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import copy
import os
from dataclasses import dataclass
from typing import List, Union
import cv2
import numpy as np
from PIL import Image
import insightface
from modules.face_restoration import FaceRestoration
from modules import codeformer_model
from modules.upscaler import UpscalerData
from modules.shared import state
from scripts.reactor_logger import logger
try:
from modules.paths_internal import models_path
except:
try:
from modules.paths import models_path
except:
model_path = os.path.abspath("models")
import warnings
np.warnings = warnings
np.warnings.filterwarnings('ignore')
providers = ["CPUExecutionProvider"]
@dataclass
class EnhancementOptions:
do_restore_first: bool = True
scale: int = 1
upscaler: UpscalerData = None
upscale_visibility: float = 0.5
face_restorer: FaceRestoration = None
restorer_visibility: float = 0.5
codeformer_weight: float = 0.5
MESSAGED_STOPPED = False
MESSAGED_SKIPPED = False
def reset_messaged():
global MESSAGED_STOPPED, MESSAGED_SKIPPED
if not state.interrupted:
MESSAGED_STOPPED = False
if not state.skipped:
MESSAGED_SKIPPED = False
def check_process_halt(msgforced: bool = False):
global MESSAGED_STOPPED, MESSAGED_SKIPPED
if state.interrupted:
if not MESSAGED_STOPPED or msgforced:
logger.info("Stopped by User")
MESSAGED_STOPPED = True
return True
if state.skipped:
if not MESSAGED_SKIPPED or msgforced:
logger.info("Skipped by User")
MESSAGED_SKIPPED = True
return True
return False
FS_MODEL = None
CURRENT_FS_MODEL_PATH = None
ANALYSIS_MODEL = None
def getAnalysisModel():
global ANALYSIS_MODEL
if ANALYSIS_MODEL is None:
ANALYSIS_MODEL = insightface.app.FaceAnalysis(
name="buffalo_l", providers=providers, root=os.path.join(models_path, "insightface") # note: allowed_modules=['detection', 'genderage']
)
return ANALYSIS_MODEL
def getFaceSwapModel(model_path: str):
global FS_MODEL
global CURRENT_FS_MODEL_PATH
if CURRENT_FS_MODEL_PATH is None or CURRENT_FS_MODEL_PATH != model_path:
CURRENT_FS_MODEL_PATH = model_path
FS_MODEL = insightface.model_zoo.get_model(model_path, providers=providers)
return FS_MODEL
def restore_face(image: Image, enhancement_options: EnhancementOptions):
result_image = image
if check_process_halt(msgforced=True):
return result_image
if enhancement_options.face_restorer is not None:
original_image = result_image.copy()
logger.info("Restoring the face with %s", enhancement_options.face_restorer.name())
numpy_image = np.array(result_image)
if enhancement_options.face_restorer.name() == "CodeFormer":
numpy_image = codeformer_model.codeformer.restore(
numpy_image, w=enhancement_options.codeformer_weight
)
else:
numpy_image = enhancement_options.face_restorer.restore(numpy_image)
restored_image = Image.fromarray(numpy_image)
result_image = Image.blend(
original_image, restored_image, enhancement_options.restorer_visibility
)
return result_image
def upscale_image(image: Image, enhancement_options: EnhancementOptions):
result_image = image
if check_process_halt(msgforced=True):
return result_image
if enhancement_options.upscaler is not None and enhancement_options.upscaler.name != "None":
original_image = result_image.copy()
logger.info(
"Upscaling with %s scale = %s",
enhancement_options.upscaler.name,
enhancement_options.scale,
)
result_image = enhancement_options.upscaler.scaler.upscale(
original_image, enhancement_options.scale, enhancement_options.upscaler.data_path
)
if enhancement_options.scale == 1:
result_image = Image.blend(
original_image, result_image, enhancement_options.upscale_visibility
)
return result_image
def enhance_image(image: Image, enhancement_options: EnhancementOptions):
result_image = image
if check_process_halt(msgforced=True):
return result_image
if enhancement_options.do_restore_first:
result_image = restore_face(result_image, enhancement_options)
result_image = upscale_image(result_image, enhancement_options)
else:
result_image = upscale_image(result_image, enhancement_options)
result_image = restore_face(result_image, enhancement_options)
return result_image
def get_gender(face, face_index):
gender = [
x.sex
for x in face
]
gender.reverse()
try:
face_gender = gender[face_index]
except:
logger.error("Gender Detection: No face with index = %s was found", face_index)
return "None"
return face_gender
def get_face_gender(
face,
face_index,
gender_condition,
