997 lines
36 KiB
Python
997 lines
36 KiB
Python
from typing import Any, List
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import cv2
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import insightface
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import threading
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import numpy as np
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import modules.globals
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import logging
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import time
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import modules.processors.frame.core
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from modules.core import update_status
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from modules.face_analyser import get_one_face, get_many_faces, default_source_face
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from modules.typing import Face, Frame
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from modules.utilities import (
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conditional_download,
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is_image,
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is_video,
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)
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from modules.cluster_analysis import find_closest_centroid
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import os
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FACE_SWAPPER = None
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THREAD_LOCK = threading.Lock()
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NAME = "DLC.FACE-SWAPPER"
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abs_dir = os.path.dirname(os.path.abspath(__file__))
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models_dir = os.path.join(
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os.path.dirname(os.path.dirname(os.path.dirname(abs_dir))), "models"
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)
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def pre_check() -> bool:
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download_directory_path = abs_dir
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conditional_download(
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download_directory_path,
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[
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"https://huggingface.co/hacksider/deep-live-cam/blob/main/inswapper_128_fp16.onnx"
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],
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)
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return True
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def pre_start() -> bool:
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if not modules.globals.map_faces and not is_image(modules.globals.source_path):
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update_status("Select an image for source path.", NAME)
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return False
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elif not modules.globals.map_faces and not get_one_face(
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cv2.imread(modules.globals.source_path)
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):
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update_status("No face in source path detected.", NAME)
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return False
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if not is_image(modules.globals.target_path) and not is_video(
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modules.globals.target_path
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):
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update_status("Select an image or video for target path.", NAME)
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return False
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return True
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def get_face_swapper() -> Any:
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global FACE_SWAPPER
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with THREAD_LOCK:
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if FACE_SWAPPER is None:
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model_path = os.path.join(models_dir, "inswapper_128_fp16.onnx")
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FACE_SWAPPER = insightface.model_zoo.get_model(
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model_path, providers=modules.globals.execution_providers
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)
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return FACE_SWAPPER
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def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
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face_swapper = get_face_swapper()
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# Apply the face swap with optimized settings for better performance
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swapped_frame = face_swapper.get(
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temp_frame, target_face, source_face, paste_back=True
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)
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if modules.globals.mouth_mask:
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# Create a mask for the target face
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face_mask = create_face_mask(target_face, temp_frame)
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# Create the mouth mask
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mouth_mask, mouth_cutout, mouth_box, lower_lip_polygon = (
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create_lower_mouth_mask(target_face, temp_frame)
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)
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# Apply the mouth area
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swapped_frame = apply_mouth_area(
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swapped_frame, mouth_cutout, mouth_box, face_mask, lower_lip_polygon
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)
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if modules.globals.show_mouth_mask_box:
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mouth_mask_data = (mouth_mask, mouth_cutout, mouth_box, lower_lip_polygon)
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swapped_frame = draw_mouth_mask_visualization(
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swapped_frame, target_face, mouth_mask_data
