858 lines
35 KiB
Python
858 lines
35 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 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.hair_segmenter import segment_hair
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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_obj: Face, target_face: Face, source_frame_full: Frame, temp_frame: Frame) -> Frame:
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face_swapper = get_face_swapper()
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# Apply the face swap
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swapped_frame = face_swapper.get(
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temp_frame, target_face, source_face_obj, paste_back=True
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)
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final_swapped_frame = swapped_frame.copy() # Initialize final_swapped_frame
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# START of Hair Blending Logic
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if source_face_obj.kps is not None and target_face.kps is not None and source_face_obj.kps.shape[0] >=2 and target_face.kps.shape[0] >=2 : # kps are 5x2 landmarks
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hair_only_mask_source = segment_hair(source_frame_full)
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# Ensure kps are float32 for estimateAffinePartial2D
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source_kps_float = source_face_obj.kps.astype(np.float32)
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target_kps_float = target_face.kps.astype(np.float32)
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# b. Estimate Transformation Matrix
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# Using LMEDS for robustness
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matrix, _ = cv2.estimateAffinePartial2D(source_kps_float, target_kps_float, method=cv2.LMEDS)
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if matrix is not None:
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# c. Warp Source Hair and its Mask
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dsize = (temp_frame.shape[1], temp_frame.shape[0]) # width, height
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# Ensure hair_only_mask_source is 8-bit single channel
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if hair_only_mask_source.ndim == 3 and hair_only_mask_source.shape[2] == 3:
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hair_only_mask_source_gray = cv2.cvtColor(hair_only_mask_source, cv2.COLOR_BGR2GRAY)
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else:
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hair_only_mask_source_gray = hair_only_mask_source
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# Threshold to ensure binary mask for warping
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_, hair_only_mask_source_binary = cv2.threshold(hair_only_mask_source_gray, 127, 255, cv2.THRESH_BINARY)
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warped_hair_mask = cv2.warpAffine(hair_only_mask_source_binary, matrix, dsize)
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warped_source_hair_image = cv2.warpAffine(source_frame_full, matrix, dsize)
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# d. Color Correct Warped Source Hair
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# Using swapped_frame (face-swapped output) as the target for color correction
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color_corrected_warped_hair = apply_color_transfer(warped_source_hair_image, swapped_frame)
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# e. Blend Hair onto Swapped Frame
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# Ensure warped_hair_mask is binary (0 or 255) after warping
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_, warped_hair_mask_binary = cv2.threshold(warped_hair_mask, 127, 255, cv2.THRESH_BINARY)
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# Preferred: cv2.seamlessClone
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x, y, w, h = cv2.boundingRect(warped_hair_mask_binary)
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if w > 0 and h > 0:
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center = (x + w // 2, y + h // 2)
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# seamlessClone expects target image, source image, mask, center, method
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# The mask should be single channel 8-bit.
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# The source (color_corrected_warped_hair) and target (swapped_frame) should be 8-bit 3-channel.
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# Check if swapped_frame is suitable for seamlessClone (it should be the base)
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# Ensure color_corrected_warped_hair is also 8UC3
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if color_corrected_warped_hair.shape == swapped_frame.shape and \
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color_corrected_warped_hair.dtype == swapped_frame.dtype and \
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warped_hair_mask_binary.dtype == np.uint8:
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try:
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final_swapped_frame = cv2.seamlessClone(color_corrected_warped_hair, swapped_frame, warped_hair_mask_binary, center, cv2.NORMAL_CLONE)
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except cv2.error as e:
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logging.warning(f"cv2.seamlessClone failed: {e}. Falling back to simple blending.")
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# Fallback: Simple Blending (if seamlessClone fails)
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warped_hair_mask_3ch = cv2.cvtColor(warped_hair_mask_binary, cv2.COLOR_GRAY2BGR) > 0 # boolean mask
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final_swapped_frame[warped_hair_mask_3ch] = color_corrected_warped_hair[warped_hair_mask_3ch]
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else:
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logging.warning("Mismatch in shape/type for seamlessClone. Falling back to simple blending.")
