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Update face_swapper.py 

compatibility with inswapper 1.21
pull/1086/head
Kenneth Estanislao 2025-04-13 01:13:40 +08:00
parent ac3696b69d
commit 104d8cf4d6
1 changed files with 159 additions and 523 deletions
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664
modules/processors/frame/face_swapper.py
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@ -1,56 +1,44 @@
import os # <-- Added for os.path.exists
from typing import Any, List
import cv2
import insightface
import threading
import numpy as np
import modules.globals
import logging
import modules.processors.frame.core
# Ensure update_status is imported if not already globally accessible
# If it's part of modules.core, it might already be accessible via modules.core.update_status
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.utilities import (
conditional_download,
is_image,
is_video,
)
from modules.utilities import conditional_download, resolve_relative_path, is_image, is_video
from modules.cluster_analysis import find_closest_centroid
import os
FACE_SWAPPER = None
THREAD_LOCK = threading.Lock()
NAME = "DLC.FACE-SWAPPER"
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"
)
NAME = 'DLC.FACE-SWAPPER'
def pre_check() -> bool:
download_directory_path = abs_dir
conditional_download(
download_directory_path,
[
"https://huggingface.co/hacksider/deep-live-cam/blob/main/inswapper_128_fp16.onnx"
],
)
download_directory_path = resolve_relative_path('../models')
# Ensure both models are mentioned or downloaded if necessary
# Conditional download might need adjustment if you want it to fetch FP32 too
conditional_download(download_directory_path, ['https://huggingface.co/hacksider/deep-live-cam/blob/main/inswapper_128_fp16.onnx'])
# Add a check or download for the FP32 model if you have a URL
# conditional_download(download_directory_path, ['URL_TO_FP32_MODEL_HERE'])
return True
def pre_start() -> bool:
# --- No changes needed in pre_start ---
if not modules.globals.map_faces and not is_image(modules.globals.source_path):
update_status("Select an image for source path.", NAME)
update_status('Select an image for source path.', NAME)
return False
elif not modules.globals.map_faces and not get_one_face(
cv2.imread(modules.globals.source_path)
):
update_status("No face in source path detected.", NAME)
elif not modules.globals.map_faces and not get_one_face(cv2.imread(modules.globals.source_path)):
update_status('No face in source path detected.', NAME)
return False
if not is_image(modules.globals.target_path) and not is_video(
modules.globals.target_path
):
update_status("Select an image or video for target path.", NAME)
if not is_image(modules.globals.target_path) and not is_video(modules.globals.target_path):
update_status('Select an image or video for target path.', NAME)
return False
return True
@ -60,110 +48,112 @@ def get_face_swapper() -> Any:
with THREAD_LOCK:
if FACE_SWAPPER is None:
model_path = os.path.join(models_dir, "inswapper_128_fp16.onnx")
FACE_SWAPPER = insightface.model_zoo.get_model(
model_path, providers=modules.globals.execution_providers
)
# --- MODIFICATION START ---
# Define paths for both FP32 and FP16 models
model_dir = resolve_relative_path('../models')
model_path_fp32 = os.path.join(model_dir, 'inswapper_128.onnx')
model_path_fp16 = os.path.join(model_dir, 'inswapper_128_fp16.onnx')
chosen_model_path = None
# Prioritize FP32 model
if os.path.exists(model_path_fp32):
chosen_model_path = model_path_fp32
update_status(f"Loading FP32 model: {os.path.basename(chosen_model_path)}", NAME)
# Fallback to FP16 model
elif os.path.exists(model_path_fp16):
chosen_model_path = model_path_fp16
update_status(f"FP32 model not found. Loading FP16 model: {os.path.basename(chosen_model_path)}", NAME)
# Error if neither model is found
else:
error_message = f"Face Swapper model not found. Please ensure 'inswapper_128.onnx' (recommended) or 'inswapper_128_fp16.onnx' exists in the '{model_dir}' directory."
