add ctc reader

fluid/ocr_recognition/ctc_reader.py

+import os
+import cv2
+import numpy as np
+
+from paddle.v2.image import load_image
+
+
+class DataGenerator(object):
+    def __init__(self):
+        pass
+
+    def train_reader(self, img_root_dir, img_label_list):
+        '''
+        Reader interface for training.
+
+		:param img_root_dir: The root path of the image for training.
+        :type file_list: str 
+
+        :param img_label_list: The path of the <image_name, label> file for training.
+        :type file_list: str 
+
+        '''
+        # sort by height, e.g. idx
+        img_label_lines = []
+        for line in open(img_label_list):
+            # h, w, img_name, labels
+            items = line.split(' ')
+            idx = "{:0>5d}".format(int(items[0]))
+            img_label_lines.append(idx + ' ' + line)
+        img_label_lines.sort()
+
+        def reader():
+            for line in img_label_lines:
+                # h, w, img_name, labels
+                items = line.split(' ')[1:]
+
+                assert len(items) == 4
+
+                label = [int(c) for c in items[-1].split(',')]
+
+                img = load_image(os.path.join(img_root_dir, items[2]))
+                img = np.transpose(img, (2, 0, 1))
+                #img = img[np.newaxis, ...]
+
+                yield img, label
+
+        return reader
+
+    def test_reader(self, img_root_dir, img_label_list):
+        '''
+        Reader interface for inference.
+
+		:param img_root_dir: The root path of the images for training.
+        :type file_list: str 
+
+        :param img_label_list: The path of the <image_name, label> file for testing.
+        :type file_list: list
+        '''
+
+        def reader():
+            for line in open(img_label_list):
+                # h, w, img_name, labels
+                items = line.split(' ')
+
+                assert len(items) == 4
+
+                label = [int(c) for c in items[-1].split(',')]
+
+                img = load_image(os.path.join(img_root_dir, items[2]))
+                img = np.transpose(img, (2, 0, 1))
+                #img = img[np.newaxis, ...]
+
+                yield img, label
+
+        return reader