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Deep-Live-Cam
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Merge 2d0c5bc8d0 into 2b70131e6a

pull/1411/merge
asateesh99 2025-07-16 01:36:27 +02:00 committed by GitHub
parent 2b70131e6a 2d0c5bc8d0
commit 82fcd7916a
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3 changed files with 227 additions and 202 deletions
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2
modules/globals.py
View File

@ -41,3 +41,5 @@ show_mouth_mask_box = False
mask_feather_ratio = 8
mask_down_size = 0.50
mask_size = 1
# Removed all performance optimization variables

423
modules/processors/frame/face_swapper.py
View File

@ -70,7 +70,7 @@ def get_face_swapper() -> Any:
def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
face_swapper = get_face_swapper()
# Apply the face swap
# Simple face swap - maximum FPS
swapped_frame = face_swapper.get(
temp_frame, target_face, source_face, paste_back=True
)
@ -98,28 +98,211 @@ def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
return swapped_frame
# Simple face position smoothing for stability
_last_face_position = None
_position_smoothing = 0.7 # Higher = more stable, lower = more responsive
def swap_face_stable(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
"""Ultra-fast face swap - maximum FPS priority"""
# Skip all complex processing for maximum FPS
face_swapper = get_face_swapper()
swapped_frame = face_swapper.get(temp_frame, target_face, source_face, paste_back=True)
# Skip all post-processing to maximize FPS
return swapped_frame
def swap_face_ultra_fast(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
"""Fast face swap with mouth mask support and forehead protection"""
face_swapper = get_face_swapper()
swapped_frame = face_swapper.get(temp_frame, target_face, source_face, paste_back=True)
# Fix forehead hair issue - blend forehead area back to original
swapped_frame = fix_forehead_hair_issue(swapped_frame, target_face, temp_frame)
# Add mouth mask functionality back (only if enabled)
if modules.globals.mouth_mask:
# Create a mask for the target face
face_mask = create_face_mask(target_face, temp_frame)
# Create the mouth mask
mouth_mask, mouth_cutout, mouth_box, lower_lip_polygon = (
create_lower_mouth_mask(target_face, temp_frame)
)
# Apply the mouth area
swapped_frame = apply_mouth_area(
swapped_frame, mouth_cutout, mouth_box, face_mask, lower_lip_polygon
)
if modules.globals.show_mouth_mask_box:
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
)
return swapped_frame
def fix_forehead_hair_issue(swapped_frame: Frame, target_face: Face, original_frame: Frame) -> Frame:
"""Fix hair falling on forehead by blending forehead area back to original"""
try:
# Get face bounding box
bbox = target_face.bbox.astype(int)
x1, y1, x2, y2 = bbox
# Ensure coordinates are within frame bounds
h, w = swapped_frame.shape[:2]
x1, y1 = max(0, x1), max(0, y1)
x2, y2 = min(w, x2), min(h, y2)
if x2 <= x1 or y2 <= y1:
return swapped_frame
# Focus on forehead area (upper 35% of face)
forehead_height = int((y2 - y1) * 0.35)
forehead_y2 = y1 + forehead_height
if forehead_y2 > y1:
# Extract forehead regions
swapped_forehead = swapped_frame[y1:forehead_y2, x1:x2]
original_forehead = original_frame[y1:forehead_y2, x1:x2]
# Create a soft blend mask for forehead area
mask = np.ones(swapped_forehead.shape[:2], dtype=np.float32)
# Apply strong Gaussian blur for very soft blending
mask = cv2.GaussianBlur(mask, (31, 31), 10)
mask = mask[:, :, np.newaxis]
# Blend forehead areas (keep much more of original to preserve hair)
blended_forehead = (swapped_forehead * 0.3 + original_forehead * 0.7).astype(np.uint8)
# Apply the blended forehead back
swapped_frame[y1:forehead_y2, x1:x2] = blended_forehead
return swapped_frame
except Exception:
return swapped_frame
def improve_forehead_matching(swapped_frame: Frame, source_face: Face, target_face: Face, original_frame: Frame) -> Frame:
