From ab3b73631b51f23448acbd357d069b05972e54b3 Mon Sep 17 00:00:00 2001 From: KRSHH <136873090+KRSHH@users.noreply.github.com> Date: Thu, 30 Jan 2025 20:04:31 +0530 Subject: [PATCH] Shift masking features to face_masking.py --- modules/processors/frame/face_masking.py | 580 ++++++++++++++ modules/processors/frame/face_swapper.py | 926 ++--------------------- 2 files changed, 625 insertions(+), 881 deletions(-) create mode 100644 modules/processors/frame/face_masking.py diff --git a/modules/processors/frame/face_masking.py b/modules/processors/frame/face_masking.py new file mode 100644 index 0000000..3ae963e --- /dev/null +++ b/modules/processors/frame/face_masking.py @@ -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 \ No newline at end of file diff --git a/modules/processors/frame/face_swapper.py b/modules/processors/frame/face_swapper.py index cdfa7e6..26330d3 100644 --- a/modules/processors/frame/face_swapper.py +++ b/modules/processors/frame/face_swapper.py @@ -14,6 +14,14 @@ from modules.utilities import ( is_video, ) 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 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) if modules.globals.mouth_mask: - # 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 + # Create and apply mouth mask + mouth_mask_data = create_lower_mouth_mask(target_face, temp_frame) + swapped_frame = apply_mask_area( + swapped_frame, + mouth_mask_data[1], # mouth_cutout + mouth_mask_data[2], # mouth_box + face_mask, + mouth_mask_data[3] # mouth_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 + swapped_frame = draw_mask_visualization( + swapped_frame, + mouth_mask_data, + "Lower Mouth Mask" ) if modules.globals.eyes_mask: - # Create the eyes mask - eyes_mask, eyes_cutout, eyes_box, eyes_polygon = ( - create_eyes_mask(target_face, temp_frame) - ) - - # Apply the eyes area - swapped_frame = apply_eyes_area( - swapped_frame, eyes_cutout, eyes_box, face_mask, eyes_polygon + # Create and apply eyes mask + eyes_mask_data = create_eyes_mask(target_face, temp_frame) + swapped_frame = apply_mask_area( + swapped_frame, + eyes_mask_data[1], # eyes_cutout + eyes_mask_data[2], # eyes_box + face_mask, + eyes_mask_data[3] # eyes_polygon ) if modules.globals.show_eyes_mask_box: - eyes_mask_data = (eyes_mask, eyes_cutout, eyes_box, eyes_polygon) - swapped_frame = draw_eyes_mask_visualization( - swapped_frame, target_face, eyes_mask_data + swapped_frame = draw_mask_visualization( + swapped_frame, + eyes_mask_data, + "Eyes Mask", + draw_method="ellipse" ) if modules.globals.eyebrows_mask: - # Create the eyebrows mask - eyebrows_mask, eyebrows_cutout, eyebrows_box, eyebrows_polygon = ( - create_eyebrows_mask(target_face, temp_frame) - ) - - # Apply the eyebrows area - swapped_frame = apply_eyebrows_area( - swapped_frame, eyebrows_cutout, eyebrows_box, face_mask, eyebrows_polygon + # Create and apply eyebrows mask + eyebrows_mask_data = create_eyebrows_mask(target_face, temp_frame) + swapped_frame = apply_mask_area( + swapped_frame, + eyebrows_mask_data[1], # eyebrows_cutout + eyebrows_mask_data[2], # eyebrows_box + face_mask, + eyebrows_mask_data[3] # eyebrows_polygon ) if modules.globals.show_eyebrows_mask_box: - eyebrows_mask_data = (eyebrows_mask, eyebrows_cutout, eyebrows_box, eyebrows_polygon) - swapped_frame = draw_eyebrows_mask_visualization( - swapped_frame, target_face, eyebrows_mask_data + swapped_frame = draw_mask_visualization( + swapped_frame, + eyebrows_mask_data, + "Eyebrows Mask" ) 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( 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