Reorganized switches

modules/globals.py

@@ -21,7 +21,7 @@ keep_audio = True
 keep_frames = False
 many_faces = False
 map_faces = False
-color_correction = False  # New global variable for color correction toggle
+color_correction = False
 nsfw_filter = False
 video_encoder = None
 video_quality = None
@@ -41,5 +41,9 @@ show_mouth_mask_box = False
 mask_feather_ratio = 8
 mask_down_size = 0.50
 mask_size = 1
+eyes_mask = False
+show_eyes_mask_box = False
+eyebrows_mask = False
+show_eyebrows_mask_box = False
 use_fake_face = False
 fake_face_path = None

modules/processors/frame/face_masking.py

@@ -0,0 +1,633 @@
+import cv2
+import numpy as np
+from modules.typing import Face, Frame
+import modules.globals
+
+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)
+
+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 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 create_eyes_mask(face: Face, frame: Frame) -> (np.ndarray, np.ndarray, tuple, np.ndarray):
+    mask = np.zeros(frame.shape[:2], dtype=np.uint8)
+    eyes_cutout = None
+    landmarks = face.landmark_2d_106
+    if landmarks is not None:
+        # Left eye landmarks (87-96) and right eye landmarks (33-42)
+        left_eye = landmarks[87:96]
+        right_eye = landmarks[33:42]
+        
+        # Calculate centers and dimensions for each eye
+        left_eye_center = np.mean(left_eye, axis=0).astype(np.int32)
+        right_eye_center = np.mean(right_eye, axis=0).astype(np.int32)
+        
+        # Calculate eye dimensions
+        def get_eye_dimensions(eye_points):
+            x_coords = eye_points[:, 0]
+            y_coords = eye_points[:, 1]
+            width = int((np.max(x_coords) - np.min(x_coords)) * (1 + modules.globals.mask_down_size))
+            height = int((np.max(y_coords) - np.min(y_coords)) * (1 + modules.globals.mask_down_size))
+            return width, height
+        
+        left_width, left_height = get_eye_dimensions(left_eye)
+        right_width, right_height = get_eye_dimensions(right_eye)
+        
+        # Add extra padding
+        padding = int(max(left_width, right_width) * 0.2)
+        
+        # Calculate bounding box for both eyes
+        min_x = min(left_eye_center[0] - left_width//2, right_eye_center[0] - right_width//2) - padding
+        max_x = max(left_eye_center[0] + left_width//2, right_eye_center[0] + right_width//2) + padding
+        min_y = min(left_eye_center[1] - left_height//2, right_eye_center[1] - right_height//2) - padding
+        max_y = max(left_eye_center[1] + left_height//2, right_eye_center[1] + right_height//2) + padding
+        
+        # Ensure coordinates are within frame bounds
+        min_x = max(0, min_x)
+        min_y = max(0, min_y)
+        max_x = min(frame.shape[1], max_x)
+        max_y = min(frame.shape[0], max_y)
+        
+        # Create mask for the eyes region
+        mask_roi = np.zeros((max_y - min_y, max_x - min_x), dtype=np.uint8)
+        
+        # Draw ellipses for both eyes
+        left_center = (left_eye_center[0] - min_x, left_eye_center[1] - min_y)
+        right_center = (right_eye_center[0] - min_x, right_eye_center[1] - min_y)
+        
+        # Calculate axes lengths (half of width and height)
+        left_axes = (left_width//2, left_height//2)
+        right_axes = (right_width//2, right_height//2)
+        
+        # Draw filled ellipses
+        cv2.ellipse(mask_roi, left_center, left_axes, 0, 0, 360, 255, -1)
+        cv2.ellipse(mask_roi, right_center, right_axes, 0, 0, 360, 255, -1)
+        
+        # Apply Gaussian blur to soften 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
+        eyes_cutout = frame[min_y:max_y, min_x:max_x].copy()
+        
+        # Create polygon points for visualization
+        def create_ellipse_points(center, axes):
+            t = np.linspace(0, 2*np.pi, 32)
+            x = center[0] + axes[0] * np.cos(t)
+            y = center[1] + axes[1] * np.sin(t)
+            return np.column_stack((x, y)).astype(np.int32)
+        
+        # Generate points for both ellipses
+        left_points = create_ellipse_points((left_eye_center[0], left_eye_center[1]), (left_width//2, left_height//2))
+        right_points = create_ellipse_points((right_eye_center[0], right_eye_center[1]), (right_width//2, right_height//2))
+        
+        # Combine points for both eyes
+        eyes_polygon = np.vstack([left_points, right_points])