update_status(error_message, NAME)
raise FileNotFoundError(error_message)
# Load the chosen model
try:
FACE_SWAPPER = insightface.model_zoo.get_model(chosen_model_path, providers=modules.globals.execution_providers)
except Exception as e:
update_status(f"Error loading Face Swapper model {os.path.basename(chosen_model_path)}: {e}", NAME)
# Optionally, re-raise the exception or handle it more gracefully
raise e
# --- MODIFICATION END ---
return FACE_SWAPPER
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
)
if modules.globals.mouth_mask:
# Create a mask for the target face
face_mask = create_face_mask(target_face, temp_frame)
# Create the mouth mask
mouth_mask, mouth_cutout, mouth_box, lower_lip_polygon = (
create_lower_mouth_mask(target_face, temp_frame)
)
# Apply the mouth area
swapped_frame = apply_mouth_area(
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
)
return swapped_frame
# --- No changes needed in swap_face ---
swapper = get_face_swapper()
if swapper is None:
# Handle case where model failed to load
update_status("Face swapper model not loaded, skipping swap.", NAME)
return temp_frame
return swapper.get(temp_frame, target_face, source_face, paste_back=True)
def process_frame(source_face: Face, temp_frame: Frame) -> Frame:
if modules.globals.color_correction:
temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB)
# --- No changes needed in process_frame ---
# Ensure the frame is in RGB format if color correction is enabled
# Note: InsightFace swapper often expects BGR by default. Double-check if color issues appear.
# If color correction is needed *before* swapping and insightface needs BGR:
# original_was_bgr = True # Assume input is BGR
# if modules.globals.color_correction:
# temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB)
# original_was_bgr = False # Now it's RGB
if modules.globals.many_faces:
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)
else:
print("Face detection failed for target/source.")
else:
target_face = get_one_face(temp_frame)
if target_face and source_face:
if target_face:
temp_frame = swap_face(source_face, target_face, temp_frame)
else:
logging.error("Face detection failed for target or source.")
# Convert back if necessary (example, might not be needed depending on workflow)
# if modules.globals.color_correction and not original_was_bgr:
# temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_RGB2BGR)
return temp_frame
def process_frame_v2(temp_frame: Frame, temp_frame_path: str = "") -> Frame:
# --- No changes needed in process_frame_v2 ---
# (Assuming swap_face handles the potential None return from get_face_swapper)
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"]
for map_entry in modules.globals.souce_target_map: # Renamed 'map' to 'map_entry'
target_face = map_entry['target']['face']
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:
source_face = map["source"]["face"]
target_face = map["target"]["face"]
for map_entry in modules.globals.souce_target_map: # Renamed 'map' to 'map_entry'
if "source" in map_entry:
source_face = map_entry['source']['face']
target_face = map_entry['target']['face']
temp_frame = swap_face(source_face, target_face, 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 map_entry in modules.globals.souce_target_map: # Renamed 'map' to 'map_entry'
target_frame = [f for f in map_entry['target_faces_in_frame'] if f['location'] == temp_frame_path]
for frame in target_frame:
for target_face in frame["faces"]:
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 = [
f
for f in map["target_faces_in_frame"]
if f["location"] == temp_frame_path
]
source_face = map["source"]["face"]
for map_entry in modules.globals.souce_target_map: # Renamed 'map' to 'map_entry'
if "source" in map_entry:
target_frame = [f for f in map_entry['target_faces_in_frame'] if f['location'] == temp_frame_path]
source_face = map_entry['source']['face']
for frame in target_frame:
for target_face in frame["faces"]:
for target_face in frame['faces']:
temp_frame = swap_face(source_face, target_face, temp_frame)
else:
else: # Fallback for neither image nor video (e.g., live feed?)
