623 lines
21 KiB
Python
623 lines
21 KiB
Python
from typing import Any, List
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import cv2
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import insightface
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import threading
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import numpy as np
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import modules.globals
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import logging
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import modules.processors.frame.core
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from modules.core import update_status
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from modules.face_analyser import get_one_face, get_many_faces, default_source_face
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from modules.typing import Face, Frame
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from modules.utilities import (
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conditional_download,
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is_image,
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is_video,
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)
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from modules.cluster_analysis import find_closest_centroid
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import os
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FACE_SWAPPER = None
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THREAD_LOCK = threading.Lock()
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NAME = "DLC.FACE-SWAPPER"
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abs_dir = os.path.dirname(os.path.abspath(__file__))
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models_dir = os.path.join(
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os.path.dirname(os.path.dirname(os.path.dirname(abs_dir))), "models"
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)
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def pre_check() -> bool:
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download_directory_path = abs_dir
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conditional_download(
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download_directory_path,
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[
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"https://huggingface.co/hacksider/deep-live-cam/blob/main/inswapper_128_fp16.onnx"
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],
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)
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return True
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def pre_start() -> bool:
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if not modules.globals.map_faces and not is_image(modules.globals.source_path):
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update_status("Select an image for source path.", NAME)
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return False
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elif not modules.globals.map_faces and not get_one_face(
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cv2.imread(modules.globals.source_path)
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):
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update_status("No face in source path detected.", NAME)
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return False
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if not is_image(modules.globals.target_path) and not is_video(
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modules.globals.target_path
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):
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update_status("Select an image or video for target path.", NAME)
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return False
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return True
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def get_face_swapper() -> Any:
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global FACE_SWAPPER
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with THREAD_LOCK:
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if FACE_SWAPPER is None:
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model_path = os.path.join(models_dir, "inswapper_128_fp16.onnx")
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FACE_SWAPPER = insightface.model_zoo.get_model(
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model_path, providers=modules.globals.execution_providers
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)
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return FACE_SWAPPER
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def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
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face_swapper = get_face_swapper()
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# Apply the face swap
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swapped_frame = face_swapper.get(
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temp_frame, target_face, source_face, paste_back=True
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)
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if modules.globals.mouth_mask:
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# Create a mask for the target face
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face_mask = create_face_mask(target_face, temp_frame)
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# Create the mouth mask
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mouth_mask, mouth_cutout, mouth_box, lower_lip_polygon = (
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create_lower_mouth_mask(target_face, temp_frame)
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)
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# Apply the mouth area
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swapped_frame = apply_mouth_area(
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swapped_frame, mouth_cutout, mouth_box, face_mask, lower_lip_polygon
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)
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if modules.globals.show_mouth_mask_box:
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mouth_mask_data = (mouth_mask, mouth_cutout, mouth_box, lower_lip_polygon)
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swapped_frame = draw_mouth_mask_visualization(
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swapped_frame, target_face, mouth_mask_data
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)
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return swapped_frame
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def process_frame(source_face: Face, temp_frame: Frame) -> Frame:
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if modules.globals.color_correction:
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temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB)
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if modules.globals.many_faces:
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many_faces = get_many_faces(temp_frame)
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if many_faces:
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for target_face in many_faces:
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if source_face and target_face:
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temp_frame = swap_face(source_face, target_face, temp_frame)
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else:
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print("Face detection failed for target/source.")
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else:
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target_face = get_one_face(temp_frame)
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if target_face and source_face:
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temp_frame = swap_face(source_face, target_face, temp_frame)
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else:
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logging.error("Face detection failed for target or source.")
