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feat: Implement hair swapping and enhance realism 

This commit introduces the capability to swap hair along with the face from a source image to a target image/video or live webcam feed.

Key changes include:

1.  **Hair Segmentation:**
    - Integrated the `isjackwild/segformer-b0-finetuned-segments-skin-hair-clothing` model from Hugging Face using the `transformers` library.
    - Added `modules/hair_segmenter.py` with a `segment_hair` function to produce a binary hair mask from an image.
    - Updated `requirements.txt` with `transformers`.

2.  **Combined Face-Hair Mask:**
    - Implemented `create_face_and_hair_mask` in `modules/processors/frame/face_swapper.py` to generate a unified mask for both face (from landmarks) and segmented hair from the source image.

3.  **Enhanced Swapping Logic:**
    - Modified `swap_face` and related processing functions (`process_frame`, `process_frame_v2`, `process_frames`, `process_image`) to utilize the full source image (`source_frame_full`).
    - The `swap_face` function now performs the standard face swap and then:
        - Segments hair from the `source_frame_full`.
        - Warps the hair and its mask to the target face's position using an affine transformation estimated from facial landmarks.
        - Applies color correction (`apply_color_transfer`) to the warped hair.
        - Blends the hair onto the target frame, preferably using `cv2.seamlessClone` for improved realism.
    - Existing mouth mask logic is preserved and applied to the final composited frame.

4.  **Webcam Integration:**
    - Updated the webcam processing loop in `modules/ui.py` (`create_webcam_preview`) to correctly load and pass the `source_frame_full` to the frame processors.
    - This enables hair swapping in live webcam mode.
    - Added error handling for source image loading in webcam mode.

This set of changes addresses your request for more realistic face swaps that include hair. Further testing and refinement of blending parameters may be beneficial for optimal results across all scenarios.
pull/1313/head
google-labs-jules[bot] 2025-05-21 18:47:31 +00:00
parent d5a3fb0c47
commit 37486f03e7
4 changed files with 449 additions and 75 deletions
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81
modules/hair_segmenter.py 100644
View File

@ -0,0 +1,81 @@
import torch
import numpy as np
from PIL import Image
from transformers import SegformerImageProcessor, SegformerForSemanticSegmentation
import cv2 # Imported for BGR to RGB conversion, though PIL can also do it.
def segment_hair(image_np: np.ndarray) -> np.ndarray:
"""
Segments hair from an image.
Args:
image_np: NumPy array representing the image (BGR format from OpenCV).
Returns:
NumPy array representing the binary hair mask.
"""
processor = SegformerImageProcessor.from_pretrained("isjackwild/segformer-b0-finetuned-segments-skin-hair-clothing")
model = SegformerForSemanticSegmentation.from_pretrained("isjackwild/segformer-b0-finetuned-segments-skin-hair-clothing")
# Convert BGR (OpenCV) to RGB (PIL)
image_rgb = cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB)
image_pil = Image.fromarray(image_rgb)
inputs = processor(images=image_pil, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits # Shape: batch_size, num_labels, height, width
# Upsample logits to original image size
upsampled_logits = torch.nn.functional.interpolate(
logits,
size=(image_np.shape[0], image_np.shape[1]), # H, W
mode='bilinear',
align_corners=False
)
segmentation_map = upsampled_logits.argmax(dim=1).squeeze().cpu().numpy()
# Label 2 is for hair in this model
hair_mask = np.where(segmentation_map == 2, 255, 0).astype(np.uint8)
return hair_mask
if __name__ == '__main__':
# This is a conceptual test.
# In a real scenario, you would load an image using OpenCV or Pillow.
# For example:
# sample_image_np = cv2.imread("path/to/your/image.jpg")
# if sample_image_np is not None:
# hair_mask_output = segment_hair(sample_image_np)
# cv2.imwrite("hair_mask_output.png", hair_mask_output)
# print("Hair mask saved to hair_mask_output.png")
# else:
# print("Failed to load sample image.")
print("Conceptual test: Hair segmenter module created.")
# Create a dummy image for a basic test run if no image is available.
dummy_image_np = np.zeros((100, 100, 3), dtype=np.uint8) # 100x100 BGR image
dummy_image_np[:, :, 1] = 255 # Make it green to distinguish from black mask
try:
print("Running segment_hair with a dummy image...")
hair_mask_output = segment_hair(dummy_image_np)
print(f"segment_hair returned a mask of shape: {hair_mask_output.shape}")
# Check if the output is a 2D array (mask) and has the same H, W as input
assert hair_mask_output.shape == (dummy_image_np.shape[0], dummy_image_np.shape[1])
# Check if the mask is binary (0 or 255)
assert np.all(np.isin(hair_mask_output, [0, 255]))
print("Dummy image test successful. Hair mask seems to be generated correctly.")
# Attempt to save the dummy mask (optional, just for visual confirmation if needed)
# cv2.imwrite("dummy_hair_mask_output.png", hair_mask_output)
# print("Dummy hair mask saved to dummy_hair_mask_output.png")
except ImportError as e:
print(f"An ImportError occurred: {e}. This might be due to missing dependencies like transformers, torch, or Pillow.")
print("Please ensure all required packages are installed by updating requirements.txt and installing them.")
except Exception as e:
print(f"An error occurred during the dummy image test: {e}")
print("This could be due to issues with model loading, processing, or other runtime errors.")
print("To perform a full test, replace the dummy image with a real image path.")