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# Fallback: Simple Blending
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warped_hair_mask_3ch = cv2.cvtColor(warped_hair_mask_binary, cv2.COLOR_GRAY2BGR) > 0
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final_swapped_frame[warped_hair_mask_3ch] = color_corrected_warped_hair[warped_hair_mask_3ch]
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else:
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# Mask is empty, no hair to blend, final_swapped_frame remains as is (copy of swapped_frame)
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logging.info("Warped hair mask is empty. Skipping hair blending.")
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# final_swapped_frame is already a copy of swapped_frame
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else:
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logging.warning("Failed to estimate affine transformation matrix for hair. Skipping hair blending.")
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# final_swapped_frame is already a copy of swapped_frame
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else:
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if source_face_obj.kps is None or target_face.kps is None:
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logging.warning("Source or target keypoints (kps) are None. Skipping hair blending.")
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else:
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logging.warning(f"Not enough keypoints for hair transformation. Source kps: {source_face_obj.kps.shape if source_face_obj.kps is not None else 'None'}, Target kps: {target_face.kps.shape if target_face.kps is not None else 'None'}. Skipping hair blending.")
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# final_swapped_frame is already a copy of swapped_frame
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# END of Hair Blending Logic
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# f. Mouth Mask Logic
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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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# Apply to final_swapped_frame if hair blending happened, otherwise to swapped_frame
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final_swapped_frame = apply_mouth_area(
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final_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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final_swapped_frame = draw_mouth_mask_visualization(
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final_swapped_frame, target_face, mouth_mask_data
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)
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return final_swapped_frame
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def process_frame(source_face_obj: Face, source_frame_full: Frame, temp_frame: Frame) -> Frame:
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if modules.globals.color_correction:
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temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB)
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if modules.globals.many_faces:
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many_faces = get_many_faces(temp_frame)
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if many_faces:
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for target_face in many_faces:
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if source_face_obj and target_face:
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temp_frame = swap_face(source_face_obj, target_face, source_frame_full, temp_frame)
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else:
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print("Face detection failed for target/source.")
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else:
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target_face = get_one_face(temp_frame)
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if target_face and source_face_obj:
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temp_frame = swap_face(source_face_obj, target_face, source_frame_full, temp_frame)
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else:
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logging.error("Face detection failed for target or source.")
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return temp_frame
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# process_frame_v2 needs to accept source_frame_full as well
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def process_frame_v2(source_frame_full: Frame, temp_frame: Frame, temp_frame_path: str = "") -> Frame:
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if is_image(modules.globals.target_path):
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if modules.globals.many_faces:
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source_face_obj = default_source_face() # This function needs to be checked if it needs source_frame_full
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if source_face_obj: # Ensure default_source_face actually returns a face
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for map_item in modules.globals.source_target_map: # Renamed map to map_item to avoid conflict
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target_face = map_item["target"]["face"]
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temp_frame = swap_face(source_face_obj, target_face, source_frame_full, temp_frame)
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elif not modules.globals.many_faces:
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for map_item in modules.globals.source_target_map: # Renamed map to map_item
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if "source" in map_item:
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source_face_obj = map_item["source"]["face"]
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target_face = map_item["target"]["face"]
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temp_frame = swap_face(source_face_obj, target_face, source_frame_full, temp_frame)
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elif is_video(modules.globals.target_path):
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if modules.globals.many_faces:
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source_face_obj = default_source_face() # This function needs to be checked
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if source_face_obj:
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for map_item in modules.globals.source_target_map: # Renamed map to map_item
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target_frames_data = [ # Renamed target_frame to target_frames_data
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f
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for f in map_item["target_faces_in_frame"]
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if f["location"] == temp_frame_path
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]
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for frame_data in target_frames_data: # Renamed frame to frame_data
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for target_face in frame_data["faces"]:
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temp_frame = swap_face(source_face_obj, target_face, source_frame_full, temp_frame)
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elif not modules.globals.many_faces:
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for map_item in modules.globals.source_target_map: # Renamed map to map_item
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if "source" in map_item:
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target_frames_data = [ # Renamed target_frame to target_frames_data