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return temp_frame
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def process_frame_v2(temp_frame: Frame, temp_frame_path: str = "") -> Frame:
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if is_image(modules.globals.target_path):
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if modules.globals.many_faces:
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source_face = default_source_face()
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for map in modules.globals.source_target_map:
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target_face = map["target"]["face"]
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temp_frame = swap_face(source_face, target_face, temp_frame)
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elif not modules.globals.many_faces:
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for map in modules.globals.source_target_map:
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if "source" in map:
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source_face = map["source"]["face"]
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target_face = map["target"]["face"]
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temp_frame = swap_face(source_face, target_face, temp_frame)
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elif is_video(modules.globals.target_path):
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if modules.globals.many_faces:
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source_face = default_source_face()
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for map in modules.globals.source_target_map:
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target_frame = [
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f
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for f in map["target_faces_in_frame"]
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if f["location"] == temp_frame_path
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]
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for frame in target_frame:
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for target_face in frame["faces"]:
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temp_frame = swap_face(source_face, target_face, temp_frame)
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elif not modules.globals.many_faces:
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for map in modules.globals.source_target_map:
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if "source" in map:
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target_frame = [
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f
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for f in map["target_faces_in_frame"]
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if f["location"] == temp_frame_path
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]
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source_face = map["source"]["face"]
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for frame in target_frame:
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for target_face in frame["faces"]:
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temp_frame = swap_face(source_face, target_face, temp_frame)
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else:
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detected_faces = get_many_faces(temp_frame)
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if modules.globals.many_faces:
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if detected_faces:
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source_face = default_source_face()
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for target_face in detected_faces:
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temp_frame = swap_face(source_face, target_face, temp_frame)
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elif not modules.globals.many_faces:
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if detected_faces:
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if len(detected_faces) <= len(
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modules.globals.simple_map["target_embeddings"]
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):
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for detected_face in detected_faces:
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closest_centroid_index, _ = find_closest_centroid(
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modules.globals.simple_map["target_embeddings"],
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detected_face.normed_embedding,
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)
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temp_frame = swap_face(
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modules.globals.simple_map["source_faces"][
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closest_centroid_index
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],
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detected_face,
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temp_frame,
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)
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else:
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detected_faces_centroids = []
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for face in detected_faces:
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detected_faces_centroids.append(face.normed_embedding)
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i = 0
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for target_embedding in modules.globals.simple_map[
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"target_embeddings"
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]:
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closest_centroid_index, _ = find_closest_centroid(
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detected_faces_centroids, target_embedding
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)
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temp_frame = swap_face(
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modules.globals.simple_map["source_faces"][i],
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detected_faces[closest_centroid_index],
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temp_frame,
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)
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i += 1
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return temp_frame
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def process_frames(
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source_path: str, temp_frame_paths: List[str], progress: Any = None
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) -> None:
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if not modules.globals.map_faces:
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source_face = get_one_face(cv2.imread(source_path))
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for temp_frame_path in temp_frame_paths:
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temp_frame = cv2.imread(temp_frame_path)
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try:
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result = process_frame(source_face, temp_frame)
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cv2.imwrite(temp_frame_path, result)
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except Exception as exception:
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print(exception)
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pass
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if progress:
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progress.update(1)
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else:
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for temp_frame_path in temp_frame_paths:
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temp_frame = cv2.imread(temp_frame_path)
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try:
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result = process_frame_v2(temp_frame, temp_frame_path)
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cv2.imwrite(temp_frame_path, result)
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except Exception as exception:
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print(exception)
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pass
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if progress:
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progress.update(1)
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def process_image(source_path: str, target_path: str, output_path: str) -> None:
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if not modules.globals.map_faces:
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source_face = get_one_face(cv2.imread(source_path))
