Add ROADMAP.md describing future plans

Author: einarf

README.md

@@ -38,6 +38,8 @@ or can be cloned on [github](https://github.com/pywavefront/PyWavefront).
 pip install pywavefront
 ```
 
+Also check out the [roadmap](https://github.com/pywavefront/PyWavefront/blob/master/ROADMAP.md) for future plans.
+
 ## Usage
 
 Basic example loading an obj file:

ROADMAP.md

@@ -0,0 +1,83 @@
+
+# PyWavefront 2.0
+
+## Introduction
+
+PyWavefront is used by projects and people from both data science and pure 3D
+rendering. Providing visualisation capabilities also seems to be important for
+a lot of users. There are ties to [pyglet](https://bitbucket.org/pyglet/pyglet/wiki/Home)
+we probably should not break.
+
+Currently the project mainly has 3D rendering in mind. We see that in the parser
+were interlaved vertex data is genereted on the fly. This is not always what
+users want and will need improvements. Collecting face data was also added
+fairly recently but is limited to trinagles to make it consistent with the
+rest of the library.
+
+The plan is to revamp the project structure and making the parse result a lot more flexible supporting triangles and quads + other optimization. The goal is to make the library easier to use for data science as well as 3d rendering. Parsing should only
+collect the actual data. We then provide methods for obtaining the data in
+various ways after the parse is done.
+
+
+## Improved Visualization
+
+To provide a modern cross-platform visualization system moving to 
+[ModernGL](https://github.com/cprogrammer1994/ModernGL) is probably
+the way to go using PyQt5 as the default rendering window.
+
+**It's still important to also keep a pyglet option as a lot of
+users would expect this to be present.**
+
+We can simply brush up the old pyglet visualitzation module and
+migrate that to using shaders when Pyglet 2.x is out.
+
+## Introduce pywavefront Command
+
+Bring PyWavefront to the command line making common operations easily
+accessible.
+
+```bash
+# Renders the obj file
+pywavefront show test.obj
+
+# Cache management
+pywavefront gen_cache test.obj
+pywavefront del_cache test.obj
+
+# .. possibly other commands
+```
+
+## Memory Management
+
+TLDR; Use `numpy` to store internal data using 6 times less memory.
+
+We are currently collecting and storing indices and postions using native
+python types.
+
+```py
+>> import sys
+>> data = [x for x in range(1000000)]
+>> f"Array size: {(len(data) * sys.getsizeof(int())) // 1024 // 1024} MB")
+Array size: 22.8876953125 MB
+>> f"sizeof int: {sys.getsizeof(int())} bytes"
+sizeof int: 24 bytes
+>> f"sizeof float: {sys.getsizeof(float())} bytes"
+sizeof float: 24 bytes
+```
+
+As we can see an `int` and `float` actually takes 24 bytes in python.
+This is 6 times more than necessary
+
+With `numpy` we can solve this entirely
+
+```py
+import numpy as np
+data = np.arange(1_000_000)
+f"Item size: {data.itemsize}"
+Item size: 4
+f"Array size: {len(data) * data.itemsize / 1024 / 1024} MB"
+Array size: 3.814697265625 MB
+```
+
+An advantage is also that we can reshape the array without
+allocating new memory.