369
modules/processors/frame/face_swapper.py
View File

@ -9,6 +9,7 @@ import modules.processors.frame.core
from modules.core import update_status
from modules.face_analyser import get_one_face, get_many_faces, default_source_face
from modules.typing import Face, Frame
from modules.hair_segmenter import segment_hair
from modules.utilities import (
conditional_download,
is_image,
@ -67,14 +68,93 @@ def get_face_swapper() -> Any:
return FACE_SWAPPER
def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
def swap_face(source_face_obj: Face, target_face: Face, source_frame_full: Frame, 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
temp_frame, target_face, source_face_obj, paste_back=True
)
final_swapped_frame = swapped_frame.copy() # Initialize final_swapped_frame
# START of Hair Blending Logic
if source_face_obj.kps is not None and target_face.kps is not None and source_face_obj.kps.shape[0] >=2 and target_face.kps.shape[0] >=2 : # kps are 5x2 landmarks
hair_only_mask_source = segment_hair(source_frame_full)
# Ensure kps are float32 for estimateAffinePartial2D
source_kps_float = source_face_obj.kps.astype(np.float32)
target_kps_float = target_face.kps.astype(np.float32)
# b. Estimate Transformation Matrix
# Using LMEDS for robustness
matrix, _ = cv2.estimateAffinePartial2D(source_kps_float, target_kps_float, method=cv2.LMEDS)
if matrix is not None:
# c. Warp Source Hair and its Mask
dsize = (temp_frame.shape[1], temp_frame.shape[0]) # width, height
# Ensure hair_only_mask_source is 8-bit single channel
if hair_only_mask_source.ndim == 3 and hair_only_mask_source.shape[2] == 3:
hair_only_mask_source_gray = cv2.cvtColor(hair_only_mask_source, cv2.COLOR_BGR2GRAY)
else:
hair_only_mask_source_gray = hair_only_mask_source
# Threshold to ensure binary mask for warping
_, hair_only_mask_source_binary = cv2.threshold(hair_only_mask_source_gray, 127, 255, cv2.THRESH_BINARY)
warped_hair_mask = cv2.warpAffine(hair_only_mask_source_binary, matrix, dsize)
warped_source_hair_image = cv2.warpAffine(source_frame_full, matrix, dsize)
# d. Color Correct Warped Source Hair
# Using swapped_frame (face-swapped output) as the target for color correction
color_corrected_warped_hair = apply_color_transfer(warped_source_hair_image, swapped_frame)
# e. Blend Hair onto Swapped Frame
# Ensure warped_hair_mask is binary (0 or 255) after warping
_, warped_hair_mask_binary = cv2.threshold(warped_hair_mask, 127, 255, cv2.THRESH_BINARY)
# Preferred: cv2.seamlessClone
x, y, w, h = cv2.boundingRect(warped_hair_mask_binary)
if w > 0 and h > 0:
center = (x + w // 2, y + h // 2)
# seamlessClone expects target image, source image, mask, center, method
# The mask should be single channel 8-bit.
# The source (color_corrected_warped_hair) and target (swapped_frame) should be 8-bit 3-channel.
# Check if swapped_frame is suitable for seamlessClone (it should be the base)
# Ensure color_corrected_warped_hair is also 8UC3
if color_corrected_warped_hair.shape == swapped_frame.shape and \
color_corrected_warped_hair.dtype == swapped_frame.dtype and \
warped_hair_mask_binary.dtype == np.uint8:
try:
final_swapped_frame = cv2.seamlessClone(color_corrected_warped_hair, swapped_frame, warped_hair_mask_binary, center, cv2.NORMAL_CLONE)
except cv2.error as e:
logging.warning(f"cv2.seamlessClone failed: {e}. Falling back to simple blending.")
# Fallback: Simple Blending (if seamlessClone fails)
warped_hair_mask_3ch = cv2.cvtColor(warped_hair_mask_binary, cv2.COLOR_GRAY2BGR) > 0 # boolean mask
final_swapped_frame[warped_hair_mask_3ch] = color_corrected_warped_hair[warped_hair_mask_3ch]
else:
logging.warning("Mismatch in shape/type for seamlessClone. Falling back to simple blending.")
# Fallback: Simple Blending
warped_hair_mask_3ch = cv2.cvtColor(warped_hair_mask_binary, cv2.COLOR_GRAY2BGR) > 0
final_swapped_frame[warped_hair_mask_3ch] = color_corrected_warped_hair[warped_hair_mask_3ch]
