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ds2sim-0.5.0
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Fun with Machine Learning and Space Ships
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | ds2sim-0.5.0 |
| نام | ds2sim |
| نسخه کتابخانه | 0.5.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Oliver Nagy |
| ایمیل نویسنده | olitheolix@gmail.com |
| آدرس صفحه اصلی | https://github.com/olitheolix/ds2sim |
| آدرس اینترنتی | https://pypi.org/project/ds2sim/ |
| مجوز | Apache Software License 2.0 |
# DS2Sim
A simple Space Sim to experiment with machine learning.
It wraps [Horde3D](http://www.horde3d.org/) with Cython and uses it to render
scenes like these.
<img src="docs/img/example_scene.jpg" width="400">
You can use it to create your own training data, but it is easier
to use the data set from the [DS2Data](https://github.com/olitheolix/ds2data)
project. Not only does that project have a representative training set of the
relevant objects, it also comes with a pre-rendered flight path to test your
model.
Once you have your model you can plug it into the viewer application (see
[View The Space Simulation](#View-The-Space-Simulation) section for details),
and fly through the scene while it identifies the objects. You may also replace
the manual controls with another AI to make it fly autonomously.
## Installation
First, this will only work with an NVidia GPU, because the project uses
[headless rendering](https://devblogs.nvidia.com/parallelforall/egl-eye-opengl-visualization-without-x-server/).
If you do not have an NVidia card, you may still train and test ML models with
the data from the [DS2Data](https://github.com/olitheolix/ds2data) set.
Next, since this is a Cython wrapper for Horde3D, you will need - you guessed
it - Cython and Horde3D. To compile and install Horde3D, activate the virtual
environment of your choice, install Cython, and then clone/compile/install
Horde3D like so:
```bash
git clone https://github.com/olitheolix/Horde3D
mkdir -p Horde3D/build
cd Horde3D/build
git checkout ds2sim
cmake .. -DCMAKE_BUILD_TYPE=RELEASE -DCMAKE_INSTALL_PREFIX=`python -c 'import sys; print(sys.prefix)'`
make install
```
Afterwards, install *DS2Sim* via PIP:
```bash
pip install ds2sim
```
## View the Space Simulation
This consists of two parts: a web server to supply the rendered images, and a
Qt application to display them.
Start the web server and load the default scene:
```bash
ds2server --default-scene
```
Then put the following code into a file. Note that almost the entire file is
boilerplate for Qt.
```python
import sys
import ds2sim.viewer
import numpy as np
import PyQt5.QtGui as QtGui
import PyQt5.QtCore as QtCore
import PyQt5.QtWidgets as QtWidgets
# For convenience.
QPen, QColor, QRectF = QtGui.QPen, QtGui.QColor, QtCore.QRectF
DS2Text = ds2sim.viewer.DS2Text
class MyClassifier(ds2sim.viewer.ClassifierCamera):
def classifyImage(self, img):
pass
# Qt boilerplate to start the application.
app = QtWidgets.QApplication(sys.argv)
widget = MyClassifier('Camera', host='127.0.0.1', port=9095)
widget.show()
app.exec_()
```
The only part here that is not boilerplate is `MyClassifier`, and even that
does nothing right now.
When you run this program you should see the scene. Click in it, and use the
ESDF keys, as well as the mouse, to fly through the scene.
## Plug Your ML Model Into The Simulation
The real fun is, of course, to use ML to find and identify all the cubes while
you fly around. To do so, overload the `classifyImage` method in the previous
demo like so:
```python
# For convenience.
QPen, QColor, QRectF = QtGui.QPen, QtGui.QColor, QtCore.QRectF
DS2Text = ds2server.viewer.DS2Text
class MyClassifier(ds2sim.viewer.ClassifierCamera):
def classifyImage(self, img):
# `img` is always a <height, width, 3> NumPy image.
assert img.dtype == np.uint8
# Pass the image to your ML model.
# myAwesomeClassifier(img)
# Define a red bounding box.
x, y, width, height = 0.3, 0.4, 0.3, 0.3
bbox = [QPen(QColor(255, 0, 0)), QRectF(x, y, width, height)]
# Define a green text label.
x, y = 0.3, 0.4
text = [QPen(QColor(100, 200, 0)), DS2Text(x, y, 'Found Something')]
# Install the overlays.
self.setMLOverlays([bbox, text])
```
The `classifyImage` method will be called for each frame. It always receives
one RGB image as a NumPy array. Pass that image to your classifier to identify
the objects in the scene.
When you have found out which objects are where, you can add overlays to
highlight them. Every overlay is a 2-tuple: a `QPen` to define the colour, and
a primitive to draw. Currently, `QRect` and `DS2Textures` are the only
supported primitives. Pass all overlays to `setMLOverlays` and it will show up
in the scene.
The example code from above will produce an output like this.
Single Frame | Spaceflight
:-------------------------:|:-------------------------:
<img src="docs/img/viewer_box.jpg" width="400">|<img src="docs/img/animated.gif" width="400">
نحوه نصب
نصب پکیج whl ds2sim-0.5.0:
pip install ds2sim-0.5.0.whl
نصب پکیج tar.gz ds2sim-0.5.0:
pip install ds2sim-0.5.0.tar.gz