564 lines
21 KiB
Python
564 lines
21 KiB
Python
import copy
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import os
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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 scripts.reactor_helpers import get_image_md5hash, get_Device
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from modules.face_restoration import FaceRestoration
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try: # A1111
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from modules import codeformer_model
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except: # SD.Next
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from modules.postprocess import codeformer_model
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from modules.upscaler import UpscalerData
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from modules.shared import state
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from scripts.reactor_logger import logger
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try:
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from modules.paths_internal import models_path
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except:
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try:
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from modules.paths import models_path
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except:
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model_path = os.path.abspath("models")
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import warnings
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PILImage = Image.Image
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CV2ImgU8 = np.ndarray[int, np.dtype[uint8]]
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Face = IFace
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BoxCoords = Tuple[int, int, int, int]
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class Gender(Enum):
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AUTO = -1
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FEMALE = 0
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MALE = 1
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np.warnings = warnings
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np.warnings.filterwarnings('ignore')
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DEVICE = get_Device()
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if DEVICE == "CUDA":
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PROVIDERS = ["CUDAExecutionProvider"]
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else:
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PROVIDERS = ["CPUExecutionProvider"]
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@dataclass
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class EnhancementOptions:
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do_restore_first: bool = True
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scale: int = 1
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upscaler: UpscalerData = None
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upscale_visibility: float = 0.5
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face_restorer: FaceRestoration = None
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restorer_visibility: float = 0.5
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codeformer_weight: float = 0.5
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MESSAGED_STOPPED = False
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MESSAGED_SKIPPED = False
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def reset_messaged():
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global MESSAGED_STOPPED, MESSAGED_SKIPPED
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if not state.interrupted:
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MESSAGED_STOPPED = False
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if not state.skipped:
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MESSAGED_SKIPPED = False
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def check_process_halt(msgforced: bool = False):
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global MESSAGED_STOPPED, MESSAGED_SKIPPED
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if state.interrupted:
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if not MESSAGED_STOPPED or msgforced:
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logger.status("Stopped by User")
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MESSAGED_STOPPED = True
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return True
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if state.skipped:
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if not MESSAGED_SKIPPED or msgforced:
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logger.status("Skipped by User")
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MESSAGED_SKIPPED = True
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return True
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return False
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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 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=os.path.join(models_path, "insightface") # note: allowed_modules=['detection', 'genderage']
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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 restore_face(image: Image, enhancement_options: EnhancementOptions):
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result_image = image
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if check_process_halt(msgforced=True):
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return result_image
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if enhancement_options.face_restorer is not None:
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original_image = result_image.copy()
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logger.status("Restoring the face with %s", enhancement_options.face_restorer.name())
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numpy_image = np.array(result_image)
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if enhancement_options.face_restorer.name() == "CodeFormer":
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numpy_image = codeformer_model.codeformer.restore(
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numpy_image, w=enhancement_options.codeformer_weight
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)
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else:
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numpy_image = enhancement_options.face_restorer.restore(numpy_image)
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restored_image = Image.fromarray(numpy_image)
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result_image = Image.blend(
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original_image, restored_image, enhancement_options.restorer_visibility
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)
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return result_image
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def upscale_image(image: Image, enhancement_options: EnhancementOptions):
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result_image = image
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if check_process_halt(msgforced=True):
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return result_image
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if enhancement_options.upscaler is not None and enhancement_options.upscaler.name != "None":
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original_image = result_image.copy()
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logger.status(
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"Upscaling with %s scale = %s",
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enhancement_options.upscaler.name,
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enhancement_options.scale,
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)
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result_image = enhancement_options.upscaler.scaler.upscale(
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original_image, enhancement_options.scale, enhancement_options.upscaler.data_path
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)
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if enhancement_options.scale == 1:
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result_image = Image.blend(
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original_image, result_image, enhancement_options.upscale_visibility
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)
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return result_image
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def enhance_image(image: Image, enhancement_options: EnhancementOptions):
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result_image = image
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if check_process_halt(msgforced=True):
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return result_image
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if enhancement_options.do_restore_first:
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result_image = restore_face(result_image, enhancement_options)
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result_image = upscale_image(result_image, enhancement_options)
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else:
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result_image = upscale_image(result_image, enhancement_options)
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result_image = restore_face(result_image, enhancement_options)
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return result_image
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def get_gender(face, face_index):
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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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try:
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face_gender = gender[face_index]
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except:
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logger.error("Gender Detection: No face with index = %s was found", face_index)
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return "None"
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return face_gender
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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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gender_detected,
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):
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face_gender = gender_detected
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if face_gender == "None":
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return None, 0
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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 get_face_age(face, face_index):
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age = [
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x.age
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for x in face
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]
