645 lines
24 KiB
Python
645 lines
24 KiB
Python
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
|
|
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.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"
|
|
)
|
|
|
|
|
|
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"
|
|
],
|
|
)
|
|
return True
|
|
|
|
|
|
def pre_start() -> bool:
|
|
if not modules.globals.map_faces and not is_image(modules.globals.source_path):
|
|
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)
|
|
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)
|
|
return False
|
|
return True
|
|
|
|
|
|
def get_face_swapper() -> Any:
|
|
global FACE_SWAPPER
|
|
|
|
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
|
|
)
|
|
return FACE_SWAPPER
|
|
|
|
|
|
def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
|
|
face_swapper = get_face_swapper()
|
|
|
|
# Simple face swap - maximum FPS
|
|
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
|
|
|
|
|
|
# Simple face position smoothing for stability
|
|
_last_face_position = None
|
|
_position_smoothing = 0.7 # Higher = more stable, lower = more responsive
|
|
|
|
def swap_face_stable(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
|
|
"""Ultra-fast face swap - maximum FPS priority"""
|
|
# Skip all complex processing for maximum FPS
|
|
face_swapper = get_face_swapper()
|
|
swapped_frame = face_swapper.get(temp_frame, target_face, source_face, paste_back=True)
|
|
|
|
# Skip all post-processing to maximize FPS
|
|
return swapped_frame
|
|
|
|
|
|
def swap_face_ultra_fast(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
|
|
"""Fast face swap with mouth mask support and forehead protection"""
|
|
face_swapper = get_face_swapper()
|
|
swapped_frame = face_swapper.get(temp_frame, target_face, source_face, paste_back=True)
|
|
|
|
# Fix forehead hair issue - blend forehead area back to original
|
|
swapped_frame = fix_forehead_hair_issue(swapped_frame, target_face, temp_frame)
|
|
|
|
# Add mouth mask functionality back (only if enabled)
|
|
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
|
|
|
|
|
|
def fix_forehead_hair_issue(swapped_frame: Frame, target_face: Face, original_frame: Frame) -> Frame:
|
|
"""Fix hair falling on forehead by blending forehead area back to original"""
|
|
try:
|
|
# Get face bounding box
|
|
bbox = target_face.bbox.astype(int)
|
|
x1, y1, x2, y2 = bbox
|
|
|
|
# Ensure coordinates are within frame bounds
|
|
h, w = swapped_frame.shape[:2]
|
|
x1, y1 = max(0, x1), max(0, y1)
|
|
x2, y2 = min(w, x2), min(h, y2)
|
|
|
|
if x2 <= x1 or y2 <= y1:
|
|
return swapped_frame
|
|
|
|
# Focus on forehead area (upper 35% of face)
|
|
forehead_height = int((y2 - y1) * 0.35)
|
|
forehead_y2 = y1 + forehead_height
|
|
|
|
if forehead_y2 > y1:
|
|
# Extract forehead regions
|
|
swapped_forehead = swapped_frame[y1:forehead_y2, x1:x2]
|
|
original_forehead = original_frame[y1:forehead_y2, x1:x2]
|
|
|
|
# Create a soft blend mask for forehead area
|
|
mask = np.ones(swapped_forehead.shape[:2], dtype=np.float32)
|
|
|
|
# Apply strong Gaussian blur for very soft blending
|
|
mask = cv2.GaussianBlur(mask, (31, 31), 10)
|
|
mask = mask[:, :, np.newaxis]
|
|
|
|
# Blend forehead areas (keep much more of original to preserve hair)
|
|
blended_forehead = (swapped_forehead * 0.3 + original_forehead * 0.7).astype(np.uint8)
|
|
|
|
# Apply the blended forehead back
|
|
swapped_frame[y1:forehead_y2, x1:x2] = blended_forehead
|
|
|
|
return swapped_frame
|
|
|
|
except Exception:
|
|
return swapped_frame
|
|
|
|
|
|
def improve_forehead_matching(swapped_frame: Frame, source_face: Face, target_face: Face, original_frame: Frame) -> Frame:
|
|
"""Create precise face mask - only swap core facial features (eyes, nose, cheeks, chin)"""
|
|
try:
|
|
# Get face landmarks for precise masking
|
|
if hasattr(target_face, 'landmark_2d_106') and target_face.landmark_2d_106 is not None:
|
|
landmarks = target_face.landmark_2d_106.astype(np.int32)
|
|
|
|
# Create precise face mask excluding forehead and hair
|
|
mask = create_precise_face_mask(landmarks, swapped_frame.shape[:2])
|
|
|
|
if mask is not None:
|
|
# Apply the precise mask
|
|
mask_3d = mask[:, :, np.newaxis] / 255.0