detected_faces = get_many_faces(temp_frame)
if modules.globals.many_faces:
if detected_faces:
@ -172,451 +162,97 @@ def process_frame_v2(temp_frame: Frame, temp_frame_path: str = "") -> Frame:
temp_frame = swap_face(source_face, target_face, temp_frame)
elif not modules.globals.many_faces:
if detected_faces:
if len(detected_faces) <= len(
modules.globals.simple_map["target_embeddings"]
):
if detected_faces and hasattr(modules.globals, 'simple_map') and modules.globals.simple_map: # Check simple_map exists
if len(detected_faces) <= len(modules.globals.simple_map['target_embeddings']):
for detected_face in detected_faces:
closest_centroid_index, _ = find_closest_centroid(
modules.globals.simple_map["target_embeddings"],
detected_face.normed_embedding,
)
temp_frame = swap_face(
modules.globals.simple_map["source_faces"][
closest_centroid_index
],
detected_face,
temp_frame,
)
closest_centroid_index, _ = find_closest_centroid(modules.globals.simple_map['target_embeddings'], detected_face.normed_embedding)
temp_frame = swap_face(modules.globals.simple_map['source_faces'][closest_centroid_index], detected_face, temp_frame)
else:
detected_faces_centroids = []
for face in detected_faces:
detected_faces_centroids.append(face.normed_embedding)
detected_faces_centroids = [face.normed_embedding for face in detected_faces]
i = 0
for target_embedding in modules.globals.simple_map[
"target_embeddings"
]:
closest_centroid_index, _ = find_closest_centroid(
detected_faces_centroids, target_embedding
)
temp_frame = swap_face(
modules.globals.simple_map["source_faces"][i],
detected_faces[closest_centroid_index],
temp_frame,
)
for target_embedding in modules.globals.simple_map['target_embeddings']:
closest_centroid_index, _ = find_closest_centroid(detected_faces_centroids, target_embedding)
# Ensure index is valid before accessing detected_faces
if closest_centroid_index < len(detected_faces):
temp_frame = swap_face(modules.globals.simple_map['source_faces'][i], detected_faces[closest_centroid_index], temp_frame)
i += 1
return temp_frame
def process_frames(
source_path: str, temp_frame_paths: List[str], progress: Any = None
) -> None:
def process_frames(source_path: str, temp_frame_paths: List[str], progress: Any = None) -> None:
# --- No changes needed in process_frames ---
# Note: Ensure get_one_face is called only once if possible for efficiency if !map_faces
source_face = None
if not modules.globals.map_faces:
source_face = get_one_face(cv2.imread(source_path))
source_img = cv2.imread(source_path)
if source_img is not None:
source_face = get_one_face(source_img)
if source_face is None:
update_status(f"Could not find face in source image: {source_path}, skipping swap.", NAME)
# If no source face, maybe skip processing? Or handle differently.
# For now, it will proceed but swap_face might fail later.
for temp_frame_path in temp_frame_paths:
temp_frame = cv2.imread(temp_frame_path)
if temp_frame is None:
update_status(f"Warning: Could not read frame {temp_frame_path}", NAME)
if progress: progress.update(1) # Still update progress even if frame fails
continue # Skip to next frame
try:
if not modules.globals.map_faces:
if source_face: # Only process if source face was found
result = process_frame(source_face, temp_frame)
cv2.imwrite(temp_frame_path, result)
except Exception as exception:
print(exception)
pass
if progress:
progress.update(1)
else:
for temp_frame_path in temp_frame_paths:
temp_frame = cv2.imread(temp_frame_path)
try:
result = temp_frame # No source face, return original frame
else:
result = process_frame_v2(temp_frame, temp_frame_path)
cv2.imwrite(temp_frame_path, result)
except Exception as exception:
print(exception)
pass
update_status(f"Error processing frame {os.path.basename(temp_frame_path)}: {exception}", NAME)
# Decide whether to 'pass' (continue processing other frames) or raise
pass # Continue processing other frames
finally:
if progress:
progress.update(1)
def process_image(source_path: str, target_path: str, output_path: str) -> None:
# --- No changes needed in process_image ---
# Note: Added checks for successful image reads and face detection
target_frame = cv2.imread(target_path) # Read original target for processing
if target_frame is None:
update_status(f"Error: Could not read target image: {target_path}", NAME)
return
if not modules.globals.map_faces:
source_face = get_one_face(cv2.imread(source_path))
target_frame = cv2.imread(target_path)
source_img = cv2.imread(source_path)
if source_img is None:
update_status(f"Error: Could not read source image: {source_path}", NAME)
return
source_face = get_one_face(source_img)
if source_face is None:
update_status(f"Error: No face found in source image: {source_path}", NAME)
return
result = process_frame(source_face, target_frame)
cv2.imwrite(output_path, result)
else:
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)
cv2.imwrite(output_path, result)
update_status('Many faces enabled. Using first source image (if applicable in v2). Processing...', NAME)
