From 07e30fe781c04f6e4b3f95cde51d36f8c55206fd Mon Sep 17 00:00:00 2001
From: Kenneth Estanislao <hacksider@gmail.com>
Date: Thu, 17 Apr 2025 02:03:34 +0800
Subject: [PATCH] Revert "Update face_swapper.py"

This reverts commit 104d8cf4d6b2ff526198ef9c5af87a8120e990bd.
---
 modules/processors/frame/face_swapper.py | 682 +++++++++++++++++------
 1 file changed, 523 insertions(+), 159 deletions(-)

diff --git a/modules/processors/frame/face_swapper.py b/modules/processors/frame/face_swapper.py
index b09e600..36b83d6 100644
--- a/modules/processors/frame/face_swapper.py
+++ b/modules/processors/frame/face_swapper.py
@@ -1,44 +1,56 @@
-import os # <-- Added for os.path.exists
 from typing import Any, List
 import cv2
 import insightface
 import threading
-
+import numpy as np
 import modules.globals
+import logging
 import modules.processors.frame.core
-# Ensure update_status is imported if not already globally accessible
-# If it's part of modules.core, it might already be accessible via modules.core.update_status
 from modules.core import update_status
 from modules.face_analyser import get_one_face, get_many_faces, default_source_face
 from modules.typing import Face, Frame
-from modules.utilities import conditional_download, resolve_relative_path, is_image, is_video
+from modules.utilities import (
+    conditional_download,
+    is_image,
+    is_video,
+)
 from modules.cluster_analysis import find_closest_centroid
+import os
 
 FACE_SWAPPER = None
 THREAD_LOCK = threading.Lock()
-NAME = 'DLC.FACE-SWAPPER'
+NAME = "DLC.FACE-SWAPPER"
+
+abs_dir = os.path.dirname(os.path.abspath(__file__))
+models_dir = os.path.join(
+    os.path.dirname(os.path.dirname(os.path.dirname(abs_dir))), "models"
+)
 
 
 def pre_check() -> bool:
-    download_directory_path = resolve_relative_path('../models')
-    # Ensure both models are mentioned or downloaded if necessary
-    # Conditional download might need adjustment if you want it to fetch FP32 too
-    conditional_download(download_directory_path, ['https://huggingface.co/hacksider/deep-live-cam/blob/main/inswapper_128_fp16.onnx'])
-    # Add a check or download for the FP32 model if you have a URL
-    # conditional_download(download_directory_path, ['URL_TO_FP32_MODEL_HERE'])
+    download_directory_path = abs_dir
+    conditional_download(
+        download_directory_path,
+        [
+            "https://huggingface.co/hacksider/deep-live-cam/blob/main/inswapper_128_fp16.onnx"
+        ],
+    )
     return True
 
 
 def pre_start() -> bool:
-    # --- No changes needed in pre_start ---
     if not modules.globals.map_faces and not is_image(modules.globals.source_path):
-        update_status('Select an image for source path.', NAME)
+        update_status("Select an image for source path.", NAME)
         return False
-    elif not modules.globals.map_faces and not get_one_face(cv2.imread(modules.globals.source_path)):
-        update_status('No face in source path detected.', NAME)
+    elif not modules.globals.map_faces and not get_one_face(
+        cv2.imread(modules.globals.source_path)
+    ):
+        update_status("No face in source path detected.", NAME)
         return False
-    if not is_image(modules.globals.target_path) and not is_video(modules.globals.target_path):
-        update_status('Select an image or video for target path.', NAME)
+    if not is_image(modules.globals.target_path) and not is_video(
+        modules.globals.target_path
+    ):
+        update_status("Select an image or video for target path.", NAME)
         return False
     return True
 
@@ -48,112 +60,110 @@ def get_face_swapper() -> Any:
 