else:
# Mask is empty, no hair to blend, final_swapped_frame remains as is (copy of swapped_frame)
logging.info("Warped hair mask is empty. Skipping hair blending.")
# final_swapped_frame is already a copy of swapped_frame
else:
logging.warning("Failed to estimate affine transformation matrix for hair. Skipping hair blending.")
# final_swapped_frame is already a copy of swapped_frame
else:
if source_face_obj.kps is None or target_face.kps is None:
logging.warning("Source or target keypoints (kps) are None. Skipping hair blending.")
else:
logging.warning(f"Not enough keypoints for hair transformation. Source kps: {source_face_obj.kps.shape if source_face_obj.kps is not None else 'None'}, Target kps: {target_face.kps.shape if target_face.kps is not None else 'None'}. Skipping hair blending.")
# final_swapped_frame is already a copy of swapped_frame
# END of Hair Blending Logic
# f. Mouth Mask Logic
if modules.globals.mouth_mask:
# Create a mask for the target face
face_mask = create_face_mask(target_face, temp_frame)
@ -85,20 +165,21 @@ def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
)
# Apply the mouth area
swapped_frame = apply_mouth_area(
swapped_frame, mouth_cutout, mouth_box, face_mask, lower_lip_polygon
# Apply to final_swapped_frame if hair blending happened, otherwise to swapped_frame
final_swapped_frame = apply_mouth_area(
final_swapped_frame, mouth_cutout, mouth_box, face_mask, lower_lip_polygon
)
if modules.globals.show_mouth_mask_box:
mouth_mask_data = (mouth_mask, mouth_cutout, mouth_box, lower_lip_polygon)
swapped_frame = draw_mouth_mask_visualization(
swapped_frame, target_face, mouth_mask_data
final_swapped_frame = draw_mouth_mask_visualization(
final_swapped_frame, target_face, mouth_mask_data
)
return swapped_frame
return final_swapped_frame
def process_frame(source_face: Face, temp_frame: Frame) -> Frame:
def process_frame(source_face_obj: Face, source_frame_full: Frame, temp_frame: Frame) -> Frame:
if modules.globals.color_correction:
temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB)
@ -106,70 +187,73 @@ 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:
temp_frame = swap_face(source_face, target_face, temp_frame)
if source_face_obj and target_face:
temp_frame = swap_face(source_face_obj, target_face, source_frame_full, temp_frame)
else:
print("Face detection failed for target/source.")
else:
target_face = get_one_face(temp_frame)
if target_face and source_face:
temp_frame = swap_face(source_face, target_face, temp_frame)
if target_face and source_face_obj:
temp_frame = swap_face(source_face_obj, target_face, source_frame_full, temp_frame)
else:
logging.error("Face detection failed for target or source.")
return temp_frame
def process_frame_v2(temp_frame: Frame, temp_frame_path: str = "") -> Frame:
# process_frame_v2 needs to accept source_frame_full as well
def process_frame_v2(source_frame_full: Frame, temp_frame: Frame, temp_frame_path: str = "") -> Frame:
if is_image(modules.globals.target_path):
if modules.globals.many_faces:
source_face = default_source_face()
for map in modules.globals.source_target_map:
target_face = map["target"]["face"]
temp_frame = swap_face(source_face, target_face, temp_frame)
source_face_obj = default_source_face() # This function needs to be checked if it needs source_frame_full
if source_face_obj: # Ensure default_source_face actually returns a face
for map_item in modules.globals.source_target_map: # Renamed map to map_item to avoid conflict
target_face = map_item["target"]["face"]
temp_frame = swap_face(source_face_obj, target_face, source_frame_full, temp_frame)
elif not modules.globals.many_faces:
for map in modules.globals.source_target_map:
if "source" in map:
source_face = map["source"]["face"]
target_face = map["target"]["face"]
temp_frame = swap_face(source_face, target_face, temp_frame)
for map_item in modules.globals.source_target_map: # Renamed map to map_item
