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from functools import lru_cache |
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from typing import List, Sequence, Tuple |
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import cv2 |
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import numpy |
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from cv2.typing import Size |
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from facefusion.types import Anchors, Angle, BoundingBox, Distance, FaceDetectorModel, FaceLandmark5, FaceLandmark68, Mask, Matrix, Points, Scale, Score, Translation, VisionFrame, WarpTemplate, WarpTemplateSet |
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WARP_TEMPLATE_SET : WarpTemplateSet =\ |
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{ |
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'arcface_112_v1': numpy.array( |
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[ |
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[ 0.35473214, 0.45658929 ], |
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[ 0.64526786, 0.45658929 ], |
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[ 0.50000000, 0.61154464 ], |
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[ 0.37913393, 0.77687500 ], |
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[ 0.62086607, 0.77687500 ] |
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]), |
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'arcface_112_v2': numpy.array( |
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[ |
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[ 0.34191607, 0.46157411 ], |
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[ 0.65653393, 0.45983393 ], |
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[ 0.50022500, 0.64050536 ], |
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[ 0.37097589, 0.82469196 ], |
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[ 0.63151696, 0.82325089 ] |
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]), |
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'arcface_128_v2': numpy.array( |
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[ |
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[ 0.36167656, 0.40387734 ], |
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[ 0.63696719, 0.40235469 ], |
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[ 0.50019687, 0.56044219 ], |
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[ 0.38710391, 0.72160547 ], |
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[ 0.61507734, 0.72034453 ] |
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]), |
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'dfl_whole_face': numpy.array( |
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[ |
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[ 0.35342266, 0.39285716 ], |
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[ 0.62797622, 0.39285716 ], |
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[ 0.48660713, 0.54017860 ], |
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[ 0.38839287, 0.68750011 ], |
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[ 0.59821427, 0.68750011 ] |
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]), |
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'ffhq_512': numpy.array( |
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[ |
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[ 0.37691676, 0.46864664 ], |
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[ 0.62285697, 0.46912813 ], |
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[ 0.50123859, 0.61331904 ], |
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[ 0.39308822, 0.72541100 ], |
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[ 0.61150205, 0.72490465 ] |
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]), |
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'mtcnn_512': numpy.array( |
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[ |
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[ 0.36562865, 0.46733799 ], |
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[ 0.63305391, 0.46585885 ], |
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[ 0.50019127, 0.61942959 ], |
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[ 0.39032951, 0.77598822 ], |
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[ 0.61178945, 0.77476328 ] |
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]), |
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'styleganex_384': numpy.array( |
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[ |
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[ 0.42353745, 0.52289879 ], |
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[ 0.57725008, 0.52319972 ], |
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[ 0.50123859, 0.61331904 ], |
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[ 0.43364461, 0.68337652 ], |
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[ 0.57015325, 0.68306005 ] |
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]) |
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} |
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def estimate_matrix_by_face_landmark_5(face_landmark_5 : FaceLandmark5, warp_template : WarpTemplate, crop_size : Size) -> Matrix: |
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normed_warp_template = WARP_TEMPLATE_SET.get(warp_template) * crop_size |
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affine_matrix = cv2.estimateAffinePartial2D(face_landmark_5, normed_warp_template, method = cv2.RANSAC, ransacReprojThreshold = 100)[0] |
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return affine_matrix |
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def warp_face_by_face_landmark_5(temp_vision_frame : VisionFrame, face_landmark_5 : FaceLandmark5, warp_template : WarpTemplate, crop_size : Size) -> Tuple[VisionFrame, Matrix]: |
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affine_matrix = estimate_matrix_by_face_landmark_5(face_landmark_5, warp_template, crop_size) |
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crop_vision_frame = cv2.warpAffine(temp_vision_frame, affine_matrix, crop_size, borderMode = cv2.BORDER_REPLICATE, flags = cv2.INTER_AREA) |
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return crop_vision_frame, affine_matrix |
