add dataset

This commit is contained in:
xxxxxx
2019-05-28 23:55:56 +08:00
parent aa1c91780a
commit b457a2bf87
8 changed files with 3865 additions and 21 deletions

View File

@@ -0,0 +1,83 @@
"""
generates HWDB data into tfrecord
"""
import struct
import numpy as np
import cv2
from alfred.utils.log import logger as logging
import tensorflow as tf
import glob
class CASIAHWDBGNT(object):
"""
A .gnt file may contains many images and charactors
"""
def __init__(self, f_p):
self.f_p = f_p
def get_data_iter(self):
header_size = 10
with open(self.f_p, 'rb') as f:
while True:
header = np.fromfile(f, dtype='uint8', count=header_size)
if not header.size:
break
sample_size = header[0] + (header[1]<<8) + (header[2]<<16) + (header[3]<<24)
tagcode = header[5] + (header[4]<<8)
width = header[6] + (header[7]<<8)
height = header[8] + (header[9]<<8)
if header_size + width*height != sample_size:
break
image = np.fromfile(f, dtype='uint8', count=width*height).reshape((height, width))
yield image, tagcode
def run():
all_hwdb_gnt_files = glob.glob('./hwdb_raw/HWDB1.1trn_gnt/*.gnt')
logging.info('got all {} gnt files.'.format(len(all_hwdb_gnt_files)))
logging.info('gathering charset...')
charset = []
for gnt in all_hwdb_gnt_files:
hwdb = CASIAHWDBGNT(gnt)
for img, tagcode in hwdb.get_data_iter():
try:
label = struct.pack('>H', tagcode).decode('gb2312')
label = label.replace('\x00', '')
charset.append(label)
except Exception as e:
continue
charset = sorted(set(charset))
logging.info('all got {} charactors.'.format(len(charset)))
with open('charactors.txt', 'w') as f:
f.writelines('\n'.join(charset))
tfrecord_f = 'casia_hwdb_1.0_1.1.tfrecord'
i = 0
with tf.io.TFRecordWriter(tfrecord_f) as tfrecord_writer:
for gnt in all_hwdb_gnt_files:
hwdb = CASIAHWDBGNT(gnt)
for img, tagcode in hwdb.get_data_iter():
try:
img = cv.resize(img, (64, 64))
label = struct.pack('>H', tagcode).decode('gb2312')
label = label.replace('\x00', '')
index = charset.index(label)
# save img, label as example
example = tf.train.Example(features=tf.train.Features(
feature={
"label": tf.train.Feature(int64_list=tf.train.Int64List(value=[index])),
'image': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img]))
}))
tfrecord_writer.write(example.SerializeToString())
if i%500:
logging.info('solved {} examples.'.format(i))
i += 1
except Exception as e:
logging.error(e)
continue
logging.info('done.')
if __name__ == "__main__":
run()