("ffmpeg encoding failed: {}", e)))?; 278 + 279 + if !status.success() { 280 + return Err(SpatialError::Other( 281 + "ffmpeg encoding exited with error".to_string(), 282 + )); 283 + } 284 + 285 + Ok(()) 286 + } 287 + 288 + pub async fn process_video( 289 + input_path: &Path, 290 + output_path: &Path, 291 + config: SpatialConfig, 292 + progress_cb: Option<ProgressCallback>, 293 + ) -> SpatialResult<()> { 294 + if !input_path.exists() { 295 + return Err(SpatialError::IoError(format!( 296 + "Input file not found: {:?}", 297 + input_path 298 + ))); 299 + } 300 + 301 + let metadata = get_video_metadata(input_path).await?; 302 + 303 + crate::model::ensure_model_exists::<fn(u64, u64)>(&config.encoder_size, None).await?; 304 + 305 + #[cfg(all(target_os = "macos", feature = "coreml"))] 306 + let estimator = { 307 + let model_path = crate::model::find_model(&config.encoder_size)?; 308 + let model_str = model_path.to_str().ok_or_else(|| { 309 + SpatialError::ModelError("Invalid model path encoding".to_string()) 310 + })?; 311 + std::sync::Arc::new(crate::depth_coreml::CoreMLDepthEstimator::new(model_str)?) 312 + }; 313 + 314 + let mut frame_rx = extract_frames(input_path, &metadata).await?; 315 + 316 + let (processed_tx, processed_rx) = mpsc::channel::<(DynamicImage, DynamicImage)>(10); 317 + 318 + let encode_handle = tokio::spawn(encode_stereo_video( 319 + output_path.to_path_buf(), 320 + metadata.clone(), 321 + processed_rx, 322 + )); 323 + 324 + let mut frame_count = 0u32; 325 + let total_frames = metadata.total_frames; 326 + 327 + if let Some(ref cb) = progress_cb { 328 + cb(VideoProgress::new(0, total_frames, "extracting".to_string())); 329 + } 330 + 331 + while let Some(frame_data) = frame_rx.recv().await { 332 + let frame = frame_to_image(&frame_data, metadata.width, metadata.height)?; 333 + 334 + frame_count += 1; 335 + if let Some(ref cb) = progress_cb { 336 + if frame_count % 10 == 0 || frame_count == total_frames { 337 + cb(VideoProgress::new( 338 + frame_count, 339 + total_frames, 340 + "processing".to_string(), 341 + )); 342 + } 343 + } 344 + 345 + #[cfg(all(target_os = "macos", feature = "coreml"))] 346 + let depth_map = estimator.estimate(&frame)?; 347 + 348 + #[cfg(not(all(target_os = "macos", feature = "coreml")))] 349 + let depth_map = { 350 + #[cfg(feature = "onnx")] 351 + { 352 + // For ONNX, we'd need to cache the estimator too 353 + // For now, this is a placeholder - ONNX video is not the primary path 354 + let model_path = crate::model::find_model(&config.encoder_size)?; 355 + let est = crate::depth::OnnxDepthEstimator::new(model_path.to_str().unwrap())?; 356 + est.estimate(&frame)? 357 + } 358 + #[cfg(not(feature = "onnx"))] 359 + { 360 + return Err(SpatialError::ConfigError( 361 + "No depth backend enabled. Enable 'coreml' or 'onnx' feature.".to_string(), 362 + )); 363 + } 364 + }; 365 + 366 + let (left, right) = generate_stereo_pair(&frame, &depth_map, config.max_disparity)?; 367 + 368 + if processed_tx.send((left, right)).await.is_err() { 369 + return Err(SpatialError::Other( 370 + "Encoder stopped unexpectedly".to_string(), 371 + )); 372 + } 373 + } 374 + 375 + drop(processed_tx); 376 + 377 + if let Some(ref cb) = progress_cb { 378 + cb(VideoProgress::new( 379 + total_frames, 380 + total_frames, 381 + "encoding".to_string(), 382 + )); 383 + } 384 + 385 + encode_handle 386 + .await 387 + .map_err(|e| SpatialError::Other(format!("Encoding task failed: {}", e)))??; 388 + 389 + if let Some(ref cb) = progress_cb { 390 + cb(VideoProgress::new( 391 + total_frames, 392 + total_frames, 393 + "complete".to_string(), 394 + )); 395 + } 396 + 397 + Ok(()) 398 + }