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Shift masking features to face_masking.py

privacy
KRSHH 2025-01-30 20:04:31 +05:30
parent d8fc1ffa04
commit ab3b73631b
2 changed files with 625 additions and 881 deletions
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580
modules/processors/frame/face_masking.py 100644
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@ -0,0 +1,580 @@
import cv2
import numpy as np
from modules.typing import Face, Frame
import modules.globals
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)
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 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 create_eyes_mask(face: Face, frame: Frame) -> (np.ndarray, np.ndarray, tuple, np.ndarray):
mask = np.zeros(frame.shape[:2], dtype=np.uint8)
eyes_cutout = None
landmarks = face.landmark_2d_106
if landmarks is not None:
# Left eye landmarks (87-96) and right eye landmarks (33-42)
left_eye = landmarks[87:96]
right_eye = landmarks[33:42]
# Calculate centers and dimensions for each eye
left_eye_center = np.mean(left_eye, axis=0).astype(np.int32)
right_eye_center = np.mean(right_eye, axis=0).astype(np.int32)
# Calculate eye dimensions
def get_eye_dimensions(eye_points):
x_coords = eye_points[:, 0]
y_coords = eye_points[:, 1]
width = int((np.max(x_coords) - np.min(x_coords)) * (1 + modules.globals.mask_down_size))
height = int((np.max(y_coords) - np.min(y_coords)) * (1 + modules.globals.mask_down_size))
return width, height
left_width, left_height = get_eye_dimensions(left_eye)
right_width, right_height = get_eye_dimensions(right_eye)
# Add extra padding
padding = int(max(left_width, right_width) * 0.2)
# Calculate bounding box for both eyes
min_x = min(left_eye_center[0] - left_width//2, right_eye_center[0] - right_width//2) - padding
max_x = max(left_eye_center[0] + left_width//2, right_eye_center[0] + right_width//2) + padding
min_y = min(left_eye_center[1] - left_height//2, right_eye_center[1] - right_height//2) - padding
max_y = max(left_eye_center[1] + left_height//2, right_eye_center[1] + right_height//2) + padding
# Ensure coordinates are within frame bounds
min_x = max(0, min_x)
min_y = max(0, min_y)
max_x = min(frame.shape[1], max_x)
max_y = min(frame.shape[0], max_y)
# Create mask for the eyes region
mask_roi = np.zeros((max_y - min_y, max_x - min_x), dtype=np.uint8)
# Draw ellipses for both eyes
left_center = (left_eye_center[0] - min_x, left_eye_center[1] - min_y)
right_center = (right_eye_center[0] - min_x, right_eye_center[1] - min_y)
# Calculate axes lengths (half of width and height)
left_axes = (left_width//2, left_height//2)
right_axes = (right_width//2, right_height//2)
# Draw filled ellipses
cv2.ellipse(mask_roi, left_center, left_axes, 0, 0, 360, 255, -1)
cv2.ellipse(mask_roi, right_center, right_axes, 0, 0, 360, 255, -1)
# Apply Gaussian blur to soften 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
eyes_cutout = frame[min_y:max_y, min_x:max_x].copy()
# Create polygon points for visualization
def create_ellipse_points(center, axes):
t = np.linspace(0, 2*np.pi, 32)
x = center[0] + axes[0] * np.cos(t)
y = center[1] + axes[1] * np.sin(t)
return np.column_stack((x, y)).astype(np.int32)
# Generate points for both ellipses
left_points = create_ellipse_points((left_eye_center[0], left_eye_center[1]), (left_width//2, left_height//2))
right_points = create_ellipse_points((right_eye_center[0], right_eye_center[1]), (right_width//2, right_height//2))
# Combine points for both eyes
eyes_polygon = np.vstack([left_points, right_points])
return mask, eyes_cutout, (min_x, min_y, max_x, max_y), eyes_polygon