operated: str,
gender_detected,
):
face_gender = gender_detected
if face_gender == "None":
return None, 0
logger.info("%s Face %s: Detected Gender -%s-", operated, face_index, face_gender)
if (gender_condition == 1 and face_gender == "F") or (gender_condition == 2 and face_gender == "M"):
logger.info("OK - Detected Gender matches Condition")
try:
return sorted(face, key=lambda x: x.bbox[0])[face_index], 0
except IndexError:
return None, 0
else:
logger.info("WRONG - Detected Gender doesn't match Condition")
return sorted(face, key=lambda x: x.bbox[0])[face_index], 1
def get_face_age(face, face_index):
age = [
x.age
for x in face
]
age.reverse()
try:
face_age = age[face_index]
except:
logger.error("Age Detection: No face with index = %s was found", face_index)
return "None"
return face_age
# def reget_face_single(img_data: np.ndarray, face, det_size, face_index, gender_source, gender_target):
# det_size_half = (det_size[0] // 2, det_size[1] // 2)
# return get_face_single(img_data, face, face_index=face_index, det_size=det_size_half, gender_source=gender_source, gender_target=gender_target)
def half_det_size(det_size):
logger.info("Trying to halve 'det_size' parameter")
return (det_size[0] // 2, det_size[1] // 2)
def analyze_faces(img_data: np.ndarray, det_size=(640, 640)):
face_analyser = copy.deepcopy(getAnalysisModel())
face_analyser.prepare(ctx_id=0, det_size=det_size)
return face_analyser.get(img_data)
def get_face_single(img_data: np.ndarray, face, face_index=0, det_size=(640, 640), gender_source=0, gender_target=0):
buffalo_path = os.path.join(models_path, "insightface/models/buffalo_l.zip")
if os.path.exists(buffalo_path):
os.remove(buffalo_path)
face_age = "None"
try:
face_age = get_face_age(face, face_index)
except:
logger.error("Cannot detect any Age for Face index = %s", face_index)
face_gender = "None"
try:
face_gender = get_gender(face, face_index)
gender_detected = face_gender
face_gender = "Female" if face_gender == "F" else ("Male" if face_gender == "M" else "None")
except:
logger.error("Cannot detect any Gender for Face index = %s", face_index)
if gender_source != 0:
if len(face) == 0 and det_size[0] > 320 and det_size[1] > 320:
det_size_half = half_det_size(det_size)
return get_face_single(img_data, analyze_faces(img_data, det_size_half), face_index, det_size_half, gender_source, gender_target)
faces, wrong_gender = get_face_gender(face,face_index,gender_source,"Source",gender_detected)
return faces, wrong_gender, face_age, face_gender
if gender_target != 0:
if len(face) == 0 and det_size[0] > 320 and det_size[1] > 320:
det_size_half = half_det_size(det_size)
return get_face_single(img_data, analyze_faces(img_data, det_size_half), face_index, det_size_half, gender_source, gender_target)
faces, wrong_gender = get_face_gender(face,face_index,gender_target,"Target",gender_detected)
return faces, wrong_gender, face_age, face_gender
if len(face) == 0 and det_size[0] > 320 and det_size[1] > 320:
det_size_half = half_det_size(det_size)
return get_face_single(img_data, analyze_faces(img_data, det_size_half), face_index, det_size_half, gender_source, gender_target)
try:
return sorted(face, key=lambda x: x.bbox[0])[face_index], 0, face_age, face_gender
except IndexError:
return None, 0, face_age, face_gender
def swap_face(
source_img: Image.Image,
target_img: Image.Image,
model: Union[str, None] = None,
source_faces_index: List[int] = [0],
faces_index: List[int] = [0],
enhancement_options: Union[EnhancementOptions, None] = None,
gender_source: int = 0,
gender_target: int = 0,
):
result_image = target_img
if check_process_halt():
return result_image, [], 0
if model is not None:
if isinstance(source_img, str): # source_img is a base64 string
import base64, io
if 'base64,' in source_img: # check if the base64 string has a data URL scheme
# split the base64 string to get the actual base64 encoded image data
base64_data = source_img.split('base64,')[-1]
# decode base64 string to bytes
img_bytes = base64.b64decode(base64_data)
else:
# if no data URL scheme, just decode
img_bytes = base64.b64decode(source_img)
source_img = Image.open(io.BytesIO(img_bytes))
source_img = cv2.cvtColor(np.array(source_img), cv2.COLOR_RGB2BGR)
target_img = cv2.cvtColor(np.array(target_img), cv2.COLOR_RGB2BGR)
output: List = []
output_info: str = ""
swapped = 0
logger.info("Analyzing Source Image...")
