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Some edits on the UI

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- Grouped the face enhancers
- Make the mouth mask just a slider
- Removed the redundant switches
This commit is contained in:
Kenneth Estanislao
2026-03-13 22:03:28 +08:00
parent 30b27c2b71
commit 3c8b259a3f
5 changed files with 176 additions and 185 deletions
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modules/processors/frame/face_masking.py
+11 -8
View File
@@ -82,8 +82,8 @@ def create_lower_mouth_mask(
landmarks = face.landmark_2d_106
if landmarks is not None:
# Use outer mouth landmarks (52-63) to capture the lips only
lower_lip_order = list(range(52, 64))
# Use outer mouth landmarks (52-71) to capture the full mouth area
lower_lip_order = list(range(52, 72))
if max(lower_lip_order) >= landmarks.shape[0]:
return mask, mouth_cutout, mouth_box, lower_lip_polygon
@@ -94,13 +94,16 @@ def create_lower_mouth_mask(
center = np.mean(lower_lip_landmarks, axis=0)
# Expand the landmarks outward using the mouth_mask_size
# Use a more conservative expansion to avoid affecting face shape
expansion_factor = (
1 + modules.globals.mask_down_size * modules.globals.mouth_mask_size
)
expanded_landmarks = (lower_lip_landmarks - center) * expansion_factor + center
mouth_mask_size = getattr(modules.globals, "mouth_mask_size", 0.0) # 0-100 slider
expansion_factor = 1 + (mouth_mask_size / 100.0) * 2.5
# Removed specific top/chin extensions to preserve face shape
# Expand with extra downward bias toward chin
offsets = lower_lip_landmarks - center
chin_bias = 1 + (mouth_mask_size / 100.0) * 1.5
scale_y = np.where(offsets[:, 1] > 0, expansion_factor * chin_bias, expansion_factor)
expanded_landmarks = lower_lip_landmarks.copy()
expanded_landmarks[:, 0] = center[0] + offsets[:, 0] * expansion_factor
expanded_landmarks[:, 1] = center[1] + offsets[:, 1] * scale_y
# Convert back to integer coordinates
expanded_landmarks = expanded_landmarks.astype(np.int32)
modules/processors/frame/face_swapper.py
+48 -88
View File
@@ -136,10 +136,12 @@ def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
if not hasattr(source_face, 'normed_embedding') or source_face.normed_embedding is None:
return temp_frame
# Store a copy of the original frame before swapping for opacity blending
# Store a copy of the original frame before swapping for opacity blending and mouth mask
opacity = getattr(modules.globals, "opacity", 1.0)
opacity = max(0.0, min(1.0, opacity))
original_frame = temp_frame if opacity >= 1.0 else temp_frame.copy()
mouth_mask_enabled = getattr(modules.globals, "mouth_mask", False)
# Always copy if mouth mask is enabled (we need the unmodified original for mouth cutout)
original_frame = temp_frame.copy() if (opacity < 1.0 or mouth_mask_enabled) else temp_frame
# Pre-swap Input Check with optimization
if temp_frame.dtype != np.uint8:
@@ -190,28 +192,28 @@ def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
# --- Post-swap Processing (Masking, Opacity, etc.) ---
# Now, work with the guaranteed uint8 'swapped_frame'
if getattr(modules.globals, "mouth_mask", False): # Check if mouth_mask is enabled
if mouth_mask_enabled: # Check if mouth_mask is enabled
# Create a mask for the target face
face_mask = create_face_mask(target_face, temp_frame) # Use temp_frame (original shape) for mask creation geometry
face_mask = create_face_mask(target_face, original_frame) # Use original_frame for mask creation geometry