def create_eyebrows_mask(face: Face, frame: Frame) -> (np.ndarray, np.ndarray, tuple, np.ndarray):
mask = np.zeros(frame.shape[:2], dtype=np.uint8)
eyebrows_cutout = None
landmarks = face.landmark_2d_106
if landmarks is not None:
# Left eyebrow landmarks (97-105) and right eyebrow landmarks (43-51)
left_eyebrow = landmarks[97:105].astype(np.float32)
right_eyebrow = landmarks[43:51].astype(np.float32)
# Calculate centers and dimensions for each eyebrow
left_center = np.mean(left_eyebrow, axis=0)
right_center = np.mean(right_eyebrow, axis=0)
# Calculate bounding box with padding
all_points = np.vstack([left_eyebrow, right_eyebrow])
min_x = np.min(all_points[:, 0]) - 25
max_x = np.max(all_points[:, 0]) + 25
min_y = np.min(all_points[:, 1]) - 20
max_y = np.max(all_points[:, 1]) + 15
# Ensure coordinates are within frame bounds
min_x = max(0, int(min_x))
min_y = max(0, int(min_y))
max_x = min(frame.shape[1], int(max_x))
max_y = min(frame.shape[0], int(max_y))
# Create mask for the eyebrows region
mask_roi = np.zeros((max_y - min_y, max_x - min_x), dtype=np.uint8)
try:
# Convert points to local coordinates
left_local = left_eyebrow - [min_x, min_y]
right_local = right_eyebrow - [min_x, min_y]
def create_curved_eyebrow(points):
if len(points) >= 5:
# Sort points by x-coordinate
sorted_idx = np.argsort(points[:, 0])
sorted_points = points[sorted_idx]
# Calculate dimensions
x_min, y_min = np.min(sorted_points, axis=0)
x_max, y_max = np.max(sorted_points, axis=0)
width = x_max - x_min
height = y_max - y_min
# Create more points for smoother curve
num_points = 50
x = np.linspace(x_min, x_max, num_points)
# Fit cubic curve through points for more natural arch
coeffs = np.polyfit(sorted_points[:, 0], sorted_points[:, 1], 3)
y = np.polyval(coeffs, x)
# Create points for top and bottom curves with varying offsets
top_offset = np.linspace(height * 0.4, height * 0.3, num_points) # Varying offset for more natural shape
bottom_offset = np.linspace(height * 0.2, height * 0.15, num_points)
# Add some randomness to the offsets for more natural look
top_offset += np.random.normal(0, height * 0.02, num_points)
bottom_offset += np.random.normal(0, height * 0.01, num_points)
# Smooth the offsets
top_offset = cv2.GaussianBlur(top_offset.reshape(-1, 1), (1, 3), 1).reshape(-1)
bottom_offset = cv2.GaussianBlur(bottom_offset.reshape(-1, 1), (1, 3), 1).reshape(-1)
top_curve = y - top_offset
bottom_curve = y + bottom_offset
# Create curved endpoints
end_points = 5
start_curve = np.column_stack((
np.linspace(x[0] - width * 0.05, x[0], end_points),
np.linspace(bottom_curve[0], top_curve[0], end_points)
))
end_curve = np.column_stack((
np.linspace(x[-1], x[-1] + width * 0.05, end_points),
np.linspace(bottom_curve[-1], top_curve[-1], end_points)
))
# Combine all points to form a smooth contour
contour_points = np.vstack([
start_curve,
np.column_stack((x, top_curve)),
end_curve,
np.column_stack((x[::-1], bottom_curve[::-1]))
])
# Add padding and smooth the shape
center = np.mean(contour_points, axis=0)
vectors = contour_points - center
padded_points = center + vectors * 1.2 # 20% padding
# Convert to integer coordinates and draw
cv2.fillPoly(mask_roi, [padded_points.astype(np.int32)], 255)
return padded_points
return points
# Generate and draw eyebrow shapes
left_shape = create_curved_eyebrow(left_local)
right_shape = create_curved_eyebrow(right_local)
# Apply multi-stage blurring for natural feathering
# First, strong Gaussian blur for initial softening
mask_roi = cv2.GaussianBlur(mask_roi, (21, 21), 7)
