from typing import Any, List, Literal from argparse import ArgumentParser import cv2 import numpy import deepfuze.globals import deepfuze.processors.frame.core as frame_processors from deepfuze import config, process_manager, wording from deepfuze.face_analyser import get_one_face, get_many_faces, find_similar_faces, clear_face_analyser from deepfuze.face_masker import create_static_box_mask, create_occlusion_mask, create_region_mask, clear_face_occluder, clear_face_parser from deepfuze.face_helper import warp_face_by_face_landmark_5, categorize_age, categorize_gender from deepfuze.face_store import get_reference_faces from deepfuze.content_analyser import clear_content_analyser from deepfuze.typing import Face, VisionFrame, UpdateProgress, ProcessMode, QueuePayload from deepfuze.vision import read_image, read_static_image, write_image from deepfuze.processors.frame.typings import FaceDebuggerInputs from deepfuze.processors.frame import globals as frame_processors_globals, choices as frame_processors_choices NAME = __name__.upper() def get_frame_processor() -> None: pass def clear_frame_processor() -> None: pass def get_options(key : Literal['model']) -> None: pass def set_options(key : Literal['model'], value : Any) -> None: pass def register_args(program : ArgumentParser) -> None: program.add_argument('--face-debugger-items', help = wording.get('help.face_debugger_items').format(choices = ', '.join(frame_processors_choices.face_debugger_items)), default = config.get_str_list('frame_processors.face_debugger_items', 'face-landmark-5/68 face-mask'), choices = frame_processors_choices.face_debugger_items, nargs = '+', metavar = 'FACE_DEBUGGER_ITEMS') def apply_args(program : ArgumentParser) -> None: args = program.parse_args() frame_processors_globals.face_debugger_items = args.face_debugger_items def pre_check() -> bool: return True def post_check() -> bool: return True def pre_process(mode : ProcessMode) -> bool: return True def post_process() -> None: read_static_image.cache_clear() if deepfuze.globals.video_memory_strategy == 'strict' or deepfuze.globals.video_memory_strategy == 'moderate': clear_frame_processor() if deepfuze.globals.video_memory_strategy == 'strict': clear_face_analyser() clear_content_analyser() clear_face_occluder() clear_face_parser() def debug_face(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame: primary_color = (0, 0, 255) secondary_color = (0, 255, 0) tertiary_color = (255, 255, 0) bounding_box = target_face.bounding_box.astype(numpy.int32) temp_vision_frame = temp_vision_frame.copy() has_face_landmark_5_fallback = numpy.array_equal(target_face.landmarks.get('5'), target_face.landmarks.get('5/68')) has_face_landmark_68_fallback = numpy.array_equal(target_face.landmarks.get('68'), target_face.landmarks.get('68/5')) if 'bounding-box' in frame_processors_globals.face_debugger_items: cv2.rectangle(temp_vision_frame, (bounding_box[0], bounding_box[1]), (bounding_box[2], bounding_box[3]), primary_color, 2) if 'face-mask' in frame_processors_globals.face_debugger_items: crop_vision_frame, affine_matrix = warp_face_by_face_landmark_5(temp_vision_frame, target_face.landmarks.get('5/68'), 'arcface_128_v2', (512, 512)) inverse_matrix = cv2.invertAffineTransform(affine_matrix) temp_size = temp_vision_frame.shape[:2][::-1] crop_mask_list = [] if 'box' in deepfuze.globals.face_mask_types: box_mask = create_static_box_mask(crop_vision_frame.shape[:2][::-1], 0, deepfuze.globals.face_mask_padding) crop_mask_list.append(box_mask) if 'occlusion' in deepfuze.globals.face_mask_types: occlusion_mask = create_occlusion_mask(crop_vision_frame) crop_mask_list.append(occlusion_mask) if 'region' in deepfuze.globals.face_mask_types: region_mask = create_region_mask(crop_vision_frame, deepfuze.globals.face_mask_regions) crop_mask_list.append(region_mask) crop_mask = numpy.minimum.reduce(crop_mask_list).clip(0, 1) crop_mask = (crop_mask * 255).astype(numpy.uint8) inverse_vision_frame = cv2.warpAffine(crop_mask, inverse_matrix, temp_size) inverse_vision_frame = cv2.threshold(inverse_vision_frame, 100, 255, cv2.THRESH_BINARY)[1] inverse_vision_frame[inverse_vision_frame > 0] = 255 inverse_contours = cv2.findContours(inverse_vision_frame, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)[0] cv2.drawContours(temp_vision_frame, inverse_contours, -1, tertiary_color if has_face_landmark_5_fallback else secondary_color, 2) if 'face-landmark-5' in frame_processors_globals.face_debugger_items and numpy.any(target_face.landmarks.get('5')): face_landmark_5 = target_face.landmarks.get('5').astype(numpy.int32) for index in