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README.md

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+# TextBoxes++-TensorFlow
+TextBoxes++ re-implementation using tensorflow.
+This project is greatly inspired by [slim project](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/slim)
+And many functions are modified based on [SSD-tensorflow project](https://github.com/balancap/SSD-Tensorflow)
+
+Author:
+	Zhisheng Zou [email protected]
+
+# environment
+` python2.7/python3.5 ` 
+
+`tensorflow-gpu 1.8.0`
+
+`at least one gpu`
+
+# how to use
+
+1. Getting the  xml file like this [example xml](./demo/example/image0.xml) and put the image together because we need the format like this [standard xml](./demo/example/standard.xml)
+   1. picture format: *.png or *.PNG
+2. Getting the xml and flags
+   ensure the XML file is under the same directory as the corresponding image.execute the code: [convert_xml_format.py](./tools/convert_xml_format.py)
+   1. `python tools/convert_xml_format.py -i in_dir  -s split_flag -l save_logs -o output_dir` 
+   2. in_dir means the absolute directory which contains the pic and xml
+   3. split_flag means whether or not to split the datasets
+   4. save_logs means whether to save train_xml.txt
+   5. output_dir means where to save xmls
+3. Getting the tfrecords
+   1. `python gene_tfrecords.py --xml_img_txt_path=./logs/train_xml.txt --output_dir=tfrecords` 
+   2. xml_img_txt_path like this [train xml](./logs/train_xml.txt)
+   3. output_dir means where to save tfrecords
+4. Training
+   1. `python train.py --train_dir =some_path --dataset_dir=some_path --checkpoint_path=some_path`
+   2. train_dir  store the checkpoints when training
+   3. dataset_dir store the tfrecords for training
+   4. checkpoint_path store the model which needs to be fine tuned
+5. Testing
+   1. `python test.py -m /home/model.ckpt-858 -o test`
+   2. -m which means the model
+   3. -o which means output_result_dir
+   4. -i which means the test img dir
+   5. -c which means use which device to run the test
+   6. -n which means the nms threshold
+   7. -s which means the score threshold
+
+
+
+# Note:
+
+1. when you training the model, you can run the eval_result.py to eval your model and save the result

