Prevent get_video_frame() from seeking to invalid frame positions.
The default frame_number=0 now resolves to the first frame instead of -1, and oversized frame requests clamp to the final valid frame instead of seeking past the end. Empty or invalid videos now return None safely after releasing the capture.
Affected files: capturer.py
Signed-off-by: Nguyen Van Nam <nam.nv205106@gmail.com>
`find_cluster_centroids()` iterates `k` from 1..`max_k` unconditionally. If `len(embeddings) < max_k`, `KMeans(n_clusters=k)` will raise `ValueError` when `k` exceeds the number of samples. This is an unhandled crash path on small datasets.
Affected files: cluster_analysis.py
Signed-off-by: Nguyen Van Nam <nam.nv205106@gmail.com>
Address Sourcery review feedback on PR #1879:
- Move OPENVINO_PROVIDER_CONFIG from _onnx_enhancer.py to
platform_info.py (a leaf module with no modules.* imports), so
the enhancer and face_swapper no longer import each other just to
share a constant. _onnx_enhancer re-exports it; face_swapper now
imports it at module top level instead of inside get_face_swapper().
- Narrow run.py's SystemExit handling: catch SystemExit separately
and print a [startup] message so the failure is visible instead
of being swallowed alongside ImportError/FileNotFoundError.
- Catch SystemExit from add_openvino_libs_to_path() so a missing
OpenVINO installation never causes a hard exit on Windows
- Replace hard-coded GPU+FP16 with AUTO:GPU,NPU,CPU device priority,
letting OpenVINO pick the best available accelerator
- Extract shared OPENVINO_PROVIDER_CONFIG constant to avoid
duplication between _onnx_enhancer and face_swapper
- Defer thread-suggestion evaluation until after execution_providers
is assigned, fixing a latent timing bug that affected OpenVINO,
CUDA, and DML thread hints
- Add add_openvino_libs_to_path() call in run.py before any ONNX
InferenceSession creation to register openvino.dll directory
- Detect and advertise OpenVINOExecutionProvider in platform_info
banner and accelerator label
- Prioritize openvino over dml in suggest_default_execution_provider
- Configure OpenVINO EP with GPU + FP16 device options for optimal
performance (~13 FPS on Intel GPU vs ~1 FPS CPU fallback)
- Set thread hint to 1 when OpenVINO EP is active
Swap the manual pip install + ruff check steps for astral-sh/ruff-action@v4.0.0.
Same pinned ruff 0.15.7, but with --output-format=github so violations appear
as inline annotations on the PR diff instead of a flat log.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Introduces pyproject.toml + .github/workflows/ruff.yml that gate
E701, E711, E712, F401, F541 on every PR and push to main.
Fixes the existing findings for those rules:
- Remove unused imports (sklearn.silhouette_score, numpy in several
files, typing.Optional, get_one_face, gpu_cvt_color, sys,
insightface.face_align)
- Annotate the intentional tkinter_fix side-effect import with
`# noqa: F401`
- Split multi-statement `if x: y` one-liners onto separate lines
- Replace `state == True` / `state == False` with truthiness checks
- Drop `f` prefix from f-strings with no placeholders
F841 (unused-variable), E402 (module-level-import-not-at-top), and
F821 (undefined-name) are left out of the gate for now — they surface
real findings (including a latent NameError in face_swapper.py) that
require human review to fix safely.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Skip prepending a directory that is already on LD_LIBRARY_PATH, so a
repeated import of run.py does not bloat the variable.
Addresses review feedback on #1826.
Mirrors the Windows preload block from #1775. When onnxruntime-gpu is
installed via pip with nvidia-cudnn-cu12, the .so files sit under
venv/lib/pythonX.Y/site-packages/nvidia/<pkg>/lib/ and the dynamic
linker never sees them. LD_LIBRARY_PATH cannot be set after Python
starts.
Pre-loads every lib*.so* via ctypes.CDLL with RTLD_GLOBAL before
onnxruntime opens its CUDA provider. Also extends LD_LIBRARY_PATH so
child processes (ffmpeg) inherit the path.
Fixes "libcudnn.so.9: cannot open shared object file" on pip-only
Linux installs.
ORT's CoreML EP GatherOpBuilder::IsOpSupportedImpl explicitly rejects
rank-0 (scalar) index tensors. StyleGAN-derived models (GFPGAN's 1024
variant has 16 of them, one per style-code slice) hit this in the
generator, and the resulting CPU fallbacks split the CoreML subgraph
into multiple partitions with boundary crossings on every inference.
Add a load-time ONNX rewrite that promotes each scalar index to [1] and
squeezes the added axis on the Gather output — semantically identical
but CoreML-compatible. GFPGAN now runs as a single CoreML partition with
zero CPU-fallback nodes; inference drops from ~87 ms to ~81 ms on an
M-series Mac.
The fix has been filed upstream as microsoft/onnxruntime#28180 — the
existing code comment in gather_op_builder.cc already describes this
exact workaround, it just isn't applied. Once the upstream fix ships
and the ORT floor is raised, this pass can be deleted.
Addresses Sourcery feedback on PR #1776: _get_soft_alpha caches a single
NxN template keyed by N, which is correct for the inswapper model
(128x128 aligned-face space) but would silently mis-warp if a caller
ever passed a non-square aligned face. Assert the shape instead of
silently assuming it.
The processing thread was running cvtColor on the full-resolution 1920×1080
frame before queueing it for display. Since the display thread immediately
resizes the frame to the preview window (~5× smaller pixel count), doing
the colour conversion on the resized buffer is cheaper overall.
Processing thread now queues BGR; display thread resizes then cvtColor.
Both face_swapper._fast_paste_back and face_enhancer._paste_back were
doing a numpy float32 round-trip per frame: convert the target crop and
the warped face to float32, blend, clip, cast back to uint8. That's four
crop-sized allocations plus unvectorized elementwise math.
Replace with a fused uint8 blend using cv2.merge + cv2.multiply + cv2.add,
which cv2 dispatches to SIMD (NEON on Apple Silicon / AVX on x86). Stored
alpha templates switched from float32 [0, 1] to uint8 [0, 255] so no
conversion is needed per frame. CUDA paths also simplified — upload uint8
alpha (less bandwidth) and scale on device.
Micro-bench on 1000x1000 RGB crop:
current (float32 numpy): 9.43 ms
cv2 uint8 fused: 1.16 ms (8.1× faster, max diff 2/255)
Visual diff is imperceptible (quantization noise in the last step).
_fast_paste_back used to erode and Gaussian-blur the warped alpha mask in
output coordinates with kernel sizes proportional to the on-screen face
bbox. That made the per-frame cost ~O(area * k^2) — a face filling half
the frame took ~8x the compositing work of one filling a quarter, which
is why FPS fell off when leaning into the camera.
Instead, build a feathered alpha template once at aligned-face resolution
(128x128 for inswapper) and warp the soft mask per-frame. The affine
transform preserves the relative feather width, so the visual output is
equivalent; the per-frame cost is now O(crop_area) with no size-scaled
erode/blur and no size-scaled padding.
Also collapses the CPU fallback onto the same shape — it previously did
a full-frame warpAffine twice per call, which scaled with the whole
frame instead of the face crop.
- core._run_pipe_pipeline: hand the background detector its own copy of
the frame. The frame processors mutate in place via paste-back, which
was racing with concurrent face detection on the same buffer.
- face_swapper._init_cuda_graph_session: replace the
`swapper.session.run` monkey-patch with a `_CudaGraphSessionAdapter`
that proxies every attribute to the underlying session and only
overrides `.run()`. Guarded so repeat init does not double-wrap.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