"""Create precise face mask - only swap core facial features (eyes, nose, cheeks, chin)"""
try:
# Get face landmarks for precise masking
if hasattr(target_face, 'landmark_2d_106') and target_face.landmark_2d_106 is not None:
landmarks = target_face.landmark_2d_106.astype(np.int32)
# Create precise face mask excluding forehead and hair
mask = create_precise_face_mask(landmarks, swapped_frame.shape[:2])
if mask is not None:
# Apply the precise mask
mask_3d = mask[:, :, np.newaxis] / 255.0
# Blend only the core facial features
result = (swapped_frame * mask_3d + original_frame * (1 - mask_3d)).astype(np.uint8)
return result
# Fallback: use bounding box method but exclude forehead
bbox = target_face.bbox.astype(int)
x1, y1, x2, y2 = bbox
# Ensure coordinates are within frame bounds
h, w = swapped_frame.shape[:2]
x1, y1 = max(0, x1), max(0, y1)
x2, y2 = min(w, x2), min(h, y2)
if x2 <= x1 or y2 <= y1:
return swapped_frame
# Exclude forehead area (upper 25% of face) to avoid hair swapping
forehead_height = int((y2 - y1) * 0.25)
face_start_y = y1 + forehead_height
if face_start_y < y2:
# Only blend the lower face area (eyes, nose, cheeks, chin)
swapped_face_area = swapped_frame[face_start_y:y2, x1:x2]
original_face_area = original_frame[face_start_y:y2, x1:x2]
# Create soft mask for the face area only
mask = np.ones(swapped_face_area.shape[:2], dtype=np.float32)
mask = cv2.GaussianBlur(mask, (15, 15), 5)
mask = mask[:, :, np.newaxis]
# Apply the face area back (keep original forehead/hair)
swapped_frame[face_start_y:y2, x1:x2] = swapped_face_area
return swapped_frame
except Exception:
return swapped_frame
def create_precise_face_mask(landmarks: np.ndarray, frame_shape: tuple) -> np.ndarray:
"""Create precise mask for core facial features only (exclude forehead and hair)"""
try:
mask = np.zeros(frame_shape, dtype=np.uint8)
# For 106-point landmarks, use correct indices
# Face contour (jawline) - points 0-32
jaw_line = landmarks[0:33]
# Eyes area - approximate indices for 106-point model
left_eye_area = landmarks[33:42] # Left eye region
right_eye_area = landmarks[87:96] # Right eye region
# Eyebrows (start from eyebrow level, not forehead)
left_eyebrow = landmarks[43:51] # Left eyebrow
right_eyebrow = landmarks[97:105] # Right eyebrow
# Create face contour that excludes forehead
# Start from eyebrow level and go around the face
face_contour_points = []
# Add eyebrow points (this will be our "top" instead of forehead)
face_contour_points.extend(left_eyebrow)
face_contour_points.extend(right_eyebrow)
# Add jawline points (bottom and sides of face)
face_contour_points.extend(jaw_line)
# Convert to numpy array
face_contour_points = np.array(face_contour_points)
# Create convex hull for the core face area (excluding forehead)
hull = cv2.convexHull(face_contour_points)
cv2.fillConvexPoly(mask, hull, 255)
# Apply Gaussian blur for soft edges
mask = cv2.GaussianBlur(mask, (21, 21), 7)
return mask
except Exception as e:
print(f"Error creating precise face mask: {e}")
return None
def process_frame(source_face: Face, temp_frame: Frame) -> Frame:
if modules.globals.color_correction:
temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB)
# Skip color correction for maximum FPS
# if modules.globals.color_correction:
# temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB)
if modules.globals.many_faces:
many_faces = get_many_faces(temp_frame)
if many_faces:
for target_face in many_faces:
if source_face and target_face:
temp_frame = swap_face(source_face, target_face, temp_frame)
else:
print("Face detection failed for target/source.")
temp_frame = swap_face_ultra_fast(source_face, target_face, temp_frame)
else:
target_face = get_one_face(temp_frame)
if target_face and source_face:
temp_frame = swap_face(source_face, target_face, temp_frame)
else:
logging.error("Face detection failed for target or source.")
