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FIX: Gender detection face index logic

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Issue #234
This commit is contained in:
Gourieff | 古仁
2026-05-13 00:07:42 +07:00
parent a7628ccdff
commit be42ca37ee
2 changed files with 117 additions and 140 deletions
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reactor_core/inswap.py
+2 -2
View File
@@ -231,8 +231,8 @@ class Attribute(BaseONNXModel):
"""Анализатор атрибутов (выдает пол и возраст)"""
def __init__(self, model_file, providers=None):
super().__init__(model_file, providers)
self.input_mean = 127.5
self.input_std = 128.0
self.input_mean = 0.0
self.input_std = 1.0
self.input_size = tuple(self.input_shape[2:4][::-1])
def get(self, img, face):
scripts/reactor_swapper.py
+115 -138
View File
@@ -142,35 +142,36 @@ def get_face_gender(
operated: str,
order: str,
):
filtered_faces = [
f for f in face
if (gender_condition == 0) or
(gender_condition == 1 and f.sex == "F") or
(gender_condition == 2 and f.sex == "M")
]
gender = "Female" if gender_condition == 1 else "Male" if gender_condition == 0 else ""
if len(filtered_faces) == 0:
if gender_condition != 0:
logger.status(f"No faces found for -{gender}-")
return None, 0, None # treat as "wrong gender" to skip
faces_sorted = sort_by_order(filtered_faces, order)
# 1. Сортируем ВСЕ найденные лица (без фильтрации!)
faces_sorted = sort_by_order(face, order)
# 2. Проверяем, существует ли вообще лицо с таким визуальным индексом
if face_index >= len(faces_sorted):
logger.info("Requested face index (%s) is out of bounds (max available index is %s)", face_index, len(faces_sorted))
logger.info("Requested face index (%s) is out of bounds (max available index is %s)", face_index, len(faces_sorted) - 1)
return None, 0, None
# 3. Берем конкретное лицо по его позиции на фото (например, второе справа)
face_selected = faces_sorted[face_index]
logger.info("%s Face %s: Detected Gender -%s-", operated, face_index, face_selected.sex)
# Если фильтр по полу отключен (no) - сразу отдаем лицо в работу
if gender_condition == 0:
return face_selected, 0, face_index
expected_gender = "F" if gender_condition == 1 else "M"
if gender_condition != 0 and face_selected.sex != expected_gender:
logger.info(f"{operated} Face {face_index}: WRONG gender ({face_selected.sex})")
return face_selected, 1, face_index # <-- есть, но не тот пол
# 4. Проверяем пол выбранного лица
# face.gender: 0 = female, 1 = male
# gender_condition: 1 = female, 2 = male
expected_gender = 0 if gender_condition == 1 else 1
actual_gender = getattr(face_selected, 'gender', -1)
sel_gender_str = "Male" if actual_gender == 1 else "Female" if actual_gender == 0 else "Unknown"
logger.info("%s Face %s: Detected Gender -%s-", operated, face_index, sel_gender_str)
# Если пол не совпадает с тем, что заказал юзер
if actual_gender != expected_gender:
logger.info(f"{operated} Face {face_index}: WRONG gender ({sel_gender_str})")
return face_selected, 1, face_index # 1 означает флаг wrong_gender = True (цикл его пропустит)
# Если всё идеально
return face_selected, 0, face_index
def half_det_size(det_size):
@@ -183,7 +184,10 @@ def analyze_faces(img_data: np.ndarray, det_size=(640, 640)):
faces = []
try:
faces = face_analyser.get(img_data)
except:
except Exception as e:
# import traceback
# traceback.print_exc()
# logger.error(f"Error during face analysis: {e}")
logger.error("No faces found")
# Try halving det_size if no faces are found
@@ -318,22 +322,27 @@ def swap_face(
logger.status("Using Hashed Target Face(s) Model...")
target_faces = TARGET_FACES
# No use in trying to swap faces if no faces are found
if len(target_faces) == 0:
logger.status("Cannot detect any Target, skipping swapping...")