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)
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return swapped_frame
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def swap_face_enhanced(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
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"""Fast face swapping - optimized for maximum FPS"""
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face_swapper = get_face_swapper()
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# Apply the face swap - this is the core operation
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swapped_frame = face_swapper.get(
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temp_frame, target_face, source_face, paste_back=True
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)
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# Skip expensive post-processing to maintain high FPS
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# Only apply mouth mask if specifically enabled
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if modules.globals.mouth_mask:
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# Create a mask for the target face
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face_mask = create_face_mask(target_face, temp_frame)
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# Create the mouth mask
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mouth_mask, mouth_cutout, mouth_box, lower_lip_polygon = (
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create_lower_mouth_mask(target_face, temp_frame)
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)
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# Apply the mouth area
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swapped_frame = apply_mouth_area(
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swapped_frame, mouth_cutout, mouth_box, face_mask, lower_lip_polygon
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)
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if modules.globals.show_mouth_mask_box:
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mouth_mask_data = (mouth_mask, mouth_cutout, mouth_box, lower_lip_polygon)
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swapped_frame = draw_mouth_mask_visualization(
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swapped_frame, target_face, mouth_mask_data
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)
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return swapped_frame
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def enhance_face_swap_quality(swapped_frame: Frame, source_face: Face, target_face: Face, original_frame: Frame) -> Frame:
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"""Apply quality enhancements to the swapped face"""
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try:
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# Get face bounding box
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bbox = target_face.bbox.astype(int)
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x1, y1, x2, y2 = bbox
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# Ensure coordinates are within frame bounds
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h, w = swapped_frame.shape[:2]
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x1, y1 = max(0, x1), max(0, y1)
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x2, y2 = min(w, x2), min(h, y2)
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if x2 <= x1 or y2 <= y1:
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return swapped_frame
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# Extract face regions
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swapped_face = swapped_frame[y1:y2, x1:x2]
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original_face = original_frame[y1:y2, x1:x2]
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# Apply color matching
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color_matched = apply_advanced_color_matching(swapped_face, original_face)
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# Apply edge smoothing
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smoothed = apply_edge_smoothing(color_matched, original_face)
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# Blend back into frame
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swapped_frame[y1:y2, x1:x2] = smoothed
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return swapped_frame
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except Exception as e:
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# Return original swapped frame if enhancement fails
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return swapped_frame
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def apply_advanced_color_matching(swapped_face: np.ndarray, target_face: np.ndarray) -> np.ndarray:
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"""Apply advanced color matching between swapped and target faces"""
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try:
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# Convert to LAB color space for better color matching
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swapped_lab = cv2.cvtColor(swapped_face, cv2.COLOR_BGR2LAB).astype(np.float32)
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target_lab = cv2.cvtColor(target_face, cv2.COLOR_BGR2LAB).astype(np.float32)
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# Calculate statistics for each channel
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swapped_mean = np.mean(swapped_lab, axis=(0, 1))
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swapped_std = np.std(swapped_lab, axis=(0, 1))
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target_mean = np.mean(target_lab, axis=(0, 1))
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target_std = np.std(target_lab, axis=(0, 1))
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# Apply color transfer
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for i in range(3):
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if swapped_std[i] > 0:
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swapped_lab[:, :, i] = (swapped_lab[:, :, i] - swapped_mean[i]) * (target_std[i] / swapped_std[i]) + target_mean[i]
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# Convert back to BGR
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result = cv2.cvtColor(np.clip(swapped_lab, 0, 255).astype(np.uint8), cv2.COLOR_LAB2BGR)
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return result
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except Exception:
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return swapped_face
