from argparse import ArgumentParser from types import ModuleType from typing import List import cv2 import numpy import facefusion.jobs.job_manager import facefusion.jobs.job_store from facefusion import config, content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer, logger, state_manager, translator, video_manager from facefusion.common_helper import get_middle from facefusion.face_creator import scale_face from facefusion.face_helper import warp_face_by_face_landmark_5 from facefusion.face_masker import create_area_mask, create_box_mask, create_occlusion_mask, create_region_mask from facefusion.face_selector import select_faces from facefusion.filesystem import in_directory, is_image, is_video, same_file_extension from facefusion.processors.modules.face_debugger import choices as face_debugger_choices from facefusion.processors.modules.face_debugger.types import FaceDebuggerInputs from facefusion.processors.types import ProcessorOutputs from facefusion.program_helper import find_argument_group from facefusion.types import ApplyStateItem, Args, Face, InferencePool, ProcessMode, VisionFrame from facefusion.vision import read_static_image, read_static_video_frame def get_inference_pool() -> InferencePool: 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 = translator.get('help.items', __package__).format(choices = ', '.join(face_debugger_choices.face_debugger_items)), default = config.get_str_list('processors', 'face_debugger_items', 'face-landmark-5/68 face-mask'), choices = face_debugger_choices.face_debugger_items, nargs = '+', metavar = 'FACE_DEBUGGER_ITEMS') facefusion.jobs.job_store.register_step_keys([ 'face_debugger_items' ]) def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None: apply_state_item('face_debugger_items', args.get('face_debugger_items')) def get_common_modules() -> List[ModuleType]: return [ content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer ] def pre_check() -> bool: for common_module in get_common_modules(): if not common_module.pre_check(): return False return True def pre_process(mode : ProcessMode) -> bool: if mode in [ 'output', 'preview' ] and not is_image(state_manager.get_item('target_path')) and not is_video(state_manager.get_item('target_path')): logger.error(translator.get('choose_image_or_video_target') + translator.get('exclamation_mark'), __name__) return False if mode == 'output' and not in_directory(state_manager.get_item('output_path')): logger.error(translator.get('specify_image_or_video_output') + translator.get('exclamation_mark'), __name__) return False if mode == 'output' and not same_file_extension(state_manager.get_item('target_path'), state_manager.get_item('output_path')): logger.error(translator.get('match_target_and_output_extension') + translator.get('exclamation_mark'), __name__) return False return True def post_process() -> None: read_static_image.cache_clear() read_static_video_frame.cache_clear() video_manager.clear_video_pool() if state_manager.get_item('video_memory_strategy') == 'strict': for common_module in get_common_modules(): common_module.clear_inference_pool() def debug_face(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame: face_debugger_items = state_manager.get_item('face_debugger_items') if 'bounding-box' in face_debugger_items: temp_vision_frame = draw_bounding_box(target_face, temp_vision_frame) if 'face-mask' in face_debugger_items: temp_vision_frame = draw_face_mask(target_face, temp_vision_frame) if 'face-landmark-5' in face_debugger_items: temp_vision_frame = draw_face_landmark_5(target_face, temp_vision_frame) if 'face-landmark-5/68' in face_debugger_items: temp_vision_frame = draw_face_landmark_5_68(target_face, temp_vision_frame) if 'face-landmark-68' in face_debugger_items: temp_vision_frame = draw_face_landmark_68(target_face, temp_vision_frame) if 'face-landmark-68/5' in face_debugger_items: temp_vision_frame = draw_face_landmark_68_5(target_face, temp_vision_frame) return temp_vision_frame def draw_bounding_box(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame: temp_vision_frame = numpy.ascontiguousarray(temp_vision_frame) bounding_box = target_face.bounding_box.astype(numpy.int32) x1, y1, x2, y2 = bounding_box box_color = 0, 0, 255 border_scale = calculate_scale(temp_vision_frame) border_color = 100, 100, 255 cv2.rectangle(temp_vision_frame, (x1, y1), (x2, y2), box_color, border_scale) if target_face.angle == 0: cv2.line(temp_vision_frame, (x1, y1), (x2, y1), border_color, border_scale + 1) if target_face.angle == 180: cv2.line(temp_vision_frame, (x1, y2), (x2, y2), border_color, border_scale + 1) if target_face.angle == 90: cv2.line(temp_vision_frame, (x2, y1), (x2, y2), border_color, border_scale + 1) if target_face.angle == 270: cv2.line(temp_vision_frame, (x1, y1), (x1, y2), border_color, border_scale + 1) return temp_vision_frame def