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generator.py
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generator.py
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import numpy as np
import cv2
from PIL import Image
import time
import tensorflow as tf
from tensorflow.python.keras.utils.data_utils import Sequence
import config
class TFDataFeeder(Sequence):
'''
Use tf.data to load raw image data
'''
def __init__(self, tfdataset, batch_size, dataset_len, pair=False):
# print('TFDataFeeder: batch size ', batch_size,
# ' dataset len', dataset_len, 'prefetch autotune')
self.pair = pair
self.batch_size = batch_size
self.tfdataset = tfdataset.batch(self.batch_size)
self.tfdataset = self.tfdataset.prefetch(tf.data.experimental.AUTOTUNE)
self.iterator = iter(self.tfdataset)
self.dataset_len = dataset_len
def __len__(self):
return int(np.floor(self.dataset_len / self.batch_size))
def __getitem__(self, index):
if (config.arc == 'SAGE'):
batch_sampleID, batch_orientation, batch_eyelandmark,\
batch_leye_im, batch_reye_im, batch_label = self.iterator.get_next()
return [batch_orientation, batch_eyelandmark, batch_leye_im, batch_reye_im], batch_label
else:
batch_frameID, batch_orientation, batch_face_grid, batch_face_im,\
batch_leye_im, batch_reye_im, batch_label = self.iterator.get_next()
return [batch_orientation, batch_leye_im, batch_reye_im, batch_face_im, batch_face_grid], batch_label
def reset(self):
self.iterator = iter(self.tfdataset)
return self
def on_epoch_end(self):
self.iterator = iter(self.tfdataset)
class TFDataFeeder_pipeline(Sequence):
'''
Used for whole pipeline model
Output format: orientation, im
'''
def __init__(self, tfdataset, batch_size, dataset_len):
# print('TFDataFeeder: batch size ', batch_size,
# ' dataset len', dataset_len, 'prefetch autotune')
self.batch_size = batch_size
self.tfdataset = tfdataset.batch(self.batch_size)
self.tfdataset = self.tfdataset.prefetch(tf.data.experimental.AUTOTUNE)
self.iterator = iter(self.tfdataset)
self.dataset_len = dataset_len
def __len__(self):
return int(np.floor(self.dataset_len / self.batch_size))
def __getitem__(self, index):
batch_sampleID, batch_orientation, batch_im, batch_label = self.iterator.get_next()
return [batch_orientation, batch_im], batch_label
def reset(self):
self.iterator = iter(self.tfdataset)
return self
def on_epoch_end(self):
self.iterator = iter(self.tfdataset)
class TFDataFeeder_iTracker(Sequence):
'''
Used for iTracker architecture
Output format: orientation, leye_grid, reye_grid, leye_im, reye_im
'''
def __init__(self, tfdataset, batch_size, dataset_len):
# print('TFDataFeeder: batch size ', batch_size,
# ' dataset len', dataset_len, 'prefetch autotune')
self.batch_size = batch_size
self.tfdataset = tfdataset.batch(self.batch_size)
self.tfdataset = self.tfdataset.prefetch(tf.data.experimental.AUTOTUNE)
self.iterator = iter(self.tfdataset)
self.dataset_len = dataset_len
def __len__(self):
return int(np.floor(self.dataset_len / self.batch_size))
def __getitem__(self, index):
batch_sampleID, batch_orientation, batch_grid_im, batch_face_im,\
batch_leye_im, batch_reye_im, batch_label = self.iterator.get_next()
return [batch_orientation, batch_leye_im, batch_reye_im, batch_face_im, batch_grid_im], batch_label
def reset(self):
self.iterator = iter(self.tfdataset)
return self
def on_epoch_end(self):
self.iterator = iter(self.tfdataset)