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def warp_face_by_bounding_box(temp_vision_frame : VisionFrame, bounding_box : BoundingBox, crop_size : Size) -> Tuple[VisionFrame, Matrix]: |
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source_points = numpy.array([ [ bounding_box[0], bounding_box[1] ], [bounding_box[2], bounding_box[1] ], [ bounding_box[0], bounding_box[3] ] ]).astype(numpy.float32) |
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target_points = numpy.array([ [ 0, 0 ], [ crop_size[0], 0 ], [ 0, crop_size[1] ] ]).astype(numpy.float32) |
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affine_matrix = cv2.getAffineTransform(source_points, target_points) |
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if bounding_box[2] - bounding_box[0] > crop_size[0] or bounding_box[3] - bounding_box[1] > crop_size[1]: |
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interpolation_method = cv2.INTER_AREA |
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else: |
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interpolation_method = cv2.INTER_LINEAR |
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crop_vision_frame = cv2.warpAffine(temp_vision_frame, affine_matrix, crop_size, flags = interpolation_method) |
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return crop_vision_frame, affine_matrix |
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def warp_face_by_translation(temp_vision_frame : VisionFrame, translation : Translation, scale : float, crop_size : Size) -> Tuple[VisionFrame, Matrix]: |
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affine_matrix = numpy.array([ [ scale, 0, translation[0] ], [ 0, scale, translation[1] ] ]) |
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crop_vision_frame = cv2.warpAffine(temp_vision_frame, affine_matrix, crop_size) |
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return crop_vision_frame, affine_matrix |
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def paste_back(temp_vision_frame : VisionFrame, crop_vision_frame : VisionFrame, crop_mask : Mask, affine_matrix : Matrix) -> VisionFrame: |
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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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inverse_mask = cv2.warpAffine(crop_mask, inverse_matrix, temp_size).clip(0, 1) |
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inverse_vision_frame = cv2.warpAffine(crop_vision_frame, inverse_matrix, temp_size, borderMode = cv2.BORDER_REPLICATE) |
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paste_vision_frame = temp_vision_frame.copy() |
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paste_vision_frame[:, :, 0] = inverse_mask * inverse_vision_frame[:, :, 0] + (1 - inverse_mask) * temp_vision_frame[:, :, 0] |
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paste_vision_frame[:, :, 1] = inverse_mask * inverse_vision_frame[:, :, 1] + (1 - inverse_mask) * temp_vision_frame[:, :, 1] |
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paste_vision_frame[:, :, 2] = inverse_mask * inverse_vision_frame[:, :, 2] + (1 - inverse_mask) * temp_vision_frame[:, :, 2] |
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return paste_vision_frame |
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@lru_cache(maxsize = None) |
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def create_static_anchors(feature_stride : int, anchor_total : int, stride_height : int, stride_width : int) -> Anchors: |
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y, x = numpy.mgrid[:stride_height, :stride_width][::-1] |
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anchors = numpy.stack((y, x), axis = -1) |
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anchors = (anchors * feature_stride).reshape((-1, 2)) |
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anchors = numpy.stack([ anchors ] * anchor_total, axis = 1).reshape((-1, 2)) |
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return anchors |
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def create_rotated_matrix_and_size(angle : Angle, size : Size) -> Tuple[Matrix, Size]: |
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rotated_matrix = cv2.getRotationMatrix2D((size[0] / 2, size[1] / 2), angle, 1) |
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rotated_size = numpy.dot(numpy.abs(rotated_matrix[:, :2]), size) |
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rotated_matrix[:, -1] += (rotated_size - size) * 0.5 |
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rotated_size = int(rotated_size[0]), int(rotated_size[1]) |
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return rotated_matrix, rotated_size |
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def create_bounding_box(face_landmark_68 : FaceLandmark68) -> BoundingBox: |
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min_x, min_y = numpy.min(face_landmark_68, axis = 0) |
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max_x, max_y = numpy.max(face_landmark_68, axis = 0) |
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bounding_box = normalize_bounding_box(numpy.array([ min_x, min_y, max_x, max_y ])) |
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return bounding_box |
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def normalize_bounding_box(bounding_box : BoundingBox) -> BoundingBox: |
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x1, y1, x2, y2 = bounding_box |
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x1, x2 = sorted([ x1, x2 ]) |
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y1, y2 = sorted([ y1, y2 ]) |
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return numpy.array([ x1, y1, x2, y2 ]) |
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def transform_points(points : Points, matrix : Matrix) -> Points: |
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points = points.reshape(-1, 1, 2) |
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points = cv2.transform(points, matrix) |
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points = points.reshape(-1, 2) |
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return points |
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def transform_bounding_box(bounding_box : BoundingBox, matrix : Matrix) -> BoundingBox: |
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points = numpy.array( |