# Second, medium blur for transition areas
mask_roi = cv2.GaussianBlur(mask_roi, (11, 11), 3)
# Finally, light blur for fine details
mask_roi = cv2.GaussianBlur(mask_roi, (5, 5), 1)
# Normalize mask values
mask_roi = cv2.normalize(mask_roi, None, 0, 255, cv2.NORM_MINMAX)
# 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
eyebrows_cutout = frame[min_y:max_y, min_x:max_x].copy()
# Combine points for visualization
eyebrows_polygon = np.vstack([
left_shape + [min_x, min_y],
right_shape + [min_x, min_y]
]).astype(np.int32)
except Exception as e:
# Fallback to simple polygons if curve fitting fails
left_local = left_eyebrow - [min_x, min_y]
right_local = right_eyebrow - [min_x, min_y]
cv2.fillPoly(mask_roi, [left_local.astype(np.int32)], 255)
cv2.fillPoly(mask_roi, [right_local.astype(np.int32)], 255)
mask_roi = cv2.GaussianBlur(mask_roi, (21, 21), 7)
mask[min_y:max_y, min_x:max_x] = mask_roi
eyebrows_cutout = frame[min_y:max_y, min_x:max_x].copy()
eyebrows_polygon = np.vstack([left_eyebrow, right_eyebrow]).astype(np.int32)
return mask, eyebrows_cutout, (min_x, min_y, max_x, max_y), eyebrows_polygon
def apply_mask_area(
frame: np.ndarray,
cutout: np.ndarray,
box: tuple,
face_mask: np.ndarray,
polygon: np.ndarray,
) -> np.ndarray:
min_x, min_y, max_x, max_y = box
box_width = max_x - min_x
box_height = max_y - min_y
if (
cutout is None
or box_width is None
or box_height is None
or face_mask is None
or polygon is None
):
return frame
try:
resized_cutout = cv2.resize(cutout, (box_width, box_height))
roi = frame[min_y:max_y, min_x:max_x]
if roi.shape != resized_cutout.shape:
resized_cutout = cv2.resize(
resized_cutout, (roi.shape[1], roi.shape[0])
)
color_corrected_area = apply_color_transfer(resized_cutout, roi)
# Create mask for the area
polygon_mask = np.zeros(roi.shape[:2], dtype=np.uint8)
# Split points for left and right parts if needed
if len(polygon) > 50: # Arbitrary threshold to detect if we have multiple parts
mid_point = len(polygon) // 2
left_points = polygon[:mid_point] - [min_x, min_y]
right_points = polygon[mid_point:] - [min_x, min_y]
cv2.fillPoly(polygon_mask, [left_points], 255)
cv2.fillPoly(polygon_mask, [right_points], 255)
else:
adjusted_polygon = polygon - [min_x, min_y]
cv2.fillPoly(polygon_mask, [adjusted_polygon], 255)
# Apply strong initial feathering
polygon_mask = cv2.GaussianBlur(polygon_mask, (21, 21), 7)
# Apply additional feathering
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()
# Apply additional smoothing to the mask edges
feathered_mask = cv2.GaussianBlur(feathered_mask, (5, 5), 1)
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_area * 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 draw_mask_visualization(
frame: Frame,
mask_data: tuple,
label: str,
draw_method: str = "polygon"
) -> Frame:
mask, cutout, (min_x, min_y, max_x, max_y), polygon = 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)
if draw_method == "ellipse" and len(polygon) > 50: # For eyes
# Split points for left and right parts
mid_point = len(polygon) // 2
left_points = polygon[:mid_point]
right_points = polygon[mid_point:]
try:
# Fit ellipses to points - need at least 5 points
if len(left_points) >= 5 and len(right_points) >= 5:
# Convert points to the correct format for ellipse fitting
left_points = left_points.astype(np.float32)
right_points = right_points.astype(np.float32)
# Fit ellipses
left_ellipse = cv2.fitEllipse(left_points)
right_ellipse = cv2.fitEllipse(right_points)
# Draw the ellipses
cv2.ellipse(vis_frame, left_ellipse, (0, 255, 0), 2)
cv2.ellipse(vis_frame, right_ellipse, (0, 255, 0), 2)
except Exception as e:
