- Added interaction helpers for cutout region validation and shape menu interaction.
- Created main operator for converting image regions into cutout shapes, including AI artifact generation.
- Introduced object creation logic for cutout shapes, handling depth and normal textures.
- Defined cutout shape identifiers and node groups for various shape types.
- Implemented depth texture writing and prediction artifact generation for MoGe-2.
- Developed server jobs for generating cutout artifacts, depth planes, and refining image regions.
- Added region refinement logic to enhance gesture masks using BEN2.
- Added a new cutout tool for selecting regions in images, allowing users to choose mesh shapes based on selected areas.
- Introduced depth generation capabilities for rasterized images, including normal data extraction.
- Created jobs for generating depth planes and region data, enhancing the AI data generation workflow.
- Developed a selection refinement process for cutout regions, improving accuracy in generated outputs.
- Established a new region module for handling rasterized image regions and their associated AI generation tasks.
- Enhanced node graph builders for asset generation, facilitating smoother integration of new features.
- Implemented `build_mesh_plane.py` to create a geometry node group for mesh planes with customizable parameters.
- Created `build_shadeless.py` for a shader node group that displays color without scene lighting while preserving shadows and reflections.
- Added `validate.py` to validate the integrity of the newly created node groups and their inputs.
- Introduced `validate_cutout.py` to ensure the functionality of cutout node groups and their dependencies.
- Developed `validate_mesh_plane.py` to test the mesh plane creation and its modifier settings.
- Implemented cotangent Poisson field and Poisson balloon profile in cutout_profile.py.
- Added teddy balloon heights calculation and related functions in cutout_teddy.py.
- Introduced interaction utilities for image editing in interaction.py.
- Developed depth normalization and analysis functions in cutout_depth.py.
- Created a validation script for cutout assets in validate_cutout_asset.py.
- Added upscale operator to handle image upscaling by 4x using AI models.
- Introduced new jobs for upscale processing in the server, including debug and standard upscale jobs.
- Created a new ONNX model handler for upscale operations, supporting tiled inference and resource management.
- Updated tests to cover new upscale functionality, including input validation and output verification.
- Added documentation for the upscale feature, detailing usage and model options.
- Implemented the Image Perspective tool for editing image corners in Blender.
- Created a new operator `EditImagePerspective` to handle the perspective transformation.
- Added a preview feature for the perspective adjustment.
- Introduced the `ImagePolylineTool` for editing image masks with polylines.
- Developed backend support for Real-ESRGAN model inference, including ONNX integration.
- Added functionality for image masking and background removal.
- Created unit tests for the Real-ESRGAN ONNX model to ensure correct preprocessing and inference.
- Implemented ExtractImageRegion operator for extracting image regions using box or lasso selection.
- Added RemoveImageRegionBackground job for background removal from selected image regions.
- Created utility functions for polygon bounds, selection canvas creation, and applying selection masks.
- Integrated image processing using PIL and numpy for efficient pixel manipulation.
- Enhanced user experience with modal drawing and status updates during extraction.
- Implemented a comprehensive test suite for the clipboard image module, covering various functionalities including operator registration, context handling, and image processing.
- Created a new mesh plane asset with customizable parameters and integrated it into the geometry node system.
- Developed a validation script to ensure the correct behavior of the mesh plane asset, including input handling and output verification.
- Implemented `DownloadModel` and `DownloadRequiredModels` operators for downloading models.
- Created `ClearModels` operator to clear model resources.
- Added `ConvertToSurface` operator for converting image empties into textured surfaces.
- Introduced `GenerateDa3Surface` and `GenerateMoge2Surface` operators for generating surfaces using DA3 and MoGe-2 models respectively.
- Developed utility functions for handling image mesh data, assigning attributes, and managing geometry nodes.
- Established runtime configuration and post-installation model downloading in `runtime.py`.
- Implemented surface generation functionality in `surface.py`, including geometry estimation and texture writing.
- Introduced media input handling in `input.py`, supporting image and video frame extraction.
- Added model management for geometry adapters in `models/__init__.py`.
- Created BEN2 and DA3 model inference logic in `ben2.py` and `da3.py`, respectively.
