Merge 2d0c5bc8d0 into 87d982e6f8

Add Golem.de (German IT news magazine) article

README.md

@@ -348,6 +348,7 @@ Looking for a CLI mode? Using the -s/--source argument will make the run program
  - [*"That's Crazy, Oh God. That's Fucking Freaky Dude... That's So Wild Dude"*](https://www.youtube.com/watch?time_continue=1074&v=py4Tc-Y8BcY) - SomeOrdinaryGamers
  - [*"Alright look look look, now look chat, we can do any face we want to look like chat"*](https://www.youtube.com/live/mFsCe7AIxq8?feature=shared&t=2686) - IShowSpeed
  - [*"They do a pretty good job matching poses, expression and even the lighting"*](https://www.youtube.com/watch?v=wnCghLjqv3s&t=551s) - TechLinked (LTT)
+ - [*"Als Sean Connery an der Redaktionskonferenz teilnahm"*](https://www.golem.de/news/deepfakes-als-sean-connery-an-der-redaktionskonferenz-teilnahm-2408-188172.html) - Golem.de (German)
 
 
 ## Credits

modules/globals.py

@@ -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

modules/processors/frame/face_swapper.py

@@ -75,7 +75,7 @@ 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
     )
@@ -103,28 +103,211 @@ 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:
+    # Skip color correction for maximum FPS
-        temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB)
+    # 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)
+                    temp_frame = swap_face_ultra_fast(source_face, target_face, temp_frame)
-                else:
-                    print("Face detection failed for target/source.")
     else:
         target_face = get_one_face(temp_frame)
         if target_face and source_face:
-            temp_frame = swap_face(source_face, target_face, temp_frame)
+            temp_frame = swap_face_ultra_fast(source_face, target_face, temp_frame)
-        else:
-            logging.error("Face detection failed for target or source.")
     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:
@@ -276,7 +459,6 @@ 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,
@@ -300,192 +482,74 @@ def create_lower_mouth_mask(
             2,
             65,
         ]
-        lower_lip_landmarks = landmarks[lower_lip_order].astype(
+        lower_lip_landmarks = landmarks[lower_lip_order].astype(np.float32)
-            np.float32
-        )  # Use float for precise calculations
 
-        # Calculate the center of the landmarks
         center = np.mean(lower_lip_landmarks, axis=0)
-
+        expansion_factor = 1 + modules.globals.mask_down_size
-        # Expand the landmarks outward
-        expansion_factor = (
-            1 + modules.globals.mask_down_size
-        )  # Adjust this for more or less expansion
         expanded_landmarks = (lower_lip_landmarks - center) * expansion_factor + center
 
-        # Extend the top lip part
+        toplip_indices = [20, 0, 1, 2, 3, 4, 5]
-        toplip_indices = [
+        toplip_extension = modules.globals.mask_size * 0.5
-            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
         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]
-        chin_indices = [
+        chin_extension = 2 * 0.2
-            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
         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)
-        padding = int((max_x - min_x) * 0.1)  # 10% padding
         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)
-
+        # Improved smoothing for mouth mask
-        # Apply Gaussian blur to soften the mask edges
+        mask_roi = cv2.GaussianBlur(mask_roi, (25, 25), 8)
-        mask_roi = cv2.GaussianBlur(mask_roi, (15, 15), 5)
-
-        # Place the mask ROI in the full-sized mask
         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(
+def draw_mouth_mask_visualization(frame: Frame, face: Face, mouth_mask_data: tuple) -> Frame:
-    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 = (
+        mask, mouth_cutout, (min_x, min_y, max_x, max_y), lower_lip_polygon = mouth_mask_data
-            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)
-
+        cv2.putText(vis_frame, "Lower Mouth Mask", (min_x, min_y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
-        # 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,
-        )
-
         return vis_frame
     return frame
 
 
-def apply_mouth_area(
+def apply_mouth_area(frame: np.ndarray, mouth_cutout: np.ndarray, mouth_box: tuple, face_mask: np.ndarray, mouth_polygon: np.ndarray) -> np.ndarray:
-    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 (
+    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:
-        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:
@@ -493,44 +557,33 @@ 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 = cv2.resize(resized_mouth_cutout, (roi.shape[1], roi.shape[0]))
-                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
+        # Improved feathering for smoother mouth mask
-        feather_amount = min(
+        feather_amount = min(35, box_width // modules.globals.mask_feather_ratio, box_height // modules.globals.mask_feather_ratio)
-            30,
+        feathered_mask = cv2.GaussianBlur(polygon_mask.astype(float), (0, 0), feather_amount * 1.2)
-            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
-        )
         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 = (
+        blended = (color_corrected_mouth * combined_mask + roi * (1 - combined_mask)).astype(np.uint8)
-            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
@@ -540,10 +593,7 @@ 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]
@@ -551,39 +601,22 @@ 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], [forehead_right]])
-        face_outline = np.vstack(
+        padding = int(np.linalg.norm(right_side_face[0] - left_side_face[-1]) * 0.05)
-            [
-                [forehead_left],
-                right_side_face,
-                left_side_face[
-                    ::-1
-                ],  # Reverse left side to create a continuous outline
-                [forehead_right],
-            ]
-        )
 
-        # 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:
@@ -595,33 +628,23 @@ 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)

requirements.txt

@@ -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