import cv2 import numpy as np from modules.typing import Face, Frame import modules.globals from modules.gpu_processing import gpu_gaussian_blur, gpu_resize, gpu_cvt_color def apply_color_transfer(source, target): """ Apply color transfer from target to source image using LAB color space. Uses float32 throughout for performance (sufficient precision for 8-bit images). """ # Convert to float32 [0,1] range for proper LAB conversion source_f32 = source.astype(np.float32) / 255.0 target_f32 = target.astype(np.float32) / 255.0 source_lab = cv2.cvtColor(source_f32, cv2.COLOR_BGR2LAB) target_lab = cv2.cvtColor(target_f32, cv2.COLOR_BGR2LAB) source_mean, source_std = cv2.meanStdDev(source_lab) target_mean, target_std = cv2.meanStdDev(target_lab) # Reshape mean and std to be broadcastable (already float64 from meanStdDev, cast to f32) source_mean = source_mean.reshape(1, 1, 3).astype(np.float32) source_std = np.maximum(source_std.reshape(1, 1, 3), 1e-6).astype(np.float32) target_mean = target_mean.reshape(1, 1, 3).astype(np.float32) target_std = target_std.reshape(1, 1, 3).astype(np.float32) # Perform the color transfer in LAB space result_lab = (source_lab - source_mean) * (target_std / source_std) + target_mean # Convert back to BGR and uint8 result_bgr = cv2.cvtColor(result_lab, cv2.COLOR_LAB2BGR) return np.clip(result_bgr * 255.0, 0, 255).astype(np.uint8) 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 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 face_outline = landmarks[0:33] hull = cv2.convexHull(face_outline) # Vectorized hull padding — expand each point outward from center center = np.mean(face_outline, axis=0, dtype=np.float32) hull_pts = hull.reshape(-1, 2).astype(np.float32) directions = hull_pts - center norms = np.linalg.norm(directions, axis=1, keepdims=True) norms = np.maximum(norms, 1e-6) # avoid division by zero directions /= norms hull_padded = (hull_pts + directions * padding).astype(np.int32) # Fill the padded convex hull cv2.fillConvexPoly(mask, hull_padded, 255) # Smooth the mask edges (GPU-accelerated when available) mask = gpu_gaussian_blur(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 lower_lip_polygon = None mouth_box = (0,0,0,0) landmarks = face.landmark_2d_106 if landmarks is not None: # Use outer mouth landmarks (52-71) to capture the full mouth area lower_lip_order = list(range(52, 72)) if max(lower_lip_order) >= landmarks.shape[0]: return mask, mouth_cutout, mouth_box, lower_lip_polygon lower_lip_landmarks = landmarks[lower_lip_order].astype(np.float32) # Calculate the center of the landmarks center = np.mean(lower_lip_landmarks, axis=0) # Expand the landmarks outward using the mouth_mask_size mouth_mask_size = getattr(modules.globals, "mouth_mask_size", 0.0) # 0-100 slider expansion_factor = 1 + (mouth_mask_size / 100.0) * 2.5 # Expand with extra downward bias toward chin offsets = lower_lip_landmarks - center chin_bias = 1 + (mouth_mask_size / 100.0) * 1.5 scale_y = np.where(offsets[:, 1] > 0, expansion_factor * chin_bias, expansion_factor) expanded_landmarks = lower_lip_landmarks.copy() expanded_landmarks[:, 0] = center[0] + offsets[:, 0] * expansion_factor expanded_landmarks[:, 1] = center[1] + offsets[:, 1] * scale_y # Convert back to integer coordinates expanded_landmarks = expanded_landmarks.astype(np.int32) # 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) # Shift polygon coordinates relative to the ROI's top-left corner polygon_relative_to_roi = expanded_landmarks - [min_x, min_y] cv2.fillPoly(mask_roi, [polygon_relative_to_roi], 255) # Apply Gaussian blur to soften the mask edges (GPU-accelerated when available) mask_roi = gpu_gaussian_blur(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 mouth_box = (min_x, min_y, max_x, max_y) return mask, mouth_cutout, mouth_box, 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 with size adjustment 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 * modules.globals.eyes_mask_size)) height = int((np.max(y_coords) - np.min(y_coords)) * (1 + modules.globals.mask_down_size * modules.globals.eyes_mask_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 (GPU-accelerated when available) mask_roi = gpu_gaussian_blur(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 adjusted by size all_points = np.vstack([left_eyebrow, right_eyebrow]) padding_factor = modules.globals.eyebrows_mask_size min_x = np.min(all_points[:, 0]) - 25 * padding_factor max_x = np.max(all_points[:, 0]) + 25 * padding_factor min_y = np.min(all_points[:, 1]) - 20 * padding_factor max_y = np.max(all_points[:, 1]) + 15 * padding_factor # 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 (GPU-accelerated when available) # First, strong Gaussian blur for initial softening mask_roi = gpu_gaussian_blur(mask_roi, (21, 21), 7) # Second, medium blur for transition areas mask_roi = gpu_gaussian_blur(mask_roi, (11, 11), 3) # Finally, light blur for fine details mask_roi = gpu_gaussian_blur(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 = gpu_gaussian_blur(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 = gpu_resize(cutout, (box_width, box_height)) roi = frame[min_y:max_y, min_x:max_x] if roi.shape != resized_cutout.shape: resized_cutout = gpu_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 (GPU-accelerated when available) polygon_mask = gpu_gaussian_blur(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(np.float32), (0, 0), feather_amount ) max_val = feathered_mask.max() if max_val > 1e-6: feathered_mask *= np.float32(1.0 / max_val) # 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.astype(np.float32) * np.float32(1.0 / 255.0)) combined_mask_3ch = combined_mask[:, :, np.newaxis] inv_mask = np.float32(1.0) - combined_mask_3ch blended = ( color_corrected_area * combined_mask_3ch + roi * inv_mask ).astype(np.uint8) # Apply face mask to blended result face_mask_f32 = face_mask_roi[:, :, np.newaxis].astype(np.float32) * np.float32(1.0 / 255.0) face_mask_3channel = np.broadcast_to(face_mask_f32, blended.shape) final_blend = blended * face_mask_3channel + roi * (np.float32(1.0) - 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