if "source" in map_item:
source_face_obj = map_item["source"]["face"]
target_face = map_item["target"]["face"]
temp_frame = swap_face(source_face_obj, target_face, source_frame_full, temp_frame)
elif is_video(modules.globals.target_path):
if modules.globals.many_faces:
source_face = default_source_face()
for map in modules.globals.source_target_map:
target_frame = [
f
for f in map["target_faces_in_frame"]
if f["location"] == temp_frame_path
]
for frame in target_frame:
for target_face in frame["faces"]:
temp_frame = swap_face(source_face, target_face, temp_frame)
elif not modules.globals.many_faces:
for map in modules.globals.source_target_map:
if "source" in map:
target_frame = [
source_face_obj = default_source_face() # This function needs to be checked
if source_face_obj:
for map_item in modules.globals.source_target_map: # Renamed map to map_item
target_frames_data = [ # Renamed target_frame to target_frames_data
f
for f in map["target_faces_in_frame"]
for f in map_item["target_faces_in_frame"]
if f["location"] == temp_frame_path
]
source_face = map["source"]["face"]
for frame in target_frame:
for target_face in frame["faces"]:
temp_frame = swap_face(source_face, target_face, temp_frame)
for frame_data in target_frames_data: # Renamed frame to frame_data
for target_face in frame_data["faces"]:
temp_frame = swap_face(source_face_obj, target_face, source_frame_full, temp_frame)
else:
elif not modules.globals.many_faces:
for map_item in modules.globals.source_target_map: # Renamed map to map_item
if "source" in map_item:
target_frames_data = [ # Renamed target_frame to target_frames_data
f
for f in map_item["target_faces_in_frame"]
if f["location"] == temp_frame_path
]
source_face_obj = map_item["source"]["face"]
for frame_data in target_frames_data: # Renamed frame to frame_data
for target_face in frame_data["faces"]:
temp_frame = swap_face(source_face_obj, target_face, source_frame_full, temp_frame)
else: # This is the live cam / generic case
detected_faces = get_many_faces(temp_frame)
if modules.globals.many_faces:
if detected_faces:
source_face = default_source_face()
for target_face in detected_faces:
temp_frame = swap_face(source_face, target_face, temp_frame)
source_face_obj = default_source_face() # This function needs to be checked
if source_face_obj:
for target_face in detected_faces:
temp_frame = swap_face(source_face_obj, target_face, source_frame_full, temp_frame)
elif not modules.globals.many_faces:
if detected_faces:
@ -181,12 +265,13 @@ def process_frame_v2(temp_frame: Frame, temp_frame_path: str = "") -> Frame:
modules.globals.simple_map["target_embeddings"],
detected_face.normed_embedding,
)
# Assuming simple_map["source_faces"] are Face objects
# And default_source_face() logic might need to be more complex if source_frame_full is always from a single source_path
source_face_obj_from_map = modules.globals.simple_map["source_faces"][closest_centroid_index]
temp_frame = swap_face(
modules.globals.simple_map["source_faces"][
closest_centroid_index
],
detected_face,
source_face_obj_from_map, # This is source_face_obj
detected_face, # This is target_face
source_frame_full, # This is source_frame_full
temp_frame,
)
else:
@ -200,10 +285,11 @@ def process_frame_v2(temp_frame: Frame, temp_frame_path: str = "") -> Frame:
closest_centroid_index, _ = find_closest_centroid(
detected_faces_centroids, target_embedding
)
source_face_obj_from_map = modules.globals.simple_map["source_faces"][i]
temp_frame = swap_face(
modules.globals.simple_map["source_faces"][i],
detected_faces[closest_centroid_index],
source_face_obj_from_map, # source_face_obj
detected_faces[closest_centroid_index], # target_face
source_frame_full, # source_frame_full
temp_frame,
)
i += 1
@ -213,44 +299,83 @@ def process_frame_v2(temp_frame: Frame, temp_frame_path: str = "") -> Frame:
def process_frames(
source_path: str, temp_frame_paths: List[str], progress: Any = None
