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50 lines
No EOL
2 KiB
Python
50 lines
No EOL
2 KiB
Python
from enum import IntEnum
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import cv2
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import numpy as np
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from random import randint
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from facelib import FaceType
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class TrainingDataType(IntEnum):
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IMAGE = 0 #raw image
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FACE_BEGIN = 1
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FACE = 1 #aligned face unsorted
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FACE_YAW_SORTED = 2 #sorted by yaw
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FACE_YAW_SORTED_AS_TARGET = 3 #sorted by yaw and included only yaws which exist in TARGET also automatic mirrored
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FACE_END = 3
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QTY = 4
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class TrainingDataSample(object):
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def __init__(self, filename=None, face_type=None, shape=None, landmarks=None, yaw=None, mirror=None, nearest_target_list=None):
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self.filename = filename
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self.face_type = face_type
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self.shape = shape
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self.landmarks = np.array(landmarks) if landmarks is not None else None
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self.yaw = yaw
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self.mirror = mirror
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self.nearest_target_list = nearest_target_list
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def copy_and_set(self, filename=None, face_type=None, shape=None, landmarks=None, yaw=None, mirror=None, nearest_target_list=None):
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return TrainingDataSample(
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filename=filename if filename is not None else self.filename,
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face_type=face_type if face_type is not None else self.face_type,
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shape=shape if shape is not None else self.shape,
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landmarks=landmarks if landmarks is not None else self.landmarks.copy(),
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yaw=yaw if yaw is not None else self.yaw,
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mirror=mirror if mirror is not None else self.mirror,
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nearest_target_list=nearest_target_list if nearest_target_list is not None else self.nearest_target_list)
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def load_bgr(self):
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img = cv2.imread (self.filename).astype(np.float32) / 255.0
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if self.mirror:
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img = img[:,::-1].copy()
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return img
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def get_random_nearest_target_sample(self):
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if self.nearest_target_list is None:
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return None
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return self.nearest_target_list[randint (0, len(self.nearest_target_list)-1)] |