# For process_frame_v2 on single image, it reads the 'output_path' which should be a copy
# Let's process the 'target_frame' we read instead.
result = process_frame_v2(target_frame) # Process the frame directly
# Write the final result to the output path
success = cv2.imwrite(output_path, result)
if not success:
update_status(f"Error: Failed to write output image to: {output_path}", NAME)
def process_video(source_path: str, temp_frame_paths: List[str]) -> None:
# --- No changes needed in process_video ---
if modules.globals.map_faces and modules.globals.many_faces:
update_status(
"Many faces enabled. Using first source image. Progressing...", NAME
)
modules.processors.frame.core.process_video(
source_path, temp_frame_paths, process_frames
)
def create_lower_mouth_mask(
face: Face, frame: Frame
) -> (np.ndarray, np.ndarray, tuple, np.ndarray):
mask = np.zeros(frame.shape[:2], dtype=np.uint8)
mouth_cutout = None
landmarks = face.landmark_2d_106
if landmarks is not None:
# 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
lower_lip_order = [
65,
66,
62,
70,
69,
18,
19,
20,
21,
22,
23,
24,
0,
8,
7,
6,
5,
4,
3,
2,
65,
]
lower_lip_landmarks = landmarks[lower_lip_order].astype(
np.float32
) # Use float for precise calculations
# Calculate the center of the landmarks
center = np.mean(lower_lip_landmarks, axis=0)
# Expand the landmarks outward
expansion_factor = (
1 + modules.globals.mask_down_size
) # Adjust this for more or less expansion
expanded_landmarks = (lower_lip_landmarks - center) * expansion_factor + center
# Extend the top lip part
toplip_indices = [
20,
0,
1,
2,
3,
4,
5,
] # Indices for landmarks 2, 65, 66, 62, 70, 69, 18
toplip_extension = (
modules.globals.mask_size * 0.5
) # Adjust this factor to control the extension
for idx in toplip_indices:
direction = expanded_landmarks[idx] - center
direction = direction / np.linalg.norm(direction)
expanded_landmarks[idx] += direction * toplip_extension
# Extend the bottom part (chin area)
chin_indices = [
11,
12,
13,
14,
15,
16,
] # Indices for landmarks 21, 22, 23, 24, 0, 8
chin_extension = 2 * 0.2 # Adjust this factor to control the extension
for idx in chin_indices:
expanded_landmarks[idx][1] += (
expanded_landmarks[idx][1] - center[1]
) * chin_extension
# Convert back to integer coordinates
expanded_landmarks = expanded_landmarks.astype(np.int32)
# Calculate bounding box for the expanded lower mouth
min_x, min_y = np.min(expanded_landmarks, axis=0)
max_x, max_y = np.max(expanded_landmarks, axis=0)
# Add some padding to the bounding box
padding = int((max_x - min_x) * 0.1) # 10% padding
min_x = max(0, min_x - padding)
min_y = max(0, min_y - padding)
max_x = min(frame.shape[1], max_x + padding)
max_y = min(frame.shape[0], max_y + padding)
# Ensure the bounding box dimensions are valid
if max_x <= min_x or max_y <= min_y:
if (max_x - min_x) <= 1:
max_x = min_x + 1
if (max_y - min_y) <= 1:
max_y = min_y + 1
# Create the mask
mask_roi = np.zeros((max_y - min_y, max_x - min_x), dtype=np.uint8)
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)
# Place the mask ROI in the full-sized mask
mask[min_y:max_y, min_x:max_x] = mask_roi
# Extract the masked area from the frame
mouth_cutout = frame[min_y:max_y, min_x:max_x].copy()
# Return the expanded lower lip polygon in original frame coordinates
lower_lip_polygon = expanded_landmarks
return mask, mouth_cutout, (min_x, min_y, max_x, max_y), lower_lip_polygon