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target_frame = cv2.imread(target_path)
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result = process_frame(source_face, target_frame)
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cv2.imwrite(output_path, result)
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else:
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if modules.globals.many_faces:
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update_status(
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"Many faces enabled. Using first source image. Progressing...", NAME
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)
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target_frame = cv2.imread(output_path)
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result = process_frame_v2(target_frame)
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cv2.imwrite(output_path, result)
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def process_video(source_path: str, temp_frame_paths: List[str]) -> None:
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if modules.globals.map_faces and modules.globals.many_faces:
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update_status(
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"Many faces enabled. Using first source image. Progressing...", NAME
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)
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modules.processors.frame.core.process_video(
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source_path, temp_frame_paths, process_frames
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)
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def create_lower_mouth_mask(
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face: Face, frame: Frame
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) -> (np.ndarray, np.ndarray, tuple, np.ndarray):
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mask = np.zeros(frame.shape[:2], dtype=np.uint8)
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mouth_cutout = None
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landmarks = face.landmark_2d_106
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if landmarks is not None:
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# 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
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lower_lip_order = [
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65,
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66,
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62,
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70,
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69,
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18,
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19,
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20,
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21,
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22,
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23,
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24,
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0,
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8,
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7,
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6,
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5,
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4,
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3,
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2,
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65,
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]
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lower_lip_landmarks = landmarks[lower_lip_order].astype(
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np.float32
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) # Use float for precise calculations
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# Calculate the center of the landmarks
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center = np.mean(lower_lip_landmarks, axis=0)
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# Expand the landmarks outward
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expansion_factor = (
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1 + modules.globals.mask_down_size
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) # Adjust this for more or less expansion
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expanded_landmarks = (lower_lip_landmarks - center) * expansion_factor + center
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# Extend the top lip part
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toplip_indices = [
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20,
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0,
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1,
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2,
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3,
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4,
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5,
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] # Indices for landmarks 2, 65, 66, 62, 70, 69, 18
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toplip_extension = (
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modules.globals.mask_size * 0.5
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) # Adjust this factor to control the extension
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for idx in toplip_indices:
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direction = expanded_landmarks[idx] - center
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direction = direction / np.linalg.norm(direction)
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expanded_landmarks[idx] += direction * toplip_extension
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# Extend the bottom part (chin area)
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chin_indices = [
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11,
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12,
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13,
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14,
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15,
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16,
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] # Indices for landmarks 21, 22, 23, 24, 0, 8
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chin_extension = 2 * 0.2 # Adjust this factor to control the extension
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for idx in chin_indices:
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expanded_landmarks[idx][1] += (
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expanded_landmarks[idx][1] - center[1]
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) * chin_extension
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# Convert back to integer coordinates
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expanded_landmarks = expanded_landmarks.astype(np.int32)
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# Calculate bounding box for the expanded lower mouth
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min_x, min_y = np.min(expanded_landmarks, axis=0)
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max_x, max_y = np.max(expanded_landmarks, axis=0)
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# Add some padding to the bounding box
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padding = int((max_x - min_x) * 0.1) # 10% padding
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min_x = max(0, min_x - padding)
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min_y = max(0, min_y - padding)
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max_x = min(frame.shape[1], max_x + padding)
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max_y = min(frame.shape[0], max_y + padding)
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# Ensure the bounding box dimensions are valid
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if max_x <= min_x or max_y <= min_y:
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if (max_x - min_x) <= 1:
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max_x = min_x + 1
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if (max_y - min_y) <= 1:
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max_y = min_y + 1
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# Create the mask
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mask_roi = np.zeros((max_y - min_y, max_x - min_x), dtype=np.uint8)
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cv2.fillPoly(mask_roi, [expanded_landmarks - [min_x, min_y]], 255)
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# Apply Gaussian blur to soften the mask edges
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mask_roi = cv2.GaussianBlur(mask_roi, (15, 15), 5)