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def apply_edge_smoothing(face: np.ndarray, reference: np.ndarray) -> np.ndarray:
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"""Apply edge smoothing to reduce artifacts"""
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try:
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# Create a soft mask for blending edges
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mask = np.ones(face.shape[:2], dtype=np.float32)
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# Apply Gaussian blur to create soft edges
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kernel_size = max(5, min(face.shape[0], face.shape[1]) // 20)
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if kernel_size % 2 == 0:
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kernel_size += 1
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mask = cv2.GaussianBlur(mask, (kernel_size, kernel_size), 0)
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mask = mask[:, :, np.newaxis]
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# Blend with reference for smoother edges
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blended = face * mask + reference * (1 - mask)
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return blended.astype(np.uint8)
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except Exception:
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return face
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def swap_face_enhanced_with_occlusion(source_face: Face, target_face: Face, temp_frame: Frame, original_frame: Frame) -> Frame:
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"""Simplified enhanced face swapping - just use the regular enhanced swap"""
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# Just use the regular enhanced swap to avoid any issues
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return swap_face_enhanced(source_face, target_face, temp_frame)
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def create_enhanced_face_mask(face: Face, frame: Frame) -> np.ndarray:
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"""Create an enhanced face mask that better handles occlusion"""
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mask = np.zeros(frame.shape[:2], dtype=np.uint8)
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try:
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# Use landmarks if available for more precise masking
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if hasattr(face, 'landmark_2d_106') and face.landmark_2d_106 is not None:
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landmarks = face.landmark_2d_106.astype(np.int32)
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# Create face contour from landmarks
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face_contour = []
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# Face outline (jawline and forehead)
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face_outline_indices = list(range(0, 33)) # Jawline and face boundary
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for idx in face_outline_indices:
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if idx < len(landmarks):
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face_contour.append(landmarks[idx])
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if len(face_contour) > 3:
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face_contour = np.array(face_contour)
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# Create convex hull for smoother mask
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hull = cv2.convexHull(face_contour)
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# Expand the hull slightly for better coverage
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center = np.mean(hull, axis=0)
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expanded_hull = []
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for point in hull:
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direction = point[0] - center
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direction = direction / np.linalg.norm(direction) if np.linalg.norm(direction) > 0 else direction
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expanded_point = point[0] + direction * 10 # Expand by 10 pixels
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expanded_hull.append(expanded_point)
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expanded_hull = np.array(expanded_hull, dtype=np.int32)
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cv2.fillConvexPoly(mask, expanded_hull, 255)
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else:
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# Fallback to bounding box
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bbox = face.bbox.astype(int)
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x1, y1, x2, y2 = bbox
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cv2.rectangle(mask, (x1, y1), (x2, y2), 255, -1)
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else:
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# Fallback to bounding box if no landmarks
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bbox = face.bbox.astype(int)
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x1, y1, x2, y2 = bbox
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cv2.rectangle(mask, (x1, y1), (x2, y2), 255, -1)
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# Apply Gaussian blur for soft edges
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mask = cv2.GaussianBlur(mask, (15, 15), 5)
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except Exception as e:
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print(f"Error creating enhanced face mask: {e}")
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# Fallback to simple rectangle mask
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bbox = face.bbox.astype(int)
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x1, y1, x2, y2 = bbox
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cv2.rectangle(mask, (x1, y1), (x2, y2), 255, -1)
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mask = cv2.GaussianBlur(mask, (15, 15), 5)
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return mask
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def apply_occlusion_aware_blending(swapped_frame: Frame, original_frame: Frame, face_mask: np.ndarray, bbox: np.ndarray) -> Frame:
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"""Apply occlusion-aware blending to handle hands/objects covering the face"""
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try:
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x1, y1, x2, y2 = bbox
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# Ensure coordinates are within bounds