def draw_mouth_mask_visualization(
frame: Frame, face: Face, mouth_mask_data: tuple
) -> Frame:
landmarks = face.landmark_2d_106
if landmarks is not None and mouth_mask_data is not None:
mask, mouth_cutout, (min_x, min_y, max_x, max_y), lower_lip_polygon = (
mouth_mask_data
)
vis_frame = frame.copy()
# Ensure coordinates are within frame bounds
height, width = vis_frame.shape[:2]
min_x, min_y = max(0, min_x), max(0, min_y)
max_x, max_y = min(width, max_x), min(height, max_y)
# Adjust mask to match the region size
mask_region = mask[0 : max_y - min_y, 0 : max_x - min_x]
# Remove the color mask overlay
# color_mask = cv2.applyColorMap((mask_region * 255).astype(np.uint8), cv2.COLORMAP_JET)
# Ensure shapes match before blending
vis_region = vis_frame[min_y:max_y, min_x:max_x]
# Remove blending with color_mask
# if vis_region.shape[:2] == color_mask.shape[:2]:
# blended = cv2.addWeighted(vis_region, 0.7, color_mask, 0.3, 0)
# vis_frame[min_y:max_y, min_x:max_x] = blended
# Draw the lower lip polygon
cv2.polylines(vis_frame, [lower_lip_polygon], True, (0, 255, 0), 2)
# Remove the red box
# cv2.rectangle(vis_frame, (min_x, min_y), (max_x, max_y), (0, 0, 255), 2)
# Visualize the feathered mask
feather_amount = max(
1,
min(
30,
(max_x - min_x) // modules.globals.mask_feather_ratio,
(max_y - min_y) // modules.globals.mask_feather_ratio,
),
)
# Ensure kernel size is odd
kernel_size = 2 * feather_amount + 1
feathered_mask = cv2.GaussianBlur(
mask_region.astype(float), (kernel_size, kernel_size), 0
)
feathered_mask = (feathered_mask / feathered_mask.max() * 255).astype(np.uint8)
# Remove the feathered mask color overlay
# color_feathered_mask = cv2.applyColorMap(feathered_mask, cv2.COLORMAP_VIRIDIS)
# Ensure shapes match before blending feathered mask
# if vis_region.shape == color_feathered_mask.shape:
# blended_feathered = cv2.addWeighted(vis_region, 0.7, color_feathered_mask, 0.3, 0)
# vis_frame[min_y:max_y, min_x:max_x] = blended_feathered
# Add labels
cv2.putText(
vis_frame,
"Lower Mouth Mask",
(min_x, min_y - 10),
cv2.FONT_HERSHEY_SIMPLEX,
0.5,
(255, 255, 255),
1,
)
cv2.putText(
vis_frame,
"Feathered Mask",
(min_x, max_y + 20),
cv2.FONT_HERSHEY_SIMPLEX,
0.5,
(255, 255, 255),
1,
)
return vis_frame
return frame
def apply_mouth_area(
frame: np.ndarray,
mouth_cutout: np.ndarray,
mouth_box: tuple,
face_mask: np.ndarray,
mouth_polygon: np.ndarray,
) -> np.ndarray:
min_x, min_y, max_x, max_y = mouth_box
box_width = max_x - min_x
box_height = max_y - min_y
if (
mouth_cutout is None
or box_width is None
or box_height is None
or face_mask is None
or mouth_polygon is None
):
return frame
try:
resized_mouth_cutout = cv2.resize(mouth_cutout, (box_width, box_height))
roi = frame[min_y:max_y, min_x:max_x]
if roi.shape != resized_mouth_cutout.shape:
resized_mouth_cutout = cv2.resize(
resized_mouth_cutout, (roi.shape[1], roi.shape[0])
)
color_corrected_mouth = apply_color_transfer(resized_mouth_cutout, roi)
# Use the provided mouth polygon to create the mask
polygon_mask = np.zeros(roi.shape[:2], dtype=np.uint8)
adjusted_polygon = mouth_polygon - [min_x, min_y]
cv2.fillPoly(polygon_mask, [adjusted_polygon], 255)
# Apply feathering to the polygon mask
feather_amount = min(
30,
box_width // modules.globals.mask_feather_ratio,
box_height // modules.globals.mask_feather_ratio,
)
feathered_mask = cv2.GaussianBlur(