# If ellipse fitting fails, draw simple rectangles as fallback
left_rect = cv2.boundingRect(left_points)
right_rect = cv2.boundingRect(right_points)
cv2.rectangle(vis_frame,
(left_rect[0], left_rect[1]),
(left_rect[0] + left_rect[2], left_rect[1] + left_rect[3]),
(0, 255, 0), 2)
cv2.rectangle(vis_frame,
(right_rect[0], right_rect[1]),
(right_rect[0] + right_rect[2], right_rect[1] + right_rect[3]),
(0, 255, 0), 2)
else: # For mouth and eyebrows
# Draw the polygon
if len(polygon) > 50: # If we have multiple parts
mid_point = len(polygon) // 2
left_points = polygon[:mid_point]
right_points = polygon[mid_point:]
cv2.polylines(vis_frame, [left_points], True, (0, 255, 0), 2, cv2.LINE_AA)
cv2.polylines(vis_frame, [right_points], True, (0, 255, 0), 2, cv2.LINE_AA)
else:
cv2.polylines(vis_frame, [polygon], True, (0, 255, 0), 2, cv2.LINE_AA)
# Add label
cv2.putText(
vis_frame,
label,
(min_x, min_y - 10),
cv2.FONT_HERSHEY_SIMPLEX,
0.5,
(255, 255, 255),
1,
)
return vis_frame

926
modules/processors/frame/face_swapper.py
View File

@ -14,6 +14,14 @@ from modules.utilities import (
is_video, is_video,
) )
from modules.cluster_analysis import find_closest_centroid from modules.cluster_analysis import find_closest_centroid
from modules.processors.frame.face_masking import (
create_face_mask,
create_lower_mouth_mask,
create_eyes_mask,
create_eyebrows_mask,
apply_mask_area,
draw_mask_visualization
)
import os import os
FACE_SWAPPER = None FACE_SWAPPER = None
@ -78,54 +86,58 @@ def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
face_mask = create_face_mask(target_face, temp_frame) face_mask = create_face_mask(target_face, temp_frame)
if modules.globals.mouth_mask: if modules.globals.mouth_mask:
# Create the mouth mask # Create and apply mouth mask
mouth_mask, mouth_cutout, mouth_box, lower_lip_polygon = ( mouth_mask_data = create_lower_mouth_mask(target_face, temp_frame)
create_lower_mouth_mask(target_face, temp_frame) swapped_frame = apply_mask_area(
) swapped_frame,
mouth_mask_data[1], # mouth_cutout
# Apply the mouth area mouth_mask_data[2], # mouth_box
swapped_frame = apply_mouth_area( face_mask,
swapped_frame, mouth_cutout, mouth_box, face_mask, lower_lip_polygon mouth_mask_data[3] # mouth_polygon
) )
if modules.globals.show_mouth_mask_box: if modules.globals.show_mouth_mask_box:
mouth_mask_data = (mouth_mask, mouth_cutout, mouth_box, lower_lip_polygon) swapped_frame = draw_mask_visualization(
swapped_frame = draw_mouth_mask_visualization( swapped_frame,
swapped_frame, target_face, mouth_mask_data mouth_mask_data,
"Lower Mouth Mask"
) )
if modules.globals.eyes_mask: if modules.globals.eyes_mask:
# Create the eyes mask # Create and apply eyes mask
eyes_mask, eyes_cutout, eyes_box, eyes_polygon = ( eyes_mask_data = create_eyes_mask(target_face, temp_frame)
create_eyes_mask(target_face, temp_frame) swapped_frame = apply_mask_area(
) swapped_frame,
eyes_mask_data[1], # eyes_cutout
# Apply the eyes area eyes_mask_data[2], # eyes_box
swapped_frame = apply_eyes_area( face_mask,
swapped_frame, eyes_cutout, eyes_box, face_mask, eyes_polygon eyes_mask_data[3] # eyes_polygon
) )
if modules.globals.show_eyes_mask_box: if modules.globals.show_eyes_mask_box:
eyes_mask_data = (eyes_mask, eyes_cutout, eyes_box, eyes_polygon) swapped_frame = draw_mask_visualization(
swapped_frame = draw_eyes_mask_visualization( swapped_frame,
swapped_frame, target_face, eyes_mask_data eyes_mask_data,
"Eyes Mask",
draw_method="ellipse"
) )
if modules.globals.eyebrows_mask: if modules.globals.eyebrows_mask:
# Create the eyebrows mask # Create and apply eyebrows mask
eyebrows_mask, eyebrows_cutout, eyebrows_box, eyebrows_polygon = ( eyebrows_mask_data = create_eyebrows_mask(target_face, temp_frame)