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h, w = swapped_frame.shape[:2]
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x1, y1 = max(0, x1), max(0, y1)
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x2, y2 = min(w, x2), min(h, y2)
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if x2 <= x1 or y2 <= y1:
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return swapped_frame
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# Extract face regions
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swapped_face_region = swapped_frame[y1:y2, x1:x2]
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original_face_region = original_frame[y1:y2, x1:x2]
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face_mask_region = face_mask[y1:y2, x1:x2]
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# Detect potential occlusion using edge detection and color analysis
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occlusion_mask = detect_occlusion(original_face_region, swapped_face_region)
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# Combine face mask with occlusion detection
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combined_mask = face_mask_region.astype(np.float32) / 255.0
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occlusion_factor = (255 - occlusion_mask).astype(np.float32) / 255.0
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# Apply occlusion-aware blending
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final_mask = combined_mask * occlusion_factor
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final_mask = final_mask[:, :, np.newaxis]
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# Blend the regions
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blended_region = (swapped_face_region * final_mask +
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original_face_region * (1 - final_mask)).astype(np.uint8)
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# Copy back to full frame
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result_frame = swapped_frame.copy()
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result_frame[y1:y2, x1:x2] = blended_region
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return result_frame
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except Exception as e:
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print(f"Error in occlusion-aware blending: {e}")
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return swapped_frame
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def detect_occlusion(original_region: np.ndarray, swapped_region: np.ndarray) -> np.ndarray:
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"""Detect potential occlusion areas (hands, objects) in the face region"""
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try:
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# Convert to different color spaces for analysis
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original_hsv = cv2.cvtColor(original_region, cv2.COLOR_BGR2HSV)
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original_lab = cv2.cvtColor(original_region, cv2.COLOR_BGR2LAB)
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# Detect skin-like regions (potential hands)
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# HSV ranges for skin detection
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lower_skin = np.array([0, 20, 70], dtype=np.uint8)
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upper_skin = np.array([20, 255, 255], dtype=np.uint8)
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skin_mask1 = cv2.inRange(original_hsv, lower_skin, upper_skin)
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lower_skin2 = np.array([160, 20, 70], dtype=np.uint8)
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upper_skin2 = np.array([180, 255, 255], dtype=np.uint8)
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skin_mask2 = cv2.inRange(original_hsv, lower_skin2, upper_skin2)
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skin_mask = cv2.bitwise_or(skin_mask1, skin_mask2)
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# Edge detection to find object boundaries
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gray = cv2.cvtColor(original_region, cv2.COLOR_BGR2GRAY)
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edges = cv2.Canny(gray, 50, 150)
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# Dilate edges to create thicker boundaries
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kernel = np.ones((3, 3), np.uint8)
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edges_dilated = cv2.dilate(edges, kernel, iterations=2)
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# Combine skin detection and edge detection
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occlusion_mask = cv2.bitwise_or(skin_mask, edges_dilated)
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# Apply morphological operations to clean up the mask
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kernel = np.ones((5, 5), np.uint8)
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occlusion_mask = cv2.morphologyEx(occlusion_mask, cv2.MORPH_CLOSE, kernel)
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occlusion_mask = cv2.morphologyEx(occlusion_mask, cv2.MORPH_OPEN, kernel)
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# Apply Gaussian blur for smooth transitions
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occlusion_mask = cv2.GaussianBlur(occlusion_mask, (11, 11), 3)
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return occlusion_mask
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except Exception as e:
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print(f"Error in occlusion detection: {e}")
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# Return empty mask if detection fails
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return np.zeros(original_region.shape[:2], dtype=np.uint8)
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def apply_subtle_occlusion_protection(swapped_frame: Frame, original_frame: Frame, target_face: Face) -> Frame:
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"""Apply very subtle occlusion protection - only affects obvious hand/object areas"""
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try:
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# Get face bounding box
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bbox = target_face.bbox.astype(int)
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x1, y1, x2, y2 = bbox
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# Ensure coordinates are within frame bounds
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h, w = swapped_frame.shape[:2]
