add PASCAL VOC 2012 download and convert. add README.md (#47)

* add PASCAL VOC2012 download and convert scripts

dataset/README.md

+# 数据下载
+## PASCAL VOC 2012数据集
+下载 PASCAL VOC 2012数据集并将分割部分的假彩色标注图(`SegmentationClass`文件夹)转换成灰度图并存储在`SegmentationClassAug`文件夹, 并在文件夹`ImageSets/Segmentation`下重新生成列表文件`train.list、val.list和trainval.list。
+
+```shell
+# 下载数据集并进行解压转换
+python download_and_convert_voc2012.py
+```
+
+如果已经下载好PASCAL VOC 2012数据集，将数据集移至dataset目录后使用下述命令直接进行转换即可。
+
+```shell
+# 数据集转换
+python convert_voc2012.py
+```
+
+## Oxford-IIIT Pet数据集
+我们使用了Oxford-IIIT中的猫和狗两个类别数据制作了一个小数据集mini_pet，更多关于数据集的介绍请参考[Oxford-IIIT Pet](https://www.robots.ox.ac.uk/~vgg/data/pets/)。
+
+```shell
+# 下载数据集并进行解压
+python dataset/download_pet.py
+```
+
+## Cityscapes数据集
+运行下述命令下载并解压Cityscapes数据集。
+
+```shell
+# 下载数据集并进行解压
+python dataset/download_cityscapes.py
+```

dataset/convert_voc2012.py

+# Copyright (c) 2019  PaddlePaddle 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.
+
+import sys
+import os
+import numpy as np
+import os
+from PIL import Image
+import glob
+
+LOCAL_PATH = os.path.dirname(os.path.abspath(__file__))
+def remove_colormap(filename):
+    gray_anno = np.array(Image.open(filename))
+    return gray_anno
+
+
+def save_annotation(annotation, filename):
+    annotation = annotation.astype(dtype=np.uint8)
+    annotation = Image.fromarray(annotation)
+    annotation.save(filename)
+
+def convert_list(origin_file, seg_file, output_folder):
+    with open(seg_file, 'w') as fid_seg:
+        with open(origin_file) as fid_ori:
+            lines = fid_ori.readlines()
+            for line in lines:
+                line = line.strip()
+                line = '.'.join([line, 'jpg'])
+                img_name = os.path.join("JPEGImages", line)
+                line = line.replace('jpg', 'png')
+                anno_name = os.path.join(output_folder.split(os.sep)[-1], line)
+                new_line = ' '.join([img_name, anno_name])
+                fid_seg.write(new_line + "\n")
+
+if __name__ == "__main__":
+    pascal_root = "./VOCtrainval_11-May-2012/VOC2012"
+    pascal_root = os.path.join(LOCAL_PATH, pascal_root)
+    seg_folder = os.path.join(pascal_root, "SegmentationClass")
+    txt_folder = os.path.join(pascal_root, "ImageSets/Segmentation")
+    train_path = os.path.join(txt_folder, "train.txt")
+    val_path = os.path.join(txt_folder, "val.txt")
+    trainval_path = os.path.join(txt_folder, "trainval.txt")
+
+    # 标注图转换后存储目录
+    output_folder = os.path.join(pascal_root, "SegmentationClassAug")
+    
+    print("annotation convert and file list convert")
+    if not os.path.exists(os.path.join(LOCAL_PATH, output_folder)):
+        os.mkdir(os.path.join(LOCAL_PATH, output_folder))
+    annotation_names = glob.glob(os.path.join(seg_folder, '*.png'))
+    for annotation_name in annotation_names:
+        annotation = remove_colormap(annotation_name)
+        filename = os.path.basename(annotation_name)
+        save_name = os.path.join(output_folder, filename)
+        save_annotation(annotation, save_name)
+
+    convert_list(train_path, train_path.replace('txt', 'list'), output_folder)
+    convert_list(val_path, val_path.replace('txt', 'list'), output_folder)
+    convert_list(trainval_path, trainval_path.replace('txt', 'list'), output_folder)
+

dataset/download_and_convert_voc2012.py

+# Copyright (c) 2019  PaddlePaddle 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.
+
+import sys
+import os
+import numpy as np
+import os
+import glob
+
+LOCAL_PATH = os.path.dirname(os.path.abspath(__file__))
+TEST_PATH = os.path.join(LOCAL_PATH, "..", "test")
+sys.path.append(TEST_PATH)
+
+from test_utils import download_file_and_uncompress
+from convert_voc2012 import convert_list
+from convert_voc2012 import remove_colormap
+from convert_voc2012 import save_annotation
+
+
+
+def download_pet_dataset(savepath, extrapath):
+    url = "https://paddleseg.bj.bcebos.com/dataset/VOCtrainval_11-May-2012.tar"
+    download_file_and_uncompress(
+        url=url, savepath=savepath, extrapath=extrapath)
+
+if __name__ == "__main__":
+    download_pet_dataset(LOCAL_PATH, LOCAL_PATH)
+    print("Dataset download finish!")
+
+    pascal_root = "./VOCtrainval_11-May-2012/VOC2012"
+    pascal_root = os.path.join(LOCAL_PATH, pascal_root)
+    seg_folder = os.path.join(pascal_root, "SegmentationClass")
+    txt_folder = os.path.join(pascal_root, "ImageSets/Segmentation")
+    train_path = os.path.join(txt_folder, "train.txt")
+    val_path = os.path.join(txt_folder, "val.txt")
+    trainval_path = os.path.join(txt_folder, "trainval.txt")
+    
+    # 标注图转换后存储目录
+    output_folder = os.path.join(pascal_root, "SegmentationClassAug")
+    
+    print("annotation convert and file list convert")
+    if not os.path.exists(output_folder):
+        os.mkdir(output_folder)
+    annotation_names = glob.glob(os.path.join(seg_folder, '*.png'))
+    for annotation_name in annotation_names:
+        annotation = remove_colormap(annotation_name)
+        filename = os.path.basename(annotation_name)
+        save_name = os.path.join(output_folder, filename)
+        save_annotation(annotation, save_name)
+
+    convert_list(train_path, train_path.replace('txt', 'list'), output_folder)
+    convert_list(val_path, val_path.replace('txt', 'list'), output_folder)
+    convert_list(trainval_path, trainval_path.replace('txt', 'list'), output_folder)
+