280 lines
11 KiB
Python
280 lines
11 KiB
Python
import copy
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import os
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import shutil
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from dataclasses import dataclass
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from typing import List, Union
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import cv2
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import numpy as np
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from PIL import Image
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import insightface
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from insightface.app.common import Face
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try:
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import torch.cuda as cuda
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except:
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cuda = None
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from .reactor_logger import logger
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from .reactor_utils import move_path, get_image_md5hash
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import folder_paths
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import warnings
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np.warnings = warnings
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np.warnings.filterwarnings('ignore')
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if cuda is not None:
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if cuda.is_available():
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providers = ["CUDAExecutionProvider"]
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else:
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providers = ["CPUExecutionProvider"]
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else:
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providers = ["CPUExecutionProvider"]
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models_path_old = os.path.join(os.path.dirname(os.path.dirname(__file__)), "models")
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insightface_path_old = os.path.join(models_path_old, "insightface")
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insightface_models_path_old = os.path.join(insightface_path_old, "models")
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models_path = folder_paths.models_dir
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insightface_path = os.path.join(models_path, "insightface")
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insightface_models_path = os.path.join(insightface_path, "models")
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if os.path.exists(models_path_old):
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move_path(insightface_models_path_old, insightface_models_path)
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move_path(insightface_path_old, insightface_path)
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move_path(models_path_old, models_path)
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if os.path.exists(insightface_path) and os.path.exists(insightface_path_old):
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shutil.rmtree(insightface_path_old)
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shutil.rmtree(models_path_old)
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FS_MODEL = None
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CURRENT_FS_MODEL_PATH = None
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ANALYSIS_MODEL = None
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SOURCE_FACES = None
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SOURCE_IMAGE_HASH = None
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TARGET_FACES = None
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TARGET_IMAGE_HASH = None
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def get_current_faces_model():
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global SOURCE_FACES
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return SOURCE_FACES
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def getAnalysisModel():
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global ANALYSIS_MODEL
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if ANALYSIS_MODEL is None:
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ANALYSIS_MODEL = insightface.app.FaceAnalysis(
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name="buffalo_l", providers=providers, root=insightface_path
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)
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return ANALYSIS_MODEL
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def getFaceSwapModel(model_path: str):
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global FS_MODEL
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global CURRENT_FS_MODEL_PATH
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if CURRENT_FS_MODEL_PATH is None or CURRENT_FS_MODEL_PATH != model_path:
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CURRENT_FS_MODEL_PATH = model_path
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FS_MODEL = insightface.model_zoo.get_model(model_path, providers=providers)
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return FS_MODEL
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def get_face_gender(
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face,
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face_index,
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gender_condition,
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operated: str
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):
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gender = [
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x.sex
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for x in face
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]
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gender.reverse()
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face_gender = gender[face_index]
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logger.status("%s Face %s: Detected Gender -%s-", operated, face_index, face_gender)
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if (gender_condition == 1 and face_gender == "F") or (gender_condition == 2 and face_gender == "M"):
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logger.status("OK - Detected Gender matches Condition")
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try:
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return sorted(face, key=lambda x: x.bbox[0])[face_index], 0
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except IndexError:
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return None, 0
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else:
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logger.status("WRONG - Detected Gender doesn't match Condition")
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return sorted(face, key=lambda x: x.bbox[0])[face_index], 1
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def half_det_size(det_size):
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logger.status("Trying to halve 'det_size' parameter")
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return (det_size[0] // 2, det_size[1] // 2)
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def analyze_faces(img_data: np.ndarray, det_size=(640, 640)):
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face_analyser = copy.deepcopy(getAnalysisModel())
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face_analyser.prepare(ctx_id=0, det_size=det_size)
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return face_analyser.get(img_data)
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def get_face_single(img_data: np.ndarray, face, face_index=0, det_size=(640, 640), gender_source=0, gender_target=0):
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buffalo_path = os.path.join(insightface_models_path, "buffalo_l.zip")
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if os.path.exists(buffalo_path):
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os.remove(buffalo_path)
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if gender_source != 0:
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if len(face) == 0 and det_size[0] > 320 and det_size[1] > 320:
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det_size_half = half_det_size(det_size)
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return get_face_single(img_data, analyze_faces(img_data, det_size_half), face_index, det_size_half, gender_source, gender_target)
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return get_face_gender(face,face_index,gender_source,"Source")
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if gender_target != 0:
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if len(face) == 0 and det_size[0] > 320 and det_size[1] > 320:
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det_size_half = half_det_size(det_size)
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return get_face_single(img_data, analyze_faces(img_data, det_size_half), face_index, det_size_half, gender_source, gender_target)
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return get_face_gender(face,face_index,gender_target,"Target")
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if len(face) == 0 and det_size[0] > 320 and det_size[1] > 320:
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det_size_half = half_det_size(det_size)
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return get_face_single(img_data, analyze_faces(img_data, det_size_half), face_index, det_size_half, gender_source, gender_target)
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try:
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return sorted(face, key=lambda x: x.bbox[0])[face_index], 0
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except IndexError:
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return None, 0
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def swap_face(