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x1, y1 = max(0, x1), max(0, y1)
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x2, y2 = min(w, x2), min(h, y2)
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if x2 <= x1 or y2 <= y1:
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return swapped_frame
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# Extract face regions
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swapped_region = swapped_frame[y1:y2, x1:x2]
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original_region = original_frame[y1:y2, x1:x2]
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# Very conservative occlusion detection - only detect obvious hands/objects
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occlusion_mask = detect_obvious_occlusion(original_region)
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# Only apply protection if significant occlusion is detected
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occlusion_percentage = np.sum(occlusion_mask > 128) / (occlusion_mask.shape[0] * occlusion_mask.shape[1])
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if occlusion_percentage > 0.15: # Only if more than 15% of face is occluded
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# Create a very soft blend mask
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blend_mask = (255 - occlusion_mask).astype(np.float32) / 255.0
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blend_mask = cv2.GaussianBlur(blend_mask, (21, 21), 7) # Very soft edges
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blend_mask = blend_mask[:, :, np.newaxis]
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# Very subtle blending - mostly keep the swapped face
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protected_region = (swapped_region * (0.7 + 0.3 * blend_mask) +
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original_region * (0.3 * (1 - blend_mask))).astype(np.uint8)
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# Copy back to full frame
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result_frame = swapped_frame.copy()
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result_frame[y1:y2, x1:x2] = protected_region
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return result_frame
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# If no significant occlusion, return original swapped frame
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return swapped_frame
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except Exception as e:
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# If anything fails, just return the swapped frame
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return swapped_frame
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def detect_obvious_occlusion(region: np.ndarray) -> np.ndarray:
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"""Detect only very obvious occlusion (hands, large objects) - much more conservative"""
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try:
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# Convert to HSV for better skin detection
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hsv = cv2.cvtColor(region, cv2.COLOR_BGR2HSV)
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# More restrictive skin detection for hands
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lower_skin = np.array([0, 30, 80], dtype=np.uint8) # More restrictive
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upper_skin = np.array([15, 255, 255], dtype=np.uint8)
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skin_mask1 = cv2.inRange(hsv, lower_skin, upper_skin)
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lower_skin2 = np.array([165, 30, 80], dtype=np.uint8)
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upper_skin2 = np.array([180, 255, 255], dtype=np.uint8)
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skin_mask2 = cv2.inRange(hsv, lower_skin2, upper_skin2)
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skin_mask = cv2.bitwise_or(skin_mask1, skin_mask2)
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# Very conservative edge detection
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gray = cv2.cvtColor(region, cv2.COLOR_BGR2GRAY)
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edges = cv2.Canny(gray, 80, 160) # Higher thresholds for obvious edges only
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# Combine but be very conservative
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occlusion_mask = cv2.bitwise_and(skin_mask, edges) # Must be both skin-like AND have edges
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# Clean up with morphological operations
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kernel = np.ones((7, 7), np.uint8)
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occlusion_mask = cv2.morphologyEx(occlusion_mask, cv2.MORPH_CLOSE, kernel)
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occlusion_mask = cv2.morphologyEx(occlusion_mask, cv2.MORPH_OPEN, kernel)
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# Only keep significant connected components
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num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(occlusion_mask)
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filtered_mask = np.zeros_like(occlusion_mask)
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for i in range(1, num_labels):
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area = stats[i, cv2.CC_STAT_AREA]
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if area > 200: # Only keep larger occlusions
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filtered_mask[labels == i] = 255
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# Apply very light Gaussian blur
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|
filtered_mask = cv2.GaussianBlur(filtered_mask, (5, 5), 1)
|
|
|
|
return filtered_mask
|
|
|
|
except Exception:
|
|
# Return empty mask if detection fails
|
|
return np.zeros(region.shape[:2], dtype=np.uint8)
|
|
|
|
|
|
def process_frame(source_face: Face, temp_frame: Frame) -> Frame:
|
|
"""Ultra-fast process_frame - maximum FPS priority"""
|
|
|
|
# Apply color correction if enabled
|
|
if modules.globals.color_correction:
|
|
temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB)
|
|
|
|
if modules.globals.many_faces:
|
|
many_faces = get_many_faces(temp_frame)
|
|
if many_faces:
|
|
for target_face in many_faces:
|
|
if source_face and target_face:
|
|
temp_frame = swap_face(source_face, target_face, temp_frame)
|
|
else:
|
|
print("Face detection failed for target/source.")
|
|
else:
|
|
target_face = get_one_face(temp_frame)
