from typing import Any, List, Tuple import cv2 import numpy as np import insightface import threading import modules.globals 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, resolve_relative_path, is_image, is_video, ) from modules.cluster_analysis import find_closest_centroid FACE_SWAPPER = None THREAD_LOCK = threading.Lock() NAME = "DLC.FACE-SWAPPER" # Add mouth landmarks indices for masking MOUTH_LANDMARKS = list( range(46, 68) ) # Common indices for mouth landmarks in facial detection def pre_check() -> bool: download_directory_path = resolve_relative_path("../models") 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 = resolve_relative_path("../models/inswapper_128_fp16.onnx") FACE_SWAPPER = insightface.model_zoo.get_model( model_path, providers=modules.globals.execution_providers ) return FACE_SWAPPER def create_mouth_mask(face: Face, frame_shape: Tuple[int, int]) -> np.ndarray: """Create a mask for the mouth region""" mask = np.zeros(frame_shape[:2], dtype=np.uint8) # Get mouth landmarks from the face landmarks = face.kps mouth_points = landmarks[MOUTH_LANDMARKS].astype(np.int32) # Create a polygon around the mouth region cv2.fillPoly(mask, [mouth_points], 255) # Dilate the mask slightly to ensure smooth blending kernel = np.ones((5, 5), np.uint8) mask = cv2.dilate(mask, kernel, iterations=2) # Blur the mask edges mask = cv2.GaussianBlur(mask, (15, 15), 10) return mask def blend_with_mask( swapped_frame: Frame, original_frame: Frame, mask: np.ndarray ) -> Frame: """Blend the swapped face with the original frame using the mouth mask""" mask_3channel = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR) / 255.0 # Blend the images based on the mask blended = swapped_frame * (1 - mask_3channel) + original_frame * mask_3channel return blended.astype(np.uint8) def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame: # Store the original frame for mouth preservation original_frame = temp_frame.copy() # Perform the face swap swapped_frame = get_face_swapper().get( temp_frame, target_face, source_face, paste_back=True ) def process_frame(source_face: Face, temp_frame: Frame) -> Frame: # Ensure the frame is in RGB format if color correction is enabled if modules.globals.color_correction: temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB) if modules.globals.many_faces: many_faces = get_many_faces(temp_frame) if many_faces: for target_face in many_faces: temp_frame = swap_face(source_face, target_face, temp_frame) else: target_face = get_one_face(temp_frame) if target_face: temp_frame = swap_face(source_face, target_face, temp_frame) return temp_frame def process_frame_v2(temp_frame: Frame, temp_frame_path: str = "") -> Frame: if is_image(modules.globals.target_path): if modules.globals.many_faces: source_face = default_source_face() for map in modules.globals.souce_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.souce_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.souce_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.souce_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 )