) -> None:
source_img = cv2.imread(source_path)
if source_img is None:
logging.error(f"Failed to read source image from {source_path}")
return
if not modules.globals.map_faces:
source_face = get_one_face(cv2.imread(source_path))
source_face_obj = get_one_face(source_img) # Use source_img here
if not source_face_obj:
logging.error(f"No face detected in source image {source_path}")
return
for temp_frame_path in temp_frame_paths:
temp_frame = cv2.imread(temp_frame_path)
if temp_frame is None:
logging.warning(f"Failed to read temp_frame from {temp_frame_path}, skipping.")
continue
try:
result = process_frame(source_face, temp_frame)
result = process_frame(source_face_obj, source_img, temp_frame)
cv2.imwrite(temp_frame_path, result)
except Exception as exception:
print(exception)
logging.error(f"Error processing frame {temp_frame_path}: {exception}", exc_info=True)
pass
if progress:
progress.update(1)
else:
else: # This is for map_faces == True
# In map_faces=True, source_face is determined per mapping.
# process_frame_v2 will need source_frame_full for hair,
# which should be the original source_path image.
for temp_frame_path in temp_frame_paths:
temp_frame = cv2.imread(temp_frame_path)
if temp_frame is None:
logging.warning(f"Failed to read temp_frame from {temp_frame_path}, skipping.")
continue
try:
result = process_frame_v2(temp_frame, temp_frame_path)
# Pass source_img (as source_frame_full) to process_frame_v2
result = process_frame_v2(source_img, temp_frame, temp_frame_path)
cv2.imwrite(temp_frame_path, result)
except Exception as exception:
print(exception)
logging.error(f"Error processing frame {temp_frame_path} with map_faces: {exception}", exc_info=True)
pass
if progress:
progress.update(1)
def process_image(source_path: str, target_path: str, output_path: str) -> None:
source_img = cv2.imread(source_path)
if source_img is None:
logging.error(f"Failed to read source image from {source_path}")
return
target_frame = cv2.imread(target_path)
if target_frame is None:
logging.error(f"Failed to read target image from {target_path}")
return
if not modules.globals.map_faces:
source_face = get_one_face(cv2.imread(source_path))
target_frame = cv2.imread(target_path)
result = process_frame(source_face, target_frame)
source_face_obj = get_one_face(source_img) # Use source_img here
if not source_face_obj:
logging.error(f"No face detected in source image {source_path}")
return
result = process_frame(source_face_obj, source_img, target_frame)
cv2.imwrite(output_path, result)
else:
# map_faces == True for process_image
# process_frame_v2 expects source_frame_full as its first argument.
# The output_path is often the same as target_path initially for images.
# We read the target_frame (which will be modified)
target_frame_for_v2 = cv2.imread(output_path) # Or target_path, depending on desired workflow
if target_frame_for_v2 is None:
logging.error(f"Failed to read image for process_frame_v2 from {output_path}")
return
if modules.globals.many_faces:
update_status(
"Many faces enabled. Using first source image. Progressing...", NAME
)
target_frame = cv2.imread(output_path)
result = process_frame_v2(target_frame)
# Pass source_img (as source_frame_full) to process_frame_v2
result = process_frame_v2(source_img, target_frame_for_v2, target_path) # target_path as temp_frame_path hint
cv2.imwrite(output_path, result)
@ -620,3 +745,113 @@ def apply_color_transfer(source, target):
source = (source - source_mean) * (target_std / source_std) + target_mean
return cv2.cvtColor(np.clip(source, 0, 255).astype("uint8"), cv2.COLOR_LAB2BGR)
def create_face_and_hair_mask(source_face: Face, source_frame: Frame) -> np.ndarray:
"""
Creates a combined mask for the face and hair from the source image.
"""
# 1. Generate the basic face mask (adapted from create_face_mask)
face_only_mask = np.zeros(source_frame.shape[:2], dtype=np.uint8)
