Article Outline
Python pil example 'synthtext'
Functions in program:
def get_path(path):
def preprocess_words(word_ar):
Modules used in program:
import scipy.io
import torchvision.datasets as datasets
import torch
import io
import pickle
import warnings
import time
import os
import numpy as np
python synthtext
Python pil example: synthtext
import numpy as np
import os
import time
import warnings
import pickle
# from accimage import Image
from PIL import Image
import io
try:
from fasterzip import ZipFile
fastzip = True
except:
warnings.warn('For faster loading of zip, you can install fasterzip via '
'`pip install https://github.com/TkTech/fasterzip/archive/master.zip`')
from zipfile import ZipFile
fastzip = False
import torch
from torch.utils.data import DataLoader, Dataset
import torchvision.datasets as datasets
import scipy.io
def preprocess_words(word_ar):
words = []
for ii in range(np.shape(word_ar)[0]):
s = word_ar[ii]
start = 0
while s[start] == ' ' or s[start] == '\n':
start += 1
for i in range(start + 1, len(s) + 1):
if i == len(s) or s[i] == '\n' or s[i] == ' ':
if start != i:
words.append(s[start : i])
start = i + 1
return words
def get_path(path):
if fastzip:
return path.encode()
else:
return path
class SynthTextDataset(Dataset):
def __init__(self, zip_path, cache_path=None):
self.zip_path = zip_path
self.cache_path = cache_path
def lazy_init(self):
"""
we lazily initialize rather than opening the Zip file before-hand because,
ZipFile is not thread/fork safe. If we dont lazily initialize,
then a bunch of file read errors show up, that are false-positives (the zip itself is fine)
"""
if hasattr(self, 'images'):
return
zip_path = self.zip_path
cache_path = self.cache_path
tm = time.time()
self.zip = ZipFile(get_path(zip_path))
print('opening zip file....done in {} seconds'.format(time.time() - tm))
if cache_path is None:
cache_path = os.path.join(os.path.dirname(zip_path), 'gt.pkl')
tm = time.time()
if os.path.exists(cache_path):
with open(cache_path, 'rb') as f:
gt = pickle.load(f)
else:
if fastzip:
with self.zip.read(get_path(os.path.join('SynthText', 'gt.mat'))) as file_contents:
gt_ = scipy.io.loadmat(io.BytesIO(file_contents))
else:
file_contents = self.zip.read(get_path(os.path.join('SynthText', 'gt.mat')))
gt_ = scipy.io.loadmat(io.BytesIO(file_contents))
gt = {
'imnames': gt_['imnames'],
'wordBB' : gt_['wordBB'],
'txt' : gt_['txt'],
}
del gt_
try:
with open(cache_path, 'wb') as f:
pickle.dump(gt, f, protocol=pickle.HIGHEST_PROTOCOL)
except e:
warnings.warn("Couldn't write SynthTextDataset cache at {}".format(cache_path))
self.images = gt['imnames'][0]
self.bboxes = gt['wordBB'][0]
self.text = gt['txt'][0]
del gt
print('loading metadata done in {} seconds'.format(time.time() - tm))
def __len__(self):
return 858750 # size of SynthText dataset
def __getitem__(self, index):
self.lazy_init()
path = str(self.images[index][0])
boxes = self.bboxes[index]
words = preprocess_words(self.text[index])
im = 'SynthText/' + path
if len(np.shape(boxes)) == 2:
boxes = np.array([boxes])
boxes = np.transpose(boxes, (1, 2, 0))
boxes = np.transpose(boxes, (2, 1, 0)) # num_boxes, 4 points, 2 xy
try:
if fastzip:
with self.zip.read(get_path(im)) as imbytes:
# pil_img = Image(bytes(imbytes))
pil_img = Image.open(io.BytesIO(bytes(imbytes)))
else:
imbytes = self.zip.read(get_path(im))
pil_img = Image.open(io.BytesIO(imbytes))
# pil_img = Image(bytes(imbytes))
except Exception as e:
print(e, index, path)
raise e
return pil_img, boxes, words
if __name__ == '__main__':
import argparse
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--synthtext', default=None, type=str,
help='path for synthtext dataset')
args = parser.parse_args()
return args
args = parse_args()
time1 = time.time()
dataset = SynthTextDataset(args.synthtext)
print('| Time taken for data init %.2f' % (time.time() - time1))
sample = dataset[0]
print(sample)
Python links
- Learn Python: https://pythonbasics.org/
- Python Tutorial: https://pythonprogramminglanguage.com