create_eyebrows_mask(target_face, temp_frame) swapped_frame = apply_mask_area(
) swapped_frame,
eyebrows_mask_data[1], # eyebrows_cutout
# Apply the eyebrows area eyebrows_mask_data[2], # eyebrows_box
swapped_frame = apply_eyebrows_area( face_mask,
swapped_frame, eyebrows_cutout, eyebrows_box, face_mask, eyebrows_polygon eyebrows_mask_data[3] # eyebrows_polygon
) )
if modules.globals.show_eyebrows_mask_box: if modules.globals.show_eyebrows_mask_box:
eyebrows_mask_data = (eyebrows_mask, eyebrows_cutout, eyebrows_box, eyebrows_polygon) swapped_frame = draw_mask_visualization(
swapped_frame = draw_eyebrows_mask_visualization( swapped_frame,
swapped_frame, target_face, eyebrows_mask_data eyebrows_mask_data,
"Eyebrows Mask"
) )
return swapped_frame return swapped_frame
@ -289,851 +301,3 @@ def process_video(source_path: str, temp_frame_paths: List[str]) -> None:
modules.processors.frame.core.process_video( modules.processors.frame.core.process_video(
source_path, temp_frame_paths, process_frames 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)
def create_eyes_mask(face: Face, frame: Frame) -> (np.ndarray, np.ndarray, tuple, np.ndarray):
mask = np.zeros(frame.shape[:2], dtype=np.uint8)
eyes_cutout = None
landmarks = face.landmark_2d_106
if landmarks is not None:
# Left eye landmarks (87-96) and right eye landmarks (33-42)
left_eye = landmarks[87:96]
right_eye = landmarks[33:42]
# Calculate centers and dimensions for each eye
left_eye_center = np.mean(left_eye, axis=0).astype(np.int32)
right_eye_center = np.mean(right_eye, axis=0).astype(np.int32)
# Calculate eye dimensions
def get_eye_dimensions(eye_points):
x_coords = eye_points[:, 0]
y_coords = eye_points[:, 1]
width = int((np.max(x_coords) - np.min(x_coords)) * (1 + modules.globals.mask_down_size))
height = int((np.max(y_coords) - np.min(y_coords)) * (1 + modules.globals.mask_down_size))
return width, height
left_width, left_height = get_eye_dimensions(left_eye)
right_width, right_height = get_eye_dimensions(right_eye)
# Add extra padding
padding = int(max(left_width, right_width) * 0.2)
# Calculate bounding box for both eyes
min_x = min(left_eye_center[0] - left_width//2, right_eye_center[0] - right_width//2) - padding
max_x = max(left_eye_center[0] + left_width//2, right_eye_center[0] + right_width//2) + padding
min_y = min(left_eye_center[1] - left_height//2, right_eye_center[1] - right_height//2) - padding
max_y = max(left_eye_center[1] + left_height//2, right_eye_center[1] + right_height//2) + padding
# Ensure coordinates are within frame bounds
min_x = max(0, min_x)
min_y = max(0, min_y)
max_x = min(frame.shape[1], max_x)
max_y = min(frame.shape[0], max_y)
# Create mask for the eyes region
mask_roi = np.zeros((max_y - min_y, max_x - min_x), dtype=np.uint8)
# Draw ellipses for both eyes
left_center = (left_eye_center[0] - min_x, left_eye_center[1] - min_y)
right_center = (right_eye_center[0] - min_x, right_eye_center[1] - min_y)
# Calculate axes lengths (half of width and height)
left_axes = (left_width//2, left_height//2)
right_axes = (right_width//2, right_height//2)
# Draw filled ellipses
cv2.ellipse(mask_roi, left_center, left_axes, 0, 0, 360, 255, -1)
cv2.ellipse(mask_roi, right_center, right_axes, 0, 0, 360, 255, -1)
# Apply Gaussian blur to soften 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
eyes_cutout = frame[min_y:max_y, min_x:max_x].copy()
# Create polygon points for visualization
def create_ellipse_points(center, axes):
t = np.linspace(0, 2*np.pi, 32)