+        
+    return mask, eyes_cutout, (min_x, min_y, max_x, max_y), eyes_polygon
+
+def create_curved_eyebrow(points):
+    if len(points) >= 5:
+        # Sort points by x-coordinate
+        sorted_idx = np.argsort(points[:, 0])
+        sorted_points = points[sorted_idx]
+        
+        # Calculate dimensions
+        x_min, y_min = np.min(sorted_points, axis=0)
+        x_max, y_max = np.max(sorted_points, axis=0)
+        width = x_max - x_min
+        height = y_max - y_min
+        
+        # Create more points for smoother curve
+        num_points = 50
+        x = np.linspace(x_min, x_max, num_points)
+        
+        # Fit quadratic curve through points for more natural arch
+        coeffs = np.polyfit(sorted_points[:, 0], sorted_points[:, 1], 2)
+        y = np.polyval(coeffs, x)
+        
+        # Increased offsets to create more separation
+        top_offset = height * 0.5  # Increased from 0.3 to shift up more
+        bottom_offset = height * 0.2  # Increased from 0.1 to shift down more
+        
+        # Create smooth curves
+        top_curve = y - top_offset
+        bottom_curve = y + bottom_offset
+        
+        # Create curved endpoints with more pronounced taper
+        end_points = 5
+        start_x = np.linspace(x[0] - width * 0.15, x[0], end_points)  # Increased taper
+        end_x = np.linspace(x[-1], x[-1] + width * 0.15, end_points)  # Increased taper
+        
+        # Create tapered ends
+        start_curve = np.column_stack((
+            start_x,
+            np.linspace(bottom_curve[0], top_curve[0], end_points)
+        ))
+        end_curve = np.column_stack((
+            end_x,
+            np.linspace(bottom_curve[-1], top_curve[-1], end_points)
+        ))
+        
+        # Combine all points to form a smooth contour
+        contour_points = np.vstack([
+            start_curve,
+            np.column_stack((x, top_curve)),
+            end_curve,
+            np.column_stack((x[::-1], bottom_curve[::-1]))
+        ])
+        
+        # Add slight padding for better coverage
+        center = np.mean(contour_points, axis=0)
+        vectors = contour_points - center
+        padded_points = center + vectors * 1.2  # Increased padding slightly
+        
+        return padded_points
+    return points
+
+def create_eyebrows_mask(face: Face, frame: Frame) -> (np.ndarray, np.ndarray, tuple, np.ndarray):
+    mask = np.zeros(frame.shape[:2], dtype=np.uint8)
+    eyebrows_cutout = None
+    landmarks = face.landmark_2d_106
+    if landmarks is not None:
+        # Left eyebrow landmarks (97-105) and right eyebrow landmarks (43-51)
+        left_eyebrow = landmarks[97:105].astype(np.float32)
+        right_eyebrow = landmarks[43:51].astype(np.float32)
+        
+        # Calculate centers and dimensions for each eyebrow
+        left_center = np.mean(left_eyebrow, axis=0)
+        right_center = np.mean(right_eyebrow, axis=0)
+        
+        # Calculate bounding box with padding
+        all_points = np.vstack([left_eyebrow, right_eyebrow])
+        min_x = np.min(all_points[:, 0]) - 25
+        max_x = np.max(all_points[:, 0]) + 25
+        min_y = np.min(all_points[:, 1]) - 20
+        max_y = np.max(all_points[:, 1]) + 15
+        
+        # Ensure coordinates are within frame bounds
+        min_x = max(0, int(min_x))
+        min_y = max(0, int(min_y))
+        max_x = min(frame.shape[1], int(max_x))
+        max_y = min(frame.shape[0], int(max_y))
+        
+        # Create mask for the eyebrows region
+        mask_roi = np.zeros((max_y - min_y, max_x - min_x), dtype=np.uint8)
+        
+        try:
+            # Convert points to local coordinates
+            left_local = left_eyebrow - [min_x, min_y]
+            right_local = right_eyebrow - [min_x, min_y]
+            
+            def create_curved_eyebrow(points):
+                if len(points) >= 5:
+                    # Sort points by x-coordinate
+                    sorted_idx = np.argsort(points[:, 0])
+                    sorted_points = points[sorted_idx]
+                    
+                    # Calculate dimensions
+                    x_min, y_min = np.min(sorted_points, axis=0)
+                    x_max, y_max = np.max(sorted_points, axis=0)
+                    width = x_max - x_min
+                    height = y_max - y_min
+                    
+                    # Create more points for smoother curve