# Create the mouth mask using original geometry
# Create the mouth mask using the ORIGINAL frame (before swap) for cutout
mouth_mask, mouth_cutout, mouth_box, lower_lip_polygon = (
create_lower_mouth_mask(target_face, temp_frame) # Use temp_frame (original) for cutout
create_lower_mouth_mask(target_face, original_frame) # Use original_frame for real mouth cutout
)
# Apply the mouth area only if mouth_cutout exists
if mouth_cutout is not None and mouth_box != (0,0,0,0): # Add check for valid box
# Apply mouth area (from original) onto the 'swapped_frame'
if mouth_cutout is not None and mouth_box != (0,0,0,0):
# Apply mouth area (from original) onto the 'swapped_frame'
swapped_frame = apply_mouth_area(
swapped_frame, mouth_cutout, mouth_box, face_mask, lower_lip_polygon
)
# Draw bounding box only while slider is being dragged
if getattr(modules.globals, "show_mouth_mask_box", False):
mouth_mask_data = (mouth_mask, mouth_cutout, mouth_box, lower_lip_polygon)
# Draw visualization on the swapped_frame *before* opacity blending
swapped_frame = draw_mouth_mask_visualization(
swapped_frame, target_face, mouth_mask_data
)
mouth_mask_data = (mouth_mask, mouth_cutout, mouth_box, lower_lip_polygon)
swapped_frame = draw_mouth_mask_visualization(
swapped_frame, target_face, mouth_mask_data
)
# --- Poisson Blending ---
if getattr(modules.globals, "poisson_blend", False):
@@ -750,9 +752,9 @@ def create_lower_mouth_mask(
return mask, mouth_cutout, mouth_box, lower_lip_polygon
try: # Wrap main logic in try-except
# Use outer mouth landmarks (52-63) to capture the lips only
# This avoids including the chin/jawline, preserving the face shape from the swap
lower_lip_order = list(range(52, 64))
# Use outer mouth landmarks (52-71) to capture the full mouth area
# This covers both upper and lower lips for proper mouth preservation
lower_lip_order = list(range(52, 72))
# Check if all indices are valid for the loaded landmarks (already partially done by < 106 check)
if max(lower_lip_order) >= landmarks.shape[0]:
@@ -772,9 +774,18 @@ def create_lower_mouth_mask(
return mask, mouth_cutout, mouth_box, lower_lip_polygon
mask_down_size = getattr(modules.globals, "mask_down_size", 0.1) # Default 0.1
expansion_factor = 1 + mask_down_size
expanded_landmarks = (lower_lip_landmarks - center) * expansion_factor + center
mouth_mask_size = getattr(modules.globals, "mouth_mask_size", 0.0) # 0-100 slider
# 0=tight lip outline, 50=covers mouth area, 100=mouth to chin
expansion_factor = 1 + (mouth_mask_size / 100.0) * 2.5
# Expand landmarks from center, with extra downward bias toward chin
offsets = lower_lip_landmarks - center
# Add extra downward expansion for points below center (toward chin)
chin_bias = 1 + (mouth_mask_size / 100.0) * 1.5 # extra vertical stretch downward
scale_y = np.where(offsets[:, 1] > 0, expansion_factor * chin_bias, expansion_factor)
expanded_landmarks = lower_lip_landmarks.copy()
expanded_landmarks[:, 0] = center[0] + offsets[:, 0] * expansion_factor
expanded_landmarks[:, 1] = center[1] + offsets[:, 1] * scale_y
# Ensure landmarks are finite after adjustments
if not np.all(np.isfinite(expanded_landmarks)):
@@ -881,8 +892,8 @@ def draw_mouth_mask_visualization(
print(f"Error drawing polygon for visualization: {e}") # Optional debug
pass
# Optional: Draw bounding box (red rectangle)
# cv2.rectangle(vis_frame, (min_x, min_y), (max_x, max_y), (0, 0, 255), 1)
# Draw bounding box (red rectangle)
cv2.rectangle(vis_frame, (min_x, min_y), (max_x, max_y), (0, 0, 255), 2)
# Optional: Add labels
label_pos_y = min_y - 10 if min_y > 20 else max_y + 15 # Adjust position based on box location