x = center[0] + axes[0] * np.cos(t)
y = center[1] + axes[1] * np.sin(t)
return np.column_stack((x, y)).astype(np.int32)
# Generate points for both ellipses
left_points = create_ellipse_points((left_eye_center[0], left_eye_center[1]), (left_width//2, left_height//2))
right_points = create_ellipse_points((right_eye_center[0], right_eye_center[1]), (right_width//2, right_height//2))
# Combine points for both eyes
eyes_polygon = np.vstack([left_points, right_points])
return mask, eyes_cutout, (min_x, min_y, max_x, max_y), eyes_polygon
def apply_eyes_area(
frame: np.ndarray,
eyes_cutout: np.ndarray,
eyes_box: tuple,
face_mask: np.ndarray,
eyes_polygon: np.ndarray,
) -> np.ndarray:
min_x, min_y, max_x, max_y = eyes_box
box_width = max_x - min_x
box_height = max_y - min_y
if (
eyes_cutout is None
or box_width is None
or box_height is None
or face_mask is None
or eyes_polygon is None
):
return frame
try:
resized_eyes_cutout = cv2.resize(eyes_cutout, (box_width, box_height))
roi = frame[min_y:max_y, min_x:max_x]
if roi.shape != resized_eyes_cutout.shape:
resized_eyes_cutout = cv2.resize(
resized_eyes_cutout, (roi.shape[1], roi.shape[0])
)
color_corrected_eyes = apply_color_transfer(resized_eyes_cutout, roi)
# Create mask for both eyes
polygon_mask = np.zeros(roi.shape[:2], dtype=np.uint8)
# Split points for left and right eyes
mid_point = len(eyes_polygon) // 2
left_eye_points = eyes_polygon[:mid_point] - [min_x, min_y]
right_eye_points = eyes_polygon[mid_point:] - [min_x, min_y]
# Draw filled ellipses using points
left_rect = cv2.minAreaRect(left_eye_points)
right_rect = cv2.minAreaRect(right_eye_points)
# Convert rect to ellipse parameters
def rect_to_ellipse_params(rect):
center = rect[0]
size = rect[1]
angle = rect[2]
return (int(center[0]), int(center[1])), (int(size[0]/2), int(size[1]/2)), angle
# Draw filled ellipses
left_params = rect_to_ellipse_params(left_rect)
right_params = rect_to_ellipse_params(right_rect)
cv2.ellipse(polygon_mask, left_params[0], left_params[1], left_params[2], 0, 360, 255, -1)
cv2.ellipse(polygon_mask, right_params[0], right_params[1], right_params[2], 0, 360, 255, -1)
# Apply feathering
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_eyes * 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 draw_eyes_mask_visualization(
frame: Frame, face: Face, eyes_mask_data: tuple
) -> Frame:
landmarks = face.landmark_2d_106
if landmarks is not None and eyes_mask_data is not None:
mask, eyes_cutout, (min_x, min_y, max_x, max_y), eyes_polygon = eyes_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)
# Draw the eyes ellipses
mid_point = len(eyes_polygon) // 2
left_points = eyes_polygon[:mid_point]
right_points = eyes_polygon[mid_point:]
try:
# Fit ellipses to points - need at least 5 points
if len(left_points) >= 5 and len(right_points) >= 5:
# Convert points to the correct format for ellipse fitting
left_points = left_points.astype(np.float32)
right_points = right_points.astype(np.float32)
# Fit ellipses
left_ellipse = cv2.fitEllipse(left_points)
right_ellipse = cv2.fitEllipse(right_points)
# Draw the ellipses
cv2.ellipse(vis_frame, left_ellipse, (0, 255, 0), 2)
cv2.ellipse(vis_frame, right_ellipse, (0, 255, 0), 2)
except Exception as e:
# If ellipse fitting fails, draw simple rectangles as fallback
left_rect = cv2.boundingRect(left_points)
right_rect = cv2.boundingRect(right_points)
cv2.rectangle(vis_frame,
(left_rect[0], left_rect[1]),
(left_rect[0] + left_rect[2], left_rect[1] + left_rect[3]),
(0, 255, 0), 2)
cv2.rectangle(vis_frame,
(right_rect[0], right_rect[1]),
(right_rect[0] + right_rect[2], right_rect[1] + right_rect[3]),
