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Deep-Live-Cam
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Merge 5db23597e9 into f0fae811d8

pull/1376/merge
rehanbgmi 2025-06-30 14:40:31 +09:00 committed by GitHub
parent f0fae811d8 5db23597e9
commit c705de8811
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3 changed files with 88 additions and 23 deletions
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22
modules/face_analyser.py
View File

@ -2,6 +2,7 @@ import os
import shutil
from typing import Any
import insightface
import logging # Added logging import
import cv2
import numpy as np
@ -25,18 +26,27 @@ def get_face_analyser() -> Any:
def get_one_face(frame: Frame) -> Any:
face = get_face_analyser().get(frame)
faces = get_face_analyser().get(frame)
if not faces:
logging.debug("Face_analyser: get_one_face: No faces found by insightface.")
return None
try:
return min(face, key=lambda x: x.bbox[0])
return min(faces, key=lambda x: x.bbox[0])
except ValueError:
logging.debug("Face_analyser: get_one_face: ValueError, likely no faces after all.")
return None
def get_many_faces(frame: Frame) -> Any:
try:
return get_face_analyser().get(frame)
except IndexError:
return None
faces = get_face_analyser().get(frame)
if not faces: # Check if faces is None or an empty list
logging.debug("Face_analyser: get_many_faces: No faces found by insightface.")
# Depending on what insightface returns for no faces,
# you might return None or an empty list.
# If .get() returns an empty list for no faces, this check is sufficient.
# If .get() returns None, this is also fine.
return faces # Return original (None or empty list)
return faces
def has_valid_map() -> bool:
for map in modules.globals.source_target_map:

3
modules/processors/frame/face_enhancer.py
View File

@ -82,7 +82,8 @@ def get_face_enhancer() -> Any:
selected_device = torch.device("cpu")
device_priority.append("CPU")
FACE_ENHANCER = gfpgan.GFPGANer(model_path=model_path, upscale=1, device=selected_device)
upscale_factor = getattr(modules.globals, 'gfpgan_upscale_factor', 2)
FACE_ENHANCER = gfpgan.GFPGANer(model_path=model_path, upscale=upscale_factor, device=selected_device)
# for debug:
print(f"Selected device: {selected_device} and device priority: {device_priority}")