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f
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for f in map_item["target_faces_in_frame"]
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if f["location"] == temp_frame_path
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]
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source_face_obj = map_item["source"]["face"]
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for frame_data in target_frames_data: # Renamed frame to frame_data
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for target_face in frame_data["faces"]:
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temp_frame = swap_face(source_face_obj, target_face, source_frame_full, temp_frame)
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else: # This is the live cam / generic case
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detected_faces = get_many_faces(temp_frame)
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if modules.globals.many_faces:
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if detected_faces:
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source_face_obj = default_source_face() # This function needs to be checked
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if source_face_obj:
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for target_face in detected_faces:
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temp_frame = swap_face(source_face_obj, target_face, source_frame_full, temp_frame)
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elif not modules.globals.many_faces:
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if detected_faces:
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if len(detected_faces) <= len(
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modules.globals.simple_map["target_embeddings"]
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):
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for detected_face in detected_faces:
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closest_centroid_index, _ = find_closest_centroid(
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modules.globals.simple_map["target_embeddings"],
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detected_face.normed_embedding,
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)
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# Assuming simple_map["source_faces"] are Face objects
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# And default_source_face() logic might need to be more complex if source_frame_full is always from a single source_path
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source_face_obj_from_map = modules.globals.simple_map["source_faces"][closest_centroid_index]
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temp_frame = swap_face(
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source_face_obj_from_map, # This is source_face_obj
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detected_face, # This is target_face
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source_frame_full, # This is source_frame_full
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temp_frame,
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)
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else:
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detected_faces_centroids = []
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for face in detected_faces:
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detected_faces_centroids.append(face.normed_embedding)
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i = 0
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for target_embedding in modules.globals.simple_map[
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"target_embeddings"
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]:
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closest_centroid_index, _ = find_closest_centroid(
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detected_faces_centroids, target_embedding
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)
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source_face_obj_from_map = modules.globals.simple_map["source_faces"][i]
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temp_frame = swap_face(
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source_face_obj_from_map, # source_face_obj
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detected_faces[closest_centroid_index], # target_face
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source_frame_full, # source_frame_full
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temp_frame,
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)
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i += 1
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return temp_frame
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def process_frames(
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source_path: str, temp_frame_paths: List[str], progress: Any = None
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) -> None:
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source_img = cv2.imread(source_path)
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if source_img is None:
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logging.error(f"Failed to read source image from {source_path}")
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return
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if not modules.globals.map_faces:
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source_face_obj = get_one_face(source_img) # Use source_img here
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if not source_face_obj:
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logging.error(f"No face detected in source image {source_path}")
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return
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for temp_frame_path in temp_frame_paths:
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temp_frame = cv2.imread(temp_frame_path)
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if temp_frame is None:
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logging.warning(f"Failed to read temp_frame from {temp_frame_path}, skipping.")
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continue
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try:
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result = process_frame(source_face_obj, source_img, temp_frame)
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cv2.imwrite(temp_frame_path, result)
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except Exception as exception:
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logging.error(f"Error processing frame {temp_frame_path}: {exception}", exc_info=True)
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pass
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if progress:
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progress.update(1)
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else: # This is for map_faces == True
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# In map_faces=True, source_face is determined per mapping.
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# process_frame_v2 will need source_frame_full for hair,
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# which should be the original source_path image.
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for temp_frame_path in temp_frame_paths:
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temp_frame = cv2.imread(temp_frame_path)
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if temp_frame is None:
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logging.warning(f"Failed to read temp_frame from {temp_frame_path}, skipping.")
