p71924506
/
carla
mirror of https://github.com/carla-simulator/carla.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

New iteration with semantic pic and additional code lines

This commit is contained in:
sergi-e 2020-09-21 22:30:57 +02:00 committed by bernat
parent 81de76fa18
commit 3140ba3f2e
2 changed files with 14 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

BIN
Docs/img/ref_sensors_semantic.jpg
View File

Binary file not shown.
Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 75 KiB

After

Width:  |  Height:  |  Size: 97 KiB

16
Docs/ref_sensors.md
View File

@ -80,6 +80,12 @@ in_meters = 1000 * normalized
The output [carla.Image](python_api.md#carla.Image) should then be saved to disk using a [carla.colorConverter](python_api.md#carla.ColorConverter) that will turn the distance stored in RGB channels into a __[0,1]__ float containing the distance and then translate this to grayscale. The output [carla.Image](python_api.md#carla.Image) should then be saved to disk using a [carla.colorConverter](python_api.md#carla.ColorConverter) that will turn the distance stored in RGB channels into a __[0,1]__ float containing the distance and then translate this to grayscale.
There are two options in [carla.colorConverter](python_api.md#carla.ColorConverter) to get a depth view: __Depth__ and __Logaritmic depth__. The precision is milimetric in both, but the logarithmic approach provides better results for closer objects. There are two options in [carla.colorConverter](python_api.md#carla.ColorConverter) to get a depth view: __Depth__ and __Logaritmic depth__. The precision is milimetric in both, but the logarithmic approach provides better results for closer objects.
```py
...
raw_image.save_to_disk("path/to/save/converted/image",carla.Depth)
```
![ImageDepth](img/ref_sensors_depth.jpg) ![ImageDepth](img/ref_sensors_depth.jpg)
@ -1435,8 +1441,16 @@ __2.__ Run the simulation using `python3 config.py --fps=10`.
This camera classifies every object in sight by displaying it in a different color according to its tags (e.g., pedestrians in a different color than vehicles). This camera classifies every object in sight by displaying it in a different color according to its tags (e.g., pedestrians in a different color than vehicles).
When the simulation starts, every element in scene is created with a tag. So it happens when an actor is spawned. The objects are classified by their relative file path in the project. For example, meshes stored in `Unreal/CarlaUE4/Content/Static/Pedestrians` are tagged as `Pedestrian`. When the simulation starts, every element in scene is created with a tag. So it happens when an actor is spawned. The objects are classified by their relative file path in the project. For example, meshes stored in `Unreal/CarlaUE4/Content/Static/Pedestrians` are tagged as `Pedestrian`.
![ImageSemanticSegmentation](img/ref_sensors_semantic.jpg)
The server provides an image with the tag information __encoded in the red channel__: A pixel with a red value of `x` belongs to an object with tag `x`. The server provides an image with the tag information __encoded in the red channel__: A pixel with a red value of `x` belongs to an object with tag `x`.
This raw [carla.Image](python_api.md#carla.Image) can be stored and converted it with the help of __CityScapesPalette__ in [carla.ColorConverter](python_api.md#carla.ColorConverter) to apply the tags information and show picture with the semantic segmentation. This raw [carla.Image](python_api.md#carla.Image) can be stored and converted it with the help of __CityScapesPalette__ in [carla.ColorConverter](python_api.md#carla.ColorConverter) to apply the tags information and show picture with the semantic segmentation.
```py
...
raw_image.save_to_disk("path/to/save/converted/image",carla.cityScapesPalette)
```
The following tags are currently available: The following tags are currently available:
<table class ="defTable"> <table class ="defTable">
@ -1569,8 +1583,6 @@ The following tags are currently available:
**Adding new tags**: **Adding new tags**:
It requires some C++ coding. Add a new label to the `ECityObjectLabel` enum in "Tagger.h", and its corresponding filepath check inside `GetLabelByFolderName()` function in "Tagger.cpp". It requires some C++ coding. Add a new label to the `ECityObjectLabel` enum in "Tagger.h", and its corresponding filepath check inside `GetLabelByFolderName()` function in "Tagger.cpp".
![ImageSemanticSegmentation](img/ref_sensors_semantic.jpg)
#### Basic camera attributes #### Basic camera attributes
<table class ="defTable"> <table class ="defTable">