318 lines
11 KiB
Python
318 lines
11 KiB
Python
from typing import Any, List, Tuple, Optional
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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 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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resolve_relative_path,
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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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FACE_SWAPPER = None
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THREAD_LOCK = threading.Lock()
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NAME = "DLC.FACE-SWAPPER"
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BLUR_AMOUNT = 12
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def pre_check() -> bool:
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download_directory_path = resolve_relative_path("../models")
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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 = resolve_relative_path("../models/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 create_face_mask(face: Face, frame: Frame) -> np.ndarray:
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"""Create a binary mask for the face region."""
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mask = np.zeros(frame.shape[:2], dtype=np.uint8)
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landmarks = face.landmark_2d_106
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if landmarks is not None:
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hull = cv2.convexHull(landmarks.astype(np.int32))
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cv2.fillConvexPoly(mask, hull, 255)
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return mask
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def create_lower_mouth_mask(
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face: Face, frame: Frame
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) -> Tuple[np.ndarray, np.ndarray, Tuple[int, int, int, int], np.ndarray]:
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"""Create a mask for the lower mouth region."""
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mask = np.zeros(frame.shape[:2], dtype=np.uint8)
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landmarks = face.landmark_2d_106
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if landmarks is not None:
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# Extract mouth landmarks
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mouth_points = landmarks[84:96] # Adjust indices based on your landmark format
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lower_lip = mouth_points[6:12] # Lower lip points
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# Create polygon for lower mouth area
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lower_lip_polygon = cv2.convexHull(lower_lip.astype(np.int32))
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cv2.fillConvexPoly(mask, lower_lip_polygon, 255)
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# Get bounding box
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x, y, w, h = cv2.boundingRect(lower_lip_polygon)
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mouth_box = (x, y, w, h)
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# Extract the mouth region
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mouth_cutout = frame[y : y + h, x : x + w].copy()
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return mask, mouth_cutout, mouth_box, lower_lip_polygon
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return None, None, None, None
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def apply_mouth_area(
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frame: Frame,
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mouth_cutout: np.ndarray,
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mouth_box: Tuple[int, int, int, int],
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face_mask: np.ndarray,
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lower_lip_polygon: Optional[np.ndarray],
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) -> Frame:
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"""Apply the original mouth area back to the face-swapped frame."""
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if mouth_cutout is None or mouth_box is None:
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return frame
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x, y, w, h = mouth_box
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# Create a blurred version of the mask
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mask = np.zeros(frame.shape[:2], dtype=np.uint8)
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if lower_lip_polygon is not None:
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cv2.fillConvexPoly(mask, lower_lip_polygon, 255)
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else:
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mask[y : y + h, x : x + w] = 255
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# Blur the mask
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blurred_mask = cv2.GaussianBlur(mask, (BLUR_AMOUNT * 2 + 1, BLUR_AMOUNT * 2 + 1), 0)
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blurred_mask = blurred_mask / 255.0
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# Create 3-channel mask
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blurred_mask_3channel = np.repeat(blurred_mask[:, :, np.newaxis], 3, axis=2)
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# Blend the original mouth area with the swapped face
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frame_copy = frame.copy()
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frame_copy[y : y + h, x : x + w] = mouth_cutout
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# Combine using the blurred mask
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result = (
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frame_copy * blurred_mask_3channel + frame * (1 - blurred_mask_3channel)
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).astype(np.uint8)
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return result
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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 masks
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face_mask = create_face_mask(target_face, temp_frame)
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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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if mouth_mask is not None:
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# Apply the mouth area preservation
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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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return swapped_frame
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def process_frame(source_face: Face, temp_frame: Frame) -> Frame:
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# Ensure the frame is in RGB format if color correction is enabled
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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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temp_frame = swap_face(source_face, target_face, temp_frame)
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else:
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target_face = get_one_face(temp_frame)
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if target_face:
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temp_frame = swap_face(source_face, target_face, temp_frame)
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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.souce_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.souce_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.souce_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.souce_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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