def getValidation(self, params):
if self.validation_dict == None:
images = []
labels = []
# Read files
for sample_filepath in self.validation_list:
sample_fullpath = self.corpus_dirpath + '/' + sample_filepath + '/' + sample_filepath
# IMAGE
sample_img = cv2.imread(
sample_fullpath + '.png',
cv2.IMREAD_GRAYSCALE) # Grayscale is assumed!
height = params['img_height']
sample_img = ctc_utils.resize(sample_img, height)
images.append(ctc_utils.normalize(sample_img))
# GROUND TRUTH
if self.semantic:
sample_full_filepath = sample_fullpath + '.semantic'
else:
sample_full_filepath = sample_fullpath + '.agnostic'
sample_gt_file = open(sample_full_filepath, 'r')
sample_gt_plain = sample_gt_file.readline().rstrip().split(
ctc_utils.word_separator())
sample_gt_file.close()
labels.append([self.word2int[lab] for lab in sample_gt_plain])
# Transform to batch
image_widths = [img.shape[1] for img in images]
max_image_width = max(image_widths)
batch_images = np.ones(shape=[
len(self.validation_list), params['img_height'],
max_image_width, params['img_channels']
],
dtype=np.float32) * self.PAD_COLUMN
for i, img in enumerate(images):
batch_images[i, 0:img.shape[0], 0:img.shape[1], 0] = img
# LENGTH
width_reduction = 1
for i in range(params['conv_blocks']):
width_reduction = width_reduction * params[
'conv_pooling_size'][i][1]
lengths = [batch_images.shape[2] / width_reduction
] * batch_images.shape[0]
self.validation_dict = {
'inputs': batch_images,
'seq_lengths': np.asarray(lengths),
'targets': labels,
}
return self.validation_dict, len(self.validation_list)
def nextBatch(self, params):
images = []
labels = []
for i in range(16):
temp_filepath = self.training_data[self.curr_idx]
full_path = self.data_dirpath + '/' + temp_filepath + '/' + temp_filepath
sample_img = cv2.imread(full_path + '.png', False)
height = 128
sample_img = ctc_utils.resize(sample_img,height)
images.append(ctc_utils.normalize(sample_img))
sample_full_filepath = full_path + '.semantic'
gt_file = open(sample_full_filepath, 'r')
gt_list = gt_file.readline().rstrip().split(ctc_utils.word_separator())
gt_file.close()
labels.append([self.word2int[lab] for lab in gt_list])
self.curr_idx = (self.curr_idx + 1) % len( self.training_data )
image_widths = [img.shape[1] for img in images]
max_width = max(image_widths)
batch_images = np.ones(shape=[16,
128,
max_width,
1], dtype=np.float32)*self.PAD_COLUMN
for i, img in enumerate(images):
batch_images[i, 0:img.shape[0], 0:img.shape[1], 0] = img
width_reduction = 1
for i in range(4):
width_reduction = width_reduction * conv_pool[i][1]
lengths = [ batch_images.shape[2] / width_reduction ] * batch_images.shape[0]
return {
'inputs': batch_images,
'seq_lengths': np.asarray(lengths),
'targets': labels,
}
def nextBatch(self, params, mode = 'Train'):
images = []
labels = []
# Read files
for _ in range(params['batch_size']):
if mode == 'Train':
sample_filepath = self.training_list[self.current_idx]
sample_fullpath = self.corpus_dirpath + '/' + sample_filepath + '/' + sample_filepath
elif mode == 'Validation':
sample_filepath = self.validation_list[self.current_val_idx]
sample_fullpath = self.corpus_dirpath + '/' + sample_filepath + '/' + sample_filepath
# IMAGE
if self.distortions:
sample_img = cv2.imread(sample_fullpath + '_distorted.jpg', False) # Grayscale is assumed
else:
sample_img = cv2.imread(sample_fullpath + '.png', False) # Grayscale is assumed!
height = params['img_height']
sample_img = ctc_utils.resize(sample_img,height)
images.append(ctc_utils.normalize(sample_img))
# GROUND TRUTH
if self.semantic:
sample_full_filepath = sample_fullpath + '.semantic'
else:
sample_full_filepath = sample_fullpath + '.agnostic'
sample_gt_file = open(sample_full_filepath, 'r')
sample_gt_plain = sample_gt_file.readline().rstrip().split(ctc_utils.word_separator())
sample_gt_file.close()
labels.append([self.word2int[lab] for lab in sample_gt_plain])
if mode == 'Train':
self.current_idx = (self.current_idx + 1) % len( self.training_list )
elif mode == 'Validation':
self.current_val_idx = (self.current_val_idx + 1) % len( self.validation_list )
# Transform to batch
image_widths = [img.shape[1] for img in images]
max_image_width = max(image_widths)
batch_images = np.ones(shape=[params['batch_size'],
params['img_height'],
max_image_width,
params['img_channels']], dtype=np.float32)*self.PAD_COLUMN
for i, img in enumerate(images):
batch_images[i, 0:img.shape[0], 0:img.shape[1], 0] = img
# LENGTH
width_reduction = 1
for i in range(params['conv_blocks']):
width_reduction = width_reduction * params['conv_pooling_size'][i][1]
lengths = [ batch_images.shape[2] / width_reduction ] * batch_images.shape[0]
return {
'inputs': batch_images,
'seq_lengths': np.asarray(lengths),
'targets': labels,
}