     with THREAD_LOCK:
         if FACE_SWAPPER is None:
-            # --- MODIFICATION START ---
-            # Define paths for both FP32 and FP16 models
-            model_dir = resolve_relative_path('../models')
-            model_path_fp32 = os.path.join(model_dir, 'inswapper_128.onnx')
-            model_path_fp16 = os.path.join(model_dir, 'inswapper_128_fp16.onnx')
-            chosen_model_path = None
-
-            # Prioritize FP32 model
-            if os.path.exists(model_path_fp32):
-                chosen_model_path = model_path_fp32
-                update_status(f"Loading FP32 model: {os.path.basename(chosen_model_path)}", NAME)
-            # Fallback to FP16 model
-            elif os.path.exists(model_path_fp16):
-                chosen_model_path = model_path_fp16
-                update_status(f"FP32 model not found. Loading FP16 model: {os.path.basename(chosen_model_path)}", NAME)
-            # Error if neither model is found
-            else:
-                error_message = f"Face Swapper model not found. Please ensure 'inswapper_128.onnx' (recommended) or 'inswapper_128_fp16.onnx' exists in the '{model_dir}' directory."
-                update_status(error_message, NAME)
-                raise FileNotFoundError(error_message)
-
-            # Load the chosen model
-            try:
-                FACE_SWAPPER = insightface.model_zoo.get_model(chosen_model_path, providers=modules.globals.execution_providers)
-            except Exception as e:
-                update_status(f"Error loading Face Swapper model {os.path.basename(chosen_model_path)}: {e}", NAME)
-                # Optionally, re-raise the exception or handle it more gracefully
-                raise e
-            # --- MODIFICATION END ---
+            model_path = os.path.join(models_dir, "inswapper_128_fp16.onnx")
+            FACE_SWAPPER = insightface.model_zoo.get_model(
+                model_path, providers=modules.globals.execution_providers
+            )
     return FACE_SWAPPER
 
 
 def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
-    # --- No changes needed in swap_face ---
-    swapper = get_face_swapper()
-    if swapper is None:
-         # Handle case where model failed to load
-         update_status("Face swapper model not loaded, skipping swap.", NAME)
-         return temp_frame
-    return swapper.get(temp_frame, target_face, source_face, paste_back=True)
+    face_swapper = get_face_swapper()
+
+    # Apply the face swap
+    swapped_frame = face_swapper.get(
+        temp_frame, target_face, source_face, paste_back=True
+    )
+
+    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 process_frame(source_face: Face, temp_frame: Frame) -> Frame:
-    # --- No changes needed in process_frame ---
-    # Ensure the frame is in RGB format if color correction is enabled
-    # Note: InsightFace swapper often expects BGR by default. Double-check if color issues appear.
-    # If color correction is needed *before* swapping and insightface needs BGR:
-    # original_was_bgr = True # Assume input is BGR
-    # if modules.globals.color_correction:
-    #     temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB)
-    #     original_was_bgr = False # Now it's RGB
+    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:
-                temp_frame = swap_face(source_face, target_face, temp_frame)
+                if source_face and target_face:
+                    temp_frame = swap_face(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:
+        if target_face and source_face:
             temp_frame = swap_face(source_face, target_face, temp_frame)
-
-    # Convert back if necessary (example, might not be needed depending on workflow)
-    # if modules.globals.color_correction and not original_was_bgr:
-    #      temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_RGB2BGR)
-
+        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:
-    # --- No changes needed in process_frame_v2 ---
-    # (Assuming swap_face handles the potential None return from get_face_swapper)
     if is_image(modules.globals.target_path):
         if modules.globals.many_faces:
             source_face = default_source_face()
-            for map_entry in modules.globals.souce_target_map: # Renamed 'map' to 'map_entry'
-                target_face = map_entry['target']['face']
+            for map in modules.globals.source_target_map:
+                target_face = map["target"]["face"]
                 temp_frame = swap_face(source_face, target_face, temp_frame)
 
         elif not modules.globals.many_faces:
-            for map_entry in modules.globals.souce_target_map: # Renamed 'map' to 'map_entry'
-                if "source" in map_entry:
-                    source_face = map_entry['source']['face']
-                    target_face = map_entry['target']['face']
+            for map in modules.globals.source_target_map:
+                if "source" in map:
+                    source_face = map["source"]["face"]
+                    target_face = map["target"]["face"]
                     temp_frame = swap_face(source_face, target_face, temp_frame)
 
     elif is_video(modules.globals.target_path):
         if modules.globals.many_faces:
             source_face = default_source_face()
-            for map_entry in modules.globals.souce_target_map: # Renamed 'map' to 'map_entry'
-                target_frame = [f for f in map_entry['target_faces_in_frame'] if f['location'] == temp_frame_path]
+            for map in modules.globals.source_target_map:
+                target_frame = [
+                    f
+                    for f in map["target_faces_in_frame"]
+                    if f["location"] == temp_frame_path
+                ]
 
                 for frame in target_frame:
-                    for target_face in frame['faces']:
+                    for target_face in frame["faces"]:
                         temp_frame = swap_face(source_face, target_face, temp_frame)
 
         elif not modules.globals.many_faces:
-            for map_entry in modules.globals.souce_target_map: # Renamed 'map' to 'map_entry'
-                if "source" in map_entry:
-                    target_frame = [f for f in map_entry['target_faces_in_frame'] if f['location'] == temp_frame_path]
-                    source_face = map_entry['source']['face']
+            for map in modules.globals.source_target_map:
+                if "source" in map:
+                    target_frame = [
+                        f
+                        for f in map["target_faces_in_frame"]
+                        if f["location"] == temp_frame_path
+                    ]
+                    source_face = map["source"]["face"]
 
