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from argparse import ArgumentParser |
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from typing import List |
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import cv2 |
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import numpy |
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import facefusion.jobs.job_manager |
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import facefusion.jobs.job_store |
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import facefusion.processors.core as processors |
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from facefusion import config, content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer, logger, process_manager, state_manager, wording |
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from facefusion.face_analyser import get_many_faces, get_one_face |
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from facefusion.face_helper import warp_face_by_face_landmark_5 |
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from facefusion.face_masker import create_occlusion_mask, create_region_mask, create_static_box_mask |
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from facefusion.face_selector import find_similar_faces, sort_and_filter_faces |
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from facefusion.face_store import get_reference_faces |
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from facefusion.filesystem import in_directory, same_file_extension |
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from facefusion.processors import choices as processors_choices |
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from facefusion.processors.types import FaceDebuggerInputs |
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from facefusion.program_helper import find_argument_group |
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from facefusion.types import ApplyStateItem, Args, Face, InferencePool, ProcessMode, QueuePayload, UpdateProgress, VisionFrame |
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from facefusion.vision import read_image, read_static_image, write_image |
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def get_inference_pool() -> InferencePool: |
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pass |
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def clear_inference_pool() -> None: |
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pass |
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def register_args(program : ArgumentParser) -> None: |
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group_processors = find_argument_group(program, 'processors') |
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if group_processors: |
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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') |
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facefusion.jobs.job_store.register_step_keys([ 'face_debugger_items' ]) |
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def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None: |
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apply_state_item('face_debugger_items', args.get('face_debugger_items')) |
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def pre_check() -> bool: |
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return True |
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def pre_process(mode : ProcessMode) -> bool: |
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if mode == 'output' and not in_directory(state_manager.get_item('output_path')): |
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logger.error(wording.get('specify_image_or_video_output') + wording.get('exclamation_mark'), __name__) |
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return False |
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if mode == 'output' and not same_file_extension(state_manager.get_item('target_path'), state_manager.get_item('output_path')): |
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logger.error(wording.get('match_target_and_output_extension') + wording.get('exclamation_mark'), __name__) |
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return False |
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return True |
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def post_process() -> None: |
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read_static_image.cache_clear() |
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if state_manager.get_item('video_memory_strategy') == 'strict': |
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content_analyser.clear_inference_pool() |
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face_classifier.clear_inference_pool() |
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face_detector.clear_inference_pool() |
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face_landmarker.clear_inference_pool() |
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face_masker.clear_inference_pool() |
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face_recognizer.clear_inference_pool() |
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def debug_face(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame: |
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primary_color = (0, 0, 255) |
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primary_light_color = (100, 100, 255) |
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secondary_color = (0, 255, 0) |
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tertiary_color = (255, 255, 0) |
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bounding_box = target_face.bounding_box.astype(numpy.int32) |
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temp_vision_frame = temp_vision_frame.copy() |
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has_face_landmark_5_fallback = numpy.array_equal(target_face.landmark_set.get('5'), target_face.landmark_set.get('5/68')) |
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has_face_landmark_68_fallback = numpy.array_equal(target_face.landmark_set.get('68'), target_face.landmark_set.get('68/5')) |
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face_debugger_items = state_manager.get_item('face_debugger_items') |
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if 'bounding-box' in face_debugger_items: |
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x1, y1, x2, y2 = bounding_box |
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cv2.rectangle(temp_vision_frame, (x1, y1), (x2, y2), primary_color, 2) |
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if target_face.angle == 0: |
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cv2.line(temp_vision_frame, (x1, y1), (x2, y1), primary_light_color, 3) |
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elif target_face.angle == 180: |
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cv2.line(temp_vision_frame, (x1, y2), (x2, y2), primary_light_color, 3) |
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elif target_face.angle == 90: |
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cv2.line(temp_vision_frame, (x2, y1), (x2, y2), primary_light_color, 3) |
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elif target_face.angle == 270: |
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cv2.line(temp_vision_frame, (x1, y1), (x1, y2), primary_light_color, 3) |
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if 'face-mask' in face_debugger_items: |
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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)) |
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inverse_matrix = cv2.invertAffineTransform(affine_matrix) |
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temp_size = temp_vision_frame.shape[:2][::-1] |
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crop_masks = [] |
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if 'box' in state_manager.get_item('face_mask_types'): |
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box_mask = create_static_box_mask(crop_vision_frame.shape[:2][::-1], 0, state_manager.get_item('face_mask_padding')) |
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crop_masks.append(box_mask) |
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if 'occlusion' in state_manager.get_item('face_mask_types'): |
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occlusion_mask = create_occlusion_mask(crop_vision_frame) |
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crop_masks.append(occlusion_mask) |
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if 'region' in state_manager.get_item('face_mask_types'): |
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region_mask = create_region_mask(crop_vision_frame, state_manager.get_item('face_mask_regions')) |
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crop_masks.append(region_mask) |
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crop_mask = numpy.minimum.reduce(crop_masks).clip(0, 1) |
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crop_mask = (crop_mask * 255).astype(numpy.uint8) |
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inverse_vision_frame = cv2.warpAffine(crop_mask, inverse_matrix, temp_size) |
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inverse_vision_frame = cv2.threshold(inverse_vision_frame, 100, 255, cv2.THRESH_BINARY)[1] |
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inverse_vision_frame[inverse_vision_frame > 0] = 255 |