86
modules/processors/frame/face_swapper.py
View File

@ -21,6 +21,16 @@ FACE_SWAPPER = None
THREAD_LOCK = threading.Lock()
NAME = "DLC.FACE-SWAPPER"
def _validate_kernel_size(kernel_tuple, default_kernel_tuple):
if isinstance(kernel_tuple, tuple) and len(kernel_tuple) == 2 and \
isinstance(kernel_tuple[0], int) and kernel_tuple[0] > 0 and kernel_tuple[0] % 2 == 1 and \
isinstance(kernel_tuple[1], int) and kernel_tuple[1] > 0 and kernel_tuple[1] % 2 == 1:
return kernel_tuple
else:
logging.warning(f"Invalid kernel size {kernel_tuple} received. Must be a tuple of two positive odd integers. Falling back to default {default_kernel_tuple}.")
return default_kernel_tuple
abs_dir = os.path.dirname(os.path.abspath(__file__))
models_dir = os.path.join(
os.path.dirname(os.path.dirname(os.path.dirname(abs_dir))), "models"
@ -32,7 +42,7 @@ def pre_check() -> bool:
conditional_download(
download_directory_path,
[
"https://huggingface.co/hacksider/deep-live-cam/blob/main/inswapper_128_fp16.onnx"
"https://huggingface.co/hacksider/deep-live-cam/blob/main/inswapper_128.onnx"
],
)
return True
@ -60,7 +70,7 @@ def get_face_swapper() -> Any:
with THREAD_LOCK:
if FACE_SWAPPER is None:
model_path = os.path.join(models_dir, "inswapper_128_fp16.onnx")
model_path = os.path.join(models_dir, "inswapper_128.onnx")
FACE_SWAPPER = insightface.model_zoo.get_model(
model_path, providers=modules.globals.execution_providers
)
@ -70,18 +80,38 @@ def get_face_swapper() -> Any:
def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
face_swapper = get_face_swapper()
# Apply the face swap
swapped_frame = face_swapper.get(
temp_frame, target_face, source_face, paste_back=True
)
# Statistical color correction
if getattr(modules.globals, 'statistical_color_correction', True) and target_face.bbox is not None:
x1, y1, x2, y2 = target_face.bbox.astype(int)
original_target_face_roi = temp_frame[y1:y2, x1:x2].copy()
# Apply the face swap
swapped_frame = face_swapper.get(
temp_frame, target_face, source_face, paste_back=True
)
if original_target_face_roi.size > 0:
swapped_face_roi = swapped_frame[y1:y2, x1:x2].copy()
if swapped_face_roi.size > 0:
try:
corrected_swapped_face_roi = apply_color_transfer(swapped_face_roi, original_target_face_roi)
swapped_frame[y1:y2, x1:x2] = corrected_swapped_face_roi
except Exception as e:
logging.error(f"Failed to apply statistical color transfer: {e}. Using original swapped ROI.")
# swapped_frame already contains the uncorrected swapped_face_roi in this region
else:
# Apply the face swap without statistical color correction
swapped_frame = face_swapper.get(
temp_frame, target_face, source_face, paste_back=True
)
if modules.globals.mouth_mask:
# Create a mask for the target face
face_mask = create_face_mask(target_face, temp_frame)
face_mask = create_face_mask(target_face, swapped_frame) # Use swapped_frame here
# Create the mouth mask
mouth_mask, mouth_cutout, mouth_box, lower_lip_polygon = (
create_lower_mouth_mask(target_face, temp_frame)
create_lower_mouth_mask(target_face, swapped_frame) # Use swapped_frame here
)
# Apply the mouth area
@ -106,16 +136,26 @@ def process_frame(source_face: Face, temp_frame: Frame) -> Frame:
many_faces = get_many_faces(temp_frame)
if many_faces:
for target_face in many_faces:
if source_face and target_face:
if source_face and target_face: # target_face from many_faces will always be valid here
temp_frame = swap_face(source_face, target_face, temp_frame)
else:
print("Face detection failed for target/source.")
elif not source_face: # Check source_face specifically
logging.error("Source face is not available or no face detected in source image. Skipping swap for this target face.")
# Optionally `continue` or `break` if source_face is essential for all
elif not source_face : # if many_faces is empty AND source_face is also an issue
logging.error("Source face is not available AND no faces detected in target frame.")
else: # many_faces is empty, but source_face is ok
logging.info(f"No faces detected in the current target frame for 'many_faces' mode.")
else:
target_face = get_one_face(temp_frame)
if target_face and source_face:
temp_frame = swap_face(source_face, target_face, temp_frame)
else:
logging.error("Face detection failed for target or source.")
if not source_face:
logging.error("Source face is not available or no face detected in source image.")
elif not target_face:
logging.error(f"No face detected in the current target frame.")
else: # Should not happen if logic is right, but as a fallback
logging.error("Face detection failed for an unknown reason concerning target or source.")
return temp_frame
@ -367,7 +407,12 @@ def create_lower_mouth_mask(
cv2.fillPoly(mask_roi, [expanded_landmarks - [min_x, min_y]], 255)
# Apply Gaussian blur to soften the mask edges
mask_roi = cv2.GaussianBlur(mask_roi, (15, 15), 5)
# Default kernel size for mouth mask blur is (9,9) as a balance between performance and smoothing.
# Larger values (e.g., (15,15) - the previous hardcoded value) provide more smoothing but are slower.
# This is configurable via modules.globals.mouth_mask_blur_kernel_size.
kernel_size_mouth_config = getattr(modules.globals, 'mouth_mask_blur_kernel_size', (9, 9))
valid_kernel_mouth = _validate_kernel_size(kernel_size_mouth_config, (9, 9))
mask_roi = cv2.GaussianBlur(mask_roi, valid_kernel_mouth, 0)
# Place the mask ROI in the full-sized mask
mask[min_y:max_y, min_x:max_x] = mask_roi
@ -508,7 +553,13 @@ def apply_mouth_area(
feathered_mask = cv2.GaussianBlur(
polygon_mask.astype(float), (0, 0), feather_amount
)
feathered_mask = feathered_mask / feathered_mask.max()
mask_max_value = feathered_mask.max()
if mask_max_value < 1e-6: # Check if max is effectively zero
logging.warning("Mouth mask's feathered_mask is all zeros or near-zeros after blur. Resulting mask will be black.")
feathered_mask = np.zeros_like(polygon_mask, dtype=np.uint8)
else:
feathered_mask = (feathered_mask / mask_max_value * 255).astype(np.uint8)
face_mask_roi = face_mask[min_y:max_y, min_x:max_x]
combined_mask = feathered_mask * (face_mask_roi / 255.0)
@ -553,7 +604,8 @@ def create_face_mask(face: Face, frame: Frame) -> np.ndarray:
face_top = np.min([right_side_face[0, 1], left_side_face[-1, 1]])
forehead_height = face_top - eyebrow_top
extended_forehead_height = int(forehead_height * 5.0) # Extend by 50%
forehead_factor = getattr(modules.globals, 'forehead_extension_factor', 2.5)
extended_forehead_height = int(forehead_height * forehead_factor)
# Create forehead points
forehead_left = right_side_face[0].copy()
@ -595,7 +647,9 @@ def create_face_mask(face: Face, frame: Frame) -> np.ndarray:
cv2.fillConvexPoly(mask, hull_padded, 255)
# Smooth the mask edges
mask = cv2.GaussianBlur(mask, (5, 5), 3)
kernel_size_face_config = getattr(modules.globals, 'face_mask_blur_kernel_size', (5, 5))
valid_kernel_face = _validate_kernel_size(kernel_size_face_config, (5, 5))
mask = cv2.GaussianBlur(mask, valid_kernel_face, 0)
return mask