from typing import Any, List import cv2 import insightface import threading import numpy as np import modules.globals import logging import time import modules.processors.frame.core 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, 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" 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 = 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: if not modules.globals.map_faces and not is_image(modules.globals.source_path): 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) 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) return False return True def get_face_swapper() -> Any: global FACE_SWAPPER with THREAD_LOCK: if FACE_SWAPPER is None: 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: face_swapper = get_face_swapper() # Apply the face swap with optimized settings for better performance 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 swap_face_enhanced(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame: """Enhanced face swapping with better quality and performance optimizations""" face_swapper = get_face_swapper() # Apply the face swap swapped_frame = face_swapper.get( temp_frame, target_face, source_face, paste_back=True ) # Enhanced post-processing for better quality swapped_frame = enhance_face_swap_quality(swapped_frame, source_face, 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 ) 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 enhance_face_swap_quality(swapped_frame: Frame, source_face: Face, target_face: Face, original_frame: Frame) -> Frame: """Apply quality enhancements to the swapped face""" try: # Get face bounding box bbox = target_face.bbox.astype(int) x1, y1, x2, y2 = bbox # Ensure coordinates are within frame bounds h, w = swapped_frame.shape[:2] x1, y1 = max(0, x1), max(0, y1) x2, y2 = min(w, x2), min(h, y2) if x2 <= x1 or y2 <= y1: return swapped_frame # Extract face regions swapped_face = swapped_frame[y1:y2, x1:x2] original_face = original_frame[y1:y2, x1:x2] # Apply color matching color_matched = apply_advanced_color_matching(swapped_face, original_face) # Apply edge smoothing smoothed = apply_edge_smoothing(color_matched, original_face) # Blend back into frame swapped_frame[y1:y2, x1:x2] = smoothed return swapped_frame except Exception as e: # Return original swapped frame if enhancement fails return swapped_frame def apply_advanced_color_matching(swapped_face: np.ndarray, target_face: np.ndarray) -> np.ndarray: """Apply advanced color matching between swapped and target faces""" try: # Convert to LAB color space for better color matching swapped_lab = cv2.cvtColor(swapped_face, cv2.COLOR_BGR2LAB).astype(np.float32) target_lab = cv2.cvtColor(target_face, cv2.COLOR_BGR2LAB).astype(np.float32) # Calculate statistics for each channel swapped_mean = np.mean(swapped_lab, axis=(0, 1)) swapped_std = np.std(swapped_lab, axis=(0, 1)) target_mean = np.mean(target_lab, axis=(0, 1)) target_std = np.std(target_lab, axis=(0, 1)) # Apply color transfer for i in range(3): if swapped_std[i] > 0: swapped_lab[:, :, i] = (swapped_lab[:, :, i] - swapped_mean[i]) * (target_std[i] / swapped_std[i]) + target_mean[i] # Convert back to BGR result = cv2.cvtColor(np.clip(swapped_lab, 0, 255).astype(np.uint8), cv2.COLOR_LAB2BGR) return result except Exception: return swapped_face def apply_edge_smoothing(face: np.ndarray, reference: np.ndarray) -> np.ndarray: """Apply edge smoothing to reduce artifacts""" try: # Create a soft mask for blending edges mask = np.ones(face.shape[:2], dtype=np.float32) # Apply Gaussian blur to create soft edges kernel_size = max(5, min(face.shape[0], face.shape[1]) // 20) if kernel_size % 2 == 0: kernel_size += 1 mask = cv2.GaussianBlur(mask, (kernel_size, kernel_size), 0) mask = mask[:, :, np.newaxis] # Blend with reference for smoother edges blended = face * mask + reference * (1 - mask) return blended.astype(np.uint8) except Exception: return face def swap_face_enhanced_with_occlusion(source_face: Face, target_face: Face, temp_frame: Frame, original_frame: Frame) -> Frame: """Enhanced face swapping with occlusion handling and stabilization""" face_swapper = get_face_swapper() try: # Get face bounding box bbox = target_face.bbox.astype(int) x1, y1, x2, y2 = bbox # Ensure coordinates are within frame bounds h, w = temp_frame.shape[:2] x1, y1 = max(0, x1), max(0, y1) x2, y2 = min(w, x2), min(h, y2) if x2 <= x1 or y2 <= y1: return temp_frame # Create face mask to handle occlusion face_mask = create_enhanced_face_mask(target_face, temp_frame) # Apply face swap swapped_frame = face_swapper.get(temp_frame, target_face, source_face, paste_back=True) # Apply occlusion-aware blending final_frame = apply_occlusion_aware_blending( swapped_frame, temp_frame, face_mask, bbox ) # Enhanced