[Setting up your first perception scene](GettingStarted.md)
## Example projects using Perception
### SynthDet
<imgsrc="images/synthdet.png"/>
[SynthDet](https://github.com/Unity-Technologies/SynthDet) is an end-to-end solution for training a 2d object detection model using synthetic data.
### Unity Simulation Smart Camera Example
<imgsrc="images/smartcamera.png"/>
The [Unity Simulation Smart Camera Example](https://github.com/Unity-Technologies/Unity-Simulation-Smart-Camera-Outdoor) illustrates how Perception could be used in a smart city or autonomous vehicle simulation. Datasets can be generated locally or at scale in [Unity Simulation](https://unity.com/products/unity-simulation).
1. Create a cube by right-clicking in the Hierarchy window, select 3D Object -> Cube
1. Create 2 more cubes this way
1. Change the names of the cubes to Cube, Box, and Crate
1. Position the Cubes in front of the main Camera
1. Create a cube by right-clicking in the Hierarchy window, select 3D Object -> Cube
2. Create 2 more cubes this way
3. Change the names of the cubes to Cube, Box, and Crate
4. Position the Cubes in front of the main Camera
1. On each cube, from the inspector panel add a **Labeling** component
5. On each cube, from the inspector panel add a **Labeling** component
1. In the Project panel right click -> Perception -> Labeling Configuration
1. Select the new **Labeling Configuration**
6. In the Project panel right click -> Perception -> Labeling Configuration
7. Select the new **ID Label Config**
3. Add a numerical value to the value field
Make sure the labels all have different values, for this example use increments of 10,000 to ensure they show up as very distinct colors in the segmentation images
>Example file path on a Windows PC : `C:/Users/<User Name>/AppData/LocalLow/DefaultCompany/UnityTestFramework\2e10ec21-9d97-4cee-b5a2-7e95e299afa4\RGB18f61842-ef8d-4b31-acb5-cb1da36fb7b1`
> Example file path on a Windows PC : `C:/Users/<User Name>/AppData/LocalLow/DefaultCompany/UnityTestFramework\2e10ec21-9d97-4cee-b5a2-7e95e299afa4\RGB18f61842-ef8d-4b31-acb5-cb1da36fb7b1`
- RGB captures
- Semantic segmentation images
- Logs
- JSON dataset
- RGB captures
- Semantic segmentation images
- Logs
- JSON dataset
<imgsrc="images/rgb_2.png"align="middle"/>
_RGB image_
_Example semantic segmentation image_
_Example semantic segmentation image_
## Optional Step: Realtime visualization of labelers
The perception package now comes with the ability to show realtime results of the labeler in the scene. To enable this capability:
<imgsrc="images/visualized.png"align="middle"/>
_Example of perception running with show visualizations on_
1. To use the visualizer, verify that *Show Visualizations* is checked on in the Inspector pane. This turns on the built in labelers which includes segmentation data, 2D bounding boxes, pixel and object counts.
2. Turning on the visualizer creates new UI controls in the editor's game view. These controls allow you to atomically control each of the individual visualizers. Each individual can be turned on/off on their own. Some visualizers also include controls to change their output.
<imgsrc="images/controls.gif"align="middle"/>
_Visualization controls in action_
***Important Note:*** The perception package takes advantage of asynchronous processing to ensure reasonable frame rates of a scene. A side effect of realtime visualization is that the labelers have to be applied to the capture in its actual frame, which will potentially adversely affect the scene's framerate.
Jon Hogins
4 年前