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inverse_contours = cv2.findContours(inverse_vision_frame, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)[0] |
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cv2.drawContours(temp_vision_frame, inverse_contours, -1, tertiary_color if has_face_landmark_5_fallback else secondary_color, 2) |
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if 'face-landmark-5' in face_debugger_items and numpy.any(target_face.landmark_set.get('5')): |
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face_landmark_5 = target_face.landmark_set.get('5').astype(numpy.int32) |
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for index in range(face_landmark_5.shape[0]): |
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cv2.circle(temp_vision_frame, (face_landmark_5[index][0], face_landmark_5[index][1]), 3, primary_color, -1) |
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if 'face-landmark-5/68' in face_debugger_items and numpy.any(target_face.landmark_set.get('5/68')): |
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face_landmark_5_68 = target_face.landmark_set.get('5/68').astype(numpy.int32) |
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for index in range(face_landmark_5_68.shape[0]): |
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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) |
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if 'face-landmark-68' in face_debugger_items and numpy.any(target_face.landmark_set.get('68')): |
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face_landmark_68 = target_face.landmark_set.get('68').astype(numpy.int32) |
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for index in range(face_landmark_68.shape[0]): |
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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) |
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if 'face-landmark-68/5' in face_debugger_items and numpy.any(target_face.landmark_set.get('68')): |
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face_landmark_68 = target_face.landmark_set.get('68/5').astype(numpy.int32) |
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for index in range(face_landmark_68.shape[0]): |
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cv2.circle(temp_vision_frame, (face_landmark_68[index][0], face_landmark_68[index][1]), 3, tertiary_color, -1) |
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if bounding_box[3] - bounding_box[1] > 50 and bounding_box[2] - bounding_box[0] > 50: |
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top = bounding_box[1] |
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left = bounding_box[0] - 20 |
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if 'face-detector-score' in face_debugger_items: |
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face_score_text = str(round(target_face.score_set.get('detector'), 2)) |
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top = top + 20 |
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cv2.putText(temp_vision_frame, face_score_text, (left, top), cv2.FONT_HERSHEY_SIMPLEX, 0.5, primary_color, 2) |
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if 'face-landmarker-score' in face_debugger_items: |
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face_score_text = str(round(target_face.score_set.get('landmarker'), 2)) |
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top = top + 20 |
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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) |
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if 'age' in face_debugger_items: |
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face_age_text = str(target_face.age.start) + '-' + str(target_face.age.stop) |
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top = top + 20 |
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cv2.putText(temp_vision_frame, face_age_text, (left, top), cv2.FONT_HERSHEY_SIMPLEX, 0.5, primary_color, 2) |
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if 'gender' in face_debugger_items: |
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face_gender_text = target_face.gender |
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top = top + 20 |
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cv2.putText(temp_vision_frame, face_gender_text, (left, top), cv2.FONT_HERSHEY_SIMPLEX, 0.5, primary_color, 2) |
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if 'race' in face_debugger_items: |
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face_race_text = target_face.race |
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top = top + 20 |
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cv2.putText(temp_vision_frame, face_race_text, (left, top), cv2.FONT_HERSHEY_SIMPLEX, 0.5, primary_color, 2) |
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return temp_vision_frame |
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def get_reference_frame(source_face : Face, target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame: |
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pass |
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def process_frame(inputs : FaceDebuggerInputs) -> VisionFrame: |
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reference_faces = inputs.get('reference_faces') |
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target_vision_frame = inputs.get('target_vision_frame') |
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many_faces = sort_and_filter_faces(get_many_faces([ target_vision_frame ])) |
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if state_manager.get_item('face_selector_mode') == 'many': |
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if many_faces: |
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for target_face in many_faces: |
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target_vision_frame = debug_face(target_face, target_vision_frame) |
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if state_manager.get_item('face_selector_mode') == 'one': |
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target_face = get_one_face(many_faces) |
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if target_face: |
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target_vision_frame = debug_face(target_face, target_vision_frame) |
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if state_manager.get_item('face_selector_mode') == 'reference': |
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similar_faces = find_similar_faces(many_faces, reference_faces, state_manager.get_item('reference_face_distance')) |
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if similar_faces: |
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for similar_face in similar_faces: |
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target_vision_frame = debug_face(similar_face, target_vision_frame) |
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return target_vision_frame |
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def process_frames(source_paths : List[str], queue_payloads : List[QueuePayload], update_progress : UpdateProgress) -> None: |
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reference_faces = get_reference_faces() if 'reference' in state_manager.get_item('face_selector_mode') else None |
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for queue_payload in process_manager.manage(queue_payloads): |
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target_vision_path = queue_payload['frame_path'] |
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target_vision_frame = read_image(target_vision_path) |
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output_vision_frame = process_frame( |
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{ |
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'reference_faces': reference_faces, |
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'target_vision_frame': target_vision_frame |
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}) |
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write_image(target_vision_path, output_vision_frame) |
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update_progress(1) |
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def process_image(source_paths : List[str], target_path : str, output_path : str) -> None: |
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reference_faces = get_reference_faces() if 'reference' in state_manager.get_item('face_selector_mode') else None |
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target_vision_frame = read_static_image(target_path) |
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output_vision_frame = process_frame( |
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{ |
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'reference_faces': reference_faces, |
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'target_vision_frame': target_vision_frame |
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}) |
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write_image(output_path, output_vision_frame) |
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def process_video(source_paths : List[str], temp_frame_paths : List[str]) -> None: |
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processors.multi_process_frames(source_paths, temp_frame_paths, process_frames) |
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