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perf(processing): optimize post-processing with float32 and buffer reuse

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- 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>
This commit is contained in:
Lauri Gates
2026-02-22 21:27:31 +02:00
parent d5338a3eae
commit e93fb95903
2 changed files with 48 additions and 41 deletions
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modules/processors/frame/face_swapper.py
+9 -9
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@@ -1004,7 +1004,7 @@ def apply_mouth_area(
feather_amount = max(1, min(30, feather_base_dim // max(1, mask_feather_ratio))) # Avoid div by zero
# Ensure kernel size is odd and positive
kernel_size = 2 * feather_amount + 1
feathered_polygon_mask = cv2.GaussianBlur(polygon_mask_roi.astype(float), (kernel_size, kernel_size), 0)
feathered_polygon_mask = cv2.GaussianBlur(polygon_mask_roi.astype(np.float32), (kernel_size, kernel_size), 0)
# Normalize feathered mask to [0.0, 1.0] range
max_val = feathered_polygon_mask.max()
@@ -1019,9 +1019,9 @@ def apply_mouth_area(
# Get the corresponding ROI from the *full face mask* (already blurred)
# Ensure face_mask is float and normalized [0.0, 1.0]
if face_mask.dtype != np.float64 and face_mask.dtype != np.float32:
face_mask_float = face_mask.astype(float) / 255.0
face_mask_float = face_mask.astype(np.float32) / 255.0
else: # Assume already float [0,1] if type is float
face_mask_float = face_mask
face_mask_float = face_mask.astype(np.float32) if face_mask.dtype == np.float64 else face_mask
face_mask_roi = face_mask_float[min_y:max_y, min_x:max_x]
# Combine the feathered mouth polygon mask with the face mask ROI
@@ -1033,14 +1033,14 @@ def apply_mouth_area(
if len(frame.shape) == 3 and frame.shape[2] == 3:
combined_mask_3channel = combined_mask[:, :, np.newaxis]
# Ensure data types are compatible for blending (float or double for mask, uint8 for images)
color_corrected_mouth_uint8 = color_corrected_mouth.astype(np.uint8)
roi_uint8 = roi.astype(np.uint8)
combined_mask_float = combined_mask_3channel.astype(np.float64) # Use float64 for precision in mask
# Ensure data types are compatible for blending
# float32 provides sufficient precision for 8-bit image blending
combined_mask_f32 = combined_mask_3channel.astype(np.float32)
inv_mask = np.float32(1.0) - combined_mask_f32
# Blend: (original_mouth * combined_mask) + (swapped_face_roi * (1 - combined_mask))
blended_roi = (color_corrected_mouth_uint8 * combined_mask_float +
roi_uint8 * (1.0 - combined_mask_float))
blended_roi = (color_corrected_mouth * combined_mask_f32 +
roi * inv_mask)
# Place the blended ROI back into the frame
frame[min_y:max_y, min_x:max_x] = blended_roi.astype(np.uint8)