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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.upscaler import UpscalerData
from scripts.logger import logger
import warnings
np.warnings = warnings
np.warnings.filterwarnings('ignore')
providers = ["CPUExecutionProvider"]
models_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "models")
insightface_path = os.path.join(models_path, "insightface")
insightface_models_path = os.path.join(insightface_path, "models")
swapper_path = os.path.join(models_path, "roop")
@dataclass
class UpscaleOptions:
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
def cosine_distance(vector1: np.ndarray, vector2: np.ndarray) -> float:
vec1 = vector1.flatten()
vec2 = vector2.flatten()
dot_product = np.dot(vec1, vec2)
norm1 = np.linalg.norm(vec1)
norm2 = np.linalg.norm(vec2)
cosine_distance = 1 - (dot_product / (norm1 * norm2))
return cosine_distance
def cosine_similarity(test_vec: np.ndarray, source_vecs: List[np.ndarray]) -> float:
cos_dist = sum(cosine_distance(test_vec, source_vec) for source_vec in source_vecs)
average_cos_dist = cos_dist / len(source_vecs)
return average_cos_dist
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=insightface_path
)
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 upscale_image(image: Image, upscale_options: UpscaleOptions):
result_image = image
if upscale_options.do_restore_first:
if upscale_options.face_restorer is not None:
original_image = result_image.copy()
logger.info("Restoring face with %s", upscale_options.face_restorer.name())
numpy_image = np.array(result_image)
numpy_image = upscale_options.face_restorer.restore(numpy_image)
restored_image = Image.fromarray(numpy_image)
result_image = Image.blend(
original_image, restored_image, upscale_options.restorer_visibility
)
if upscale_options.upscaler is not None and upscale_options.upscaler.name != "None":
original_image = result_image.copy()
logger.info(
"Upscaling with %s scale = %s",
upscale_options.upscaler.name,
upscale_options.scale,
)
result_image = upscale_options.upscaler.scaler.upscale(
original_image, upscale_options.scale, upscale_options.upscaler.data_path
)
if upscale_options.scale == 1:
result_image = Image.blend(
original_image, result_image, upscale_options.upscale_visibility
)
else:
if upscale_options.upscaler is not None and upscale_options.upscaler.name != "None":
original_image = result_image.copy()
logger.info(
"Upscaling with %s scale = %s",
upscale_options.upscaler.name,
upscale_options.scale,
)
result_image = upscale_options.upscaler.scaler.upscale(
image, upscale_options.scale, upscale_options.upscaler.data_path
)
if upscale_options.scale == 1:
result_image = Image.blend(
original_image, result_image, upscale_options.upscale_visibility
)
if upscale_options.face_restorer is not None:
original_image = result_image.copy()
logger.info("Restoring face with %s", upscale_options.face_restorer.name())
numpy_image = np.array(result_image)
numpy_image = upscale_options.face_restorer.restore(numpy_image)
restored_image = Image.fromarray(numpy_image)
result_image = Image.blend(
original_image, restored_image, upscale_options.restorer_visibility
)
return result_image
def get_face_single(img_data: np.ndarray, face_index=0, det_size=(640, 640)):
face_analyser = copy.deepcopy(getAnalysisModel())
buffalo_path = os.path.join(insightface_models_path, "buffalo_l.zip")
if os.path.exists(buffalo_path):
# Remove the zip to save space
os.remove(buffalo_path)
face_analyser.prepare(ctx_id=0, det_size=det_size)
face = face_analyser.get(img_data)
if len(face) == 0 and det_size[0] > 320 and det_size[1] > 320:
det_size_half = (det_size[0] // 2, det_size[1] // 2)
return get_face_single(img_data, face_index=face_index, det_size=det_size_half)
try:
return sorted(face, key=lambda x: x.bbox[0])[face_index]
except IndexError:
return None
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],
upscale_options: Union[UpscaleOptions, None] = None,
):
result_image = target_img
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)
source_face = get_face_single(source_img, face_index=source_faces_index[0])
if len(source_faces_index) != 0 and len(source_faces_index) != 1 and len(source_faces_index) != len(faces_index):
logger.info(f'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
model_path = model_path = os.path.join(swapper_path, model)
face_swapper = getFaceSwapModel(model_path)
source_face_idx = 0
for face_num in faces_index:
if len(source_faces_index) > 1 and source_face_idx > 0:
source_face = get_face_single(source_img, face_index=source_faces_index[source_face_idx])
source_face_idx += 1
if source_face is not None:
target_face = get_face_single(target_img, face_index=face_num)
if target_face is not None:
result = face_swapper.get(result, target_face, source_face)
else:
logger.info(f"No target face found for {face_num}")
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 upscale_options is not None:
result_image = upscale_image(result_image, upscale_options)
else:
logger.info("No source face(s) found")
return result_image
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