|
|
|
|
# Blend only the core facial features
|
|
result = (swapped_frame * mask_3d + original_frame * (1 - mask_3d)).astype(np.uint8)
|
|
return result
|
|
|
|
# Fallback: use bounding box method but exclude forehead
|
|
bbox = target_face.bbox.astype(int)
|
|
x1, y1, x2, y2 = bbox
|
|
|
|
# Ensure coordinates are within frame bounds
|
|
h, w = swapped_frame.shape[:2]
|
|
x1, y1 = max(0, x1), max(0, y1)
|
|
x2, y2 = min(w, x2), min(h, y2)
|
|
|
|
if x2 <= x1 or y2 <= y1:
|
|
return swapped_frame
|
|
|
|
# Exclude forehead area (upper 25% of face) to avoid hair swapping
|
|
forehead_height = int((y2 - y1) * 0.25)
|
|
face_start_y = y1 + forehead_height
|
|
|
|
if face_start_y < y2:
|
|
# Only blend the lower face area (eyes, nose, cheeks, chin)
|
|
swapped_face_area = swapped_frame[face_start_y:y2, x1:x2]
|
|
original_face_area = original_frame[face_start_y:y2, x1:x2]
|
|
|
|
# Create soft mask for the face area only
|
|
mask = np.ones(swapped_face_area.shape[:2], dtype=np.float32)
|
|
mask = cv2.GaussianBlur(mask, (15, 15), 5)
|
|
mask = mask[:, :, np.newaxis]
|
|
|
|
# Apply the face area back (keep original forehead/hair)
|
|
swapped_frame[face_start_y:y2, x1:x2] = swapped_face_area
|
|
|
|
return swapped_frame
|
|
|
|
except Exception:
|
|
return swapped_frame
|
|
|
|
|
|
def create_precise_face_mask(landmarks: np.ndarray, frame_shape: tuple) -> np.ndarray:
|
|
"""Create precise mask for core facial features only (exclude forehead and hair)"""
|
|
try:
|
|
mask = np.zeros(frame_shape, dtype=np.uint8)
|
|
|
|
# For 106-point landmarks, use correct indices
|
|
# Face contour (jawline) - points 0-32
|
|
jaw_line = landmarks[0:33]
|
|
|
|
# Eyes area - approximate indices for 106-point model
|
|
left_eye_area = landmarks[33:42] # Left eye region
|
|
right_eye_area = landmarks[87:96] # Right eye region
|
|
|
|
# Eyebrows (start from eyebrow level, not forehead)
|
|
left_eyebrow = landmarks[43:51] # Left eyebrow
|
|
right_eyebrow = landmarks[97:105] # Right eyebrow
|
|
|
|
# Create face contour that excludes forehead
|
|
# Start from eyebrow level and go around the face
|
|
face_contour_points = []
|
|
|
|
# Add eyebrow points (this will be our "top" instead of forehead)
|
|
face_contour_points.extend(left_eyebrow)
|
|
face_contour_points.extend(right_eyebrow)
|
|
|
|
# Add jawline points (bottom and sides of face)
|
|
face_contour_points.extend(jaw_line)
|
|
|
|
# Convert to numpy array
|
|
face_contour_points = np.array(face_contour_points)
|
|
|
|
# Create convex hull for the core face area (excluding forehead)
|
|
hull = cv2.convexHull(face_contour_points)
|
|
cv2.fillConvexPoly(mask, hull, 255)
|
|
|
|
# Apply Gaussian blur for soft edges
|
|
mask = cv2.GaussianBlur(mask, (21, 21), 7)
|
|
|
|
return mask
|
|
|
|
except Exception as e:
|
|
print(f"Error creating precise face mask: {e}")
|
|
return None
|
|
|
|
|
|
def process_frame(source_face: Face, temp_frame: Frame) -> Frame:
|
|
# Skip color correction for maximum FPS
|
|
# if modules.globals.color_correction:
|
|
# temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB)
|
|
|
|
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_ultra_fast(source_face, target_face, temp_frame)
|
|
else:
|
|
target_face = get_one_face(temp_frame)
|
|
if target_face and source_face:
|
|
temp_frame = swap_face_ultra_fast(source_face, target_face, temp_frame)
|
|
return temp_frame
|
|
|
|
|
|
def process_frame_v2(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)
|
|
|
|
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)
|
|
|
|
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 = [
|
|
f
|
|
for f in map["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)
|
|
|
|
else:
|
|
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)
|
|
|
|
elif not modules.globals.many_faces:
|
|
if detected_faces:
|
|
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,
|
|
)
|
|
else:
|
|
detected_faces_centroids = []
|
|
for face in detected_faces:
|
|
detected_faces_centroids.append(face.normed_embedding)
|
|
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,
|
|
)
|
|
i += 1
|
|
return temp_frame
|
|
|
|
|
|
def process_frames(
|
|
