The macOS Apple Silicon section installed Python 3.11 but then
referenced Python 3.10 in several places:
- `brew install python-tk@3.10` → python-tk@3.11
- Linux comment "Ensure you use the installed Python 3.10" → 3.11
- CoreML section cross-reference "completed the macOS setup above
using Python 3.10" → 3.11
- `python3.10 run.py` usage command → python3.11
- "You must use Python 3.10" note → 3.11
- `brew reinstall python-tk@3.10` troubleshooting tip → 3.11
- Removed `python@3.11` from the list of conflicting versions to
uninstall (it is the required version, not a conflict)
Fixes#1632
The live webcam preview in ui.py calls process_frame_v2() on all
frame processors, but face_enhancer.py was missing this method.
This caused an AttributeError crash when the GFPGAN face enhancer
was enabled during live mode.
Fixes https://github.com/hacksider/Deep-Live-Cam/issues/1654
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Change default face swapper model to FP32 for better GPU compatibility and avoid NaN issues on certain GPUs.
Revamped `run.py` to adjust PATH variables for dependencies setup and re-added with expanded configuration.
Add ToolTip class (modules/ui_tooltip.py) and wire descriptive hover
tooltips onto every button, switch, slider, and dropdown in the main
window. Tooltips appear after a 500ms hover delay and are clamped to
screen bounds.
This requires no new dependencies — ToolTip uses only customtkinter.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
cv2_enumerate_cameras(CAP_AVFOUNDATION) probes indices 0-99 through
OpenCV's AVFoundation backend, which intermittently segfaults (exit
code 139) when invalid device indices are probed. Replace with a
bounded cv2.VideoCapture loop (range(10)) that safely skips
unavailable indices.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Replace float64 with float32 in apply_mouth_area() blending masks —
float32 provides sufficient precision for 8-bit image blending and
halves memory bandwidth
- Use float32 in apply_mask_area() mask computations
- Vectorize hull padding loop in create_face_mask() (face_masking.py)
replacing per-point Python loop with NumPy array operations
- Fix apply_color_transfer() to use proper [0,1] LAB conversion —
cv2.cvtColor with float32 input expects [0,1] range, not [0,255]
- Pre-compute inverse masks to avoid repeated (1.0 - mask) subtraction
- Use np.broadcast_to instead of np.repeat for face mask expansion
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Move opacity calculation before frame copy to skip the copy when
opacity is 1.0 (common case). Add early return path for full opacity.
Clear PREVIOUS_FRAME_RESULT instead of caching when interpolation
is disabled.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add a dedicated detection thread that runs face detection continuously
on the latest captured frame and publishes results to a shared dict.
The processing/swap thread reads cached detection results instead of
running detection inline, so it never blocks on the 15-30ms detection
cost.
Architecture change: 2 threads → 3 threads
Before: capture → [detect + swap] → display
After: capture → swap (uses cached detections) → display
↘ detect (async, writes to shared cache) ↗
Also replaces the blocking while/ROOT.update() display loop with
ROOT.after()-based scheduling, which avoids Tk event loop re-entrancy
issues and UI freezes.
Closes#1664
Kenneth Estanislao