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from functools import lru_cache |
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from typing import Tuple |
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import cv2 |
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import numpy |
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from facefusion import inference_manager, state_manager |
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from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url |
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from facefusion.face_helper import create_rotated_matrix_and_size, estimate_matrix_by_face_landmark_5, transform_points, warp_face_by_translation |
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from facefusion.filesystem import resolve_relative_path |
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from facefusion.thread_helper import conditional_thread_semaphore |
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from facefusion.types import Angle, BoundingBox, DownloadScope, DownloadSet, FaceLandmark5, FaceLandmark68, InferencePool, ModelSet, Prediction, Score, VisionFrame |
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@lru_cache(maxsize = None) |
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def create_static_model_set(download_scope : DownloadScope) -> ModelSet: |
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return\ |
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{ |
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'2dfan4': |
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{ |
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'hashes': |
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{ |
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'2dfan4': |
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{ |
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'url': resolve_download_url('models-3.0.0', '2dfan4.hash'), |
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'path': resolve_relative_path('../.assets/models/2dfan4.hash') |
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} |
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}, |
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'sources': |
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{ |
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'2dfan4': |
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{ |
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'url': resolve_download_url('models-3.0.0', '2dfan4.onnx'), |
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'path': resolve_relative_path('../.assets/models/2dfan4.onnx') |
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} |
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}, |
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'size': (256, 256) |
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}, |
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'peppa_wutz': |
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{ |
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'hashes': |
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{ |
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'peppa_wutz': |
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{ |
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'url': resolve_download_url('models-3.0.0', 'peppa_wutz.hash'), |
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'path': resolve_relative_path('../.assets/models/peppa_wutz.hash') |
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} |
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}, |
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'sources': |
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{ |
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'peppa_wutz': |
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{ |
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'url': resolve_download_url('models-3.0.0', 'peppa_wutz.onnx'), |
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'path': resolve_relative_path('../.assets/models/peppa_wutz.onnx') |
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} |
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}, |
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'size': (256, 256) |
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}, |
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'fan_68_5': |
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{ |
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'hashes': |
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{ |
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'fan_68_5': |
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{ |
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'url': resolve_download_url('models-3.0.0', 'fan_68_5.hash'), |
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'path': resolve_relative_path('../.assets/models/fan_68_5.hash') |
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} |
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}, |
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'sources': |
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{ |
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'fan_68_5': |
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{ |
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'url': resolve_download_url('models-3.0.0', 'fan_68_5.onnx'), |
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'path': resolve_relative_path('../.assets/models/fan_68_5.onnx') |
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} |
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} |
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} |
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} |
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def get_inference_pool() -> InferencePool: |
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model_names = [ state_manager.get_item('face_landmarker_model'), 'fan_68_5' ] |
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_, model_source_set = collect_model_downloads() |
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return inference_manager.get_inference_pool(__name__, model_names, model_source_set) |
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def clear_inference_pool() -> None: |
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model_names = [ state_manager.get_item('face_landmarker_model'), 'fan_68_5' ] |
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inference_manager.clear_inference_pool(__name__, model_names) |
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def collect_model_downloads() -> Tuple[DownloadSet, DownloadSet]: |
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model_set = create_static_model_set('full') |
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model_hash_set =\ |
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{ |
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'fan_68_5': model_set.get('fan_68_5').get('hashes').get('fan_68_5') |
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} |
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model_source_set =\ |
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{ |
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'fan_68_5': model_set.get('fan_68_5').get('sources').get('fan_68_5') |
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} |
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for face_landmarker_model in [ '2dfan4', 'peppa_wutz' ]: |
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if state_manager.get_item('face_landmarker_model') in [ 'many', face_landmarker_model ]: |
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model_hash_set[face_landmarker_model] = model_set.get(face_landmarker_model).get('hashes').get(face_landmarker_model) |
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model_source_set[face_landmarker_model] = model_set.get(face_landmarker_model).get('sources').get(face_landmarker_model) |
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return model_hash_set, model_source_set |
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def pre_check() -> bool: |
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model_hash_set, model_source_set = collect_model_downloads() |
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return conditional_download_hashes(model_hash_set) and conditional_download_sources(model_source_set) |
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def detect_face_landmark(vision_frame : VisionFrame, bounding_box : BoundingBox, face_angle : Angle) -> Tuple[FaceLandmark68, Score]: |
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face_landmark_2dfan4 = None |
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face_landmark_peppa_wutz = None |
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face_landmark_score_2dfan4 = 0.0 |
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face_landmark_score_peppa_wutz = 0.0 |
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if state_manager.get_item('face_landmarker_model') in [ 'many', '2dfan4' ]: |