source_path: str, temp_frame_paths: List[str], progress: Any = None
|
|
) -> None:
|
|
if not modules.globals.map_faces:
|
|
source_face = get_one_face(cv2.imread(source_path))
|
|
for temp_frame_path in temp_frame_paths:
|
|
temp_frame = cv2.imread(temp_frame_path)
|
|
try:
|
|
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 = process_frame_v2(temp_frame, temp_frame_path)
|
|
cv2.imwrite(temp_frame_path, result)
|
|
except Exception as exception:
|
|
print(exception)
|
|
pass
|
|
if progress:
|
|
progress.update(1)
|
|
|
|
|
|
def process_image(source_path: str, target_path: str, output_path: str) -> None:
|
|
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)
|
|
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)
|
|
|
|
|
|
def process_video(source_path: str, temp_frame_paths: List[str]) -> None:
|
|
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:
|
|
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)
|
|
|
|
center = np.mean(lower_lip_landmarks, axis=0)
|
|
expansion_factor = 1 + modules.globals.mask_down_size
|
|
expanded_landmarks = (lower_lip_landmarks - center) * expansion_factor + center
|
|
|
|
toplip_indices = [20, 0, 1, 2, 3, 4, 5]
|
|
toplip_extension = modules.globals.mask_size * 0.5
|
|
for idx in toplip_indices:
|
|
direction = expanded_landmarks[idx] - center
|
|
direction = direction / np.linalg.norm(direction)
|
|
expanded_landmarks[idx] += direction * toplip_extension
|
|
|
|
chin_indices = [11, 12, 13, 14, 15, 16]
|
|
chin_extension = 2 * 0.2
|
|
for idx in chin_indices:
|
|
expanded_landmarks[idx][1] += (
|
|
expanded_landmarks[idx][1] - center[1]
|
|
) * chin_extension
|
|
|
|
expanded_landmarks = expanded_landmarks.astype(np.int32)
|
|
|
|
min_x, min_y = np.min(expanded_landmarks, axis=0)
|
|
max_x, max_y = np.max(expanded_landmarks, axis=0)
|
|
|
|
padding = int((max_x - min_x) * 0.1)
|
|
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)
|
|
|
|
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
|
|
|
|
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)
|
|
# Improved smoothing for mouth mask
|
|
mask_roi = cv2.GaussianBlur(mask_roi, (25, 25), 8)
|
|
mask[min_y:max_y, min_x:max_x] = mask_roi
|
|
mouth_cutout = frame[min_y:max_y, min_x:max_x].copy()
|
|
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()
|
|
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)
|
|
cv2.polylines(vis_frame, [lower_lip_polygon], True, (0, 255, 0), 2)
|
|
cv2.putText(vis_frame, "Lower Mouth Mask", (min_x, min_y - 10), 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)
|
|
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)
|
|
|
|
# Improved feathering for smoother mouth mask
|
|
feather_amount = min(35, 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 * 1.2)
|
|
feathered_mask = feathered_mask / feathered_mask.max()
|
|
|
|
# Additional smoothing pass for extra softness
|
|
feathered_mask = cv2.GaussianBlur(feathered_mask, (7, 7), 2)
|
|
|
|
# Fix black line artifacts by ensuring smooth mask transitions
|
|
feathered_mask = np.clip(feathered_mask, 0.1, 0.9) # Avoid pure 0 and 1 values
|
|
|
|
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)
|
|
|
|
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:
|
|
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:
|
|
landmarks = landmarks.astype(np.int32)
|
|
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]
|
|
|
|
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)
|
|
|
|
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
|
|
|
|
face_outline = np.vstack([[forehead_left], right_side_face, left_side_face[::-1], [forehead_right]])
|
|
padding = int(np.linalg.norm(right_side_face[0] - left_side_face[-1]) * 0.05)
|
|
|
|
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)
|
|
cv2.fillConvexPoly(mask, hull_padded, 255)
|
|
mask = cv2.GaussianBlur(mask, (5, 5), 3)
|
|
|
|
return mask
|
|
|
|
|
|
def apply_color_transfer(source, target):
|
|
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)
|
|
|
|
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)
|
|
|
|
source = (source - source_mean) * (target_std / source_std) + target_mean
|
|
return cv2.cvtColor(np.clip(source, 0, 255).astype("uint8"), cv2.COLOR_LAB2BGR) |