Addresses an IndentationError at the definition of the
_blend_material_onto_frame helper function in
modules/processors/frame/face_swapper.py.
The fix ensures that the function definition line starts at
column 0 (no leading whitespace) and that the preceding
function's structure does not cause misinterpretation by the
Python parser. Duplicated/malformed definitions of related
helper functions were also confirmed to be removed in prior steps.
This resolves a syntax error that prevented your application from
starting.
I modified `modules/video_capture.py` so that it will explicitly try using `cv2.CAP_AVFOUNDATION` when initializing `cv2.VideoCapture` on macOS. If AVFoundation fails to open the camera, it will then fall back to the default OpenCV backend.
This adjustment should improve camera compatibility and stability on macOS, especially in situations where the default backend might not be working as expected.
Replaces Python 3.10+ type hint syntax (e.g., Frame | None)
with Python 3.9 compatible syntax (e.g., Optional[Frame])
in modules/processors/frame/face_swapper.py.
This resolves a TypeError encountered when running on Python 3.9.
Specifically, the return type of _prepare_warped_source_material_and_mask
was updated.
Removes a duplicated 'if not modules.globals.map_faces:' line within the create_webcam_preview function. This error was causing a syntax issue and preventing the application from running correctly, particularly in webcam mode.
This commit introduces shell scripts to automate the setup process and provide convenient ways to run the application on macOS.
New files added:
- setup_mac.sh: Checks for Python 3.9+ and ffmpeg, creates a virtual environment, installs pip dependencies from requirements.txt.
- run_mac.sh: Runs the application with the CPU execution provider by default.
- run_mac_cpu.sh: Explicitly runs with the CPU execution provider.
- run_mac_coreml.sh: Runs with the CoreML execution provider.
- run_mac_mps.sh: Runs with the MPS execution provider.
The README.md has also been updated with a new section detailing how to use these scripts for macOS users.
These scripts aim to simplify the initial setup and execution of the project on macOS, similar to the .bat files available for Windows.
This commit introduces the capability to swap hair along with the face from a source image to a target image/video or live webcam feed.
Key changes include:
1. **Hair Segmentation:**
- Integrated the `isjackwild/segformer-b0-finetuned-segments-skin-hair-clothing` model from Hugging Face using the `transformers` library.
- Added `modules/hair_segmenter.py` with a `segment_hair` function to produce a binary hair mask from an image.
- Updated `requirements.txt` with `transformers`.
2. **Combined Face-Hair Mask:**
- Implemented `create_face_and_hair_mask` in `modules/processors/frame/face_swapper.py` to generate a unified mask for both face (from landmarks) and segmented hair from the source image.
3. **Enhanced Swapping Logic:**
- Modified `swap_face` and related processing functions (`process_frame`, `process_frame_v2`, `process_frames`, `process_image`) to utilize the full source image (`source_frame_full`).
- The `swap_face` function now performs the standard face swap and then:
- Segments hair from the `source_frame_full`.
- Warps the hair and its mask to the target face's position using an affine transformation estimated from facial landmarks.
- Applies color correction (`apply_color_transfer`) to the warped hair.
- Blends the hair onto the target frame, preferably using `cv2.seamlessClone` for improved realism.
- Existing mouth mask logic is preserved and applied to the final composited frame.
4. **Webcam Integration:**
- Updated the webcam processing loop in `modules/ui.py` (`create_webcam_preview`) to correctly load and pass the `source_frame_full` to the frame processors.
- This enables hair swapping in live webcam mode.
- Added error handling for source image loading in webcam mode.
This set of changes addresses your request for more realistic face swaps that include hair. Further testing and refinement of blending parameters may be beneficial for optimal results across all scenarios.
- Add explicit checks for face detection results (source and target faces).
- Handle cases when face embeddings are not available, preventing AttributeError.
- Provide meaningful log messages for easier debugging in future scenarios.
Added:
- try-finally Block: This makes sure the camera.release() is called no matter how the while loops end.
- Resource Cleanup: The finally block takes care of cleaning up resources to keep the application stable.
The following changes have been implemented:
-A "clear" button has been incorporated.
-The Source x Target Mapper window has been retained following the submission of data via the "submit" button.
google-labs-jules[bot]
Teo Jia Cheng
Kenneth Estanislao
Gordon Böer
KUNJ SHAH
NeuroDonu
Adrian Zimbran
Soul Lee
Nimish Gåtam
qitian
Makaru
KRSHH
Mehdi Mousavi
Pedro Santos