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[ |
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[ bounding_box[0], bounding_box[1] ], |
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[ bounding_box[2], bounding_box[1] ], |
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[ bounding_box[2], bounding_box[3] ], |
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[ bounding_box[0], bounding_box[3] ] |
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]) |
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points = transform_points(points, matrix) |
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x1, y1 = numpy.min(points, axis = 0) |
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x2, y2 = numpy.max(points, axis = 0) |
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return normalize_bounding_box(numpy.array([ x1, y1, x2, y2 ])) |
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def distance_to_bounding_box(points : Points, distance : Distance) -> BoundingBox: |
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x1 = points[:, 0] - distance[:, 0] |
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y1 = points[:, 1] - distance[:, 1] |
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x2 = points[:, 0] + distance[:, 2] |
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y2 = points[:, 1] + distance[:, 3] |
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bounding_box = numpy.column_stack([ x1, y1, x2, y2 ]) |
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return bounding_box |
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def distance_to_face_landmark_5(points : Points, distance : Distance) -> FaceLandmark5: |
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x = points[:, 0::2] + distance[:, 0::2] |
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y = points[:, 1::2] + distance[:, 1::2] |
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face_landmark_5 = numpy.stack((x, y), axis = -1) |
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return face_landmark_5 |
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def scale_face_landmark_5(face_landmark_5 : FaceLandmark5, scale : Scale) -> FaceLandmark5: |
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face_landmark_5_scale = face_landmark_5 - face_landmark_5[2] |
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face_landmark_5_scale *= scale |
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face_landmark_5_scale += face_landmark_5[2] |
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return face_landmark_5_scale |
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def convert_to_face_landmark_5(face_landmark_68 : FaceLandmark68) -> FaceLandmark5: |
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face_landmark_5 = numpy.array( |
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[ |
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numpy.mean(face_landmark_68[36:42], axis = 0), |
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numpy.mean(face_landmark_68[42:48], axis = 0), |
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face_landmark_68[30], |
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face_landmark_68[48], |
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face_landmark_68[54] |
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]) |
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return face_landmark_5 |
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def estimate_face_angle(face_landmark_68 : FaceLandmark68) -> Angle: |
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x1, y1 = face_landmark_68[0] |
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x2, y2 = face_landmark_68[16] |
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theta = numpy.arctan2(y2 - y1, x2 - x1) |
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theta = numpy.degrees(theta) % 360 |
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angles = numpy.linspace(0, 360, 5) |
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index = numpy.argmin(numpy.abs(angles - theta)) |
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face_angle = int(angles[index] % 360) |
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return face_angle |
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def apply_nms(bounding_boxes : List[BoundingBox], scores : List[Score], score_threshold : float, nms_threshold : float) -> Sequence[int]: |
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normed_bounding_boxes = [ (x1, y1, x2 - x1, y2 - y1) for (x1, y1, x2, y2) in bounding_boxes ] |
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keep_indices = cv2.dnn.NMSBoxes(normed_bounding_boxes, scores, score_threshold = score_threshold, nms_threshold = nms_threshold) |
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return keep_indices |
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def get_nms_threshold(face_detector_model : FaceDetectorModel, face_detector_angles : List[Angle]) -> float: |
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if face_detector_model == 'many': |
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return 0.1 |
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if len(face_detector_angles) == 2: |
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return 0.3 |
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if len(face_detector_angles) == 3: |
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return 0.2 |
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if len(face_detector_angles) == 4: |
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return 0.1 |
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return 0.4 |
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def merge_matrix(matrices : List[Matrix]) -> Matrix: |
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merged_matrix = numpy.vstack([ matrices[0], [ 0, 0, 1 ] ]) |
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for matrix in matrices[1:]: |
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matrix = numpy.vstack([ matrix, [ 0, 0, 1 ] ]) |
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merged_matrix = numpy.dot(merged_matrix, matrix) |
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return merged_matrix[:2, :] |
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