update the readme

README.md

@@ -1,2 +1,33 @@
-# aster.pytorch
-ASTER in Pytorch
+# ASTER: Attentional Scene Text Recognizer with Flexible Rectification
+
+This repository implements the ASTER in pytorch. Origin software could be found in [here](https://github.com/bgshih/aster).
+
+## Train
+```
+bash scripts/stn_att_rec.sh
+```
+
+## Test
+```
+bash scripts/main_test_all.sh
+```
+
+## Reproduced results
+
+|               | IIIT5k |  SVT |  IC03 |  IC13 |  SVTP |  CUTE |
+|:-------------:|:------:|:----:|:-----:|:-----:|:-----:|:-----:|
+|  ASTER (L2R)  |  92.67 |   -  | 93.72 | 90.74 | 78.76 | 76.39 |
+| ASTER.Pytorch |  93.2  | 89.2 | 92.2  |   91  |  81.2 |  81.9 |
+|
+
+At present, the bidirectional attention decoder proposed in ASTER is not included in my implementation. 
+
+You can use the codes to bootstrap for your next text recognition research project.
+
+
+## Data preparation
+
+We give an example to construct your own datasets. Details please refer to `tools/create_svtp_lmdb.py`.
+
+
+IMPORTANT NOTICE: Although this software is licensed under MIT, our intention is to make it free for academic research purposes. If you are going to use it in a product, we suggest you [contact us]([email protected]) regarding possible patent issues.

lib/tools/create_svtp_lmdb.py

@@ -0,0 +1,98 @@
+import os
+import lmdb # install lmdb by "pip install lmdb"
+import cv2
+import numpy as np
+from tqdm import tqdm
+import six
+from PIL import Image
+import scipy.io as sio
+from tqdm import tqdm
+import re
+
+def checkImageIsValid(imageBin):
+  if imageBin is None:
+    return False
+  imageBuf = np.fromstring(imageBin, dtype=np.uint8)
+  img = cv2.imdecode(imageBuf, cv2.IMREAD_GRAYSCALE)
+  imgH, imgW = img.shape[0], img.shape[1]
+  if imgH * imgW == 0:
+    return False
+  return True
+
+
+def writeCache(env, cache):
+  with env.begin(write=True) as txn:
+    for k, v in cache.items():
+      txn.put(k.encode(), v)
+
+
+def _is_difficult(word):
+  assert isinstance(word, str)
+  return not re.match('^[\w]+$', word)
+
+
+def createDataset(outputPath, imagePathList, labelList, lexiconList=None, checkValid=True):
+  """
+  Create LMDB dataset for CRNN training.
+  ARGS:
+      outputPath    : LMDB output path
+      imagePathList : list of image path
+      labelList     : list of corresponding groundtruth texts
+      lexiconList   : (optional) list of lexicon lists
+      checkValid    : if true, check the validity of every image
+  """
+  assert(len(imagePathList) == len(labelList))
+  nSamples = len(imagePathList)
+  env = lmdb.open(outputPath, map_size=1099511627776)
+  cache = {}
+  cnt = 1
+  for i in range(nSamples):
+    imagePath = imagePathList[i]
+    label = labelList[i]
+    if len(label) == 0:
+      continue
+    if not os.path.exists(imagePath):
+      print('%s does not exist' % imagePath)
+      continue
+    with open(imagePath, 'rb') as f:
+      imageBin = f.read()
+    if checkValid:
+      if not checkImageIsValid(imageBin):
+        print('%s is not a valid image' % imagePath)
+        continue
+
+    imageKey = 'image-%09d' % cnt
+    labelKey = 'label-%09d' % cnt
+    cache[imageKey] = imageBin
+    cache[labelKey] = label.encode()
+    if lexiconList:
+      lexiconKey = 'lexicon-%09d' % cnt
+      cache[lexiconKey] = ' '.join(lexiconList[i])
+    if cnt % 1000 == 0:
+      writeCache(env, cache)
+      cache = {}
+      print('Written %d / %d' % (cnt, nSamples))
+    cnt += 1
+  nSamples = cnt-1
+  cache['num-samples'] = str(nSamples).encode()
+  writeCache(env, cache)
+  print('Created dataset with %d samples' % nSamples)
+
+if __name__ == "__main__":
+  data_dir = '/data/mkyang/datasets/English/benchmark/svtp/'
+  lmdb_output_path = '/data/mkyang/datasets/English/benchmark_lmdbs_new/svt_p_645'
+  gt_file = os.path.join(data_dir, 'gt.txt')
+  image_dir = data_dir
+  with open(gt_file, 'r') as f:
+    lines = [line.strip('\n') for line in f.readlines()]
+
+  imagePathList, labelList = [], []
+  for i, line in enumerate(lines):
+    splits = line.split(' ')
+    image_name = splits[0]
+    gt_text = splits[1]
+    print(image_name, gt_text)
+    imagePathList.append(os.path.join(image_dir, image_name))
+    labelList.append(gt_text)
+
+  createDataset(lmdb_output_path, imagePathList, labelList)