landmarks = source_face.landmark_2d_106
if landmarks is not None:
landmarks = landmarks.astype(np.int32)
# Extract facial features (same logic as create_face_mask)
right_side_face = landmarks[0:16]
left_side_face = landmarks[17:32]
# right_eye = landmarks[33:42] # Not directly used for outline
right_eye_brow = landmarks[43:51]
# left_eye = landmarks[87:96] # Not directly used for outline
left_eye_brow = landmarks[97:105]
# Calculate forehead extension (same logic as create_face_mask)
right_eyebrow_top = np.min(right_eye_brow[:, 1])
left_eyebrow_top = np.min(left_eye_brow[:, 1])
eyebrow_top = min(right_eyebrow_top, left_eyebrow_top)
face_top = np.min([right_side_face[0, 1], left_side_face[-1, 1]])
# Ensure forehead_height is not negative if eyebrows are above the topmost landmark of face sides
forehead_height = max(0, face_top - eyebrow_top)
extended_forehead_height = int(forehead_height * 5.0)
forehead_left = right_side_face[0].copy()
forehead_right = left_side_face[-1].copy()
# Ensure extended forehead points do not go into negative y values
forehead_left[1] = max(0, forehead_left[1] - extended_forehead_height)
forehead_right[1] = max(0, forehead_right[1] - extended_forehead_height)
face_outline = np.vstack(
[
[forehead_left],
right_side_face,
left_side_face[::-1],
[forehead_right],
]
)
# Calculate padding (same logic as create_face_mask)
# Ensure face_outline has at least one point before calculating norm
if face_outline.shape[0] > 1:
padding = int(
np.linalg.norm(right_side_face[0] - left_side_face[-1]) * 0.05
)
else:
padding = 5 # Default padding if not enough points
hull = cv2.convexHull(face_outline)
hull_padded = []
center = np.mean(face_outline, axis=0).squeeze() # Squeeze to handle potential extra dim
# Ensure center is a 1D array for subtraction
if center.ndim > 1:
center = np.mean(center, axis=0)
for point_contour in hull:
point = point_contour[0] # cv2.convexHull returns points wrapped in an extra array
direction = point - center
norm_direction = np.linalg.norm(direction)
if norm_direction == 0: # Avoid division by zero if point is the center
unit_direction = np.array([0,0])
else:
unit_direction = direction / norm_direction
padded_point = point + unit_direction * padding
hull_padded.append(padded_point)
if hull_padded: # Ensure hull_padded is not empty
hull_padded = np.array(hull_padded, dtype=np.int32)
cv2.fillConvexPoly(face_only_mask, hull_padded, 255)
else: # Fallback if hull_padded is empty (e.g. very few landmarks)
cv2.fillConvexPoly(face_only_mask, hull, 255) # Use unpadded hull
# Initial blur for face_only_mask is not strictly in the old one before combining,
# but can be applied here or after combining. Let's keep it like original for now.
# face_only_mask = cv2.GaussianBlur(face_only_mask, (5, 5), 3) # Original blur from create_face_mask
# 2. Generate the hair mask
# Ensure source_frame is contiguous, as some cv2 functions might require it.
source_frame_contiguous = np.ascontiguousarray(source_frame, dtype=np.uint8)
hair_mask_on_source = segment_hair(source_frame_contiguous)
# 3. Combine the masks
# Ensure masks are binary and of the same type for bitwise operations
_, face_only_mask_binary = cv2.threshold(face_only_mask, 127, 255, cv2.THRESH_BINARY)
_, hair_mask_on_source_binary = cv2.threshold(hair_mask_on_source, 127, 255, cv2.THRESH_BINARY)
# Ensure shapes match. If not, hair_mask might be different. Resize if necessary.
# This should ideally not happen if segment_hair preserves dimensions.
if face_only_mask_binary.shape != hair_mask_on_source_binary.shape:
hair_mask_on_source_binary = cv2.resize(hair_mask_on_source_binary,
(face_only_mask_binary.shape[1], face_only_mask_binary.shape[0]),
interpolation=cv2.INTER_NEAREST)
combined_mask = cv2.bitwise_or(face_only_mask_binary, hair_mask_on_source_binary)
# 4. Apply Gaussian blur to the combined mask
combined_mask = cv2.GaussianBlur(combined_mask, (5, 5), 3)
return combined_mask