+                    num_points = 50
+                    x = np.linspace(x_min, x_max, num_points)
+                    
+                    # Fit quadratic curve through points for more natural arch
+                    coeffs = np.polyfit(sorted_points[:, 0], sorted_points[:, 1], 2)
+                    y = np.polyval(coeffs, x)
+                    
+                    # Increased offsets to create more separation
+                    top_offset = height * 0.5  # Increased from 0.3 to shift up more
+                    bottom_offset = height * 0.2  # Increased from 0.1 to shift down more
+                    
+                    # Create smooth curves
+                    top_curve = y - top_offset
+                    bottom_curve = y + bottom_offset
+                    
+                    # Create curved endpoints with more pronounced taper
+                    end_points = 5
+                    start_x = np.linspace(x[0] - width * 0.15, x[0], end_points)  # Increased taper
+                    end_x = np.linspace(x[-1], x[-1] + width * 0.15, end_points)  # Increased taper
+                    
+                    # Create tapered ends
+                    start_curve = np.column_stack((
+                        start_x,
+                        np.linspace(bottom_curve[0], top_curve[0], end_points)
+                    ))
+                    end_curve = np.column_stack((
+                        end_x,
+                        np.linspace(bottom_curve[-1], top_curve[-1], end_points)
+                    ))
+                    
+                    # Combine all points to form a smooth contour
+                    contour_points = np.vstack([
+                        start_curve,
+                        np.column_stack((x, top_curve)),
+                        end_curve,
+                        np.column_stack((x[::-1], bottom_curve[::-1]))
+                    ])
+                    
+                    # Add slight padding for better coverage
+                    center = np.mean(contour_points, axis=0)
+                    vectors = contour_points - center
+                    padded_points = center + vectors * 1.2  # Increased padding slightly
+                    
+                    return padded_points
+                return points
+            
+            # Generate and draw eyebrow shapes
+            left_shape = create_curved_eyebrow(left_local)
+            right_shape = create_curved_eyebrow(right_local)
+            
+            # Apply multi-stage blurring for natural feathering
+            # First, strong Gaussian blur for initial softening
+            mask_roi = cv2.GaussianBlur(mask_roi, (21, 21), 7)
+            
+            # Second, medium blur for transition areas
+            mask_roi = cv2.GaussianBlur(mask_roi, (11, 11), 3)
+            
+            # Finally, light blur for fine details
+            mask_roi = cv2.GaussianBlur(mask_roi, (5, 5), 1)
+            
+            # Normalize mask values
+            mask_roi = cv2.normalize(mask_roi, None, 0, 255, cv2.NORM_MINMAX)
+            
+            # 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
+            eyebrows_cutout = frame[min_y:max_y, min_x:max_x].copy()
+            
+            # Combine points for visualization
+            eyebrows_polygon = np.vstack([
+                left_shape + [min_x, min_y],
+                right_shape + [min_x, min_y]
+            ]).astype(np.int32)
+            
+        except Exception as e:
+            # Fallback to simple polygons if curve fitting fails
+            left_local = left_eyebrow - [min_x, min_y]
+            right_local = right_eyebrow - [min_x, min_y]
+            cv2.fillPoly(mask_roi, [left_local.astype(np.int32)], 255)
+            cv2.fillPoly(mask_roi, [right_local.astype(np.int32)], 255)
+            mask_roi = cv2.GaussianBlur(mask_roi, (21, 21), 7)
+            mask[min_y:max_y, min_x:max_x] = mask_roi
+            eyebrows_cutout = frame[min_y:max_y, min_x:max_x].copy()
+            eyebrows_polygon = np.vstack([left_eyebrow, right_eyebrow]).astype(np.int32)
+        
+    return mask, eyebrows_cutout, (min_x, min_y, max_x, max_y), eyebrows_polygon
+
+def apply_mask_area(
+    frame: np.ndarray,
+    cutout: np.ndarray,
+    box: tuple,
+    face_mask: np.ndarray,
+    polygon: np.ndarray,
+) -> np.ndarray:
+    min_x, min_y, max_x, max_y = box