- Developed ONNX model inference utilities for BEN2 and DA3 in `onnx_ben2.py` and `onnx_da3.py`.
- Added support for MoGe-2 model inference in `moge2.py` with necessary ONNX utilities.
- Implemented output handling for depth and surface data in `depth.py` and `surface.py`.
- Created debug output utilities for DA3 and MoGe-2 predictions in `debug_da3.py` and `debug_moge2.py`.
- Enhanced ONNX runtime session management in `onnx_runtime.py`.
- Updated `__init__.py` files to include new modules and functions for better organization.
- Introduced ModelSpec dataclass to define model attributes.
- Added multiple models including DA3 Small, Base, Giant 1.1, Nested Giant Large 1.1, and MoGe-2 variants.
- Implemented functions to retrieve model details and metadata.
- Included BEN2 model specifications for background removal.
- Introduced `onnx_moge2.py` for MoGe-2 ONNX inference with NumPy post-processing.
- Added `onnx_ben2.py` for BEN2 ONNX inference with Pillow and NumPy preprocessing.
- Created `onnx_da3.py` for Depth Anything 3 inference with ONNX Runtime and NumPy.
- Implemented shared ONNX Runtime session helpers in `onnx_runtime.py`.
- Developed unit tests for server functionalities in `test_server.py`, including model caching and job management.
- Enhanced job server to handle frame extraction without requiring models.
- Implement JobClient for handling HTTP requests to the JobServer.
- Create ModalJobOperatorBase for managing Blender job lifecycle.
- Introduce JobContext and JobServer for job management and state handling.
- Add ServiceConnection and ServiceController for managing service processes.
- Implement job submission, cancellation, and resource management endpoints in the server.
- Ensure proper error handling and job state updates throughout the service.
- Updated version in pyproject.toml and blender_manifest.toml to 0.11.2.
- Enhanced uv executable path documentation in client.py.
- Added inference_mode parameter to reconstruct_request function in client.py.
- Implemented uv download functionality in install_env.py with improved error handling and checksum verification.
- Modified install_uv function to download and extract uv binary instead of using bundled version.
- Updated job progress handling in operators.py to track progress stages.
- Improved download progress reporting in worker.py and adjusted related tests.
- Refactored tests to accommodate changes in status reporting and model downloading.
- Removed vendor.lock.toml as part of the cleanup process.
- Added `ben2_onnx.py` for BEN2 ONNX inference with preprocessing.
- Introduced `da3_onnx.py` for Depth Anything 3 inference, including camera projection calculations.
- Created `moge_onnx.py` for MoGe-2 ONNX inference and post-processing.
- Implemented shared ONNX Runtime session helpers in `onnx_runtime.py`.
- Added model synchronization script `sync_models.py` for downloading and verifying ONNX models.
- Developed unit tests for BEN2, Depth Anything 3, and MoGe ONNX models to ensure functionality.
- Updated `vendor.lock.toml` to remove unused artifacts.
- Implement tests for ben2 and da3 model caching to ensure models are reused for matching runtime keys.
- Verify service state management, including client registration, job cancellation, and client expiration.
- Test discovery file creation and removal based on service ownership.
- Ensure frame extraction functionality works without models and produces expected results.
- Implemented a new geometry nodes asset in `build_geometry_nodes_asset.py` to handle mesh refinement and topology fixing.
- Created a validation script in `validate_geometry_nodes_asset.py` to ensure the integrity and functionality of the geometry node group.
- Added input handling for various parameters including resolution, iterations, and smoothing options.
- Included checks for node group structure and attributes to validate the geometry processing pipeline.
- Create a build workflow in `.github/workflows/build.yml` to automate the build process.
- Configure Python environment and install dependencies.
- Add steps for running tests and packaging the extension.
chore: Initialize project metadata and dependencies for Depth Anything 3
- Add `pyproject.toml` for project configuration and dependencies.
- Include optional dependencies for app and gs functionalities.