post-processing for better quality final_frame = enhance_face_swap_quality(final_frame, source_face, target_face, original_frame) # Apply mouth mask if enabled if modules.globals.mouth_mask: face_mask_full = create_face_mask(target_face, final_frame) mouth_mask, mouth_cutout, mouth_box, lower_lip_polygon = ( create_lower_mouth_mask(target_face, final_frame) ) final_frame = apply_mouth_area( final_frame, mouth_cutout, mouth_box, face_mask_full, lower_lip_polygon ) if modules.globals.show_mouth_mask_box: mouth_mask_data = (mouth_mask, mouth_cutout, mouth_box, lower_lip_polygon) final_frame = draw_mouth_mask_visualization( final_frame, target_face, mouth_mask_data ) return final_frame except Exception as e: print(f"Error in occlusion-aware face swap: {e}") # Fallback to regular enhanced swap return swap_face_enhanced(source_face, target_face, temp_frame) def create_enhanced_face_mask(face: Face, frame: Frame) -> np.ndarray: """Create an enhanced face mask that better handles occlusion""" mask = np.zeros(frame.shape[:2], dtype=np.uint8) try: # Use landmarks if available for more precise masking if hasattr(face, 'landmark_2d_106') and face.landmark_2d_106 is not None: landmarks = face.landmark_2d_106.astype(np.int32) # Create face contour from landmarks face_contour = [] # Face outline (jawline and forehead) face_outline_indices = list(range(0, 33)) # Jawline and face boundary for idx in face_outline_indices: if idx < len(landmarks): face_contour.append(landmarks[idx]) if len(face_contour) > 3: face_contour = np.array(face_contour) # Create convex hull for smoother mask hull = cv2.convexHull(face_contour) # Expand the hull slightly for better coverage center = np.mean(hull, axis=0) expanded_hull = [] for point in hull: direction = point[0] - center direction = direction / np.linalg.norm(direction) if np.linalg.norm(direction) > 0 else direction expanded_point = point[0] + direction * 10 # Expand by 10 pixels expanded_hull.append(expanded_point) expanded_hull = np.array(expanded_hull, dtype=np.int32) cv2.fillConvexPoly(mask, expanded_hull, 255) else: # Fallback to bounding box bbox = face.bbox.astype(int) x1, y1, x2, y2 = bbox cv2.rectangle(mask, (x1, y1), (x2, y2), 255, -1) else: # Fallback to bounding box if no landmarks bbox = face.bbox.astype(int) x1, y1, x2, y2 = bbox cv2.rectangle(mask, (x1, y1), (x2, y2), 255, -1) # Apply Gaussian blur for soft edges mask = cv2.GaussianBlur(mask, (15, 15), 5) except Exception as e: print(f"Error creating enhanced face mask: {e}") # Fallback to simple rectangle mask bbox = face.bbox.astype(int) x1, y1, x2, y2 = bbox cv2.rectangle(mask, (x1, y1), (x2, y2), 255, -1) mask = cv2.GaussianBlur(mask, (15, 15), 5) return mask def apply_occlusion_aware_blending(swapped_frame: Frame, original_frame: Frame, face_mask: np.ndarray, bbox: np.ndarray) -> Frame: """Apply occlusion-aware blending to handle hands/objects covering the face""" try: x1, y1, x2, y2 = bbox # Ensure coordinates are within bounds h, w = swapped_frame.shape[:2] x1, y1 = max(0, x1), max(0, y1) x2, y2 = min(w, x2), min(h, y2) if x2 <= x1 or y2 <= y1: return swapped_frame # Extract face regions swapped_face_region = swapped_frame[y1:y2, x1:x2] original_face_region = original_frame[y1:y2, x1:x2] face_mask_region = face_mask[y1:y2, x1:x2] # Detect potential occlusion using edge detection and color analysis occlusion_mask = detect_occlusion(original_face_region, swapped_face_region) # Combine face mask with occlusion detection combined_mask = face_mask_region.astype(np.float32) / 255.0 occlusion_factor = (255 - occlusion_mask).astype(np.float32) / 255.0 # Apply occlusion-aware blending final_mask = combined_mask * occlusion_factor final_mask = final_mask[:, :, np.newaxis] # Blend the regions blended_region = (swapped_face_region * final_mask + original_face_region * (1 - final_mask)).astype(np.uint8) # Copy back to full frame result_frame = swapped_frame.copy() result_frame[y1:y2, x1:x2] = blended_region return result_frame except Exception as e: print(f"Error in occlusion-aware blending: {e}") return swapped_frame def detect_occlusion(original_region: np.ndarray, swapped_region: np.ndarray) -> np.ndarray: """Detect potential occlusion areas (hands, objects) in the face region""" try: # Convert to different color spaces for analysis original_hsv = cv2.cvtColor(original_region, cv2.COLOR_BGR2HSV) original_lab = cv2.cvtColor(original_region, cv2.COLOR_BGR2LAB) # Detect skin-like regions (potential hands) # HSV ranges for skin detection lower_skin = np.array([0, 20, 70], dtype=np.uint8) upper_skin = np.array([20, 255, 255], dtype=np.uint8) skin_mask1 = cv2.inRange(original_hsv, lower_skin, upper_skin) lower_skin2 = np.array([160, 20, 70], dtype=np.uint8) upper_skin2 = np.array([180, 255, 255], dtype=np.uint8) skin_mask2 = cv2.inRange(original_hsv, lower_skin2, upper_skin2) skin_mask = cv2.bitwise_or(skin_mask1, skin_mask2) # Edge detection to find object boundaries gray = cv2.cvtColor(original_region, cv2.COLOR_BGR2GRAY) edges = cv2.Canny(gray, 50, 150) # Dilate edges to create thicker boundaries kernel = np.ones((3, 3), np.uint8) edges_dilated = cv2.dilate(edges, kernel, iterations=2) # Combine skin detection and edge detection occlusion_mask = cv2.bitwise_or(skin_mask, edges_dilated) # Apply morphological operations to clean up the mask kernel = np.ones((5, 5), np.uint8) occlusion_mask = cv2.morphologyEx(occlusion_mask, cv2.MORPH_CLOSE, kernel) occlusion_mask = cv2.morphologyEx(occlusion_mask, cv2.MORPH_OPEN, kernel) # Apply Gaussian blur for smooth transitions occlusion_mask = cv2.GaussianBlur(occlusion_mask, (11, 11), 3) return occlusion_mask except Exception as e: print(f"Error in occlusion detection: {e}") # Return empty mask if detection fails return np.zeros(original_region.shape[:2], dtype=np.uint8) def process_frame(source_face: Face, temp_frame: Frame) -> Frame: from modules.performance_optimizer import performance_optimizer from modules.face_tracker import face_tracker start_time = time.time() original_size = temp_frame.shape[:2][::-1] # (width, height) # Apply color correction if enabled if modules.globals.color_correction: temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB) # Preprocess frame for performance processed_frame = performance_optimizer.preprocess_frame(temp_frame) if modules.globals.many_faces: # Only detect faces if enough time has passed or cache is empty if performance_optimizer.should_detect_faces(): detected_faces = get_many_faces(processed_frame) # Apply tracking to each face tracked_faces = [] for i, face in enumerate(detected_faces or []): # Use separate tracker for each face (simplified for now) tracked_face = face_tracker.track_face(face, processed_frame) if tracked_face: tracked_faces.append(tracked_face) performance_optimizer.face_cache['many_faces'] = tracked_faces else: tracked_faces = performance_optimizer.face_cache.get('many_faces', []) if tracked_faces: for target_face in tracked_faces: if source_face and target_face: processed_frame = swap_face_enhanced_with_occlusion(source_face, target_face, processed_frame, temp_frame) else: print("Face detection failed for target/source.") else: # Use cached face detection with tracking for better performance if performance_optimizer.should_detect_faces(): detected_face = get_one_face(processed_frame) tracked_face = face_tracker.track_face(detected_face, processed_frame) performance_optimizer.face_cache['single_face'] = tracked_face else: tracked_face = performance_optimizer.face_cache.get('single_face') if tracked_face and source_face: processed_frame = swap_face_enhanced_with_occlusion(source_face, tracked_face, processed_frame, temp_frame) else: # Try to use tracking even without detection tracked_face = face_tracker.track_face(None, processed_frame) if tracked_face and source_face: processed_frame = swap_face_enhanced_with_occlusion(source_face, tracked_face, processed_frame, temp_frame) else: logging.error("Face detection and tracking failed.") # Postprocess frame back to original size final_frame = performance_optimizer.postprocess_frame(processed_frame, original_size) # Update performance stats frame_time = time.time() - start_time performance_optimizer.update_fps_stats(frame_time) return final_frame def process_frame_v2(temp_frame: Frame, temp_frame_path: str = "") -> Frame: if is_image(modules.globals.target_path): if modules.globals.many_faces: source_face = default_source_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 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 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"]: temp_frame = swap_face(source_face, target_face, temp_frame) elif not modules.globals.many_faces: 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"]: temp_frame = swap_face(source_face, target_face, temp_frame) else: detected_faces = get_many_faces(temp_frame) if modules.globals.many_faces: if detected_faces: source_face = default_source_face() for target_face in detected_faces: temp_frame = swap_face(source_face, target_face, temp_frame) elif not modules.globals.many_faces: 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, ) else: 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 ) 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: if not modules.globals.map_faces: 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: if not modules.globals.map_faces: 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. 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: if modules.globals.map_faces and modules.globals.many_faces: 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)