+    box_width = max_x - min_x
+    box_height = max_y - min_y
+
+    if (
+        cutout is None
+        or box_width is None
+        or box_height is None
+        or face_mask is None
+        or polygon is None
+    ):
+        return frame
+
+    try:
+        resized_cutout = cv2.resize(cutout, (box_width, box_height))
+        roi = frame[min_y:max_y, min_x:max_x]
+
+        if roi.shape != resized_cutout.shape:
+            resized_cutout = cv2.resize(
+                resized_cutout, (roi.shape[1], roi.shape[0])
+            )
+
+        color_corrected_area = apply_color_transfer(resized_cutout, roi)
+
+        # Create mask for the area
+        polygon_mask = np.zeros(roi.shape[:2], dtype=np.uint8)
+        
+        # Split points for left and right parts if needed
+        if len(polygon) > 50:  # Arbitrary threshold to detect if we have multiple parts
+            mid_point = len(polygon) // 2
+            left_points = polygon[:mid_point] - [min_x, min_y]
+            right_points = polygon[mid_point:] - [min_x, min_y]
+            cv2.fillPoly(polygon_mask, [left_points], 255)
+            cv2.fillPoly(polygon_mask, [right_points], 255)
+        else:
+            adjusted_polygon = polygon - [min_x, min_y]
+            cv2.fillPoly(polygon_mask, [adjusted_polygon], 255)
+
+        # Apply strong initial feathering
+        polygon_mask = cv2.GaussianBlur(polygon_mask, (21, 21), 7)
+
+        # Apply additional feathering
+        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()
+
+        # Apply additional smoothing to the mask edges
+        feathered_mask = cv2.GaussianBlur(feathered_mask, (5, 5), 1)
+
+        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_area * 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 draw_mask_visualization(
+    frame: Frame,
+    mask_data: tuple,
+    label: str,
+    draw_method: str = "polygon"
+) -> Frame:
+    mask, cutout, (min_x, min_y, max_x, max_y), polygon = 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)
+
+    if draw_method == "ellipse" and len(polygon) > 50:  # For eyes
+        # Split points for left and right parts
+        mid_point = len(polygon) // 2
+        left_points = polygon[:mid_point]
+        right_points = polygon[mid_point:]
+        
+        try:
+            # Fit ellipses to points - need at least 5 points
+            if len(left_points) >= 5 and len(right_points) >= 5:
+                # Convert points to the correct format for ellipse fitting
+                left_points = left_points.astype(np.float32)
+                right_points = right_points.astype(np.float32)
+                
+                # Fit ellipses
+                left_ellipse = cv2.fitEllipse(left_points)
+                right_ellipse = cv2.fitEllipse(right_points)
+                
+                # Draw the ellipses
+                cv2.ellipse(vis_frame, left_ellipse, (0, 255, 0), 2)
+                cv2.ellipse(vis_frame, right_ellipse, (0, 255, 0), 2)
+        except Exception as e:
+            # If ellipse fitting fails, draw simple rectangles as fallback
+            left_rect = cv2.boundingRect(left_points)
+            right_rect = cv2.boundingRect(right_points)
+            cv2.rectangle(vis_frame, 
+                        (left_rect[0], left_rect[1]), 
+                        (left_rect[0] + left_rect[2], left_rect[1] + left_rect[3]), 
+                        (0, 255, 0), 2)
+            cv2.rectangle(vis_frame,
+                        (right_rect[0], right_rect[1]),
+                        (right_rect[0] + right_rect[2], right_rect[1] + right_rect[3]),
+                        (0, 255, 0), 2)
+    else:  # For mouth and eyebrows
+        # Draw the polygon
+        if len(polygon) > 50:  # If we have multiple parts
+            mid_point = len(polygon) // 2
+            left_points = polygon[:mid_point]
+            right_points = polygon[mid_point:]
+            cv2.polylines(vis_frame, [left_points], True, (0, 255, 0), 2, cv2.LINE_AA)
+            cv2.polylines(vis_frame, [right_points], True, (0, 255, 0), 2, cv2.LINE_AA)
+        else:
+            cv2.polylines(vis_frame, [polygon], True, (0, 255, 0), 2, cv2.LINE_AA)
+
+    # Add label
+    cv2.putText(
+        vis_frame,
+        label,
+        (min_x, min_y - 10),
+        cv2.FONT_HERSHEY_SIMPLEX,
+        0.5,
+        (255, 255, 255),
+        1,
+    )
+
+    return vis_frame 