temp_frame = swap_face_ultra_fast(source_face, target_face, temp_frame)
return temp_frame
def process_frame_v2(temp_frame: Frame, temp_frame_path: str = "") -> Frame:
if is_image(modules.globals.target_path):
if modules.globals.many_faces:
@ -271,7 +454,6 @@ def create_lower_mouth_mask(
mouth_cutout = None
landmarks = face.landmark_2d_106
if landmarks is not None:
# 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
lower_lip_order = [
65,
66,
@ -295,192 +477,74 @@ def create_lower_mouth_mask(
2,
65,
]
lower_lip_landmarks = landmarks[lower_lip_order].astype(
np.float32
) # Use float for precise calculations
lower_lip_landmarks = landmarks[lower_lip_order].astype(np.float32)
# Calculate the center of the landmarks
center = np.mean(lower_lip_landmarks, axis=0)
# Expand the landmarks outward
expansion_factor = (
1 + modules.globals.mask_down_size
) # Adjust this for more or less expansion
expansion_factor = 1 + modules.globals.mask_down_size
expanded_landmarks = (lower_lip_landmarks - center) * expansion_factor + center
# Extend the top lip part
toplip_indices = [
20,
0,
1,
2,
3,
4,
5,
] # Indices for landmarks 2, 65, 66, 62, 70, 69, 18
toplip_extension = (
modules.globals.mask_size * 0.5
) # Adjust this factor to control the extension
toplip_indices = [20, 0, 1, 2, 3, 4, 5]
toplip_extension = modules.globals.mask_size * 0.5
for idx in toplip_indices:
direction = expanded_landmarks[idx] - center
direction = direction / np.linalg.norm(direction)
expanded_landmarks[idx] += direction * toplip_extension
# Extend the bottom part (chin area)
chin_indices = [
11,
12,
13,
14,
15,
16,
] # Indices for landmarks 21, 22, 23, 24, 0, 8
chin_extension = 2 * 0.2 # Adjust this factor to control the extension
chin_indices = [11, 12, 13, 14, 15, 16]
chin_extension = 2 * 0.2
for idx in chin_indices:
expanded_landmarks[idx][1] += (
expanded_landmarks[idx][1] - center[1]
) * chin_extension
# Convert back to integer coordinates
expanded_landmarks = expanded_landmarks.astype(np.int32)
# Calculate bounding box for the expanded lower mouth
min_x, min_y = np.min(expanded_landmarks, axis=0)
max_x, max_y = np.max(expanded_landmarks, axis=0)
# Add some padding to the bounding box
padding = int((max_x - min_x) * 0.1) # 10% padding
padding = int((max_x - min_x) * 0.1)
min_x = max(0, min_x - padding)
min_y = max(0, min_y - padding)
max_x = min(frame.shape[1], max_x + padding)
max_y = min(frame.shape[0], max_y + padding)
# Ensure the bounding box dimensions are valid
if max_x <= min_x or max_y <= min_y:
if (max_x - min_x) <= 1:
max_x = min_x + 1
if (max_y - min_y) <= 1:
max_y = min_y + 1
# Create the mask
mask_roi = np.zeros((max_y - min_y, max_x - min_x), dtype=np.uint8)
cv2.fillPoly(mask_roi, [expanded_landmarks - [min_x, min_y]], 255)
# Apply Gaussian blur to soften the mask edges
mask_roi = cv2.GaussianBlur(mask_roi, (15, 15), 5)
# Place the mask ROI in the full-sized mask
# Improved smoothing for mouth mask
mask_roi = cv2.GaussianBlur(mask_roi, (25, 25), 8)
mask[min_y:max_y, min_x:max_x] = mask_roi
# Extract the masked area from the frame
mouth_cutout = frame[min_y:max_y, min_x:max_x].copy()
# Return the expanded lower lip polygon in original frame coordinates
lower_lip_polygon = expanded_landmarks
return mask, mouth_cutout, (min_x, min_y, max_x, max_y), lower_lip_polygon
def draw_mouth_mask_visualization(
frame: Frame, face: Face, mouth_mask_data: tuple
) -> Frame:
def draw_mouth_mask_visualization(frame: Frame, face: Face, mouth_mask_data: tuple) -> Frame:
landmarks = face.landmark_2d_106
if landmarks is not None and mouth_mask_data is not None:
mask, mouth_cutout, (min_x, min_y, max_x, max_y), lower_lip_polygon = (
mouth_mask_data
)
mask, mouth_cutout, (min_x, min_y, max_x, max_y), lower_lip_polygon = mouth_mask_data
vis_frame = frame.copy()
# Ensure coordinates are within frame bounds
height, width = vis_frame.shape[:2]
min_x, min_y = max(0, min_x), max(0, min_y)
max_x, max_y = min(width, max_x), min(height, max_y)
