added TFRecord writer

+import tensorflow as tf
+
+def bytes_feature(values):
+    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[values]))
+
+def int64_feature(values):
+    if not isinstance(values, (tuple, list)):
+        values = [values]
+
+    return tf.train.Feature(int64_list=tf.train.Int64List(value=values))
+
+def readImage(path):
+    file = open(path, 'rb')
+    byte = file.read()
+    return byte
+
+def main():
+    ANNOTATION_PATH = 'train.txt' #annotation set (train/val/test) text file
+    IMAGE_DIRECTORY = 'image_data/' #image directory
+    SAVE_PATH = 'train.tfrecord' #save path for tfrecord
+
+    print('Tensorflow version:', tf.__version__) #tensorflow version should be 1.x
+
+    file = open(ANNOTATION_PATH, 'r')
+    lines = file.readlines()
+    file.close()
+
+    options = tf.python_io.TFRecordOptions(tf.python_io.TFRecordCompressionType.GZIP) #compress option
+    writer = tf.python_io.TFRecordWriter(path=SAVE_PATH, options=options)
+
+    for line in lines:
+        parsed = line.split(' ')
+        print('Current Doing...', parsed[1]) #debug messages
+        image = readImage(IMAGE_DIRECTORY + '/' + parsed[1])
+        boxes = []
+
+        for i in range(4, len(parsed)):
+            boxes.append(int(parsed[i]))
+        
+        data = tf.train.Example(features=tf.train.Features(feature={
+            'index': int64_feature(int(parsed[0])),
+            'image': bytes_feature(image),
+            'width': int64_feature(int(parsed[2])),
+            'height': int64_feature(int(parsed[3])),
+            'boxes': int64_feature(boxes) # boxes = [label1, xmin1, ymin1, xmax1, ymax1, label2, xmax2, ...]
+        }))
+
+        writer.write(data.SerializeToString())
+
+    writer.close()
+
+if __name__ == '__main__':
+    main()
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