modules/processors/frame/face_swapper.py

@@ -14,6 +14,14 @@ from modules.utilities import (
     is_video,
 )
 from modules.cluster_analysis import find_closest_centroid
+from modules.processors.frame.face_masking import (
+    create_face_mask,
+    create_lower_mouth_mask,
+    create_eyes_mask,
+    create_eyebrows_mask,
+    apply_mask_area,
+    draw_mask_visualization
+)
 import os
 
 FACE_SWAPPER = None
@@ -74,24 +82,62 @@ def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
         temp_frame, target_face, source_face, paste_back=True
     )
 
+    # Create face mask for both mouth and eyes masking
+    face_mask = create_face_mask(target_face, temp_frame)
+
     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
+        # Create and apply mouth mask
+        mouth_mask_data = create_lower_mouth_mask(target_face, temp_frame)
+        swapped_frame = apply_mask_area(
+            swapped_frame, 
+            mouth_mask_data[1],  # mouth_cutout
+            mouth_mask_data[2],  # mouth_box
+            face_mask, 
+            mouth_mask_data[3]   # mouth_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
+            swapped_frame = draw_mask_visualization(
+                swapped_frame, 
+                mouth_mask_data,
+                "Lower Mouth Mask"
+            )
+
+    if modules.globals.eyes_mask:
+        # Create and apply eyes mask
+        eyes_mask_data = create_eyes_mask(target_face, temp_frame)
+        swapped_frame = apply_mask_area(
+            swapped_frame, 
+            eyes_mask_data[1],  # eyes_cutout
+            eyes_mask_data[2],  # eyes_box
+            face_mask, 
+            eyes_mask_data[3]   # eyes_polygon
+        )
+
+        if modules.globals.show_eyes_mask_box:
+            swapped_frame = draw_mask_visualization(
+                swapped_frame, 
+                eyes_mask_data,
+                "Eyes Mask",
+                draw_method="ellipse"
+            )
+
+    if modules.globals.eyebrows_mask:
+        # Create and apply eyebrows mask
+        eyebrows_mask_data = create_eyebrows_mask(target_face, temp_frame)
+        swapped_frame = apply_mask_area(
+            swapped_frame, 
+            eyebrows_mask_data[1],  # eyebrows_cutout
+            eyebrows_mask_data[2],  # eyebrows_box
+            face_mask, 
+            eyebrows_mask_data[3]   # eyebrows_polygon
+        )
+
+        if modules.globals.show_eyebrows_mask_box:
+            swapped_frame = draw_mask_visualization(
+                swapped_frame, 
+                eyebrows_mask_data,
+                "Eyebrows Mask"
             )
 
     return swapped_frame
@@ -255,361 +301,3 @@ def process_video(source_path: str, temp_frame_paths: List[str]) -> None:
     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)

modules/ui.py

@@ -3,7 +3,7 @@ import webbrowser
 import customtkinter as ctk
 from typing import Callable, Tuple
 import cv2
-from cv2_enumerate_cameras import enumerate_cameras  # Add this import
+from cv2_enumerate_cameras import enumerate_cameras
 from PIL import Image, ImageOps
 import time
 import json
@@ -36,7 +36,7 @@ if platform.system() == "Windows":
 ROOT = None
 POPUP = None
 POPUP_LIVE = None
-ROOT_HEIGHT = 700
+ROOT_HEIGHT = 730
 ROOT_WIDTH = 600
 
 PREVIEW = None
@@ -167,20 +167,20 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
 
     # Image Selection Area (Top)
     source_label = ctk.CTkLabel(root, text=None)
-    source_label.place(relx=0.1, rely=0.1, relwidth=0.3, relheight=0.25)
+    source_label.place(relx=0.1, rely=0.05, relwidth=0.3, relheight=0.25)
 
     target_label = ctk.CTkLabel(root, text=None)
-    target_label.place(relx=0.6, rely=0.1, relwidth=0.3, relheight=0.25)
+    target_label.place(relx=0.6, rely=0.05, relwidth=0.3, relheight=0.25)
 
     select_face_button = ctk.CTkButton(
         root, text=_("Select a face"), cursor="hand2", command=lambda: select_source_path()
     )
-    select_face_button.place(relx=0.1, rely=0.4, relwidth=0.3, relheight=0.1)
+    select_face_button.place(relx=0.1, rely=0.35, relwidth=0.3, relheight=0.1)
 