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source_img: Union[Image.Image, None],
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target_img: Image.Image,
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model: Union[str, None] = None,
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source_faces_index: List[int] = [0],
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faces_index: List[int] = [0],
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gender_source: int = 0,
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gender_target: int = 0,
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face_model: Union[Face, None] = None,
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):
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global SOURCE_FACES, SOURCE_IMAGE_HASH, TARGET_FACES, TARGET_IMAGE_HASH
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result_image = target_img
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if model is not None:
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if isinstance(source_img, str): # source_img is a base64 string
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import base64, io
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if 'base64,' in source_img: # check if the base64 string has a data URL scheme
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# split the base64 string to get the actual base64 encoded image data
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base64_data = source_img.split('base64,')[-1]
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# decode base64 string to bytes
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img_bytes = base64.b64decode(base64_data)
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else:
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# if no data URL scheme, just decode
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img_bytes = base64.b64decode(source_img)
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source_img = Image.open(io.BytesIO(img_bytes))
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target_img = cv2.cvtColor(np.array(target_img), cv2.COLOR_RGB2BGR)
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if source_img is not None:
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source_img = cv2.cvtColor(np.array(source_img), cv2.COLOR_RGB2BGR)
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source_image_md5hash = get_image_md5hash(source_img)
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if SOURCE_IMAGE_HASH is None:
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SOURCE_IMAGE_HASH = source_image_md5hash
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source_image_same = False
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else:
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source_image_same = True if SOURCE_IMAGE_HASH == source_image_md5hash else False
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if not source_image_same:
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SOURCE_IMAGE_HASH = source_image_md5hash
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logger.info("Source Image MD5 Hash = %s", SOURCE_IMAGE_HASH)
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logger.info("Source Image the Same? %s", source_image_same)
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if SOURCE_FACES is None or not source_image_same:
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logger.status("Analyzing Source Image...")
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source_faces = analyze_faces(source_img)
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SOURCE_FACES = source_faces
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elif source_image_same:
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logger.status("Using Hashed Source Face(s) Model...")
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source_faces = SOURCE_FACES
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elif face_model is not None:
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logger.status("Using Loaded Source Face Model...")
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source_face_model = [face_model]
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source_faces = source_face_model
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else:
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logger.error("Cannot detect any Source")
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if source_faces is not None:
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target_image_md5hash = get_image_md5hash(target_img)
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if TARGET_IMAGE_HASH is None:
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TARGET_IMAGE_HASH = target_image_md5hash
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target_image_same = False
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else:
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target_image_same = True if TARGET_IMAGE_HASH == target_image_md5hash else False
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if not target_image_same:
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TARGET_IMAGE_HASH = target_image_md5hash
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logger.info("Target Image MD5 Hash = %s", TARGET_IMAGE_HASH)
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logger.info("Target Image the Same? %s", target_image_same)
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if TARGET_FACES is None or not target_image_same:
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logger.status("Analyzing Target Image...")
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target_faces = analyze_faces(target_img)
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TARGET_FACES = target_faces
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elif target_image_same:
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logger.status("Using Hashed Target Face(s) Model...")
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target_faces = TARGET_FACES
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if source_img is not None:
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source_face, wrong_gender = get_face_single(source_img, source_faces, face_index=source_faces_index[0], gender_source=gender_source)
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else:
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source_face = sorted(source_faces, key=lambda x: x.bbox[0])[source_faces_index[0]]
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wrong_gender = 0
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if len(source_faces_index) != 0 and len(source_faces_index) != 1 and len(source_faces_index) != len(faces_index):
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logger.status(f'Source Faces must have no entries (default=0), one entry, or same number of entries as target faces.')
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elif source_face is not None:
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result = target_img
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model_path = model_path = os.path.join(insightface_path, model)
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face_swapper = getFaceSwapModel(model_path)
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source_face_idx = 0
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for face_num in faces_index:
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if len(source_faces_index) > 1 and source_face_idx > 0:
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source_face, wrong_gender = get_face_single(source_img, source_faces, face_index=source_faces_index[source_face_idx], gender_source=gender_source)
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source_face_idx += 1
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if source_face is not None and wrong_gender == 0:
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target_face, wrong_gender = get_face_single(target_img, target_faces, face_index=face_num, gender_target=gender_target)
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if target_face is not None and wrong_gender == 0:
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logger.status(f"Swapping...")
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result = face_swapper.get(result, target_face, source_face)
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elif wrong_gender == 1:
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wrong_gender = 0
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if source_face_idx == len(source_faces_index):
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result_image = Image.fromarray(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))
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return result_image
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else:
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logger.status(f"No target face found for {face_num}")
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elif wrong_gender == 1:
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wrong_gender = 0
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if source_face_idx == len(source_faces_index):
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result_image = Image.fromarray(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))
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return result_image
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else:
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logger.status(f"No source face found for face number {source_face_idx}.")
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result_image = Image.fromarray(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))
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else:
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logger.status("No source face(s) in the provided Index")
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else:
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logger.status("No source face(s) found")
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return result_image
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