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continue
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try:
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# Pass source_img (as source_frame_full) to process_frame_v2
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result = process_frame_v2(source_img, temp_frame, temp_frame_path)
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cv2.imwrite(temp_frame_path, result)
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except Exception as exception:
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logging.error(f"Error processing frame {temp_frame_path} with map_faces: {exception}", exc_info=True)
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pass
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if progress:
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progress.update(1)
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def process_image(source_path: str, target_path: str, output_path: str) -> None:
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source_img = cv2.imread(source_path)
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if source_img is None:
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logging.error(f"Failed to read source image from {source_path}")
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return
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target_frame = cv2.imread(target_path)
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if target_frame is None:
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logging.error(f"Failed to read target image from {target_path}")
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return
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if not modules.globals.map_faces:
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source_face_obj = get_one_face(source_img) # Use source_img here
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if not source_face_obj:
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logging.error(f"No face detected in source image {source_path}")
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return
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result = process_frame(source_face_obj, source_img, target_frame)
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cv2.imwrite(output_path, result)
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else:
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# map_faces == True for process_image
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# process_frame_v2 expects source_frame_full as its first argument.
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# The output_path is often the same as target_path initially for images.
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# We read the target_frame (which will be modified)
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target_frame_for_v2 = cv2.imread(output_path) # Or target_path, depending on desired workflow
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if target_frame_for_v2 is None:
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logging.error(f"Failed to read image for process_frame_v2 from {output_path}")
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return
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if modules.globals.many_faces:
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update_status(
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"Many faces enabled. Using first source image. Progressing...", NAME
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)
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# Pass source_img (as source_frame_full) to process_frame_v2
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result = process_frame_v2(source_img, target_frame_for_v2, target_path) # target_path as temp_frame_path hint
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cv2.imwrite(output_path, result)
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def process_video(source_path: str, temp_frame_paths: List[str]) -> None:
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if modules.globals.map_faces and modules.globals.many_faces:
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update_status(
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"Many faces enabled. Using first source image. Progressing...", NAME
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)
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modules.processors.frame.core.process_video(
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source_path, temp_frame_paths, process_frames
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)
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def create_lower_mouth_mask(
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face: Face, frame: Frame
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) -> (np.ndarray, np.ndarray, tuple, np.ndarray):
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mask = np.zeros(frame.shape[:2], dtype=np.uint8)
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mouth_cutout = None
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landmarks = face.landmark_2d_106
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if landmarks is not None:
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# 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
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lower_lip_order = [
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65,
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66,
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62,
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70,
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69,
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18,
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19,
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20,
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21,
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22,
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23,
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24,
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0,
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8,
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7,
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6,
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5,
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4,
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3,
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2,
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65,
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]
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lower_lip_landmarks = landmarks[lower_lip_order].astype(
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np.float32
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) # Use float for precise calculations
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# Calculate the center of the landmarks
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center = np.mean(lower_lip_landmarks, axis=0)
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# Expand the landmarks outward
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expansion_factor = (
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1 + modules.globals.mask_down_size
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) # 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_face_and_hair_mask(source_face: Face, source_frame: Frame) -> np.ndarray:
|
|
"""
|
|
Creates a combined mask for the face and hair from the source image.