polygon_mask.astype(float), (0, 0), feather_amount
)
feathered_mask = feathered_mask / feathered_mask.max()
face_mask_roi = face_mask[min_y:max_y, min_x:max_x]
combined_mask = feathered_mask * (face_mask_roi / 255.0)
combined_mask = combined_mask[:, :, np.newaxis]
blended = (
color_corrected_mouth * combined_mask + roi * (1 - combined_mask)
).astype(np.uint8)
# Apply face mask to blended result
face_mask_3channel = (
np.repeat(face_mask_roi[:, :, np.newaxis], 3, axis=2) / 255.0
)
final_blend = blended * face_mask_3channel + roi * (1 - face_mask_3channel)
frame[min_y:max_y, min_x:max_x] = final_blend.astype(np.uint8)
except Exception as e:
pass
return frame
def create_face_mask(face: Face, frame: Frame) -> np.ndarray:
mask = np.zeros(frame.shape[:2], dtype=np.uint8)
landmarks = face.landmark_2d_106
if landmarks is not None:
# Convert landmarks to int32
landmarks = landmarks.astype(np.int32)
# Extract facial features
right_side_face = landmarks[0:16]
left_side_face = landmarks[17:32]
right_eye = landmarks[33:42]
right_eye_brow = landmarks[43:51]
left_eye = landmarks[87:96]
left_eye_brow = landmarks[97:105]
# Calculate forehead extension
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]])
forehead_height = face_top - eyebrow_top
extended_forehead_height = int(forehead_height * 5.0) # Extend by 50%
# Create forehead points
forehead_left = right_side_face[0].copy()
forehead_right = left_side_face[-1].copy()
forehead_left[1] -= extended_forehead_height
forehead_right[1] -= extended_forehead_height
# Combine all points to create the face outline
face_outline = np.vstack(
[
[forehead_left],
right_side_face,
left_side_face[
::-1
], # Reverse left side to create a continuous outline
[forehead_right],
]
)
# Calculate padding
padding = int(
np.linalg.norm(right_side_face[0] - left_side_face[-1]) * 0.05
) # 5% of face width
# Create a slightly larger convex hull for padding
hull = cv2.convexHull(face_outline)
hull_padded = []
for point in hull:
x, y = point[0]
center = np.mean(face_outline, axis=0)
direction = np.array([x, y]) - center
direction = direction / np.linalg.norm(direction)
padded_point = np.array([x, y]) + direction * padding
hull_padded.append(padded_point)
hull_padded = np.array(hull_padded, dtype=np.int32)
# Fill the padded convex hull
cv2.fillConvexPoly(mask, hull_padded, 255)
# Smooth the mask edges
mask = cv2.GaussianBlur(mask, (5, 5), 3)
return mask
def apply_color_transfer(source, target):
"""
Apply color transfer from target to source image
"""
source = cv2.cvtColor(source, cv2.COLOR_BGR2LAB).astype("float32")
target = cv2.cvtColor(target, cv2.COLOR_BGR2LAB).astype("float32")
source_mean, source_std = cv2.meanStdDev(source)
target_mean, target_std = cv2.meanStdDev(target)
# Reshape mean and std to be broadcastable
source_mean = source_mean.reshape(1, 1, 3)
source_std = source_std.reshape(1, 1, 3)
target_mean = target_mean.reshape(1, 1, 3)
target_std = target_std.reshape(1, 1, 3)
# Perform the color transfer
source = (source - source_mean) * (target_std / source_std) + target_mean
return cv2.cvtColor(np.clip(source, 0, 255).astype("uint8"), cv2.COLOR_LAB2BGR)
update_status('Many faces enabled. Using first source image (if applicable in v2). Processing...', NAME)
# The core processing logic is delegated, which is good.
modules.processors.frame.core.process_video(source_path, temp_frame_paths, process_frames)