     swap_faces_button = ctk.CTkButton(
         root, text="↔", cursor="hand2", command=lambda: swap_faces_paths()
     )
-    swap_faces_button.place(relx=0.45, rely=0.4, relwidth=0.1, relheight=0.1)
+    swap_faces_button.place(relx=0.45, rely=0.35, relwidth=0.1, relheight=0.1)
 
     select_target_button = ctk.CTkButton(
         root,
@@ -188,7 +188,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
         cursor="hand2",
         command=lambda: select_target_path(),
     )
-    select_target_button.place(relx=0.6, rely=0.4, relwidth=0.3, relheight=0.1)
+    select_target_button.place(relx=0.6, rely=0.35, relwidth=0.3, relheight=0.1)
 
     # AI Generated Face controls
     fake_face_value = ctk.BooleanVar(value=modules.globals.use_fake_face)
@@ -199,7 +199,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
         cursor="hand2",
         command=lambda: toggle_fake_face(fake_face_value)
     )
-    fake_face_switch.place(relx=0.1, rely=0.55)
+    fake_face_switch.place(relx=0.1, rely=0.50)
 
     # Add refresh button next to the switch
     refresh_face_button = ctk.CTkButton(
@@ -209,7 +209,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
         cursor="hand2",
         command=lambda: refresh_fake_face_clicked()
     )
-    refresh_face_button.place(relx=0.35, rely=0.55)
+    refresh_face_button.place(relx=0.35, rely=0.50)
 
     # Face Processing Options (Middle Left)
     many_faces_value = ctk.BooleanVar(value=modules.globals.many_faces)
@@ -223,7 +223,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
             save_switch_states(),
         ),
     )
-    many_faces_switch.place(relx=0.1, rely=0.60)
+    many_faces_switch.place(relx=0.1, rely=0.55)
 
     map_faces = ctk.BooleanVar(value=modules.globals.map_faces)
     map_faces_switch = ctk.CTkSwitch(
@@ -237,7 +237,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
             close_mapper_window() if not map_faces.get() else None
         ),
     )
-    map_faces_switch.place(relx=0.1, rely=0.65)
+    map_faces_switch.place(relx=0.1, rely=0.60)
 
     enhancer_value = ctk.BooleanVar(value=modules.globals.fp_ui["face_enhancer"])
     enhancer_switch = ctk.CTkSwitch(
@@ -250,30 +250,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
             save_switch_states(),
         ),
     )
-    enhancer_switch.place(relx=0.1, rely=0.70)
-
-    # Additional Options (Middle Right)
-    mouth_mask_var = ctk.BooleanVar(value=modules.globals.mouth_mask)
-    mouth_mask_switch = ctk.CTkSwitch(
-        root,
-        text=_("Mouth Mask"),
-        variable=mouth_mask_var,
-        cursor="hand2",
-        command=lambda: setattr(modules.globals, "mouth_mask", mouth_mask_var.get()),
-    )
-    mouth_mask_switch.place(relx=0.6, rely=0.55)
-
-    show_mouth_mask_box_var = ctk.BooleanVar(value=modules.globals.show_mouth_mask_box)
-    show_mouth_mask_box_switch = ctk.CTkSwitch(
-        root,
-        text=_("Show Mouth Mask Box"),
-        variable=show_mouth_mask_box_var,
-        cursor="hand2",
-        command=lambda: setattr(
-            modules.globals, "show_mouth_mask_box", show_mouth_mask_box_var.get()
-        ),
-    )
-    show_mouth_mask_box_switch.place(relx=0.6, rely=0.6)
+    enhancer_switch.place(relx=0.1, rely=0.65)
 
     keep_audio_value = ctk.BooleanVar(value=modules.globals.keep_audio)
     keep_audio_switch = ctk.CTkSwitch(
@@ -286,9 +263,9 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
             save_switch_states(),
         ),
     )
-    keep_audio_switch.place(relx=0.6, rely=0.65)
+    keep_audio_switch.place(relx=0.1, rely=0.70)
 