return result_image, bbox, swapped_indexes
# --- НОВАЯ ИДЕАЛЬНАЯ ЛОГИКА СОРТИРОВКИ ---
# 1. Заранее собираем список ТОЛЬКО ВАЛИДНЫХ исходных лиц
valid_source_faces = []
if source_img is not None:
# separated management of wrong_gender between source and target
source_face, src_wrong_gender, source_face_index = get_face_single(source_img, source_faces, face_index=source_faces_index[0], gender_source=gender_source, order=faces_order[1])
for idx in source_faces_index:
sf, src_wrong_gender, _ = get_face_single(source_img, source_faces, face_index=idx, gender_source=gender_source, order=faces_order[1])
if sf is not None and src_wrong_gender == 0:
valid_source_faces.append(sf)
else:
# source_face = sorted(source_faces, key=lambda x: x.bbox[0])[source_faces_index[0]]
source_face = sorted(source_faces, key=lambda x: (x.bbox[2] - x.bbox[0]) * (x.bbox[3] - x.bbox[1]), reverse = True)[source_faces_index[0]]
src_wrong_gender = 0
sf, src_wrong_gender, _ = get_face_single(None, source_faces, face_index=source_faces_index[0], gender_source=gender_source, order=faces_order[1])
if sf is not None and src_wrong_gender == 0:
valid_source_faces.append(sf)
if len(source_faces_index) != 0 and len(source_faces_index) != 1 and len(source_faces_index) != len(faces_index):
logger.status(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:
if len(valid_source_faces) == 0:
logger.status("No valid source face(s) found in the provided Index after gender filter")
else:
result = target_img
if "inswapper" in model:
model_path = os.path.join(insightface_path, model)
@@ -346,47 +355,40 @@ def swap_face(
source_face_idx = 0
# 2. Идем по целевым лицам
for face_num in faces_index:
# No use in trying to swap faces if no further faces are found
if face_num >= len(target_faces):
logger.status("Checked all existing target faces, skipping swapping...")
break
if len(source_faces_index) > 1 and source_face_idx > 0:
source_face, src_wrong_gender, source_face_index = get_face_single(source_img, source_faces, face_index=source_faces_index[source_face_idx], gender_source=gender_source, order=faces_order[1])
source_face_idx += 1
if source_face is not None and src_wrong_gender == 0:
target_face, wrong_gender, target_face_index = get_face_single(target_img, target_faces, face_index=face_num, gender_target=gender_target, order=faces_order[0])
if target_face is not None and wrong_gender == 0:
logger.status(f"Swapping...")
if face_boost_enabled and "hyperswap" not in model:
logger.status(f"Face Boost is enabled (inswapper/reswapper only)")
bgr_fake, M = face_swapper.get(result, target_face, source_face, paste_back=False)
bgr_fake, scale = restorer.get_restored_face(bgr_fake, face_restore_model, face_restore_visibility, codeformer_weight, interpolation)
M *= scale
result = swapper.in_swap(result, bgr_fake, M)
else:
result = face_swapper.get(result, target_face, source_face)
bbox = [tuple(map(float, target_face.bbox))]
swapped_indexes = [target_face_index]
elif wrong_gender == 1:
wrong_gender = 0
logger.status("Wrong target gender detected")
continue
target_face, wrong_gender, target_face_index = get_face_single(target_img, target_faces, face_index=face_num, gender_target=gender_target, order=faces_order[0])
if target_face is not None and wrong_gender == 0:
logger.status(f"Swapping...")
# 3. Берем валидное лицо (если их меньше, чем целей — идем по кругу)
source_face_to_use = valid_source_faces[source_face_idx % len(valid_source_faces)]
if face_boost_enabled and "hyperswap" not in model:
logger.status(f"Face Boost is enabled (inswapper/reswapper only)")
bgr_fake, M = face_swapper.get(result, target_face, source_face_to_use, paste_back=False)
bgr_fake, scale = restorer.get_restored_face(bgr_fake, face_restore_model, face_restore_visibility, codeformer_weight, interpolation)
M *= scale
result = swapper.in_swap(result, bgr_fake, M)
else:
logger.info(f"No target face found for {face_num}")
elif src_wrong_gender == 1:
src_wrong_gender = 0
logger.status("Wrong source gender detected")
result = face_swapper.get(result, target_face, source_face_to_use)
bbox.append(tuple(map(float, target_face.bbox)))
swapped_indexes.append(target_face_index)
# Продвигаем индекс исходного лица ТОЛЬКО после УСПЕШНОГО применения
if len(valid_source_faces) > 1:
source_face_idx += 1
elif wrong_gender == 1:
logger.status("Wrong target gender detected")
continue
else:
logger.status(f"No source face found for face number {source_face_idx}.")
