mdb

README.md

@@ -19,13 +19,31 @@ If you find our work useful in your research, please consider citing:
 	}
 
 
-### Installation
-
-Install <a href="https://www.tensorflow.org/get_started/os_setup" target="_blank">TensorFlow</a> and 
-<a href="http://tflearn.org/installation/" target="_blank">TFLearn</a>.
+## Dependencies
+Main requirements:
+- Python 2.7+ with Numpy, Scipy and Matplotlib
+- [Tensorflow](https://www.tensorflow.org/get_started/os_setup)
+- [TFLearn](http://tflearn.org/installation)
 
 Our code has been tested with Python 2.7, TensorFlow 1.3.0, TFLearn 0.3.2, CUDA 8.0 and cuDNN 6.0 on Ubuntu 14.04.
 
+
+## Installing the library
+
+Download the source code from the git repository:
+```
+mkdir -p $HOME/work
+cd $HOME/work
+git clone https://github.com/HoME-Platform/home-platform.git
+```
+
+Note that the library must be in the PYTHONPATH environment variable for Python to be able to find it:
+```
+export PYTHONPATH=$HOME/work/home-platform:$PYTHONPATH 
+```
+This can also be added at the end of the configuration file $HOME/.bashrc
+
+
 <!-- 
 ### Usage
 To train a model to classify point clouds sampled from 3D shapes:

src/in_out.py

@@ -10,6 +10,7 @@
 import re
 from six.moves import cPickle
 
+
 def create_dir(dir_path):
     ''' Creates a directory (or nested directories) if they don't exist.
     '''
@@ -19,7 +20,6 @@ def create_dir(dir_path):
     return dir_path
 
 
-
 def pickle_data(file_name, *args):
     '''Using (c)Pickle to save multiple python objects in a single file.
     '''
@@ -87,11 +87,13 @@ def load_all_point_clouds_under_folder(top_dir, n_threads=20, file_ending='.ply'
     '04554684': 'washer',    '02858304': 'boat',       '02992529': 'cellphone'
 }
 
+
 def snc_category_to_synth_id():
     d = snc_synth_id_to_category
     inv_map = {v: k for k, v in six.iteritems(d)}
     return inv_map
 
+
 def load_point_clouds_from_filenames(file_names, n_threads, loader, verbose=False):
     pc = loader(file_names[0])[0]
     pclouds = np.empty([len(file_names), pc.shape[0], pc.shape[1]], dtype=np.float32)