(0, 255, 0), 2)
# Add label
cv2.putText(
vis_frame,
"Eyes Mask",
(min_x, min_y - 10),
cv2.FONT_HERSHEY_SIMPLEX,
0.5,
(255, 255, 255),
1,
)
return vis_frame
return frame
def create_eyebrows_mask(face: Face, frame: Frame) -> (np.ndarray, np.ndarray, tuple, np.ndarray):
mask = np.zeros(frame.shape[:2], dtype=np.uint8)
eyebrows_cutout = None
landmarks = face.landmark_2d_106
if landmarks is not None:
# Left eyebrow landmarks (97-105) and right eyebrow landmarks (43-51)
left_eyebrow = landmarks[97:105].astype(np.float32)
right_eyebrow = landmarks[43:51].astype(np.float32)
# Calculate centers and dimensions for each eyebrow
left_center = np.mean(left_eyebrow, axis=0)
right_center = np.mean(right_eyebrow, axis=0)
# Calculate bounding box with padding
all_points = np.vstack([left_eyebrow, right_eyebrow])
min_x = np.min(all_points[:, 0]) - 25
max_x = np.max(all_points[:, 0]) + 25
min_y = np.min(all_points[:, 1]) - 20
max_y = np.max(all_points[:, 1]) + 15
# Ensure coordinates are within frame bounds
min_x = max(0, int(min_x))
min_y = max(0, int(min_y))
max_x = min(frame.shape[1], int(max_x))
max_y = min(frame.shape[0], int(max_y))
# Create mask for the eyebrows region
mask_roi = np.zeros((max_y - min_y, max_x - min_x), dtype=np.uint8)
try:
# Convert points to local coordinates
left_local = left_eyebrow - [min_x, min_y]
right_local = right_eyebrow - [min_x, min_y]
def create_curved_eyebrow(points):
if len(points) >= 5:
# Sort points by x-coordinate
sorted_idx = np.argsort(points[:, 0])
sorted_points = points[sorted_idx]
# Calculate dimensions
x_min, y_min = np.min(sorted_points, axis=0)
x_max, y_max = np.max(sorted_points, axis=0)
width = x_max - x_min
height = y_max - y_min
# Create more points for smoother curve
num_points = 50
x = np.linspace(x_min, x_max, num_points)
# Fit cubic curve through points for more natural arch
coeffs = np.polyfit(sorted_points[:, 0], sorted_points[:, 1], 3)
y = np.polyval(coeffs, x)
# Create points for top and bottom curves with varying offsets
top_offset = np.linspace(height * 0.4, height * 0.3, num_points) # Varying offset for more natural shape
bottom_offset = np.linspace(height * 0.2, height * 0.15, num_points)
# Add some randomness to the offsets for more natural look
top_offset += np.random.normal(0, height * 0.02, num_points)
bottom_offset += np.random.normal(0, height * 0.01, num_points)
# Smooth the offsets
top_offset = cv2.GaussianBlur(top_offset.reshape(-1, 1), (1, 3), 1).reshape(-1)
bottom_offset = cv2.GaussianBlur(bottom_offset.reshape(-1, 1), (1, 3), 1).reshape(-1)
top_curve = y - top_offset
bottom_curve = y + bottom_offset
# Create curved endpoints
end_points = 5
start_curve = np.column_stack((
np.linspace(x[0] - width * 0.05, x[0], end_points),
np.linspace(bottom_curve[0], top_curve[0], end_points)
))
end_curve = np.column_stack((
np.linspace(x[-1], x[-1] + width * 0.05, end_points),
np.linspace(bottom_curve[-1], top_curve[-1], end_points)
))
# Combine all points to form a smooth contour
contour_points = np.vstack([
start_curve,
np.column_stack((x, top_curve)),
end_curve,
np.column_stack((x[::-1], bottom_curve[::-1]))
])
# Add padding and smooth the shape
center = np.mean(contour_points, axis=0)
vectors = contour_points - center
padded_points = center + vectors * 1.2 # 20% padding
# Convert to integer coordinates and draw
cv2.fillPoly(mask_roi, [padded_points.astype(np.int32)], 255)
return padded_points
return points
# Generate and draw eyebrow shapes
left_shape = create_curved_eyebrow(left_local)
right_shape = create_curved_eyebrow(right_local)