# Adjust mask to match the region size
mask_region = mask[0 : max_y - min_y, 0 : max_x - min_x]
# Remove the color mask overlay
# color_mask = cv2.applyColorMap((mask_region * 255).astype(np.uint8), cv2.COLORMAP_JET)
# Ensure shapes match before blending
vis_region = vis_frame[min_y:max_y, min_x:max_x]
# Remove blending with color_mask
# if vis_region.shape[:2] == color_mask.shape[:2]:
# blended = cv2.addWeighted(vis_region, 0.7, color_mask, 0.3, 0)
# vis_frame[min_y:max_y, min_x:max_x] = blended
# Draw the lower lip polygon
cv2.polylines(vis_frame, [lower_lip_polygon], True, (0, 255, 0), 2)
# Remove the red box
# cv2.rectangle(vis_frame, (min_x, min_y), (max_x, max_y), (0, 0, 255), 2)
# Visualize the feathered mask
feather_amount = max(
1,
min(
30,
(max_x - min_x) // modules.globals.mask_feather_ratio,
(max_y - min_y) // modules.globals.mask_feather_ratio,
),
)
# Ensure kernel size is odd
kernel_size = 2 * feather_amount + 1
feathered_mask = cv2.GaussianBlur(
mask_region.astype(float), (kernel_size, kernel_size), 0
)
feathered_mask = (feathered_mask / feathered_mask.max() * 255).astype(np.uint8)
# Remove the feathered mask color overlay
# color_feathered_mask = cv2.applyColorMap(feathered_mask, cv2.COLORMAP_VIRIDIS)
# Ensure shapes match before blending feathered mask
# if vis_region.shape == color_feathered_mask.shape:
# blended_feathered = cv2.addWeighted(vis_region, 0.7, color_feathered_mask, 0.3, 0)
# vis_frame[min_y:max_y, min_x:max_x] = blended_feathered
# Add labels
cv2.putText(
vis_frame,
"Lower Mouth Mask",
(min_x, min_y - 10),
cv2.FONT_HERSHEY_SIMPLEX,
0.5,
(255, 255, 255),
1,
)
cv2.putText(
vis_frame,
"Feathered Mask",
(min_x, max_y + 20),
cv2.FONT_HERSHEY_SIMPLEX,
0.5,
(255, 255, 255),
1,
)
cv2.putText(vis_frame, "Lower Mouth Mask", (min_x, min_y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
return vis_frame
return frame
def apply_mouth_area(
frame: np.ndarray,
mouth_cutout: np.ndarray,
mouth_box: tuple,
face_mask: np.ndarray,
mouth_polygon: np.ndarray,
) -> np.ndarray:
def apply_mouth_area(frame: np.ndarray, mouth_cutout: np.ndarray, mouth_box: tuple, face_mask: np.ndarray, mouth_polygon: np.ndarray) -> np.ndarray:
min_x, min_y, max_x, max_y = mouth_box
box_width = max_x - min_x
box_height = max_y - min_y
if (
mouth_cutout is None
or box_width is None
or box_height is None
or face_mask is None
or mouth_polygon is None
):
if mouth_cutout is None or box_width is None or box_height is None or face_mask is None or mouth_polygon is None:
return frame
try:
@ -488,44 +552,33 @@ def apply_mouth_area(
roi = frame[min_y:max_y, min_x:max_x]
if roi.shape != resized_mouth_cutout.shape:
resized_mouth_cutout = cv2.resize(
resized_mouth_cutout, (roi.shape[1], roi.shape[0])
)
resized_mouth_cutout = cv2.resize(resized_mouth_cutout, (roi.shape[1], roi.shape[0]))
color_corrected_mouth = apply_color_transfer(resized_mouth_cutout, roi)
# Use the provided mouth polygon to create the mask
polygon_mask = np.zeros(roi.shape[:2], dtype=np.uint8)
adjusted_polygon = mouth_polygon - [min_x, min_y]
cv2.fillPoly(polygon_mask, [adjusted_polygon], 255)
# Apply feathering to the polygon mask
feather_amount = min(
30,
box_width // modules.globals.mask_feather_ratio,
box_height // modules.globals.mask_feather_ratio,
)
feathered_mask = cv2.GaussianBlur(
polygon_mask.astype(float), (0, 0), feather_amount
)
# Improved feathering for smoother mouth mask
feather_amount = min(35, box_width // modules.globals.mask_feather_ratio, box_height // modules.globals.mask_feather_ratio)
feathered_mask = cv2.GaussianBlur(polygon_mask.astype(float), (0, 0), feather_amount * 1.2)
feathered_mask = feathered_mask / feathered_mask.max()
# Additional smoothing pass for extra softness
feathered_mask = cv2.GaussianBlur(feathered_mask, (7, 7), 2)
# Fix black line artifacts by ensuring smooth mask transitions
feathered_mask = np.clip(feathered_mask, 0.1, 0.9) # Avoid pure 0 and 1 values
face_mask_roi = face_mask[min_y:max_y, min_x:max_x]