                     for frame in target_frame:
-                        for target_face in frame['faces']:
+                        for target_face in frame["faces"]:
                             temp_frame = swap_face(source_face, target_face, temp_frame)
-    else: # Fallback for neither image nor video (e.g., live feed?)
+
+    else:
         detected_faces = get_many_faces(temp_frame)
         if modules.globals.many_faces:
             if detected_faces:
@@ -162,97 +172,451 @@ def process_frame_v2(temp_frame: Frame, temp_frame_path: str = "") -> Frame:
                     temp_frame = swap_face(source_face, target_face, temp_frame)
 
         elif not modules.globals.many_faces:
-            if detected_faces and hasattr(modules.globals, 'simple_map') and modules.globals.simple_map: # Check simple_map exists
-                if len(detected_faces) <= len(modules.globals.simple_map['target_embeddings']):
+            if detected_faces:
+                if len(detected_faces) <= len(
+                    modules.globals.simple_map["target_embeddings"]
+                ):
                     for detected_face in detected_faces:
-                        closest_centroid_index, _ = find_closest_centroid(modules.globals.simple_map['target_embeddings'], detected_face.normed_embedding)
-                        temp_frame = swap_face(modules.globals.simple_map['source_faces'][closest_centroid_index], detected_face, temp_frame)
+                        closest_centroid_index, _ = find_closest_centroid(
+                            modules.globals.simple_map["target_embeddings"],
+                            detected_face.normed_embedding,
+                        )
+
+                        temp_frame = swap_face(
+                            modules.globals.simple_map["source_faces"][
+                                closest_centroid_index
+                            ],
+                            detected_face,
+                            temp_frame,
+                        )
                 else:
-                    detected_faces_centroids = [face.normed_embedding for face in detected_faces]
+                    detected_faces_centroids = []
+                    for face in detected_faces:
+                        detected_faces_centroids.append(face.normed_embedding)
                     i = 0
-                    for target_embedding in modules.globals.simple_map['target_embeddings']:
-                        closest_centroid_index, _ = find_closest_centroid(detected_faces_centroids, target_embedding)
-                        # Ensure index is valid before accessing detected_faces
-                        if closest_centroid_index < len(detected_faces):
-                            temp_frame = swap_face(modules.globals.simple_map['source_faces'][i], detected_faces[closest_centroid_index], temp_frame)
+                    for target_embedding in modules.globals.simple_map[
+                        "target_embeddings"
+                    ]:
+                        closest_centroid_index, _ = find_closest_centroid(
+                            detected_faces_centroids, target_embedding
+                        )
+
+                        temp_frame = swap_face(
+                            modules.globals.simple_map["source_faces"][i],
+                            detected_faces[closest_centroid_index],
+                            temp_frame,
+                        )
                         i += 1
     return temp_frame
 
 
-def process_frames(source_path: str, temp_frame_paths: List[str], progress: Any = None) -> None:
-    # --- No changes needed in process_frames ---
-    # Note: Ensure get_one_face is called only once if possible for efficiency if !map_faces
-    source_face = None
+def process_frames(
+    source_path: str, temp_frame_paths: List[str], progress: Any = None
+) -> None:
     if not modules.globals.map_faces:
-        source_img = cv2.imread(source_path)
-        if source_img is not None:
-            source_face = get_one_face(source_img)
-        if source_face is None:
-             update_status(f"Could not find face in source image: {source_path}, skipping swap.", NAME)
-             # If no source face, maybe skip processing? Or handle differently.
-             # For now, it will proceed but swap_face might fail later.
-
-    for temp_frame_path in temp_frame_paths:
-        temp_frame = cv2.imread(temp_frame_path)
-        if temp_frame is None:
-            update_status(f"Warning: Could not read frame {temp_frame_path}", NAME)
-            if progress: progress.update(1) # Still update progress even if frame fails
-            continue # Skip to next frame
-
-        try:
-            if not modules.globals.map_faces:
-                if source_face: # Only process if source face was found
-                    result = process_frame(source_face, temp_frame)
-                else:
-                    result = temp_frame # No source face, return original frame
-            else:
-                 result = process_frame_v2(temp_frame, temp_frame_path)
-
-            cv2.imwrite(temp_frame_path, result)
-        except Exception as exception:
-            update_status(f"Error processing frame {os.path.basename(temp_frame_path)}: {exception}", NAME)
-            # Decide whether to 'pass' (continue processing other frames) or raise
-            pass # Continue processing other frames
-        finally:
+        source_face = get_one_face(cv2.imread(source_path))
+        for temp_frame_path in temp_frame_paths:
+            temp_frame = cv2.imread(temp_frame_path)
+            try:
+                result = process_frame(source_face, temp_frame)
+                cv2.imwrite(temp_frame_path, result)
+            except Exception as exception:
+                print(exception)
+                pass
+            if progress:
+                progress.update(1)
+    else:
+        for temp_frame_path in temp_frame_paths:
+            temp_frame = cv2.imread(temp_frame_path)
+            try:
+                result = process_frame_v2(temp_frame, temp_frame_path)
+                cv2.imwrite(temp_frame_path, result)
+            except Exception as exception:
+                print(exception)
+                pass
             if progress:
                 progress.update(1)
 