|
|
if target_face and source_face:
|
|
temp_frame = swap_face(source_face, target_face, temp_frame)
|
|
else:
|
|
logging.error("Face detection failed for target or source.")
|
|
|
|
return temp_frame
|
|
|
|
|
|
|
|
def process_frame_v2(temp_frame: Frame, temp_frame_path: str = "") -> Frame:
|
|
if is_image(modules.globals.target_path):
|
|
if modules.globals.many_faces:
|
|
source_face = default_source_face()
|
|
for map in modules.globals.source_target_map:
|
|
target_face = map["target"]["face"]
|
|
temp_frame = swap_face(source_face, target_face, temp_frame)
|
|
|
|
elif not modules.globals.many_faces:
|
|
for map in modules.globals.source_target_map:
|
|
if "source" in map:
|
|
source_face = map["source"]["face"]
|
|
target_face = map["target"]["face"]
|
|
temp_frame = swap_face(source_face, target_face, temp_frame)
|
|
|
|
elif is_video(modules.globals.target_path):
|
|
if modules.globals.many_faces:
|
|
source_face = default_source_face()
|
|
for map in modules.globals.source_target_map:
|
|
target_frame = [
|
|
f
|
|
for f in map["target_faces_in_frame"]
|
|
if f["location"] == temp_frame_path
|
|
]
|
|
|
|
for frame in target_frame:
|
|
for target_face in frame["faces"]:
|
|
temp_frame = swap_face(source_face, target_face, temp_frame)
|
|
|
|
elif not modules.globals.many_faces:
|
|
for map in modules.globals.source_target_map:
|
|
if "source" in map:
|
|
target_frame = [
|
|
f
|
|
for f in map["target_faces_in_frame"]
|
|
if f["location"] == temp_frame_path
|
|
]
|
|
source_face = map["source"]["face"]
|
|
|
|
for frame in target_frame:
|
|
for target_face in frame["faces"]:
|
|
temp_frame = swap_face(source_face, target_face, temp_frame)
|
|
|
|
else:
|
|
detected_faces = get_many_faces(temp_frame)
|
|
if modules.globals.many_faces:
|
|
if detected_faces:
|
|
source_face = default_source_face()
|
|
for target_face in detected_faces:
|
|
temp_frame = swap_face(source_face, target_face, temp_frame)
|
|
|
|
elif not modules.globals.many_faces:
|
|
if detected_faces:
|
|
if len(detected_faces) <= len(
|
|
modules.globals.simple_map["target_embeddings"]
|
|
):
|
|
for detected_face in detected_faces:
|
|
closest_centroid_index, _ = find_closest_centroid(
|
|
modules.globals.simple_map["target_embeddings"],
|
|
detected_face.normed_embedding,
|
|
)
|
|
|
|
temp_frame = swap_face(
|
|
modules.globals.simple_map["source_faces"][
|
|
closest_centroid_index
|
|
],
|
|
detected_face,
|
|
temp_frame,
|
|
)
|
|
else:
|
|
detected_faces_centroids = []
|
|
for face in detected_faces:
|
|
detected_faces_centroids.append(face.normed_embedding)
|
|
i = 0
|
|
for target_embedding in modules.globals.simple_map[
|
|
"target_embeddings"
|
|
]:
|
|
closest_centroid_index, _ = find_closest_centroid(
|
|
detected_faces_centroids, target_embedding
|
|
)
|
|
|
|
temp_frame = swap_face(
|
|
modules.globals.simple_map["source_faces"][i],
|
|
detected_faces[closest_centroid_index],
|
|
temp_frame,
|
|
)
|
|
i += 1
|
|
return temp_frame
|
|
|
|
|
|
def process_frames(
|
|
source_path: str, temp_frame_paths: List[str], progress: Any = None
|
|
) -> None:
|
|
if not modules.globals.map_faces:
|
|
source_face = get_one_face(cv2.imread(source_path))
|
|
for temp_frame_path in temp_frame_paths:
|
|
temp_frame = cv2.imread(temp_frame_path)
|
|
try:
|
|
result = process_frame(source_face, temp_frame)
|
|
cv2.imwrite(temp_frame_path, result)
|
|
except Exception as exception:
|
|
print(exception)
|
|
pass
|
|
if progress:
|
|
progress.update(1)
|
|
else:
|
|
for temp_frame_path in temp_frame_paths:
|
|
temp_frame = cv2.imread(temp_frame_path)
|
|
try:
|
|
result = process_frame_v2(temp_frame, temp_frame_path)
|
|
cv2.imwrite(temp_frame_path, result)
|
|
except Exception as exception:
|
|
print(exception)
|
|
pass
|
|
if progress:
|
|
progress.update(1)
|
|
|
|
|
|
def process_image(source_path: str, target_path: str, output_path: str) -> None:
|
|
if not modules.globals.map_faces:
|
|
source_face = get_one_face(cv2.imread(source_path))
|
|
target_frame = cv2.imread(target_path)
|
|
result = process_frame(source_face, target_frame)
|
|
cv2.imwrite(output_path, result)
|
|
else:
|
|
if modules.globals.many_faces:
|
|
update_status(
|
|
"Many faces enabled. Using first source image. Progressing...", NAME
|
|
)
|
|
target_frame = cv2.imread(output_path)
|
|
result = process_frame_v2(target_frame)
|
|
cv2.imwrite(output_path, result)
|
|
|
|
|
|
def process_video(source_path: str, temp_frame_paths: List[str]) -> None:
|
|
if modules.globals.map_faces and modules.globals.many_faces:
|
|
update_status(
|
|
"Many faces enabled. Using first source image. Progressing...", NAME
|
|
)
|
|
modules.processors.frame.core.process_video(
|
|
source_path, temp_frame_paths, process_frames
|
|
)
|
|
|
|
|
|
def create_lower_mouth_mask(
|
|
face: Face, frame: Frame
|
|
) -> (np.ndarray, np.ndarray, tuple, np.ndarray):
|
|
mask = np.zeros(frame.shape[:2], dtype=np.uint8)
|
|
mouth_cutout = None
|
|
landmarks = face.landmark_2d_106
|
|
if landmarks is not None:
|
|
# 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)
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hull_padded = []
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for point in hull:
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x, y = point[0]
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center = np.mean(face_outline, axis=0)
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direction = np.array([x, y]) - center
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direction = direction / np.linalg.norm(direction)
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padded_point = np.array([x, y]) + direction * padding
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hull_padded.append(padded_point)
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|
|
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hull_padded = np.array(hull_padded, dtype=np.int32)
|
|
|
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# Fill the padded convex hull
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cv2.fillConvexPoly(mask, hull_padded, 255)
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|
|
|
# Smooth the mask edges
|
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mask = cv2.GaussianBlur(mask, (5, 5), 3)
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|
|
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return mask
|
|
|
|
|
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def apply_color_transfer(source, target):
|
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"""
|
|
Apply color transfer from target to source image
|
|
"""
|
|
source = cv2.cvtColor(source, cv2.COLOR_BGR2LAB).astype("float32")
|
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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)
|