data ready

.gitignore

@@ -0,0 +1 @@
+.vscode/

.idea/misc.xml

@@ -0,0 +1,4 @@
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.idea/modules.xml

@@ -0,0 +1,8 @@
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+  </component>
+</project>

.idea/ocrcn_tf2.iml

@@ -0,0 +1,11 @@
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+  <component name="TestRunnerService">
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+</module>

.idea/vcs.xml

@@ -0,0 +1,6 @@
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+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
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+</project>

.idea/workspace.xml

@@ -0,0 +1,251 @@
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+    <entry file="file://$PROJECT_DIR$/dataset/charactors.txt">
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+    <entry file="file://$PROJECT_DIR$/train.py">
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+          <folding />
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+    </entry>
+    <entry file="file://$PROJECT_DIR$/sample.png">
+      <provider selected="true" editor-type-id="images">
+        <state />
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+    <entry file="file://$PROJECT_DIR$/tests.py">
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+    <entry file="file://$PROJECT_DIR$/dataset/casia_hwdb.py">
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+      </provider>
+    </entry>
+  </component>
+</project>

dataset/.gitignore

@@ -1,2 +1,3 @@
 hwdb_raw/
-*.tfrecord
+*.tfrecord
+casia_hwdb.pyhwdb_11.tfrecord

dataset/casia_hwdb.py

@@ -6,9 +6,13 @@ we using this class to get .png and label from raw
 .gnt data
 
 """
+from alfred.dl.tf.common import mute_tf
+
+mute_tf()
 import struct
 import numpy as np
 import cv2
+import tensorflow as tf
 
 
 class CASIAHWDBGNT(object):
@@ -24,61 +28,57 @@ class CASIAHWDBGNT(object):
         with open(self.f_p, 'rb') as f:
             while True:
                 header = np.fromfile(f, dtype='uint8', count=header_size)
-                if not header.size: 
+                if not header.size:
                     break
-                sample_size = header[0] + (header[1]<<8) + (header[2]<<16) + (header[3]<<24)
+                sample_size = header[0] + (header[1] << 8) + (
-                tagcode = header[5] + (header[4]<<8)
+                        header[2] << 16) + (header[3] << 24)
-                width = header[6] + (header[7]<<8)
+                tagcode = header[5] + (header[4] << 8)
-                height = header[8] + (header[9]<<8)
+                width = header[6] + (header[7] << 8)
-                if header_size + width*height != sample_size:
+                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))
+                image = np.fromfile(f, dtype='uint8',
+                                    count=width * height).reshape(
+                    (height, width))
                 yield image, tagcode
 
 
-def resize_padding_or_crop(target_size, ori_img, padding_value=255):
+def parse_example(record):
-    if len(ori_img.shape) == 3:
+    features = tf.io.parse_single_example(record,
-        res = np.zeros([ori_img.shape[0], target_size, target_size])
+                                          features={
-    else:
+                                              'label':
-        res = np.ones([target_size, target_size])*padding_value
+                                                  tf.io.FixedLenFeature([], tf.int64),
-        end_x = target_size
+                                              'image':
-        end_y = target_size
+                                                  tf.io.FixedLenFeature([], tf.string),
-        start_x = 0
+                                          })
-        start_y = 0
+    img = tf.io.decode_raw(features['image'], out_type=tf.uint8)
-        if ori_img.shape[0] < target_size:
+    label = tf.cast(features['label'], tf.int32)
-            end_x = int((target_size + ori_img.shape[0])/2)
+    return img, label
-        if ori_img.shape[1] < target_size:
+
-            end_y = int((target_size + ori_img.shape[1])/2)
+
-        if ori_img.shape[0] < target_size:
+def load_ds():
-            start_x = int((target_size - ori_img.shape[0])/2)
+    input_files = ['casia_hwdb_1.0_1.1.tfrecord']
-        if ori_img.shape[1] < target_size:
+    ds = tf.data.TFRecordDataset(input_files)
-            start_y = int((target_size - ori_img.shape[1])/2)
+    ds = ds.map(parse_example)
-        res[start_x:end_x, start_y:end_y] = ori_img
+    return ds
-        return np.array(res, dtype=np.uint8)
+
+
+def load_charactors():
+    a = open('charactors.txt', 'r').readlines()
+    return [i.strip() for i in a]
+
 