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age.reverse()
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try:
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face_age = age[face_index]
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except:
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logger.error("Age Detection: No face with index = %s was found", face_index)
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return "None"
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return face_age
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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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logger.info("Applied Execution Provider: %s", PROVIDERS[0])
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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(models_path, "insightface/models/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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face_age = "None"
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try:
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face_age = get_face_age(face, face_index)
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except:
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logger.error("Cannot detect any Age for Face index = %s", face_index)
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face_gender = "None"
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try:
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face_gender = get_gender(face, face_index)
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gender_detected = face_gender
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face_gender = "Female" if face_gender == "F" else ("Male" if face_gender == "M" else "None")
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except:
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logger.error("Cannot detect any Gender for Face index = %s", face_index)
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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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faces, wrong_gender = get_face_gender(face,face_index,gender_source,"Source",gender_detected)
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return faces, wrong_gender, face_age, face_gender
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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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faces, wrong_gender = get_face_gender(face,face_index,gender_target,"Target",gender_detected)
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return faces, wrong_gender, face_age, face_gender
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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, face_age, face_gender
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except IndexError:
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return None, 0, face_age, face_gender
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def swap_face(
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source_img: Image.Image,
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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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enhancement_options: Union[EnhancementOptions, None] = None,
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gender_source: int = 0,
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gender_target: int = 0,
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source_hash_check: bool = True,
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target_hash_check: bool = False,
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device: str = "CPU",
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):
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global SOURCE_FACES, SOURCE_IMAGE_HASH, TARGET_FACES, TARGET_IMAGE_HASH, PROVIDERS
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result_image = target_img
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PROVIDERS = ["CUDAExecutionProvider"] if device == "CUDA" else ["CPUExecutionProvider"]
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if check_process_halt():
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return result_image, [], 0
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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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source_img = cv2.cvtColor(np.array(source_img), cv2.COLOR_RGB2BGR)
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target_img = cv2.cvtColor(np.array(target_img), cv2.COLOR_RGB2BGR)
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output: List = []
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output_info: str = ""
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swapped = 0
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if source_hash_check:
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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 Ready Source Face(s) Model...")
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source_faces = SOURCE_FACES
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else:
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logger.status("Analyzing Source Image...")
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source_faces = analyze_faces(source_img)
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if source_faces is not None:
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if target_hash_check:
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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 Ready Target Face(s) Model...")
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target_faces = TARGET_FACES
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else:
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logger.status("Analyzing Target Image...")
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target_faces = analyze_faces(target_img)
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logger.status("Detecting Source Face, Index = %s", source_faces_index[0])
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source_face, wrong_gender, source_age, source_gender = get_face_single(source_img, source_faces, face_index=source_faces_index[0], gender_source=gender_source)
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if source_age != "None" or source_gender != "None":
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logger.status("Detected: -%s- y.o. %s", source_age, source_gender)
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output_info = f"SourceFaceIndex={source_faces_index[0]};Age={source_age};Gender={source_gender}\n"
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output.append(output_info)
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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("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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face_swapper = getFaceSwapModel(model)
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source_face_idx = 0
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for face_num in faces_index:
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if check_process_halt():
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return result_image, [], 0
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if len(source_faces_index) > 1 and source_face_idx > 0:
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logger.status("Detecting Source Face, Index = %s", source_faces_index[source_face_idx])
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source_face, wrong_gender, source_age, source_gender = get_face_single(source_img, source_faces, face_index=source_faces_index[source_face_idx], gender_source=gender_source)
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if source_age != "None" or source_gender != "None":
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logger.status("Detected: -%s- y.o. %s", source_age, source_gender)
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output_info = f"SourceFaceIndex={source_faces_index[source_face_idx]};Age={source_age};Gender={source_gender}\n"
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output.append(output_info)
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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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logger.status("Detecting Target Face, Index = %s", face_num)
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target_face, wrong_gender, target_age, target_gender = get_face_single(target_img, target_faces, face_index=face_num, gender_target=gender_target)
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if target_age != "None" or target_gender != "None":
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logger.status("Detected: -%s- y.o. %s", target_age, target_gender)
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output_info = f"TargetFaceIndex={face_num};Age={target_age};Gender={target_gender}\n"
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output.append(output_info)
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if target_face is not None and wrong_gender == 0:
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logger.status("Swapping Source into Target")
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result = face_swapper.get(result, target_face, source_face)
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swapped += 1
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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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if enhancement_options is not None and len(source_faces_index) > 1:
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result_image = enhance_image(result_image, enhancement_options)
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return result_image, output, swapped
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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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if enhancement_options is not None and len(source_faces_index) > 1:
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result_image = enhance_image(result_image, enhancement_options)
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return result_image, output, swapped
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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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if enhancement_options is not None and swapped > 0:
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result_image = enhance_image(result_image, enhancement_options)
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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, output, swapped
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def merge_images_with_mask(
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image1: CV2ImgU8, image2: CV2ImgU8, mask: CV2ImgU8
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) -> CV2ImgU8:
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"""
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Merges two images using a given mask. The regions where the mask is set will be replaced with the corresponding
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areas of the second image.