logger.info(f"No target face found for {face_num}")
result_image = Image.fromarray(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))
else:
logger.status("No source face(s) in the provided Index")
else:
logger.status("No source face(s) found")
return result_image, bbox, swapped_indexes
@@ -413,26 +415,19 @@ def swap_face_many(
swapped_indexes = []
if model is not None:
if isinstance(source_img, str): # source_img is a base64 string
if isinstance(source_img, str):
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
if 'base64,' in source_img:
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_imgs = [cv2.cvtColor(np.array(target_img), cv2.COLOR_RGB2BGR) for target_img in target_imgs]
if source_img is not None:
source_img = cv2.cvtColor(np.array(source_img), cv2.COLOR_RGB2BGR)
source_image_md5hash = get_image_md5hash(source_img)
if SOURCE_IMAGE_HASH is None:
@@ -455,17 +450,14 @@ def swap_face_many(
source_faces = SOURCE_FACES
elif face_model is not None:
source_faces_index = [0]
logger.status("Using Loaded Source Face Model...")
source_face_model = [face_model]
source_faces = source_face_model
else:
logger.error("Cannot detect any Source")
if source_faces is not None:
target_faces = []
pbar = progress_bar(len(target_imgs))
@@ -495,46 +487,43 @@ def swap_face_many(
logger.info("(Image %s) Target Image the Same? %s", i, target_image_same)
if len(TARGET_FACES_LIST) == 0:
# logger.status(f"Analyzing Target Image {i}...")
target_face = analyze_faces(target_img)
TARGET_FACES_LIST = [target_face]
elif len(TARGET_FACES_LIST) == i and not target_image_same:
# logger.status(f"Analyzing Target Image {i}...")
target_face = analyze_faces(target_img)
TARGET_FACES_LIST.append(target_face)
elif len(TARGET_FACES_LIST) != i and not target_image_same:
# logger.status(f"Analyzing Target Image {i}...")
target_face = analyze_faces(target_img)
TARGET_FACES_LIST[i] = target_face
elif target_image_same:
# logger.status("(Image %s) Using Hashed Target Face(s) Model...", i)
target_face = TARGET_FACES_LIST[i]
# logger.status(f"Analyzing Target Image {i}...")
if target_face is not None:
target_faces.append(target_face)
pbar.update(1)
progress_bar_reset(pbar)
# No use in trying to swap faces if no faces are found
if len(target_faces) == 0:
logger.status("Cannot detect any Target, skipping swapping...")
return result_images, bbox, swapped_indexes
# --- НОВАЯ ИДЕАЛЬНАЯ ЛОГИКА СОРТИРОВКИ ---
valid_source_faces = []
if source_img is not None:
# separated management of wrong_gender between source and target
source_face, src_wrong_gender, source_face_index = get_face_single(source_img, source_faces, face_index=source_faces_index[0], gender_source=gender_source, order=faces_order[1])
for idx in source_faces_index:
sf, src_wrong_gender, _ = get_face_single(source_img, source_faces, face_index=idx, gender_source=gender_source, order=faces_order[1])
if sf is not None and src_wrong_gender == 0:
valid_source_faces.append(sf)
else:
# source_face = sorted(source_faces, key=lambda x: x.bbox[0])[source_faces_index[0]]
source_face = sorted(source_faces, key=lambda x: (x.bbox[2] - x.bbox[0]) * (x.bbox[3] - x.bbox[1]), reverse = True)[source_faces_index[0]]
src_wrong_gender = 0
sf, src_wrong_gender, _ = get_face_single(None, source_faces, face_index=source_faces_index[0], gender_source=gender_source, order=faces_order[1])
if sf is not None and src_wrong_gender == 0:
valid_source_faces.append(sf)
if len(source_faces_index) != 0 and len(source_faces_index) != 1 and len(source_faces_index) != len(faces_index):
logger.status(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:
if len(valid_source_faces) == 0:
logger.status("No valid source face(s) found in the provided Index after gender filter")
else:
results = target_imgs
if "inswapper" in model:
model_path = os.path.join(insightface_path, model)
@@ -546,59 +535,47 @@ def swap_face_many(
face_swapper = getFaceSwapModel(model_path)
source_face_idx = 0
pbar = progress_bar(len(target_imgs))
logger.status(f"Swapping...")
