add ctc reader

fluid/ocr_recognition/ctc_reader.py

@@ -0,0 +1,97 @@
+import os
+import cv2
+import numpy as np
+from PIL import Image
+
+from paddle.v2.image import load_image
+
+
+class DataGenerator(object):
+    def __init__(self):
+        pass
+
+    def train_reader(self, img_root_dir, img_label_list, batchsize):
+        '''
+        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 
+
+        '''
+
+        img_label_lines = []
+        if batchsize == 1:
+            to_file = "tmp.txt"
+            cmd = "cat " + img_label_list + " | awk '{print $1,$2,$3,$4;}' | shuf > " + to_file
+            print "cmd: " + cmd
+            os.system(cmd)
+            print "finish batch shuffle"
+            img_label_lines = open(to_file, 'r').readlines()
+        else:
+            to_file = "tmp.txt"
+            #cmd1: partial shuffle
+            cmd = "cat " + img_label_list + " | awk '{printf(\"%04d%.4f %s\\n\", $1, rand(), $0)}' | sort | sed 1,$((1 + RANDOM % 100))d | "
+            #cmd2: batch merge and shuffle
+            cmd += "awk '{printf $2\" \"$3\" \"$4\" \"$5\" \"; if(NR % " + str(
+                batchsize) + " == 0) print \"\";}' | shuf | "
+            #cmd3: batch split
+            cmd += "awk '{if(NF == " + str(
+                batchsize
+            ) + " * 4) {for(i = 0; i < " + str(
+                batchsize
+            ) + "; i++) print $(4*i+1)\" \"$(4*i+2)\" \"$(4*i+3)\" \"$(4*i+4);}}' > " + to_file
+            print "cmd: " + cmd
+            os.system(cmd)
+            print "finish batch shuffle"
+            img_label_lines = open(to_file, 'r').readlines()
+
+        def reader():
+            sizes = len(img_label_lines) / batchsize
+            for i in range(sizes):
+                result = []
+                sz = [0, 0]
+                for j in range(batchsize):
+                    line = img_label_lines[i * batchsize + j]
+                    # h, w, img_name, labels
+                    items = line.split(' ')
+
+                    label = [int(c) for c in items[-1].split(',')]
+                    img = Image.open(os.path.join(img_root_dir, items[
+                        2])).convert('L')  #zhuanhuidu
+                    if j == 0:
+                        sz = img.size
+                    img = img.resize((sz[0], sz[1]))
+                    img = np.array(img) - 127.5
+                    img = img[np.newaxis, ...]
+                    result.append([img, label])
+                yield result
+
+        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(' ')
+
+                label = [int(c) for c in items[-1].split(',')]
+                img = Image.open(os.path.join(img_root_dir, items[2])).convert(
+                    'L')
+                img = np.array(img) - 127.5
+                img = img[np.newaxis, ...]
+                yield img, label
+
+        return reader