source_faces = analyze_faces(source_img)
if source_faces is not None:
logger.info("Analyzing Target Image...")
target_faces = analyze_faces(target_img)
logger.info("Detecting Source Face, Index = %s", source_faces_index[0])
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)
if source_age != "None" or source_gender != "None":
logger.info("Detected: -%s- y.o. %s", source_age, source_gender)
output_info = f"SourceFaceIndex={source_faces_index[0]};Age={source_age};Gender={source_gender}\n"
output.append(output_info)
if len(source_faces_index) != 0 and len(source_faces_index) != 1 and len(source_faces_index) != len(faces_index):
logger.info("Source Faces must have no entries (default=0), one entry, or same number of entries as target faces.")
elif source_face is not None:
result = target_img
face_swapper = getFaceSwapModel(model)
source_face_idx = 0
for face_num in faces_index:
if check_process_halt():
return result_image, [], 0
if len(source_faces_index) > 1 and source_face_idx > 0:
logger.info("Detecting Source Face, Index = %s", source_faces_index[source_face_idx])
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)
if source_age != "None" or source_gender != "None":
logger.info("Detected: -%s- y.o. %s", source_age, source_gender)
output_info = f"SourceFaceIndex={source_faces_index[source_face_idx]};Age={source_age};Gender={source_gender}\n"
output.append(output_info)
source_face_idx += 1
if source_face is not None and wrong_gender == 0:
logger.info("Detecting Target Face, Index = %s", face_num)
target_face, wrong_gender, target_age, target_gender = get_face_single(target_img, target_faces, face_index=face_num, gender_target=gender_target)
if target_age != "None" or target_gender != "None":
logger.info("Detected: -%s- y.o. %s", target_age, target_gender)
output_info = f"TargetFaceIndex={face_num};Age={target_age};Gender={target_gender}\n"
output.append(output_info)
if target_face is not None and wrong_gender == 0:
logger.info("Swapping Source into Target")
result = face_swapper.get(result, target_face, source_face)
swapped += 1
elif wrong_gender == 1:
wrong_gender = 0
if source_face_idx == len(source_faces_index):
result_image = Image.fromarray(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))
if enhancement_options is not None and len(source_faces_index) > 1:
result_image = enhance_image(result_image, enhancement_options)
return result_image, output, swapped
else:
logger.info(f"No target face found for {face_num}")
elif wrong_gender == 1:
wrong_gender = 0
if source_face_idx == len(source_faces_index):
result_image = Image.fromarray(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))
if enhancement_options is not None and len(source_faces_index) > 1:
result_image = enhance_image(result_image, enhancement_options)
return result_image, output, swapped
else:
logger.info(f"No source face found for face number {source_face_idx}.")
result_image = Image.fromarray(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))
if enhancement_options is not None and swapped > 0:
result_image = enhance_image(result_image, enhancement_options)
else:
logger.info("No source face(s) in the provided Index")
else:
logger.info("No source face(s) found")
return result_image, output, swapped
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