|
|
"""
|
|
# 1. Generate the basic face mask (adapted from create_face_mask)
|
|
face_only_mask = np.zeros(source_frame.shape[:2], dtype=np.uint8)
|
|
landmarks = source_face.landmark_2d_106
|
|
if landmarks is not None:
|
|
landmarks = landmarks.astype(np.int32)
|
|
|
|
# Extract facial features (same logic as create_face_mask)
|
|
right_side_face = landmarks[0:16]
|
|
left_side_face = landmarks[17:32]
|
|
# right_eye = landmarks[33:42] # Not directly used for outline
|
|
right_eye_brow = landmarks[43:51]
|
|
# left_eye = landmarks[87:96] # Not directly used for outline
|
|
left_eye_brow = landmarks[97:105]
|
|
|
|
# Calculate forehead extension (same logic as create_face_mask)
|
|
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]])
|
|
# Ensure forehead_height is not negative if eyebrows are above the topmost landmark of face sides
|
|
forehead_height = max(0, face_top - eyebrow_top)
|
|
extended_forehead_height = int(forehead_height * 5.0)
|
|
|
|
forehead_left = right_side_face[0].copy()
|
|
forehead_right = left_side_face[-1].copy()
|
|
|
|
# Ensure extended forehead points do not go into negative y values
|
|
forehead_left[1] = max(0, forehead_left[1] - extended_forehead_height)
|
|
forehead_right[1] = max(0, forehead_right[1] - extended_forehead_height)
|
|
|
|
face_outline = np.vstack(
|
|
[
|
|
[forehead_left],
|
|
right_side_face,
|
|
left_side_face[::-1],
|
|
[forehead_right],
|
|
]
|
|
)
|
|
|
|
# Calculate padding (same logic as create_face_mask)
|
|
# Ensure face_outline has at least one point before calculating norm
|
|
if face_outline.shape[0] > 1:
|
|
padding = int(
|
|
np.linalg.norm(right_side_face[0] - left_side_face[-1]) * 0.05
|
|
)
|
|
else:
|
|
padding = 5 # Default padding if not enough points
|
|
|
|
hull = cv2.convexHull(face_outline)
|
|
hull_padded = []
|
|
center = np.mean(face_outline, axis=0).squeeze() # Squeeze to handle potential extra dim
|
|
|
|
# Ensure center is a 1D array for subtraction
|
|
if center.ndim > 1:
|
|
center = np.mean(center, axis=0)
|
|
|
|
|
|
for point_contour in hull:
|
|
point = point_contour[0] # cv2.convexHull returns points wrapped in an extra array
|
|
direction = point - center
|
|
norm_direction = np.linalg.norm(direction)
|
|
if norm_direction == 0: # Avoid division by zero if point is the center
|
|
unit_direction = np.array([0,0])
|
|
else:
|
|
unit_direction = direction / norm_direction
|
|
|
|
padded_point = point + unit_direction * padding
|
|
hull_padded.append(padded_point)
|
|
|
|
if hull_padded: # Ensure hull_padded is not empty
|
|
hull_padded = np.array(hull_padded, dtype=np.int32)
|
|
cv2.fillConvexPoly(face_only_mask, hull_padded, 255)
|
|
else: # Fallback if hull_padded is empty (e.g. very few landmarks)
|
|
cv2.fillConvexPoly(face_only_mask, hull, 255) # Use unpadded hull
|
|
|
|
|
|
# Initial blur for face_only_mask is not strictly in the old one before combining,
|
|
# but can be applied here or after combining. Let's keep it like original for now.
|
|
# face_only_mask = cv2.GaussianBlur(face_only_mask, (5, 5), 3) # Original blur from create_face_mask
|
|
|
|
# 2. Generate the hair mask
|
|
# Ensure source_frame is contiguous, as some cv2 functions might require it.
|
|
source_frame_contiguous = np.ascontiguousarray(source_frame, dtype=np.uint8)
|
|
hair_mask_on_source = segment_hair(source_frame_contiguous)
|
|
|
|
# 3. Combine the masks
|
|
# Ensure masks are binary and of the same type for bitwise operations
|
|
_, face_only_mask_binary = cv2.threshold(face_only_mask, 127, 255, cv2.THRESH_BINARY)
|
|
_, hair_mask_on_source_binary = cv2.threshold(hair_mask_on_source, 127, 255, cv2.THRESH_BINARY)
|
|
|
|
# Ensure shapes match. If not, hair_mask might be different. Resize if necessary.
|
|
# This should ideally not happen if segment_hair preserves dimensions.
|
|
if face_only_mask_binary.shape != hair_mask_on_source_binary.shape:
|
|
hair_mask_on_source_binary = cv2.resize(hair_mask_on_source_binary,
|
|
(face_only_mask_binary.shape[1], face_only_mask_binary.shape[0]),
|
|
interpolation=cv2.INTER_NEAREST)
|
|
|
|
combined_mask = cv2.bitwise_or(face_only_mask_binary, hair_mask_on_source_binary)
|
|
|
|
# 4. Apply Gaussian blur to the combined mask
|
|
combined_mask = cv2.GaussianBlur(combined_mask, (5, 5), 3)
|
|
|
|
return combined_mask
|