First commit

LICENSE.md

@@ -0,0 +1,23 @@
+
+# License information
+
+The code is released under a dual license depending on applications, research or commercial.
+
+---
+
+## COMMERCIAL PURPOSES
+
+Please contact the ONERA [www.onera.fr/en/contact-us](www.onera.fr/en/contact-us) for additional information.
+
+---
+
+## RESEARCH AND NON COMMERCIAL PURPOSES
+
+#### Code license
+
+For research and non commercial purposes, all the code and documentation of github.com/aboulch/snapnet is released under the GPLv3 license:
+
+DeLTA Toolbox, a toolbox for ONERA DeLTA project
+Copyright (C) 2017 ONERA, Alexandre Boulch
+This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or any later version.
+This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301  USA

README.md

@@ -1,2 +1,9 @@
-# delta_tb
-Toolbox for the ONERA Delta project
+# Toolbox for DeLTA project
+
+Toolbox for the [ONERA Delta project](https://delta-onera.github.io).
+
+This toolbox provides code sample developped in the project for various applications. It will be enriched as the project goes on.
+
+## [Semantic segmentation](semantic_segmentation/semantic_segmentation.md)
+
+## [License](LICENSE)

semantic_segmentation/dataset_list.py

@@ -0,0 +1,107 @@
+"""Image Folder Data loader"""
+
+import torch.utils.data as data
+import torchvision.transforms as transforms
+import numpy as np
+from PIL import Image, ImageMath, ImageOps
+import os
+import os.path
+import random
+
+
+def make_dataset(input_dir, target_dir, filenames):
+    """Create the dataset."""
+    images = []
+    # deal with multiple input
+
+    text_file = open(filenames, 'r')
+    lines = text_file.readlines()
+
+    for filename in lines:
+        filename = filename.split("\n")[0]
+        item = []
+        item.append(os.path.join(input_dir, filename))
+
+        if target_dir is not None:
+            item.append(os.path.join(target_dir, filename))
+        else:
+            item.append(None)
+
+        images.append(item)
+
+    return images
+
+
+def pil_loader(path):
+    """Load PIL images."""
+    return Image.open(path)
+
+
+def default_loader(path):
+    """Load Default loader."""
+    return pil_loader(path)
+
+
+class ImageFolderDenseFileLists(data.Dataset):
+    """Main Class for Image Folder loader."""
+
+    def __init__(self, input_root,
+                target_root=None,
+                filenames=None, loader=default_loader,
+                 training=True, mirror=True):
+        """Init function."""
+        #
+        # get the lists of images
+        imgs = make_dataset(input_root, target_root, filenames, extensions)
+
+        if len(imgs) == 0:
+            raise(RuntimeError("Found 0 images in subfolders of: " + input_root + "\n"
+                               "Supported image extensions are: " + ",".join(IMG_EXTENSIONS)))
+
+        self.input_root = input_root
+        self.target_root = target_root
+        self.imgs = imgs
+
+
+        self.loader = loader
+        self.training = training
+
+        self.mirror = mirror
+
+    def __getitem__(self, index):
+        """Get item."""
+
+        input_paths = self.imgs[index][0]
+        target_path = self.imgs[index][1]
+
+        input_img = self.loader(p)
+
+        # random flip
+        if self.mirror:
+            use_mirror = random.randint(0,1)
+            if(use_mirror):
+                input_img = ImageOps.mirror(input_img)
+
+        # apply transformation
+        input_img = self.transform(input_img)
+        transform = transforms.Compose([transforms.ToTensor()])
+        input_img = transform(input_img)
+
+        if self.training:
+            target_img = self.loader(target_path)
+            if(use_mirror):
+                target_img = ImageOps.mirror(target_img)
+            target_img = transform(target_img)
+        else:
+            target_img = np.array([index]) # index of the image in the filelist
+        target_img = np.array(target_img)
+
+        return input_img, target_img
+
+    def __len__(self):
+        """Length."""
+        return len(self.imgs)
+
+    def get_filename(self, id):
+        """Get the filename."""
+        return self.imgs[id]