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Args:
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image1 (CV2Img): The base image, which must have the same shape as image2.
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image2 (CV2Img): The image to be merged, which must have the same shape as image1.
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mask (CV2Img): A binary mask specifying the regions to be merged. The mask shape should match image1's first two dimensions.
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Returns:
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CV2Img: The merged image.
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Raises:
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ValueError: If the shapes of the images and mask do not match.
|
|
"""
|
|
|
|
if image1.shape != image2.shape or image1.shape[:2] != mask.shape:
|
|
raise ValueError("Img should have the same shape")
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|
mask = mask.astype(np.uint8)
|
|
masked_region = cv2.bitwise_and(image2, image2, mask=mask)
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|
inverse_mask = cv2.bitwise_not(mask)
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empty_region = cv2.bitwise_and(image1, image1, mask=inverse_mask)
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|
merged_image = cv2.add(empty_region, masked_region)
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|
return merged_image
|
|
|
|
|
|
def erode_mask(mask: CV2ImgU8, kernel_size: int = 3, iterations: int = 1) -> CV2ImgU8:
|
|
"""
|
|
Erodes a binary mask using a given kernel size and number of iterations.
|
|
|
|
Args:
|
|
mask (CV2Img): The binary mask to erode.
|
|
kernel_size (int, optional): The size of the kernel. Default is 3.
|
|
iterations (int, optional): The number of erosion iterations. Default is 1.
|
|
|
|
Returns:
|
|
CV2Img: The eroded mask.
|
|
"""
|
|
kernel = np.ones((kernel_size, kernel_size), np.uint8)
|
|
eroded_mask = cv2.erode(mask, kernel, iterations=iterations)
|
|
return eroded_mask
|
|
|
|
|
|
def apply_gaussian_blur(
|
|
mask: CV2ImgU8, kernel_size: Tuple[int, int] = (5, 5), sigma_x: int = 0
|
|
) -> CV2ImgU8:
|
|
"""
|
|
Applies a Gaussian blur to a mask.
|
|
|
|
Args:
|
|
mask (CV2Img): The mask to blur.
|
|
kernel_size (tuple, optional): The size of the kernel, e.g. (5, 5). Default is (5, 5).
|
|
sigma_x (int, optional): The standard deviation in the X direction. Default is 0.
|
|
|
|
Returns:
|
|
CV2Img: The blurred mask.
|
|
"""
|
|
blurred_mask = cv2.GaussianBlur(mask, kernel_size, sigma_x)
|
|
return blurred_mask
|
|
|
|
|
|
def dilate_mask(mask: CV2ImgU8, kernel_size: int = 5, iterations: int = 1) -> CV2ImgU8:
|
|
"""
|
|
Dilates a binary mask using a given kernel size and number of iterations.
|
|
|
|
Args:
|
|
mask (CV2Img): The binary mask to dilate.
|
|
kernel_size (int, optional): The size of the kernel. Default is 5.
|
|
iterations (int, optional): The number of dilation iterations. Default is 1.
|
|
|
|
Returns:
|
|
CV2Img: The dilated mask.
|
|
"""
|
|
kernel = np.ones((kernel_size, kernel_size), np.uint8)
|
|
dilated_mask = cv2.dilate(mask, kernel, iterations=iterations)
|
|
return dilated_mask
|
|
|
|
|
|
def get_face_mask(aimg: CV2ImgU8, bgr_fake: CV2ImgU8) -> CV2ImgU8:
|
|
"""
|
|
Generates a face mask by performing bitwise OR on two face masks and then dilating the result.
|
|
|
|
Args:
|
|
aimg (CV2Img): Input image for generating the first face mask.
|
|
bgr_fake (CV2Img): Input image for generating the second face mask.
|
|
|
|
Returns:
|
|
CV2Img: The combined and dilated face mask.
|
|
"""
|
|
mask1 = generate_face_mask(aimg, device=shared.device)
|
|
mask2 = generate_face_mask(bgr_fake, device=shared.device)
|
|
mask = dilate_mask(cv2.bitwise_or(mask1, mask2))
|
|
return mask
|