draw_face_mask(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame: crop_masks = [] temp_vision_frame = numpy.ascontiguousarray(temp_vision_frame) face_landmark_5 = target_face.landmark_set.get('5') face_landmark_68 = target_face.landmark_set.get('68') face_landmark_5_68 = target_face.landmark_set.get('5/68') crop_vision_frame, affine_matrix = warp_face_by_face_landmark_5(temp_vision_frame, face_landmark_5_68, 'arcface_128', (512, 512)) inverse_matrix = cv2.invertAffineTransform(affine_matrix) temp_size = temp_vision_frame.shape[:2][::-1] mask_scale = calculate_scale(temp_vision_frame) mask_color = 0, 255, 0 if numpy.array_equal(face_landmark_5, face_landmark_5_68): mask_color = 255, 255, 0 if target_face.origin == 'refill': mask_color = 0, 165, 255 if 'box' in state_manager.get_item('face_mask_types'): box_mask = create_box_mask(crop_vision_frame, 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 'area' in state_manager.get_item('face_mask_types'): face_landmark_68 = cv2.transform(face_landmark_68.reshape(1, -1, 2), affine_matrix).reshape(-1, 2) area_mask = create_area_mask(crop_vision_frame, face_landmark_68, state_manager.get_item('face_mask_areas')) crop_masks.append(area_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_contours, _ = cv2.findContours(inverse_vision_frame, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE) cv2.drawContours(temp_vision_frame, inverse_contours, -1, mask_color, mask_scale) return temp_vision_frame def draw_face_landmark_5(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame: temp_vision_frame = numpy.ascontiguousarray(temp_vision_frame) face_landmark_5 = target_face.landmark_set.get('5') point_scale = calculate_scale(temp_vision_frame) point_color = 0, 0, 255 if target_face.origin == 'refill': point_color = 0, 165, 255 if numpy.any(face_landmark_5): face_landmark_5 = face_landmark_5.astype(numpy.int32) for point in face_landmark_5: cv2.circle(temp_vision_frame, tuple(point), point_scale, point_color, -1) return temp_vision_frame def draw_face_landmark_5_68(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame: temp_vision_frame = numpy.ascontiguousarray(temp_vision_frame) face_landmark_5 = target_face.landmark_set.get('5') face_landmark_5_68 = target_face.landmark_set.get('5/68') point_scale = calculate_scale(temp_vision_frame) point_color = 0, 255, 0 if numpy.array_equal(face_landmark_5, face_landmark_5_68): point_color = 255, 255, 0 if target_face.origin == 'refill': point_color = 0, 165, 255 if numpy.any(face_landmark_5_68): face_landmark_5_68 = face_landmark_5_68.astype(numpy.int32) for point in face_landmark_5_68: cv2.circle(temp_vision_frame, tuple(point), point_scale, point_color, -1) return temp_vision_frame def draw_face_landmark_68(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame: temp_vision_frame = numpy.ascontiguousarray(temp_vision_frame) face_landmark_68 = target_face.landmark_set.get('68') face_landmark_68_5 = target_face.landmark_set.get('68/5') point_scale = calculate_scale(temp_vision_frame) point_color = 0, 255, 0 if numpy.array_equal(face_landmark_68, face_landmark_68_5): point_color = 255, 255, 0 if target_face.origin == 'refill': point_color = 0, 165, 255 if numpy.any(face_landmark_68): face_landmark_68 = face_landmark_68.astype(numpy.int32) for point in face_landmark_68: cv2.circle(temp_vision_frame, tuple(point), point_scale, point_color, -1) return temp_vision_frame def draw_face_landmark_68_5(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame: temp_vision_frame = numpy.ascontiguousarray(temp_vision_frame) face_landmark_68_5 = target_face.landmark_set.get('68/5') point_scale = calculate_scale(temp_vision_frame) point_color = 255, 255, 0 if target_face.origin == 'refill': point_color = 0, 165, 255 if numpy.any(face_landmark_68_5): face_landmark_68_5 = face_landmark_68_5.astype(numpy.int32) for point in face_landmark_68_5: cv2.circle(temp_vision_frame, tuple(point), point_scale, point_color, -1) return temp_vision_frame def calculate_scale(temp_vision_frame : VisionFrame) -> int: frame_height, _ = temp_vision_frame.shape[:2] frame_scale = round(frame_height / 270) return max(1, min(10, frame_scale)) def process_frame(inputs : FaceDebuggerInputs) -> ProcessorOutputs: reference_vision_frame = inputs.get('reference_vision_frame') source_vision_frames = inputs.get('source_vision_frames') target_vision_frames = inputs.get('target_vision_frames') temp_vision_frame = inputs.get('temp_vision_frame') temp_vision_mask = inputs.get('temp_vision_mask') target_vision_frame = get_middle(target_vision_frames) target_faces = select_faces(reference_vision_frame, source_vision_frames, target_vision_frames) if target_faces: for target_face in target_faces: target_face = scale_face(target_face, target_vision_frame, temp_vision_frame) temp_vision_frame = debug_face(target_face, temp_vision_frame) return temp_vision_frame, temp_vision_mask