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| from argparse import ArgumentParser | |
| from typing import List | |
| import cv2 | |
| import numpy | |
| import facefusion.jobs.job_manager | |
| import facefusion.jobs.job_store | |
| import facefusion.processors.core as processors | |
| from facefusion import config, content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer, logger, process_manager, state_manager, wording | |
| from facefusion.face_analyser import get_many_faces, get_one_face | |
| from facefusion.face_helper import warp_face_by_face_landmark_5 | |
| from facefusion.face_masker import create_occlusion_mask, create_region_mask, create_static_box_mask | |
| from facefusion.face_selector import categorize_age, categorize_gender, find_similar_faces, sort_and_filter_faces | |
| from facefusion.face_store import get_reference_faces | |
| from facefusion.filesystem import in_directory, same_file_extension | |
| from facefusion.processors import choices as processors_choices | |
| from facefusion.processors.typing import FaceDebuggerInputs | |
| from facefusion.program_helper import find_argument_group | |
| from facefusion.typing import Args, Face, ProcessMode, QueuePayload, UpdateProgress, VisionFrame | |
| from facefusion.vision import read_image, read_static_image, write_image | |
| def get_inference_pool() -> None: | |
| pass | |
| def clear_inference_pool() -> None: | |
| pass | |
| def register_args(program : ArgumentParser) -> None: | |
| group_processors = find_argument_group(program, 'processors') | |
| if group_processors: | |
| group_processors.add_argument('--face-debugger-items', help = wording.get('help.face_debugger_items').format(choices = ', '.join(processors_choices.face_debugger_items)), default = config.get_str_list('processors.face_debugger_items', 'face-landmark-5/68 face-mask'), choices = processors_choices.face_debugger_items, nargs = '+', metavar = 'FACE_DEBUGGER_ITEMS') | |
| facefusion.jobs.job_store.register_step_keys([ 'face_debugger_items' ]) | |
| def apply_args(args : Args) -> None: | |
| state_manager.init_item('face_debugger_items', args.get('face_debugger_items')) | |
| def pre_check() -> bool: | |
| return True | |
| def pre_process(mode : ProcessMode) -> bool: | |
| if mode == 'output' and not in_directory(state_manager.get_item('output_path')): | |
| logger.error(wording.get('specify_image_or_video_output') + wording.get('exclamation_mark'), __name__.upper()) | |
| return False | |
| if mode == 'output' and not same_file_extension([ state_manager.get_item('target_path'), state_manager.get_item('output_path') ]): | |
| logger.error(wording.get('match_target_and_output_extension') + wording.get('exclamation_mark'), __name__.upper()) | |
| return False | |
| return True | |
| def post_process() -> None: | |
| read_static_image.cache_clear() | |
| if state_manager.get_item('video_memory_strategy') == 'strict': | |
| content_analyser.clear_inference_pool() | |
| face_classifier.clear_inference_pool() | |
| face_detector.clear_inference_pool() | |
| face_landmarker.clear_inference_pool() | |
| face_masker.clear_inference_pool() | |
| face_recognizer.clear_inference_pool() | |
| def debug_face(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame: | |
| primary_color = (0, 0, 255) | |
| primary_light_color = (100, 100, 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.landmark_set.get('5'), target_face.landmark_set.get('5/68')) | |
| has_face_landmark_68_fallback = numpy.array_equal(target_face.landmark_set.get('68'), target_face.landmark_set.get('68/5')) | |
| face_debugger_items = state_manager.get_item('face_debugger_items') | |
| if 'bounding-box' in face_debugger_items: | |
| x1, y1, x2, y2 = bounding_box | |
| cv2.rectangle(temp_vision_frame, (x1, y1), (x2, y2), primary_color, 2) | |
| if target_face.angle == 0: | |
| cv2.line(temp_vision_frame, (x1, y1), (x2, y1), primary_light_color, 3) | |
| elif target_face.angle == 180: | |
| cv2.line(temp_vision_frame, (x1, y2), (x2, y2), primary_light_color, 3) | |
| elif target_face.angle == 90: | |
| cv2.line(temp_vision_frame, (x2, y1), (x2, y2), primary_light_color, 3) | |
| elif target_face.angle == 270: | |
| cv2.line(temp_vision_frame, (x1, y1), (x1, y2), primary_light_color, 3) | |
| if 'face-mask' in face_debugger_items: | |
| crop_vision_frame, affine_matrix = warp_face_by_face_landmark_5(temp_vision_frame, target_face.landmark_set.get('5/68'), 'arcface_128_v2', (512, 512)) | |
| inverse_matrix = cv2.invertAffineTransform(affine_matrix) | |
| temp_size = temp_vision_frame.shape[:2][::-1] | |
| crop_masks = [] | |
| if 'box' in state_manager.get_item('face_mask_types'): | |
| box_mask = create_static_box_mask(crop_vision_frame.shape[:2][::-1], 0, state_manager.get_item('face_mask_padding')) | |
| crop_masks.append(box_mask) | |
| if 'occlusion' in state_manager.get_item('face_mask_types'): | |
| occlusion_mask = create_occlusion_mask(crop_vision_frame) | |
| crop_masks.append(occlusion_mask) | |
| if 'region' in state_manager.get_item('face_mask_types'): | |
| region_mask = create_region_mask(crop_vision_frame, state_manager.get_item('face_mask_regions')) | |
| crop_masks.append(region_mask) | |
| crop_mask = numpy.minimum.reduce(crop_masks).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 #type:ignore[operator] | |
| 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 face_debugger_items and numpy.any(target_face.landmark_set.get('5')): | |
| face_landmark_5 = target_face.landmark_set.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 face_debugger_items and numpy.any(target_face.landmark_set.get('5/68')): | |
| face_landmark_5_68 = target_face.landmark_set.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 face_debugger_items and numpy.any(target_face.landmark_set.get('68')): | |
| face_landmark_68 = target_face.landmark_set.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 face_debugger_items and numpy.any(target_face.landmark_set.get('68')): | |
| face_landmark_68 = target_face.landmark_set.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 face_debugger_items: | |
| face_score_text = str(round(target_face.score_set.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 face_debugger_items: | |
| face_score_text = str(round(target_face.score_set.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 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 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') | |
| many_faces = sort_and_filter_faces(get_many_faces([ target_vision_frame ])) | |
| if state_manager.get_item('face_selector_mode') == 'many': | |
| if many_faces: | |
| for target_face in many_faces: | |
| target_vision_frame = debug_face(target_face, target_vision_frame) | |
| if state_manager.get_item('face_selector_mode') == 'one': | |
| target_face = get_one_face(many_faces) | |
| if target_face: | |
| target_vision_frame = debug_face(target_face, target_vision_frame) | |
| if state_manager.get_item('face_selector_mode') == 'reference': | |
| similar_faces = find_similar_faces(many_faces, reference_faces, state_manager.get_item('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 state_manager.get_item('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 state_manager.get_item('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: | |
| processors.multi_process_frames(source_paths, temp_frame_paths, process_frames) | |