https://github.com/florianpfleiderer/cyws3d-pipeline
Model Optimisation and Comparison for Improved Change Detection in Autonomous Systems
https://github.com/florianpfleiderer/cyws3d-pipeline
artficial-neural-network machine-vision object-detection robotics
Last synced: 15 days ago
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Model Optimisation and Comparison for Improved Change Detection in Autonomous Systems
- Host: GitHub
- URL: https://github.com/florianpfleiderer/cyws3d-pipeline
- Owner: florianpfleiderer
- License: mit
- Created: 2024-04-10T08:02:02.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-07-04T09:46:10.000Z (over 1 year ago)
- Last Synced: 2025-06-03T01:29:58.907Z (4 months ago)
- Topics: artficial-neural-network, machine-vision, object-detection, robotics
- Language: Python
- Homepage:
- Size: 359 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: readme.md
- License: LICENSE
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README
# CYWS3D Evaluation Pipeline on ObChange Dataset
The Goal is to perform a batched inference on selected RGB-D
Frames taken from the original Input Data in the ObChange Dataset. The Pipeline is split into
3 Modules:
1. Inference: takes the input images from data/GH30_\ performs an inference and saves the images and bboxes in data/Gh30_\/predictions
2. Annotation: split into 2 Parts:
1. Extracting Transformations for selected images from the rosbag files (separate repo)
2. Exract ground truth bounding boxes from data/ObChange/...
3. Evaluation: calculating the mAP
1. note that the boundingboxes from the inference are in one file per room, sorted in rising order following scenename and timestamp in each scene (see input_metadata.yaml for the order)
## Installation
**Clone the repository**
```
git clone --recursive git@github.com:ragavsachdeva/CYWS-3D.git
```
**Install depedencies**
```
docker build -t cyws3d:latest .
```
Or start the .devcontainer using visual studio code (recommended way due to the postCreateCommand that is also run)
**Notes**
Showing images with cv2 inside docker currently does not work and produces the following error:
```
qt.qpa.plugin: Could not load the Qt platform plugin "xcb" in "/miniconda3/envs/cyws3d-pipeline/lib/python3.9/site-packages/cv2/qt/plugins" even though it was found.
This application failed to start because no Qt platform plugin could be initialized. Reinstalling the application may fix this problem.
Available platform plugins are: xcb.
Aborted (core dumped)
```
## Datasets
The Frames taken from the ObChange Dataset are found in data/GH30_\/...
## Pre-trained model
```
wget https://thor.robots.ox.ac.uk/cyws-3d/cyws-3d.ckpt.gz
gzip -d cyws-3d.ckpt.gz
```
## Example Usage
Make sure you are inside root folder.
After initializing the project with:
`pip install -e .`
start with annotation for the specific room:
`annotate.py --room `
this annotates the dataset and draws boundingboxes for double checking in ground_truth folder into each scene.
next, create the metadata for inference:
`create_inference_metadata.py --room --depth --transformations --perspective <2d/3d>`
For inference please try running:
`inference.py --room `
This should perform a batched inference on a set of example image pairs under various settings (see [this file](data/inference/demo_data/input_metadata.yml)).
The inference create the annotated predictions and saves the bboxes into GH30_*/predictions.
For evaluation:
`evaluate.py --room `
This saves the mAP file into predictions folder and some graphs. The mAP file can be loaded later for more data analysis.