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@@ -302,8 +302,6 @@ def get_transform_mat (image_landmarks, output_size, face_type, scale=1.0):
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g_c += vec*vec_len*0.07
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elif face_type == FaceType.HEAD:
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mat = umeyama( np.concatenate ( [ image_landmarks[17:49] , image_landmarks[54:55] ] ) , landmarks_2D_new, True)[0:2]
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# assuming image_landmarks are 3D_Landmarks extracted for HEAD,
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# adjust horizontal offset according to estimated yaw
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yaw = estimate_averaged_yaw(transform_points (image_landmarks, mat, False))
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