- Define project scripts and metadata.
chore: Add requirements file for Depth Anything 3 dependencies
- Create `requirements.txt` listing all necessary packages for the project.
feat: Implement export utility functions for Depth Anything 3
- Create `src/depth_anything_3/utils/export/__init__.py` to handle various export formats.
- Implement functions for exporting predictions in different formats.
feat: Add ray utility functions for homography estimation
- Create `src/depth_anything_3/utils/ray_utils.py` with functions for computing optimal rotation and homography using RANSAC.
feat: Implement vendor preparation script
- Add `prepare_vendor.py` to manage third-party dependencies and artifacts.
- Include functions for downloading and verifying artifacts based on a lock file.
test: Add unit tests for vendor preparation script
- Create `tests/test_prepare_vendor.py` to test artifact downloading and verification.
- Implement tests for handling hash mismatches and unsupported platforms.
chore: Add vendor lock file for dependency management
- Create `vendor.lock.toml` to define artifacts and platforms for the project.
- Bump SnapForm version in uv.lock from 0.9.30 to 0.10.10.
- Introduce comprehensive technical documentation for SnapForm, detailing features, usage instructions, and development guidelines.
- Modified status messages and stage labels in `test_addon.py` for clarity and consistency.
- Added tests to ensure proper host termination and command execution in `test_addon.py`.
- Enhanced `test_bootstrap_runtime.py` with additional tests for installation behavior, including handling of CUDA and MPS devices, and improved error handling for disk space and network checks.
- Updated download progress reporting in `test_download_progress.py` to include download speed and improved message formatting.
- Added new tests in `test_worker.py` for device selection, NPZ file validation, and model downloading behavior with Hugging Face endpoints.
- Bumped version in `uv.lock` to 0.9.30.
- Added ai_host.py to handle AI tasks with FastAPI integration.
- Introduced HostState and ModelCache classes for managing client connections and model loading.
- Implemented job submission and cancellation endpoints in the AI host.
- Enhanced tests for AI host functionality, including client tracking and job cancellation.
- Updated existing tests to accommodate changes in the operator and command structure.
- Refactored runtime commands to use request-based architecture instead of command-line arguments.
- Improved model loading logic to handle CUDA memory management and reuse models efficiently.
- Introduced BEN2 model for background removal in the panel and properties.
- Updated runtime to handle background removal commands and model downloads.
- Implemented background removal logic in worker, including image processing and saving results.
- Enhanced tests to cover new background removal features and ensure proper integration.
- Updated version to 0.9.28 to reflect new features and improvements.
- Introduced MODEL_NUMBERS for better model indexing.
- Improved enum_items function to cache results and handle download status.
- Updated ActiveJob class to manage temporary input paths and added displacement_via property.
- Changed IMAGE_DEPTH_PROCESS_RES_METHOD from "lower_bound_resize" to "upper_bound_resize".
- Enhanced RunJob operator to include new properties and improved error messages.
- Modified panel layout for better user experience and added runtime installation checks.
- Updated SnapFormSettings to reflect new model properties and defaults.
- Added temporary input path management in runtime.
- Refined worker functions to improve error handling and messaging.
- Expanded tests to cover new functionality and ensure backward compatibility.
- Fixed packaging script to align with versioning conventions.
- Implemented RuntimeModelsPanel and DebugPanel for UI integration in Blender.
- Created SnapFormPreferences to manage user settings for storage and runtime configuration.
- Defined SnapFormSettings to hold various properties related to input, model selection, and export options.
- Developed runtime management functions for installation, model downloading, and reconstruction commands.
- Added status bar functionality to display job progress and status in the Blender UI.
- Implemented worker script for handling model downloads and depth reconstruction tasks.
- Introduced unit tests for download progress reporting and image depth extraction functionalities.
- Added properties for Omoo DA3 settings in properties.py, including input path, video frames, model selection, and export format.
- Created runtime management in runtime.py, handling model downloads, job paths, and command execution for reconstruction.
- Developed status bar functionality in status_bar.py to display job progress and status.
- Implemented worker.py for image and video processing, including frame extraction and model inference.
- Added comprehensive unit tests covering addon registration, model handling, export processes, and UI interactions.
- Included a lock file for dependency management with uv.