 if __name__ == "__main__":
-    gnt = CASIAHWDBGNT('samples/1001-f.gnt')
+    ds = load_ds()
-
+    charactors = load_charactors()
-    full_img = np.zeros([900, 900], dtype=np.uint8)
+    for img, label in ds.take(9):
-    charset = []
+        # start training on model...
-    i = 0
+        img = img.numpy()
-    for img, tagcode in gnt.get_data_iter():
+        img = np.resize(img, (64, 64))
-        # cv2.imshow('rr', img)
+        print(img.shape)
-        try:
+        label = label.numpy()
-            label = struct.pack('>H', tagcode).decode('gb2312')            
+        label = charactors[label]
-            img_padded = resize_padding_or_crop(90, img)
+        print(label)
-            col_idx = i%10
+        cv2.imshow('rr', img)
-            row_idx = i//10
+        cv2.waitKey(0)
-            full_img[row_idx*90:(row_idx+1)*90, col_idx*90:(col_idx+1)*90] = img_padded
+        # break
-            charset.append(label.replace('\x00', ''))
-            if i >= 99:
-                cv2.imshow('rrr', full_img)
-                cv2.imwrite('sample.png', full_img)
-                cv2.waitKey(0)
-                print(charset)
-                break
-            i += 1
-        except Exception as e:
-            # print(e.with_traceback(0))
-            print('decode error')
-            continue

dataset/convert_to_tfrecord.py

@@ -7,6 +7,7 @@ import cv2
 from alfred.utils.log import logger as logging
 import tensorflow as tf
 import glob
+import os
 
 
 class CASIAHWDBGNT(object):
@@ -39,20 +40,27 @@ def run():
     logging.info('got all {} gnt files.'.format(len(all_hwdb_gnt_files)))
     logging.info('gathering charset...')
     charset = []
-    for gnt in all_hwdb_gnt_files:
+    if os.path.exists('charactors.txt'):
-        hwdb = CASIAHWDBGNT(gnt)
+        logging.info('found exist charactors.txt...')
-        for img, tagcode in hwdb.get_data_iter():
+        with open('charactors.txt', 'r') as f:
-            try:
+            charset = f.readlines()
-                label = struct.pack('>H', tagcode).decode('gb2312')            
+            charset = [i.strip() for i in charset]
-                label = label.replace('\x00', '')
+    else:
-                charset.append(label)
+        for gnt in all_hwdb_gnt_files:
-            except Exception as e:
+            hwdb = CASIAHWDBGNT(gnt)
-                continue
+            for img, tagcode in hwdb.get_data_iter():
-    charset = sorted(set(charset))
+                try:
+                    label = struct.pack('>H', tagcode).decode('gb2312')            
+                    label = label.replace('\x00', '')
+                    charset.append(label)
+                except Exception as e:
+                    continue
+        charset = sorted(set(charset))
+        with open('charactors.txt', 'w') as f:
+            f.writelines('\n'.join(charset))
     logging.info('all got {} charactors.'.format(len(charset)))
-    with open('charactors.txt', 'w') as f:
+    logging.info('{}'.format(charset[:10]))
-        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:
@@ -60,7 +68,7 @@ def run():
             hwdb = CASIAHWDBGNT(gnt)
             for img, tagcode in hwdb.get_data_iter():
                 try:
-                    img = cv.resize(img, (64, 64))
+                    img = cv2.resize(img, (64, 64))
                     label = struct.pack('>H', tagcode).decode('gb2312')            
                     label = label.replace('\x00', '')
                     index = charset.index(label)
@@ -68,11 +76,11 @@ def run():
                     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]))
+                        'image': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img.tobytes()]))
                     }))
                     tfrecord_writer.write(example.SerializeToString())
                     if i%500:
-                        logging.info('solved {} examples.'.format(i))
+                        logging.info('solved {} examples. {}: {}'.format(i, label, index))
                     i += 1
                 except Exception as e:
                     logging.error(e)

readme.md

@@ -27,7 +27,13 @@
 
 **更新**:
 实际上，由于单个汉字图片其实很小，差不多也就最大80x80的大小，这个大小不适合转成图片保存到本地，因此我们将hwdb原始的二进制保存为tfrecord。同时也方便后面训练，可以直接从tfrecord读取图片进行训练。
+![](https://s2.ax1x.com/2019/05/29/VKSEZt.png)
 
+在我们存储完成的时候大概处理了89万个汉字，总共汉字的空间是3755个汉字。由于我们暂时仅仅使用了1.0，所以还有大概3000个汉字没有加入进来，但是处理是一样。使用本仓库来生成你的tfrecord步骤如下：
+
+- `cd dataset && python3 convert_to_tfrecord.py`, 请注意我们使用的是tf2.0；
+- 你需要修改对应的路径，等待生成完成，大概有89万个example，如果1.0和1.1都用，那估计得double。
+  
 
 ## Model