-    # Add show FPS switch
+    # Add show FPS switch right after keep_audio_switch
     show_fps_value = ctk.BooleanVar(value=modules.globals.show_fps)
     show_fps_switch = ctk.CTkSwitch(
         root,
@@ -300,7 +277,75 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
             save_switch_states(),
         ),
     )
-    show_fps_switch.place(relx=0.6, rely=0.70)
+    show_fps_switch.place(relx=0.1, rely=0.75)
+
+    # Mask Switches (Middle Right - Top Section)
+    mouth_mask_var = ctk.BooleanVar(value=modules.globals.mouth_mask)
+    mouth_mask_switch = ctk.CTkSwitch(
+        root,
+        text=_("Mouth Mask"),
+        variable=mouth_mask_var,
+        cursor="hand2",
+        command=lambda: setattr(modules.globals, "mouth_mask", mouth_mask_var.get()),
+    )
+    mouth_mask_switch.place(relx=0.6, rely=0.50)
+
+    eyes_mask_var = ctk.BooleanVar(value=modules.globals.eyes_mask)
+    eyes_mask_switch = ctk.CTkSwitch(
+        root,
+        text=_("Eyes Mask"),
+        variable=eyes_mask_var,
+        cursor="hand2",
+        command=lambda: setattr(modules.globals, "eyes_mask", eyes_mask_var.get()),
+    )
+    eyes_mask_switch.place(relx=0.6, rely=0.55)
+
+    eyebrows_mask_var = ctk.BooleanVar(value=modules.globals.eyebrows_mask)
+    eyebrows_mask_switch = ctk.CTkSwitch(
+        root,
+        text=_("Eyebrows Mask"),
+        variable=eyebrows_mask_var,
+        cursor="hand2",
+        command=lambda: setattr(modules.globals, "eyebrows_mask", eyebrows_mask_var.get()),
+    )
+    eyebrows_mask_switch.place(relx=0.6, rely=0.60)
+
+    # Box Visualization Switches (Middle Right - Bottom Section)
+    show_mouth_mask_box_var = ctk.BooleanVar(value=modules.globals.show_mouth_mask_box)
+    show_mouth_mask_box_switch = ctk.CTkSwitch(
+        root,
+        text=_("Show Mouth Box"),
+        variable=show_mouth_mask_box_var,
+        cursor="hand2",
+        command=lambda: setattr(
+            modules.globals, "show_mouth_mask_box", show_mouth_mask_box_var.get()
+        ),
+    )
+    show_mouth_mask_box_switch.place(relx=0.6, rely=0.65)
+
+    show_eyes_mask_box_var = ctk.BooleanVar(value=modules.globals.show_eyes_mask_box)
+    show_eyes_mask_box_switch = ctk.CTkSwitch(
+        root,
+        text=_("Show Eyes Box"),
+        variable=show_eyes_mask_box_var,
+        cursor="hand2",
+        command=lambda: setattr(
+            modules.globals, "show_eyes_mask_box", show_eyes_mask_box_var.get()
+        ),
+    )
+    show_eyes_mask_box_switch.place(relx=0.6, rely=0.70)
+
+    show_eyebrows_mask_box_var = ctk.BooleanVar(value=modules.globals.show_eyebrows_mask_box)
+    show_eyebrows_mask_box_switch = ctk.CTkSwitch(
+        root,
+        text=_("Show Eyebrows Box"),
+        variable=show_eyebrows_mask_box_var,
+        cursor="hand2",
+        command=lambda: setattr(
+            modules.globals, "show_eyebrows_mask_box", show_eyebrows_mask_box_var.get()
+        ),
+    )
+    show_eyebrows_mask_box_switch.place(relx=0.6, rely=0.75)
 
     # Main Control Buttons (Bottom)
     start_button = ctk.CTkButton(
@@ -368,7 +413,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
     donate_label = ctk.CTkLabel(
         root, text="Deep Live Cam", justify="center", cursor="hand2"
     )
-    donate_label.place(relx=0.1, rely=0.95, relwidth=0.8)
+    donate_label.place(relx=0.1, rely=0.94, relwidth=0.8)
     donate_label.configure(
         text_color=ctk.ThemeManager.theme.get("URL").get("text_color")
     )
@@ -767,8 +812,7 @@ def update_preview(frame_number: int = 0) -> None:
                 modules.globals.frame_processors
         ):
             temp_frame = frame_processor.process_frame(
-                get_one_face(cv2.imread(modules.globals.source_path)), temp_frame
-            )
+                get_one_face(cv2.imread(modules.globals.source_path)), temp_frame)
         image = Image.fromarray(cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB))
         image = ImageOps.contain(
             image, (PREVIEW_MAX_WIDTH, PREVIEW_MAX_HEIGHT), Image.LANCZOS