combined_mask = feathered_mask * (face_mask_roi / 255.0)
combined_mask = combined_mask[:, :, np.newaxis]
blended = (
color_corrected_mouth * combined_mask + roi * (1 - combined_mask)
).astype(np.uint8)
blended = (color_corrected_mouth * combined_mask + roi * (1 - combined_mask)).astype(np.uint8)
# Apply face mask to blended result
face_mask_3channel = (
np.repeat(face_mask_roi[:, :, np.newaxis], 3, axis=2) / 255.0
)
face_mask_3channel = np.repeat(face_mask_roi[:, :, np.newaxis], 3, axis=2) / 255.0
final_blend = blended * face_mask_3channel + roi * (1 - face_mask_3channel)
frame[min_y:max_y, min_x:max_x] = final_blend.astype(np.uint8)
except Exception as e:
except Exception:
pass
return frame
@ -535,10 +588,7 @@ def create_face_mask(face: Face, frame: Frame) -> np.ndarray:
mask = np.zeros(frame.shape[:2], dtype=np.uint8)
landmarks = face.landmark_2d_106
if landmarks is not None:
# Convert landmarks to int32
landmarks = landmarks.astype(np.int32)
# Extract facial features
right_side_face = landmarks[0:16]
left_side_face = landmarks[17:32]
right_eye = landmarks[33:42]
@ -546,39 +596,22 @@ def create_face_mask(face: Face, frame: Frame) -> np.ndarray:
left_eye = landmarks[87:96]
left_eye_brow = landmarks[97:105]
# Calculate forehead extension
right_eyebrow_top = np.min(right_eye_brow[:, 1])
left_eyebrow_top = np.min(left_eye_brow[:, 1])
eyebrow_top = min(right_eyebrow_top, left_eyebrow_top)
face_top = np.min([right_side_face[0, 1], left_side_face[-1, 1]])
forehead_height = face_top - eyebrow_top
extended_forehead_height = int(forehead_height * 5.0) # Extend by 50%
extended_forehead_height = int(forehead_height * 5.0)
# Create forehead points
forehead_left = right_side_face[0].copy()
forehead_right = left_side_face[-1].copy()
forehead_left[1] -= extended_forehead_height
forehead_right[1] -= extended_forehead_height
# Combine all points to create the face outline
face_outline = np.vstack(
[
[forehead_left],
right_side_face,
left_side_face[
::-1
], # Reverse left side to create a continuous outline
[forehead_right],
]
)
face_outline = np.vstack([[forehead_left], right_side_face, left_side_face[::-1], [forehead_right]])
padding = int(np.linalg.norm(right_side_face[0] - left_side_face[-1]) * 0.05)
# Calculate padding
padding = int(
np.linalg.norm(right_side_face[0] - left_side_face[-1]) * 0.05
) # 5% of face width
# Create a slightly larger convex hull for padding
hull = cv2.convexHull(face_outline)
hull_padded = []
for point in hull:
@ -590,33 +623,23 @@ def create_face_mask(face: Face, frame: Frame) -> np.ndarray:
hull_padded.append(padded_point)
hull_padded = np.array(hull_padded, dtype=np.int32)
# Fill the padded convex hull
cv2.fillConvexPoly(mask, hull_padded, 255)
# Smooth the mask edges
mask = cv2.GaussianBlur(mask, (5, 5), 3)
return mask
def apply_color_transfer(source, target):
"""
Apply color transfer from target to source image
"""
source = cv2.cvtColor(source, cv2.COLOR_BGR2LAB).astype("float32")
target = cv2.cvtColor(target, cv2.COLOR_BGR2LAB).astype("float32")
source_mean, source_std = cv2.meanStdDev(source)
target_mean, target_std = cv2.meanStdDev(target)
# Reshape mean and std to be broadcastable
source_mean = source_mean.reshape(1, 1, 3)
source_std = source_std.reshape(1, 1, 3)
target_mean = target_mean.reshape(1, 1, 3)
target_std = target_std.reshape(1, 1, 3)
# Perform the color transfer
source = (source - source_mean) * (target_std / source_std) + target_mean
return cv2.cvtColor(np.clip(source, 0, 255).astype("uint8"), cv2.COLOR_LAB2BGR)

2
requirements.txt
View File

@ -14,8 +14,8 @@ torch; sys_platform != 'darwin'
torch==2.5.1; sys_platform == 'darwin'
torchvision; sys_platform != 'darwin'
torchvision==0.20.1; sys_platform == 'darwin'
onnxruntime-silicon==1.16.3; sys_platform == 'darwin' and platform_machine == 'arm64'
onnxruntime-gpu==1.22.0; sys_platform != 'darwin'
tensorflow; sys_platform != 'darwin'
opennsfw2==0.10.2
protobuf==4.25.1
pygrabber