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https://github.com/pollen-robotics/pollen-vision
Simple and unified interface to zero-shot computer vision models curated for robotics use cases.
https://github.com/pollen-robotics/pollen-vision
computer-vision grasping object-detection object-segmentation robotics
Last synced: 3 days ago
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Simple and unified interface to zero-shot computer vision models curated for robotics use cases.
- Host: GitHub
- URL: https://github.com/pollen-robotics/pollen-vision
- Owner: pollen-robotics
- License: apache-2.0
- Created: 2023-11-08T10:54:32.000Z (about 1 year ago)
- Default Branch: develop
- Last Pushed: 2024-11-20T11:24:13.000Z (about 2 months ago)
- Last Synced: 2024-12-31T06:09:09.096Z (10 days ago)
- Topics: computer-vision, grasping, object-detection, object-segmentation, robotics
- Language: Python
- Homepage: https://www.pollen-robotics.com
- Size: 85.5 MB
- Stars: 102
- Watchers: 3
- Forks: 5
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
Simple and unified interface to zero-shot computer vision models curated for robotics use cases.
Check out our [HuggingFace space](https://huggingface.co/pollen-robotics) for an online demo or try pollen-vision in a [Colab notebook](https://drive.google.com/drive/folders/1Xx42Pk4exkS95iyD-5arHIYQLXyRWTXw?usp=drive_link)!
## Get started in very few lines of code!
Perform zero-shot object detection and segmentation on a live video stream from your webcam with the following code:
```python
import cv2
from pollen_vision.vision_models.object_detection import OwlVitWrapper
from pollen_vision.vision_models.object_segmentation import MobileSamWrapper
from pollen_vision.utils import Annotator, get_bboxes
owl = OwlVitWrapper()
sam = MobileSamWrapper()
annotator = Annotator()
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
predictions = owl.infer(
frame, ["paper cups"]
) # zero-shot object detection | put your classes here
bboxes = get_bboxes(predictions)
masks = sam.infer(frame, bboxes=bboxes) # zero-shot object segmentation
annotated_frame = annotator.annotate(frame, predictions, masks=masks)
cv2.imshow("frame", annotated_frame)
if cv2.waitKey(1) & 0xFF == ord("q"):
cv2.destroyAllWindows()
break
```
Supported models
We continue to work on adding new models that could be useful for robotics perception applications.
We chose to focus on zero-shot models to make it easier to use and deploy. Zero-shot models can recognize objects or segment them based on text queries, without needing to be fine-tuned on annotated datasets.
Right now, we support:
#### Object detection
- `Yolo-World` for zero-shot object detection and localization
- `Owl-Vit` for zero-shot object detection and localization
- `Recognize-Anything` for zero-shot object detection (without localization)
#### Object segmentation
- `Mobile-SAM` for (fast) zero-shot object segmentation
#### Monocular depth estimation
- `Depth Anything` for (non metric) monocular depth estimation
Below is an example of combining `Owl-Vit` and `Mobile-Sam` to detect and segment objects in a point cloud, all live.
(Note: in this example, there is no temporal or spatial filtering of any kind, we display the raw outputs of the models computed independently on each frame)
https://github.com/pollen-robotics/pollen-vision/assets/6552564/a5285627-9cba-4af5-aafb-6af3d1e6d40c
We also provide wrappers for the Luxonis cameras which we use internally. They allow to easily access the main features that are interesting to our robotics applications (RBG-D, onboard h264 encoding and onboard stereo rectification).
Installation
# Installation
```
Note: This package has been tested on Ubuntu 22.04 and macOS (with M1 Pro processor), with python3.10.
```
## Git LFS
This repository uses Git LFS to store large files. You need to install it before cloning the repository.
### Ubuntu
```console
sudo apt-get install git-lfs
```
### macOS
```console
brew install git-lfs
```
## One line installation
You can install the package directly from the repository without having to clone it first with:
```console
pip install "pollen-vision[vision] @ git+https://github.com/pollen-robotics/pollen-vision.git@main"
```
> Note: here we install the package with the `vision` extra, which includes the vision models. You can also install the `depthai_wrapper` extra to use the Luxonis depthai wrappers.
## Install from source
Clone this repository and then install the package either in "production" mode or "dev" mode.
>👉 We recommend using a virtual environment to avoid conflicts with other packages.
After cloning the repository, you can either install everything with:
```console
pip install .[all]
```
or install only the modules you want:
```console
pip install .[depthai_wrapper]
pip install .[vision]
```
To add "dev" mode dependencies (CI/CD, testing, etc):
```console
pip install -e .[dev]
```
## Luxonis depthai specific information
If this is the first time you use luxonis cameras on this computer, you need to setup the udev rules:
```
echo 'SUBSYSTEM=="usb", ATTRS{idVendor}=="03e7", MODE="0666"' | sudo tee /etc/udev/rules.d/80-movidius.rules
sudo udevadm control --reload-rules && sudo udevadm trigger
```
Gradio demo
# Gradio demo
## Test the demo online
A gradio demo is available on Pollen Robotics' [Huggingface space](https://huggingface.co/spaces/pollen-robotics/pollen-vision-demo). It allows to test the models on your own images without having to install anything.
## Run the demo locally
If you want to run the demo locally, you can install the dependencies with the following command:
```console
pip install pollen_vision[gradio]
```
You can then run the demo locally on your machine with:
```console
python pollen-vision/gradio/app.py
```
Examples
# Examples
## Vision models wrappers
Check our [example notebooks](examples/vision_models_examples/)!
## Luxonis depthai wrappers
Check our [example scripts](examples/camera_wrappers_examples/)!
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