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face_landmark_2dfan4, face_landmark_score_2dfan4 = detect_with_2dfan4(vision_frame, bounding_box, face_angle) |
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if state_manager.get_item('face_landmarker_model') in [ 'many', 'peppa_wutz' ]: |
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face_landmark_peppa_wutz, face_landmark_score_peppa_wutz = detect_with_peppa_wutz(vision_frame, bounding_box, face_angle) |
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if face_landmark_score_2dfan4 > face_landmark_score_peppa_wutz - 0.2: |
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return face_landmark_2dfan4, face_landmark_score_2dfan4 |
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return face_landmark_peppa_wutz, face_landmark_score_peppa_wutz |
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def detect_with_2dfan4(temp_vision_frame: VisionFrame, bounding_box: BoundingBox, face_angle: Angle) -> Tuple[FaceLandmark68, Score]: |
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model_size = create_static_model_set('full').get('2dfan4').get('size') |
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scale = 195 / numpy.subtract(bounding_box[2:], bounding_box[:2]).max().clip(1, None) |
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translation = (model_size[0] - numpy.add(bounding_box[2:], bounding_box[:2]) * scale) * 0.5 |
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rotated_matrix, rotated_size = create_rotated_matrix_and_size(face_angle, model_size) |
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crop_vision_frame, affine_matrix = warp_face_by_translation(temp_vision_frame, translation, scale, model_size) |
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crop_vision_frame = cv2.warpAffine(crop_vision_frame, rotated_matrix, rotated_size) |
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crop_vision_frame = conditional_optimize_contrast(crop_vision_frame) |
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crop_vision_frame = crop_vision_frame.transpose(2, 0, 1).astype(numpy.float32) / 255.0 |
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face_landmark_68, face_heatmap = forward_with_2dfan4(crop_vision_frame) |
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face_landmark_68 = face_landmark_68[:, :, :2][0] / 64 * 256 |
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face_landmark_68 = transform_points(face_landmark_68, cv2.invertAffineTransform(rotated_matrix)) |
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face_landmark_68 = transform_points(face_landmark_68, cv2.invertAffineTransform(affine_matrix)) |
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face_landmark_score_68 = numpy.amax(face_heatmap, axis = (2, 3)) |
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face_landmark_score_68 = numpy.mean(face_landmark_score_68) |
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face_landmark_score_68 = numpy.interp(face_landmark_score_68, [ 0, 0.9 ], [ 0, 1 ]) |
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return face_landmark_68, face_landmark_score_68 |
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def detect_with_peppa_wutz(temp_vision_frame : VisionFrame, bounding_box : BoundingBox, face_angle : Angle) -> Tuple[FaceLandmark68, Score]: |
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model_size = create_static_model_set('full').get('peppa_wutz').get('size') |
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scale = 195 / numpy.subtract(bounding_box[2:], bounding_box[:2]).max().clip(1, None) |
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translation = (model_size[0] - numpy.add(bounding_box[2:], bounding_box[:2]) * scale) * 0.5 |
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rotated_matrix, rotated_size = create_rotated_matrix_and_size(face_angle, model_size) |
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crop_vision_frame, affine_matrix = warp_face_by_translation(temp_vision_frame, translation, scale, model_size) |
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crop_vision_frame = cv2.warpAffine(crop_vision_frame, rotated_matrix, rotated_size) |
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crop_vision_frame = conditional_optimize_contrast(crop_vision_frame) |
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crop_vision_frame = crop_vision_frame.transpose(2, 0, 1).astype(numpy.float32) / 255.0 |
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crop_vision_frame = numpy.expand_dims(crop_vision_frame, axis = 0) |
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prediction = forward_with_peppa_wutz(crop_vision_frame) |
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face_landmark_68 = prediction.reshape(-1, 3)[:, :2] / 64 * model_size[0] |
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face_landmark_68 = transform_points(face_landmark_68, cv2.invertAffineTransform(rotated_matrix)) |
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face_landmark_68 = transform_points(face_landmark_68, cv2.invertAffineTransform(affine_matrix)) |
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face_landmark_score_68 = prediction.reshape(-1, 3)[:, 2].mean() |
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face_landmark_score_68 = numpy.interp(face_landmark_score_68, [ 0, 0.95 ], [ 0, 1 ]) |
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return face_landmark_68, face_landmark_score_68 |
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def conditional_optimize_contrast(crop_vision_frame : VisionFrame) -> VisionFrame: |
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crop_vision_frame = cv2.cvtColor(crop_vision_frame, cv2.COLOR_RGB2Lab) |
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if numpy.mean(crop_vision_frame[:, :, 0]) < 30: |
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crop_vision_frame[:, :, 0] = cv2.createCLAHE(clipLimit = 2).apply(crop_vision_frame[:, :, 0]) |
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crop_vision_frame = cv2.cvtColor(crop_vision_frame, cv2.COLOR_Lab2RGB) |
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return crop_vision_frame |
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def estimate_face_landmark_68_5(face_landmark_5 : FaceLandmark5) -> FaceLandmark68: |
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affine_matrix = estimate_matrix_by_face_landmark_5(face_landmark_5, 'ffhq_512', (1, 1)) |
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face_landmark_5 = cv2.transform(face_landmark_5.reshape(1, -1, 2), affine_matrix).reshape(-1, 2) |
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face_landmark_68_5 = forward_fan_68_5(face_landmark_5) |
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face_landmark_68_5 = cv2.transform(face_landmark_68_5.reshape(1, -1, 2), cv2.invertAffineTransform(affine_matrix)).reshape(-1, 2) |
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return face_landmark_68_5 |
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def forward_with_2dfan4(crop_vision_frame : VisionFrame) -> Tuple[Prediction, Prediction]: |
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face_landmarker = get_inference_pool().get('2dfan4') |
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with conditional_thread_semaphore(): |
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prediction = face_landmarker.run(None, |
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{ |
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'input': [ crop_vision_frame ] |
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}) |
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return prediction |
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def forward_with_peppa_wutz(crop_vision_frame : VisionFrame) -> Prediction: |
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face_landmarker = get_inference_pool().get('peppa_wutz') |
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with conditional_thread_semaphore(): |
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prediction = face_landmarker.run(None, |
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{ |
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'input': crop_vision_frame |
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})[0] |
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return prediction |
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def forward_fan_68_5(face_landmark_5 : FaceLandmark5) -> FaceLandmark68: |
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face_landmarker = get_inference_pool().get('fan_68_5') |
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with conditional_thread_semaphore(): |
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face_landmark_68_5 = face_landmarker.run(None, |
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{ |
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'input': [ face_landmark_5 ] |
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})[0][0] |
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return face_landmark_68_5 |
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