@@ -962,85 +973,34 @@ def apply_mouth_area(
# print("Warning: Mouth cutout is invalid after resize attempt.")
return frame
# --- Color Correction Step ---
# Apply color transfer from ROI (swapped face region) to the original mouth cutout
# This helps match lighting/color before blending
color_corrected_mouth = resized_mouth_cutout # Default to resized if correction fails
try:
# Ensure both images are 3 channels for color transfer
if len(resized_mouth_cutout.shape) == 3 and resized_mouth_cutout.shape[2] == 3 and \
len(roi.shape) == 3 and roi.shape[2] == 3:
color_corrected_mouth = apply_color_transfer(resized_mouth_cutout, roi)
else:
# print("Warning: Cannot apply color transfer, images not BGR.")
pass
except cv2.error as ct_e: # Handle potential errors in color transfer
# print(f"Warning: Color transfer failed: {ct_e}. Using uncorrected mouth cutout.") # Optional debug
pass
except Exception as ct_gen_e:
# print(f"Warning: Unexpected error during color transfer: {ct_gen_e}")
pass
# --- End Color Correction ---
# --- Mask Creation ---
# Create a mask based *specifically* on the mouth_polygon, relative to the ROI
# Create a mask based on the mouth_polygon, relative to the ROI
polygon_mask_roi = np.zeros(roi.shape[:2], dtype=np.uint8)
# Adjust polygon coordinates relative to the ROI's top-left corner
adjusted_polygon = mouth_polygon - [min_x, min_y]
# Draw the filled polygon on the ROI mask
cv2.fillPoly(polygon_mask_roi, [adjusted_polygon.astype(np.int32)], 255)
# Feather the polygon mask (Gaussian blur)
mask_feather_ratio = getattr(modules.globals, "mask_feather_ratio", 12) # Default 12
# Calculate feather amount based on the smaller dimension of the box
feather_base_dim = min(box_width, box_height)
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
# Feather the edges with Gaussian blur for smooth blending
feather_amount = max(1, min(30, min(box_width, box_height) // 8))
kernel_size = 2 * feather_amount + 1
feathered_polygon_mask = cv2.GaussianBlur(polygon_mask_roi.astype(np.float32), (kernel_size, kernel_size), 0)
feathered_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()
if max_val > 1e-6: # Avoid division by zero
feathered_polygon_mask = feathered_polygon_mask / max_val
# Normalize to [0.0, 1.0]
max_val = feathered_mask.max()
if max_val > 1e-6:
feathered_mask = feathered_mask / max_val
else:
feathered_polygon_mask.fill(0.0) # Mask is all black if max is near zero
# --- End Mask Creation ---
feathered_mask.fill(0.0)
# --- Refined Blending ---
# 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(np.float32) / 255.0
else: # Assume already float [0,1] if type is float
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
# Use minimum to ensure we only affect area inside both masks (mouth area within face)
# This helps blend the edges smoothly with the surrounding swapped face region
combined_mask = np.minimum(feathered_polygon_mask, face_mask_roi)
# Expand mask to 3 channels for blending (ensure it matches image channels)
# --- Blending: paste original mouth onto swapped face ---
if len(frame.shape) == 3 and frame.shape[2] == 3:
combined_mask_3channel = combined_mask[:, :, np.newaxis]
mask_3ch = feathered_mask[:, :, np.newaxis].astype(np.float32)
inv_mask = 1.0 - mask_3ch
# 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 * mask) + (swapped_face * (1 - mask))
blended_roi = (resized_mouth_cutout.astype(np.float32) * mask_3ch +
roi.astype(np.float32) * inv_mask)
# Blend: (original_mouth * combined_mask) + (swapped_face_roi * (1 - combined_mask))
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)
else:
# print("Warning: Cannot apply mouth mask blending, frame is not 3-channel BGR.")
pass # Don't modify frame if it's not BGR
frame[min_y:max_y, min_x:max_x] = np.clip(blended_roi, 0, 255).astype(np.uint8)
except Exception as e:
print(f"Error applying mouth area: {e}") # Optional debug