datasets/__init__.py

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+

datasets/dataset_utils.py

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+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Contains utilities for downloading and converting datasets."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import sys
+import tarfile
+
+from six.moves import urllib
+import tensorflow as tf
+
+LABELS_FILENAME = 'labels.txt'
+def norm(x):
+    if x < 0:
+        x = 0
+    else:
+        if x > 1:
+            x = 1
+    return x
+
+def int64_feature(value):
+    """Wrapper for inserting int64 features into Example proto.
+    """
+    if not isinstance(value, list):
+        value = [value]
+    return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
+
+
+def float_feature(value):
+    """Wrapper for inserting float features into Example proto.
+    """
+    if not isinstance(value, list):
+        value = [value]
+    return tf.train.Feature(float_list=tf.train.FloatList(value=value))
+
+
+def bytes_feature(value):
+    """Wrapper for inserting bytes features into Example proto.
+    """
+    if not isinstance(value, list):
+        value = [value]
+    return tf.train.Feature(bytes_list=tf.train.BytesList(value=value))
+
+
+def image_to_tfexample(image_data, image_format, height, width, class_id):
+    return tf.train.Example(features=tf.train.Features(feature={
+      'image/encoded': bytes_feature(image_data),
+      'image/format': bytes_feature(image_format),
+      'image/class/label': int64_feature(class_id),
+      'image/height': int64_feature(height),
+      'image/width': int64_feature(width),
+    }))
+
+
+def download_and_uncompress_tarball(tarball_url, dataset_dir):
+    """Downloads the `tarball_url` and uncompresses it locally.
+
+    Args:
+    tarball_url: The URL of a tarball file.
+    dataset_dir: The directory where the temporary files are stored.
+    """
+    filename = tarball_url.split('/')[-1]
+    filepath = os.path.join(dataset_dir, filename)
+
+    def _progress(count, block_size, total_size):
+        sys.stdout.write('\r>> Downloading %s %.1f%%' % (
+            filename, float(count * block_size) / float(total_size) * 100.0))
+        sys.stdout.flush()
+    filepath, _ = urllib.request.urlretrieve(tarball_url, filepath, _progress)
+    statinfo = os.stat(filepath)
+    print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')
+    tarfile.open(filepath, 'r:gz').extractall(dataset_dir)
+
+
+def write_label_file(labels_to_class_names, dataset_dir,
+                     filename=LABELS_FILENAME):
+    """Writes a file with the list of class names.
+
+    Args:
+    labels_to_class_names: A map of (integer) labels to class names.
+    dataset_dir: The directory in which the labels file should be written.
+    filename: The filename where the class names are written.
+    """
+    labels_filename = os.path.join(dataset_dir, filename)
+    with tf.gfile.Open(labels_filename, 'w') as f:
+        for label in labels_to_class_names:
+            class_name = labels_to_class_names[label]
+            f.write('%d:%s\n' % (label, class_name))
+
+
+def has_labels(dataset_dir, filename=LABELS_FILENAME):
+    """Specifies whether or not the dataset directory contains a label map file.
+
+    Args:
+    dataset_dir: The directory in which the labels file is found.
+    filename: The filename where the class names are written.
+
+    Returns:
+    `True` if the labels file exists and `False` otherwise.
+    """
+    return tf.gfile.Exists(os.path.join(dataset_dir, filename))
+
+
+def read_label_file(dataset_dir, filename=LABELS_FILENAME):
+    """Reads the labels file and returns a mapping from ID to class name.
+
+    Args:
+    dataset_dir: The directory in which the labels file is found.
+    filename: The filename where the class names are written.
+
+    Returns:
+    A map from a label (integer) to class name.
+    """
+    labels_filename = os.path.join(dataset_dir, filename)
+    with tf.gfile.Open(labels_filename, 'rb') as f:
+        lines = f.read()
+    lines = lines.split(b'\n')
+    lines = filter(None, lines)
+
+    labels_to_class_names = {}
+    for line in lines:
+        index = line.index(b':')
+        labels_to_class_names[int(line[:index])] = line[index+1:]
+    return labels_to_class_names
+
+
+class ImageCoder(object):
+  """Helper class that provides TensorFlow image coding utilities."""
+
+  def __init__(self):
+    # Create a single Session to run all image coding calls.
+    self._sess = tf.Session()
+
+    # Initializes function that converts PNG to JPEG data.
+    self._png_data = tf.placeholder(dtype=tf.string)
+    image = tf.image.decode_png(self._png_data, channels=3)
+    self._png_to_jpeg = tf.image.encode_jpeg(image, format='rgb', quality=100)
+
+    # Initializes function that converts CMYK JPEG data to RGB JPEG data.
+    self._cmyk_data = tf.placeholder(dtype=tf.string)
+    image = tf.image.decode_jpeg(self._cmyk_data, channels=0)
+    self._cmyk_to_rgb = tf.image.encode_jpeg(image, format='rgb', quality=100)
+
+    # Initializes function that decodes RGB JPEG data.
+    self._decode_jpeg_data = tf.placeholder(dtype=tf.string)
+    self._decode_jpeg = tf.image.decode_jpeg(self._decode_jpeg_data, channels=3)
+
+  def png_to_jpeg(self, image_data):
+    return self._sess.run(self._png_to_jpeg,
+                          feed_dict={self._png_data: image_data})
+
+  def cmyk_to_rgb(self, image_data):
+    return self._sess.run(self._cmyk_to_rgb,
+                          feed_dict={self._cmyk_data: image_data})
+
+  def decode_jpeg(self, image_data):
+    image = self._sess.run(self._decode_jpeg,
+                           feed_dict={self._decode_jpeg_data: image_data})
+    assert len(image.shape) == 3
+    assert image.shape[2] == 3
+    return image