# Apply multi-stage blurring for natural feathering
# First, strong Gaussian blur for initial softening
mask_roi = cv2.GaussianBlur(mask_roi, (21, 21), 7)
# Second, medium blur for transition areas
mask_roi = cv2.GaussianBlur(mask_roi, (11, 11), 3)
# Finally, light blur for fine details
mask_roi = cv2.GaussianBlur(mask_roi, (5, 5), 1)
# Normalize mask values
mask_roi = cv2.normalize(mask_roi, None, 0, 255, cv2.NORM_MINMAX)
# 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
eyebrows_cutout = frame[min_y:max_y, min_x:max_x].copy()
# Combine points for visualization
eyebrows_polygon = np.vstack([
left_shape + [min_x, min_y],
right_shape + [min_x, min_y]
]).astype(np.int32)
except Exception as e:
# Fallback to simple polygons if curve fitting fails
left_local = left_eyebrow - [min_x, min_y]
right_local = right_eyebrow - [min_x, min_y]
cv2.fillPoly(mask_roi, [left_local.astype(np.int32)], 255)
cv2.fillPoly(mask_roi, [right_local.astype(np.int32)], 255)
mask_roi = cv2.GaussianBlur(mask_roi, (21, 21), 7)
mask[min_y:max_y, min_x:max_x] = mask_roi
eyebrows_cutout = frame[min_y:max_y, min_x:max_x].copy()
eyebrows_polygon = np.vstack([left_eyebrow, right_eyebrow]).astype(np.int32)
return mask, eyebrows_cutout, (min_x, min_y, max_x, max_y), eyebrows_polygon
def apply_eyebrows_area(
frame: np.ndarray,
eyebrows_cutout: np.ndarray,
eyebrows_box: tuple,
face_mask: np.ndarray,
eyebrows_polygon: np.ndarray,
) -> np.ndarray:
min_x, min_y, max_x, max_y = eyebrows_box
box_width = max_x - min_x
box_height = max_y - min_y
if (
eyebrows_cutout is None
or box_width is None
or box_height is None
or face_mask is None
or eyebrows_polygon is None
):
return frame
try:
resized_eyebrows_cutout = cv2.resize(eyebrows_cutout, (box_width, box_height))
roi = frame[min_y:max_y, min_x:max_x]
if roi.shape != resized_eyebrows_cutout.shape:
resized_eyebrows_cutout = cv2.resize(
resized_eyebrows_cutout, (roi.shape[1], roi.shape[0])
)
color_corrected_eyebrows = apply_color_transfer(resized_eyebrows_cutout, roi)
# Create mask for both eyebrows
polygon_mask = np.zeros(roi.shape[:2], dtype=np.uint8)
# Split points for left and right eyebrows
mid_point = len(eyebrows_polygon) // 2
left_points = eyebrows_polygon[:mid_point] - [min_x, min_y]
right_points = eyebrows_polygon[mid_point:] - [min_x, min_y]
# Draw filled polygons
cv2.fillPoly(polygon_mask, [left_points], 255)
cv2.fillPoly(polygon_mask, [right_points], 255)
# Apply strong initial feathering
polygon_mask = cv2.GaussianBlur(polygon_mask, (21, 21), 7)
# Apply additional feathering
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()
# Apply additional smoothing to the mask edges
feathered_mask = cv2.GaussianBlur(feathered_mask, (5, 5), 1)
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_eyebrows * 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 draw_eyebrows_mask_visualization(
frame: Frame, face: Face, eyebrows_mask_data: tuple
) -> Frame:
landmarks = face.landmark_2d_106
if landmarks is not None and eyebrows_mask_data is not None:
mask, eyebrows_cutout, (min_x, min_y, max_x, max_y), eyebrows_polygon = eyebrows_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)
# Draw the eyebrows curves
mid_point = len(eyebrows_polygon) // 2
left_points = eyebrows_polygon[:mid_point]
right_points = eyebrows_polygon[mid_point:]
# Draw smooth curves with anti-aliasing
cv2.polylines(vis_frame, [left_points], True, (0, 255, 0), 2, cv2.LINE_AA)
cv2.polylines(vis_frame, [right_points], True, (0, 255, 0), 2, cv2.LINE_AA)
# Add label
cv2.putText(
vis_frame,
"Eyebrows Mask",
(min_x, min_y - 10),
cv2.FONT_HERSHEY_SIMPLEX,
0.5,
(255, 255, 255),
1,
)
return vis_frame
return frame