data ready

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)
-                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:
+                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))
+                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):
-    if len(ori_img.shape) == 3:
-        res = np.zeros([ori_img.shape[0], target_size, target_size])
-    else:
-        res = np.ones([target_size, target_size])*padding_value
-        end_x = target_size
-        end_y = target_size
-        start_x = 0
-        start_y = 0
-        if ori_img.shape[0] < target_size:
-            end_x = int((target_size + ori_img.shape[0])/2)
-        if ori_img.shape[1] < target_size:
-            end_y = int((target_size + ori_img.shape[1])/2)
-        if ori_img.shape[0] < target_size:
-            start_x = int((target_size - ori_img.shape[0])/2)
-        if ori_img.shape[1] < target_size:
-            start_y = int((target_size - ori_img.shape[1])/2)
-        res[start_x:end_x, start_y:end_y] = ori_img
-        return np.array(res, dtype=np.uint8)
+def parse_example(record):
+    features = tf.io.parse_single_example(record,
+                                          features={
+                                              'label':
+                                                  tf.io.FixedLenFeature([], tf.int64),
+                                              'image':
+                                                  tf.io.FixedLenFeature([], tf.string),
+                                          })
+    img = tf.io.decode_raw(features['image'], out_type=tf.uint8)
+    label = tf.cast(features['label'], tf.int32)
+    return img, label
+
+
+def load_ds():
+    input_files = ['casia_hwdb_1.0_1.1.tfrecord']
+    ds = tf.data.TFRecordDataset(input_files)
+    ds = ds.map(parse_example)
+    return ds
+
+
+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')
-
-    full_img = np.zeros([900, 900], dtype=np.uint8)
-    charset = []
-    i = 0
-    for img, tagcode in gnt.get_data_iter():
-        # cv2.imshow('rr', img)
-        try:
-            label = struct.pack('>H', tagcode).decode('gb2312')            
-            img_padded = resize_padding_or_crop(90, img)
-            col_idx = i%10
-            row_idx = i//10
-            full_img[row_idx*90:(row_idx+1)*90, col_idx*90:(col_idx+1)*90] = img_padded
-            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
+    ds = load_ds()
+    charactors = load_charactors()
+    for img, label in ds.take(9):
+        # start training on model...
+        img = img.numpy()
+        img = np.resize(img, (64, 64))
+        print(img.shape)
+        label = label.numpy()
+        label = charactors[label]
+        print(label)
+        cv2.imshow('rr', img)
+        cv2.waitKey(0)
+        # break