73
modules/ui.py
View File

@ -880,7 +880,7 @@ def create_webcam_preview(camera_index: int):
PREVIEW.deiconify()
frame_processors = get_frame_processors_modules(modules.globals.frame_processors)
source_image = None
# source_image = None # Replaced by source_face_obj_for_cam
prev_time = time.time()
fps_update_interval = 0.5
frame_count = 0
@ -907,23 +907,80 @@ def create_webcam_preview(camera_index: int):
)
if not modules.globals.map_faces:
if source_image is None and modules.globals.source_path:
source_image = get_one_face(cv2.imread(modules.globals.source_path))
# Case 1: map_faces is False
source_face_obj_for_cam = None
source_frame_full_for_cam = None
if modules.globals.source_path and os.path.exists(modules.globals.source_path):
source_frame_full_for_cam = cv2.imread(modules.globals.source_path)
if source_frame_full_for_cam is not None:
source_face_obj_for_cam = get_one_face(source_frame_full_for_cam)
if source_face_obj_for_cam is None:
update_status(f"Error: No face detected in source image at {modules.globals.source_path}")
# Optional: could return here or allow running without a source face if some processors handle it
else:
update_status(f"Error: Could not read source image at {modules.globals.source_path}")
cap.release()
PREVIEW.withdraw()
return
elif modules.globals.source_path:
update_status(f"Error: Source image not found at {modules.globals.source_path}")
cap.release()
PREVIEW.withdraw()
return
else:
update_status("Error: No source image selected for webcam mode.")
cap.release()
PREVIEW.withdraw()
return
for frame_processor in frame_processors:
if frame_processor.NAME == "DLC.FACE-ENHANCER":
if modules.globals.fp_ui["face_enhancer"]:
temp_frame = frame_processor.process_frame(None, temp_frame)
# Assuming face_enhancer's process_frame doesn't need source_face or source_frame_full
temp_frame = frame_processor.process_frame(None, temp_frame)
else:
temp_frame = frame_processor.process_frame(source_image, temp_frame)
if source_face_obj_for_cam and source_frame_full_for_cam is not None:
temp_frame = frame_processor.process_frame(source_face_obj_for_cam, source_frame_full_for_cam, temp_frame)
# else: temp_frame remains unchanged if source isn't ready
else:
modules.globals.target_path = None
# Case 2: map_faces is True
source_frame_full_for_cam_map_faces = None
if modules.globals.source_path and os.path.exists(modules.globals.source_path):
source_frame_full_for_cam_map_faces = cv2.imread(modules.globals.source_path)
if source_frame_full_for_cam_map_faces is None:
update_status(f"Error: Could not read source image (for hair/background) at {modules.globals.source_path}")
cap.release()
PREVIEW.withdraw()
return
elif modules.globals.source_path:
update_status(f"Error: Source image (for hair/background) not found at {modules.globals.source_path}")
cap.release()
PREVIEW.withdraw()
return
else:
update_status("Error: No global source image selected (for hair/background in map_faces mode).")
cap.release()
PREVIEW.withdraw()
return
# Also check if map is defined, though process_frame_v2 handles specific face mapping internally
if not modules.globals.source_target_map and not modules.globals.simple_map: # Check both map types
update_status("Error: No face map defined for map_faces mode.")
# This might not need a return if some processors can run without map
# but for face_swapper, it's likely needed.
# For now, we proceed and let process_frame_v2 handle it.
modules.globals.target_path = None # Standard for live mode
for frame_processor in frame_processors:
if frame_processor.NAME == "DLC.FACE-ENHANCER":
if modules.globals.fp_ui["face_enhancer"]:
temp_frame = frame_processor.process_frame_v2(temp_frame)
# Pass source_frame_full_for_cam_map_faces for signature consistency
# The enhancer can choose to ignore it if not needed.
temp_frame = frame_processor.process_frame_v2(source_frame_full_for_cam_map_faces, temp_frame)
else:
temp_frame = frame_processor.process_frame_v2(temp_frame)
if source_frame_full_for_cam_map_faces is not None:
temp_frame = frame_processor.process_frame_v2(source_frame_full_for_cam_map_faces, temp_frame)
# else: temp_frame remains unchanged if global source for map_faces isn't ready
# Calculate and display FPS
current_time = time.time()

1
requirements.txt
View File

@ -19,3 +19,4 @@ onnxruntime-gpu==1.17; sys_platform != 'darwin'
tensorflow; sys_platform != 'darwin'
opennsfw2==0.10.2
protobuf==4.23.2
transformers>=4.0.0