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andysingal/swap.py

Created November 27, 2023 16:15
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swap.py
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 insightface.app.common import Face
from scripts.reactor_globals import FACE_MODELS_PATH
from scripts.reactor_helpers import get_image_md5hash, get_Device, save_face_model, load_face_model
from scripts.console_log_patch import apply_logging_patch
from modules.face_restoration import FaceRestoration
try: # A1111
from modules import codeformer_model
except: # SD.Next
from modules.postprocess import codeformer_model
from modules.upscaler import UpscalerData
from modules.shared import state
from scripts.reactor_logger import logger
from modules.reactor_mask import apply_face_mask
try:
from modules.paths_internal import models_path
except:
try:
from modules.paths import models_path
except:
models_path = os.path.abspath("models")
import warnings
np.warnings = warnings
np.warnings.filterwarnings('ignore')
DEVICE = get_Device()
if DEVICE == "CUDA":
PROVIDERS = ["CUDAExecutionProvider"]
else:
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.status("Stopped by User")
MESSAGED_STOPPED = True
return True
if state.skipped:
if not MESSAGED_SKIPPED or msgforced:
logger.status("Skipped by User")
MESSAGED_SKIPPED = True
return True
return False
FS_MODEL = None
ANALYSIS_MODEL = None
MASK_MODEL = None
CURRENT_FS_MODEL_PATH = None
CURRENT_MASK_MODEL_PATH = None
SOURCE_FACES = None
SOURCE_IMAGE_HASH = None
TARGET_FACES = None
TARGET_IMAGE_HASH = 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.status("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.status(
"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 enhance_image_and_mask(image: Image.Image, enhancement_options: EnhancementOptions,target_img_orig:Image.Image,entire_mask_image:Image.Image)->Image.Image:
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 = Image.composite(result_image,target_img_orig,entire_mask_image)
result_image = upscale_image(result_image, enhancement_options)
else:
result_image = upscale_image(result_image, enhancement_options)
entire_mask_image = Image.fromarray(cv2.resize(np.array(entire_mask_image),result_image.size, interpolation=cv2.INTER_AREA)).convert("L")
result_image = Image.composite(result_image,target_img_orig,entire_mask_image)
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.status("%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.status("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.status("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 half_det_size(det_size):
logger.status("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)):
logger.info("Applied Execution Provider: %s", PROVIDERS[0])
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,
source_hash_check: bool = True,
target_hash_check: bool = False,
device: str = "CPU",
mask_face: bool = False,
select_source: int = 0,
face_model: str = "None",
):
global SOURCE_FACES, SOURCE_IMAGE_HASH, TARGET_FACES, TARGET_IMAGE_HASH, PROVIDERS
result_image = target_img
PROVIDERS = ["CUDAExecutionProvider"] if device == "CUDA" else ["CPUExecutionProvider"]
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))
target_img = cv2.cvtColor(np.array(target_img), cv2.COLOR_RGB2BGR)
target_img_orig = cv2.cvtColor(np.array(target_img), cv2.COLOR_RGB2BGR)
entire_mask_image = np.zeros_like(np.array(target_img))
output: List = []
output_info: str = ""
swapped = 0
if select_source == 0 and source_img is not None:
source_img = cv2.cvtColor(np.array(source_img), cv2.COLOR_RGB2BGR)
if source_hash_check:
source_image_md5hash = get_image_md5hash(source_img)
if SOURCE_IMAGE_HASH is None:
SOURCE_IMAGE_HASH = source_image_md5hash
source_image_same = False
else:
source_image_same = True if SOURCE_IMAGE_HASH == source_image_md5hash else False
if not source_image_same:
SOURCE_IMAGE_HASH = source_image_md5hash
logger.info("Source Image MD5 Hash = %s", SOURCE_IMAGE_HASH)
logger.info("Source Image the Same? %s", source_image_same)
if SOURCE_FACES is None or not source_image_same:
logger.status("Analyzing Source Image...")
source_faces = analyze_faces(source_img)
SOURCE_FACES = source_faces
elif source_image_same:
logger.status("Using Hashed Source Face(s) Model...")
source_faces = SOURCE_FACES
else:
logger.status("Analyzing Source Image...")
source_faces = analyze_faces(source_img)
elif select_source == 1 and (face_model is not None and face_model != "None"):
source_face_model = [load_face_model(face_model)]
if source_face_model is not None:
source_faces_index = [0]
source_faces = source_face_model
logger.status("Using Loaded Source Face Model...")
else:
logger.error(f"Cannot load Face Model File: {face_model}.safetensors")
else:
logger.error("Cannot detect any Source")
if source_faces is not None:
if target_hash_check:
target_image_md5hash = get_image_md5hash(target_img)
if TARGET_IMAGE_HASH is None:
TARGET_IMAGE_HASH = target_image_md5hash
target_image_same = False
else:
target_image_same = True if TARGET_IMAGE_HASH == target_image_md5hash else False
if not target_image_same:
TARGET_IMAGE_HASH = target_image_md5hash
logger.info("Target Image MD5 Hash = %s", TARGET_IMAGE_HASH)
logger.info("Target Image the Same? %s", target_image_same)
if TARGET_FACES is None or not target_image_same:
logger.status("Analyzing Target Image...")
target_faces = analyze_faces(target_img)
TARGET_FACES = target_faces
elif target_image_same:
logger.status("Using Hashed Target Face(s) Model...")
target_faces = TARGET_FACES
else:
logger.status("Analyzing Target Image...")
target_faces = analyze_faces(target_img)
logger.status("Detecting Source Face, Index = %s", source_faces_index[0])
if select_source == 0 and source_img is not None:
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)
else:
source_face = sorted(source_faces, key=lambda x: x.bbox[0])[source_faces_index[0]]
wrong_gender = 0
source_age = source_face["age"]
source_gender = "Female" if source_face["gender"] == 0 else "Male"
if source_age != "None" or source_gender != "None":
logger.status("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.status("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.status("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.status("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.status("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.status("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.status("Swapping Source into Target")
swapped_image = face_swapper.get(result, target_face, source_face)
if mask_face:
result = apply_face_mask(swapped_image=swapped_image,target_image=result,target_face=target_face,entire_mask_image=entire_mask_image)
else:
result = swapped_image
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.status(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.status(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:
if mask_face and entire_mask_image is not None:
result_image = enhance_image_and_mask(result_image, enhancement_options,Image.fromarray(target_img_orig),Image.fromarray(entire_mask_image).convert("L"))
else:
result_image = enhance_image(result_image, enhancement_options)
elif mask_face and entire_mask_image is not None and swapped > 0:
result_image = Image.composite(result_image,Image.fromarray(target_img_orig),Image.fromarray(entire_mask_image).convert("L"))
else:
logger.status("No source face(s) in the provided Index")
else:
logger.status("No source face(s) found")
return result_image, output, swapped
def build_face_model(image: Image.Image, name: str):
if image is None:
error_msg = "Please load an Image"
logger.error(error_msg)
return error_msg
if name is None:
error_msg = "Please filled out the 'Face Model Name' field"
logger.error(error_msg)
return error_msg
apply_logging_patch(1)
image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
logger.status("Building Face Model...")
face_model = analyze_faces(image)
if face_model is not None and len(face_model) > 0:
face_model_path = os.path.join(FACE_MODELS_PATH, name + ".safetensors")
save_face_model(face_model[0],face_model_path)
logger.status("--Done!--")
done_msg = f"Face model has been saved to '{face_model_path}'"
logger.status(done_msg)
return done_msg
else:
no_face_msg = "No face found, please try another image"
logger.error(no_face_msg)
return no_face_msg
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