range(face_landmark_5.shape[0]): cv2.circle(temp_vision_frame, (face_landmark_5[index][0], face_landmark_5[index][1]), 3, primary_color, -1) if 'face-landmark-5/68' in frame_processors_globals.face_debugger_items and numpy.any(target_face.landmarks.get('5/68')): face_landmark_5_68 = target_face.landmarks.get('5/68').astype(numpy.int32) for index in range(face_landmark_5_68.shape[0]): cv2.circle(temp_vision_frame, (face_landmark_5_68[index][0], face_landmark_5_68[index][1]), 3, tertiary_color if has_face_landmark_5_fallback else secondary_color, -1) if 'face-landmark-68' in frame_processors_globals.face_debugger_items and numpy.any(target_face.landmarks.get('68')): face_landmark_68 = target_face.landmarks.get('68').astype(numpy.int32) for index in range(face_landmark_68.shape[0]): cv2.circle(temp_vision_frame, (face_landmark_68[index][0], face_landmark_68[index][1]), 3, tertiary_color if has_face_landmark_68_fallback else secondary_color, -1) if 'face-landmark-68/5' in frame_processors_globals.face_debugger_items and numpy.any(target_face.landmarks.get('68')): face_landmark_68 = target_face.landmarks.get('68/5').astype(numpy.int32) for index in range(face_landmark_68.shape[0]): cv2.circle(temp_vision_frame, (face_landmark_68[index][0], face_landmark_68[index][1]), 3, primary_color, -1) if bounding_box[3] - bounding_box[1] > 50 and bounding_box[2] - bounding_box[0] > 50: top = bounding_box[1] left = bounding_box[0] - 20 if 'face-detector-score' in frame_processors_globals.face_debugger_items: face_score_text = str(round(target_face.scores.get('detector'), 2)) top = top + 20 cv2.putText(temp_vision_frame, face_score_text, (left, top), cv2.FONT_HERSHEY_SIMPLEX, 0.5, primary_color, 2) if 'face-landmarker-score' in frame_processors_globals.face_debugger_items: face_score_text = str(round(target_face.scores.get('landmarker'), 2)) top = top + 20 cv2.putText(temp_vision_frame, face_score_text, (left, top), cv2.FONT_HERSHEY_SIMPLEX, 0.5, tertiary_color if has_face_landmark_5_fallback else secondary_color, 2) if 'age' in frame_processors_globals.face_debugger_items: face_age_text = categorize_age(target_face.age) top = top + 20 cv2.putText(temp_vision_frame, face_age_text, (left, top), cv2.FONT_HERSHEY_SIMPLEX, 0.5, primary_color, 2) if 'gender' in frame_processors_globals.face_debugger_items: face_gender_text = categorize_gender(target_face.gender) top = top + 20 cv2.putText(temp_vision_frame, face_gender_text, (left, top), cv2.FONT_HERSHEY_SIMPLEX, 0.5, primary_color, 2) return temp_vision_frame def get_reference_frame(source_face : Face, target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame: pass def process_frame(inputs : FaceDebuggerInputs) -> VisionFrame: reference_faces = inputs.get('reference_faces') target_vision_frame = inputs.get('target_vision_frame') if deepfuze.globals.face_selector_mode == 'many': many_faces = get_many_faces(target_vision_frame) if many_faces: for target_face in many_faces: target_vision_frame = debug_face(target_face, target_vision_frame) if deepfuze.globals.face_selector_mode == 'one': target_face = get_one_face(target_vision_frame) if target_face: target_vision_frame = debug_face(target_face, target_vision_frame) if deepfuze.globals.face_selector_mode == 'reference': similar_faces = find_similar_faces(reference_faces, target_vision_frame, deepfuze.globals.reference_face_distance) if similar_faces: for similar_face in similar_faces: target_vision_frame = debug_face(similar_face, target_vision_frame) return target_vision_frame def process_frames(source_paths : List[str], queue_payloads : List[QueuePayload], update_progress : UpdateProgress) -> None: reference_faces = get_reference_faces() if 'reference' in deepfuze.globals.face_selector_mode else None for queue_payload in process_manager.manage(queue_payloads): target_vision_path = queue_payload['frame_path'] target_vision_frame = read_image(target_vision_path) output_vision_frame = process_frame( { 'reference_faces': reference_faces, 'target_vision_frame': target_vision_frame }) write_image(target_vision_path, output_vision_frame) update_progress(1) def process_image(source_paths : List[str], target_path : str, output_path : str) -> None: reference_faces = get_reference_faces() if 'reference' in deepfuze.globals.face_selector_mode else None target_vision_frame = read_static_image(target_path) output_vision_frame = process_frame( { 'reference_faces': reference_faces, 'target_vision_frame': target_vision_frame }) write_image(output_path, output_vision_frame) def process_video(source_paths : List[str], temp_frame_paths : List[str]) -> None: frame_processors.multi_process_frames(source_paths, temp_frame_paths, process_frames)