 
 def process_image(source_path: str, target_path: str, output_path: str) -> None:
-    # --- No changes needed in process_image ---
-    # Note: Added checks for successful image reads and face detection
-    target_frame = cv2.imread(target_path) # Read original target for processing
-    if target_frame is None:
-        update_status(f"Error: Could not read target image: {target_path}", NAME)
-        return
-
     if not modules.globals.map_faces:
-        source_img = cv2.imread(source_path)
-        if source_img is None:
-             update_status(f"Error: Could not read source image: {source_path}", NAME)
-             return
-        source_face = get_one_face(source_img)
-        if source_face is None:
-            update_status(f"Error: No face found in source image: {source_path}", NAME)
-            return
-
+        source_face = get_one_face(cv2.imread(source_path))
+        target_frame = cv2.imread(target_path)
         result = process_frame(source_face, target_frame)
+        cv2.imwrite(output_path, result)
     else:
         if modules.globals.many_faces:
-            update_status('Many faces enabled. Using first source image (if applicable in v2). Processing...', NAME)
-        # For process_frame_v2 on single image, it reads the 'output_path' which should be a copy
-        # Let's process the 'target_frame' we read instead.
-        result = process_frame_v2(target_frame) # Process the frame directly
-
-    # Write the final result to the output path
-    success = cv2.imwrite(output_path, result)
-    if not success:
-        update_status(f"Error: Failed to write output image to: {output_path}", NAME)
+            update_status(
+                "Many faces enabled. Using first source image. Progressing...", NAME
+            )
+        target_frame = cv2.imread(output_path)
+        result = process_frame_v2(target_frame)
+        cv2.imwrite(output_path, result)
 
 
 def process_video(source_path: str, temp_frame_paths: List[str]) -> None:
-    # --- No changes needed in process_video ---
     if modules.globals.map_faces and modules.globals.many_faces:
-        update_status('Many faces enabled. Using first source image (if applicable in v2). Processing...', NAME)
-    # The core processing logic is delegated, which is good.
-    modules.processors.frame.core.process_video(source_path, temp_frame_paths, process_frames)
\ No newline at end of file
+        update_status(
+            "Many faces enabled. Using first source image. Progressing...", NAME
+        )
+    modules.processors.frame.core.process_video(
+        source_path, temp_frame_paths, process_frames
+    )
+
+
+def create_lower_mouth_mask(
+    face: Face, frame: Frame
+) -> (np.ndarray, np.ndarray, tuple, np.ndarray):
+    mask = np.zeros(frame.shape[:2], dtype=np.uint8)
+    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,
+            62,
+            70,
+            69,
+            18,
+            19,
+            20,
+            21,
+            22,
+            23,
+            24,
+            0,
+            8,
+            7,
+            6,
+            5,
+            4,
+            3,
+            2,
+            65,
+        ]
+        lower_lip_landmarks = landmarks[lower_lip_order].astype(
+            np.float32
+        )  # Use float for precise calculations
+
+        # 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
+        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
+        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
+        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
+        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
+        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:
+    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
+        )
+
+        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,
+        )
+
+        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:
+    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
+    ):
+        return frame
+
+    try:
+        resized_mouth_cutout = cv2.resize(mouth_cutout, (box_width, box_height))
+        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])
+            )
+
+        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
+        )
+        feathered_mask = feathered_mask / feathered_mask.max()
+
+        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)
+
+        # Apply face mask to blended result
+        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:
+        pass
+
+    return frame
+
+
+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]
+        right_eye_brow = landmarks[43:51]
+        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%
+
+        # 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],
+            ]
+        )
+
+        # 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:
+            x, y = point[0]
+            center = np.mean(face_outline, axis=0)
+            direction = np.array([x, y]) - center
+            direction = direction / np.linalg.norm(direction)
+            padded_point = np.array([x, y]) + direction * padding
+            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)