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