for face_num in faces_index:
# No use in trying to swap faces if no further faces are found
if face_num >= len(target_faces):
logger.status("Checked all existing target faces, skipping swapping...")
break
if len(source_faces_index) > 1 and source_face_idx > 0:
source_face, src_wrong_gender, source_face_index = get_face_single(source_img, source_faces, face_index=source_faces_index[source_face_idx], gender_source=gender_source, order=faces_order[1])
source_face_idx += 1
if source_face is not None and src_wrong_gender == 0:
# Reading results to make current face swap on a previous face result
for i, (target_img, target_face) in enumerate(zip(results, target_faces)):
target_face_single, wrong_gender, target_face_index = get_face_single(target_img, target_face, face_index=face_num, gender_target=gender_target, order=faces_order[0])
if target_face_single is not None and wrong_gender == 0:
result = target_img
if face_boost_enabled and "hyperswap" not in model:
logger.status(f"Face Boost is enabled (inswapper/reswapper only)")
bgr_fake, M = face_swapper.get(target_img, target_face_single, source_face, paste_back=False)
bgr_fake, scale = restorer.get_restored_face(bgr_fake, face_restore_model, face_restore_visibility, codeformer_weight, interpolation)
M *= scale
result = swapper.in_swap(target_img, bgr_fake, M)
else:
result = face_swapper.get(target_img, target_face_single, source_face)
results[i] = result
bbox.append(tuple(map(float, target_face_single.bbox)))
swapped_indexes.append(target_face_index)
pbar.update(1)
elif wrong_gender == 1:
wrong_gender = 0
logger.status("Wrong target gender detected")
pbar.update(1)
continue
target_used_in_any_image = False
for i, (target_img, target_face_list) in enumerate(zip(results, target_faces)):
target_face_single, wrong_gender, target_face_index = get_face_single(target_img, target_face_list, face_index=face_num, gender_target=gender_target, order=faces_order[0])
if target_face_single is not None and wrong_gender == 0:
target_used_in_any_image = True
source_face_to_use = valid_source_faces[source_face_idx % len(valid_source_faces)]
result = target_img
if face_boost_enabled and "hyperswap" not in model:
bgr_fake, M = face_swapper.get(target_img, target_face_single, source_face_to_use, paste_back=False)
bgr_fake, scale = restorer.get_restored_face(bgr_fake, face_restore_model, face_restore_visibility, codeformer_weight, interpolation)
M *= scale
result = swapper.in_swap(target_img, bgr_fake, M)
else:
logger.info(f"{i}: No target face found for {face_num}")
pbar.update(1)
elif src_wrong_gender == 1:
src_wrong_gender = 0
logger.status("Wrong source gender detected")
continue
else:
logger.status(f"No source face found for face number {source_face_idx}.")
result = face_swapper.get(target_img, target_face_single, source_face_to_use)
results[i] = result
bbox.append(tuple(map(float, target_face_single.bbox)))
swapped_indexes.append(target_face_index)
pbar.update(1)
elif wrong_gender == 1:
logger.status("Wrong target gender detected")
pbar.update(1)
continue
else:
logger.info(f"{i}: No target face found for {face_num}")
pbar.update(1)
if target_used_in_any_image and len(valid_source_faces) > 1:
source_face_idx += 1
progress_bar_reset(pbar)
result_images = [Image.fromarray(cv2.cvtColor(result, cv2.COLOR_BGR2RGB)) for result in results]
else:
logger.status("No source face(s) in the provided Index")
else:
logger.status("No source face(s) found")
return result_images, bbox, swapped_indexes