Update readme with some directions

README.md

@@ -11,10 +11,76 @@
 
 **WIP - Version 0**
 
-This is a Python package for visualisation and loading of Baidu's ApolloScape dataset for vehicle vision tasks.
+This is a Python package for visualisation and loading of Baidu's ApolloScape data-set for vehicle vision tasks.
 
-Currently it is only focused on the overlapping [__Lane segmentation__][lane_segmentation] and [__Scene parsing__][scene_parsing] datasets.
+Currently it is only focused on the overlapping [__Lane segmentation__][lane_segmentation] and [__Scene parsing__][scene_parsing] data-sets.
 
+## temporary usage
+
+Currently this project is still a bit messy. I plan on cleaning it and
+improving the UI (probably inducing some heavy changes) when I will have some
+time.
+Here is some code if you want to use it in its current state:
+
+```python
+import apolloscope
+from apolloscope.ls_sp.register import Register, SequenceId, TypeId
+```
+Specify  the paths to the data-set:
+```python
+sp_path = PATH_TO_YOUR_SCENE_PARSING_FOLDER
+ls_path = PATH_TO_YOUR_LANE_SEGMENTATION_FOLDER
+
+apolloscope.root_folder.scene_parsing(sp_path)
+apolloscope.root_folder.lane_segmentation(ls_path)
+```
+The expected folder architecture is the one used in ApolloScape archive files.
+Some archives seem to have been zipped differently than others and will not 
+recreate the top-most folder (the one that has the same name as the archive), 
+you will have to create it yourself (this is for example the case for the scene 
+parsing `road02_seg_depth.tar.gz` file).
+
+This will parse the file paths in the specified folders and classifying
+them by image type and sequence in a multi-indexed data-frame (see it like a 2D
+array in which we are going to slice depending on the data we want):
+```python
+register = Register()
+```
+Say that we want to iterate at the same time on the colour data, the semantic
+segmentation and the depth maps. We define the three data types:
+```python
+image_type = TypeId(dataset="SP",
+                    section="seg",
+                    subsection="ColorImage",
+                    file_type="jpg")
+depth_type = TypeId("SP", "seg_depth", "Depth", "png")
+seg_type = TypeId("SP", "seg", "Label", "bin.png")
+```
+Suppose that we want to iterate over the frames captured on road 2,
+sequence 22, camera 5. We define the sequence:
+```python
+test_sequence = SequenceId(road=2, record=22, camera=5)
+```
+In both data types and sequences definition, all parameters are optional,
+allowing to select larger parts of the data-set. Defining
+```python
+test_sequence = SequenceId(road=2, camera=5)
+```
+will take the data of all records on road 2 filmed by camera 5.
+
+To actually select the data from `register`, we do:
+```python
+filtered_register = register.types([file_type, depth_type, seg_type])
+filtered_register = filtered_register.sequences([test_sequence])
+```
+We can then get the Pytorch dataset:
+```python
+dataset = apolloscope.ls_sp.pytorch.Dataset(filtered_register)
+```
+and use it in a pytorch dataloader in a classical way.
+In the current case, each element would be a tuple of three images
+corresponding to the three data types we defined, at a same time-stamp.
+Triplets with missing data would be dropped.
 
 [lane_segmentation]: http://apolloscape.auto/lane_segmentation.html
 [scene_parsing]: http://apolloscape.auto/scene.html