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# Place the mask ROI in the full-sized mask
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mask[min_y:max_y, min_x:max_x] = mask_roi
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# Extract the masked area from the frame
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mouth_cutout = frame[min_y:max_y, min_x:max_x].copy()
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# Return the expanded lower lip polygon in original frame coordinates
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lower_lip_polygon = expanded_landmarks
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return mask, mouth_cutout, (min_x, min_y, max_x, max_y), lower_lip_polygon
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def draw_mouth_mask_visualization(
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frame: Frame, face: Face, mouth_mask_data: tuple
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) -> Frame:
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landmarks = face.landmark_2d_106
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if landmarks is not None and mouth_mask_data is not None:
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mask, mouth_cutout, (min_x, min_y, max_x, max_y), lower_lip_polygon = (
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mouth_mask_data
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)
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vis_frame = frame.copy()
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# Ensure coordinates are within frame bounds
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height, width = vis_frame.shape[:2]
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min_x, min_y = max(0, min_x), max(0, min_y)
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max_x, max_y = min(width, max_x), min(height, max_y)
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# Adjust mask to match the region size
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mask_region = mask[0 : max_y - min_y, 0 : max_x - min_x]
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# Remove the color mask overlay
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# color_mask = cv2.applyColorMap((mask_region * 255).astype(np.uint8), cv2.COLORMAP_JET)
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# Ensure shapes match before blending
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vis_region = vis_frame[min_y:max_y, min_x:max_x]
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# Remove blending with color_mask
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# if vis_region.shape[:2] == color_mask.shape[:2]:
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# blended = cv2.addWeighted(vis_region, 0.7, color_mask, 0.3, 0)
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# vis_frame[min_y:max_y, min_x:max_x] = blended
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# Draw the lower lip polygon
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cv2.polylines(vis_frame, [lower_lip_polygon], True, (0, 255, 0), 2)
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# Remove the red box
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# cv2.rectangle(vis_frame, (min_x, min_y), (max_x, max_y), (0, 0, 255), 2)
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# Visualize the feathered mask
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feather_amount = max(
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1,
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min(
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30,
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(max_x - min_x) // modules.globals.mask_feather_ratio,
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(max_y - min_y) // modules.globals.mask_feather_ratio,
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),
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)
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# Ensure kernel size is odd
|
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kernel_size = 2 * feather_amount + 1
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feathered_mask = cv2.GaussianBlur(
|
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mask_region.astype(float), (kernel_size, kernel_size), 0
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)
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feathered_mask = (feathered_mask / feathered_mask.max() * 255).astype(np.uint8)
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# Remove the feathered mask color overlay
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# color_feathered_mask = cv2.applyColorMap(feathered_mask, cv2.COLORMAP_VIRIDIS)
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# Ensure shapes match before blending feathered mask
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# if vis_region.shape == color_feathered_mask.shape:
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# blended_feathered = cv2.addWeighted(vis_region, 0.7, color_feathered_mask, 0.3, 0)
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# vis_frame[min_y:max_y, min_x:max_x] = blended_feathered
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|
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# Add labels
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cv2.putText(
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vis_frame,
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"Lower Mouth Mask",
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(min_x, min_y - 10),
|
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cv2.FONT_HERSHEY_SIMPLEX,
|
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0.5,
|
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(255, 255, 255),
|
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1,
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)
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cv2.putText(
|
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vis_frame,
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"Feathered Mask",
|
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(min_x, max_y + 20),
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cv2.FONT_HERSHEY_SIMPLEX,
|
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0.5,
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(255, 255, 255),
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1,
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)
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return vis_frame
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return frame
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|
|
|
|
def apply_mouth_area(
|
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frame: np.ndarray,
|
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mouth_cutout: np.ndarray,
|
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mouth_box: tuple,
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face_mask: np.ndarray,
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mouth_polygon: np.ndarray,
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) -> np.ndarray:
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min_x, min_y, max_x, max_y = mouth_box
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box_width = max_x - min_x
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box_height = max_y - min_y
|
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|
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if (
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mouth_cutout is None
|
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or box_width is None
|
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or box_height is None
|
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or face_mask is None
|
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or mouth_polygon is None
|
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):
|
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return frame
|
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|
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try:
|
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resized_mouth_cutout = cv2.resize(mouth_cutout, (box_width, box_height))
|
|
roi = frame[min_y:max_y, min_x:max_x]
|
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|
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if roi.shape != resized_mouth_cutout.shape:
|
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resized_mouth_cutout = cv2.resize(
|
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resized_mouth_cutout, (roi.shape[1], roi.shape[0])
|
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)
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color_corrected_mouth = apply_color_transfer(resized_mouth_cutout, roi)
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|
|
# 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)
|