https://github.com/abraia/abraia-multiple
Abraia computer vision SDK for edge applications
https://github.com/abraia/abraia-multiple
abraia-api computer-vision convert-images hyperspectral-image hyperspectral-image-analysis image-analysis
Last synced: 4 months ago
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Abraia computer vision SDK for edge applications
- Host: GitHub
- URL: https://github.com/abraia/abraia-multiple
- Owner: abraia
- License: mit
- Created: 2017-08-31T23:46:55.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2025-12-06T19:36:00.000Z (7 months ago)
- Last Synced: 2025-12-16T12:52:07.021Z (7 months ago)
- Topics: abraia-api, computer-vision, convert-images, hyperspectral-image, hyperspectral-image-analysis, image-analysis
- Language: Jupyter Notebook
- Homepage: https://abraia.me/vision/
- Size: 195 MB
- Stars: 9
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[](https://github.com/abraia/abraia-multiple/actions/workflows/build.yml)
[](https://pypi.org/project/abraia/)
# Abraia Vision SDK
The [Abraia Vision](https://abraia.me/vision/) SDK is a Python package which provides a set of tools to develop and deploy advanced Machine Learning image applications on the edge. Moreover, with [Abraia DeepLab](https://abraia.me/deeplab/) you can easily annotate and train, your own versions of some of the best state of the art deep learning models, and get them ready to deploy with this Python SDK.
Just install the Abraia SDK and CLI on Windows, Mac, or Linux:
```sh
python -m pip install -U abraia
```
And start using deep learning models ready to work on your local devices.
## Deep learning custom models and applications
Consult your problem or directly try to annotate your images and train a state-of-the-art model for classification, detection, or segmentation using [DeepLab](https://abraia.me/deeplab/). You can directly load and run the model on the edge using the browser or this Python SDK.
### Object detection and tracking
Identify and track multiple objects with a custom detection model on videos and camera streams, enabling real-time counting applications. You just need to use
the Video class to process every frame as is done for images, and use the tracker to follow each object through
every frame.
```python
from abraia.inference import Model, Tracker
from abraia.utils import Video, render_results
model = Model("multiple/models/yolov8n.onnx")
video = Video('images/people-walking.mp4')
tracker = Tracker(frame_rate=video.frame_rate)
for frame in video:
results = model.run(frame, conf_threshold=0.5, iou_threshold=0.5)
results = tracker.update(results)
frame = render_results(frame, results)
video.show(frame)
```
### Face recognition
Identify people on images with face recognition as shown bellow.
```python
import os
from abraia.inference import FaceRecognizer
from abraia.utils import load_image, save_image, render_results
img = load_image('images/rolling-stones.jpg')
out = img.copy()
recognition = FaceRecognizer()
index = []
for src in ['mick-jagger.jpg', 'keith-richards.jpg', 'ronnie-wood.jpg', 'charlie-watts.jpg']:
img = load_image(f"images/{src}")
rslt = recognition.identify_faces(img)[0]
index.append({'name': os.path.splitext(src)[0], 'vector': rslt['vector']})
results = recognition.identify_faces(results, index)
render_results(out, results)
save_image(out, 'images/rolling-stones-identified.jpg')
```
### License plates recognition
Automatically recognize car license plates in images and video streams.
```python
from abraia.inference import PlateRecognizer
from abraia.utils import load_image, show_image, render_results
alpr = PlateRecognizer()
img = load_image('images/car.jpg')
results = alpr.detect(img)
results = alpr.recognize(img, results)
results = [result for result in results if len(result['lines'])]
for result in results:
result['label'] = '\n'.join([line.get('text', '') for line in result['lines']])
del result['score']
frame = render_results(img, results)
show_image(img)
```
### Gender Age model
Model to predict gender and age. It can be useful to anonymize minors faces.
```python
from abraia.inference import FaceRecognizer, FaceAttribute
from abraia.utils import load_image, show_image, render_results
recognition = FaceRecognizer()
attribute = FaceAttribute()
img = load_image('images/image.jpg')
results = recognition.detect_faces(img)
faces = recognition.extract_faces(img, results)
for face, result in zip(faces, results):
gender, age, score = attribute.predict(face)
result['label'] = f"{gender} {age}"
result['score'] = score
img = render_results(img, results)
show_image(img)
```
### Blur license plate
Anonymize images automatically bluring car license plates.
```python
from abraia.utils import load_image, save_image
from abraia.inference import PlateDetector
from abraia.editing import build_mask
from abraia.utils.draw import draw_blurred_mask
src = 'images/car.jpg'
img = load_image(src)
detector = PlateDetector()
plates = detector.detect(img)
mask = build_mask(img, plates, [])
out = draw_blurred_mask(img, mask)
save_image(out, 'blur-car.jpg')
```
### Semantic search
Search on images with embeddings.
```python
from tqdm import tqdm
from glob import glob
from abraia.utils import load_image
from abraia.inference.clip import Clip
from abraia.inference.ops import search_vector
clip_model = Clip()
image_paths = glob('images/*.jpg')
image_index = [{'vector': clip_model.get_image_embeddings([load_image(image_path)])[0]} for image_path in tqdm(image_paths)]
text_query = "full body person"
vector = clip_model.get_text_embeddings([text_query])[0]
idxs, scores = search_vector(vector, image_index)
print(f"Similarity score is {scores[0]} for image {image_paths[idxs[0]]}")
```
## Hyperspectral image analysis toolbox
The Multiple extension provides seamless integration of multispectral and hyperspectral images, providing support for straightforward HyperSpectral Image (HSI) analysis and classification.
Just click on one of the available Colab's notebooks to directly start testing the multispectral capabilities:
* [](https://colab.research.google.com/github/abraia/abraia-multiple/blob/master/notebooks/hyperspectral-analysis.ipynb) Hyperspectral image analysis
* [](https://colab.research.google.com/github/abraia/abraia-multiple/blob/master/notebooks/hyperspectral-classification.ipynb) Hyperspectral image classification
To install the multiple extension use the command bellow:
```sh
python -m pip install -U "abraia[multiple]"
```
To use the SDK you have to configure your [Id and Key](https://abraia.me/editor/) as environment variables:
```sh
export ABRAIA_ID=user_id
export ABRAIA_KEY=user_key
```
On Windows you need to use `set` instead of `export`:
```sh
set ABRAIA_ID=user_id
set ABRAIA_KEY=user_key
```
Then, you will be able to directly load and save ENVI files, and their metadata.
```python
from abraia.multiple import Multiple
multiple = Multiple()
img = multiple.load_image('test.hdr')
meta = multiple.load_metadata('test.hdr')
multiple.save_image('test.hdr', img, metadata=meta)
```
### Upload and load HSI data
To start with, we may [upload some data](https://abraia.me/deeplab/) directly using the graphical interface, or using the multiple api:
```python
multiple.upload_file('PaviaU.mat')
```
Now, we can load the hyperspectral image data (HSI cube) directly from the cloud:
```python
img = multiple.load_image('PaviaU.mat')
```
### Basic HSI visualization
Hyperspectral images cannot be directly visualized, so we can get some random bands from our HSI cube, and visualize these bands as like any other monochannel image.
```python
from abraia.multiple import hsi
imgs, indexes = hsi.random(img)
hsi.plot_images(imgs, cmap='jet')
```
Or, we can reduce the dimensionality applying principal components analysis (PCA). We can get the first three principal components into a three bands pseudoimage, and visualize this pseudoimage.
```python
pc_img = hsi.principal_components(img)
hsi.plot_image(pc_img, 'Principal components')
```
## License
This software is licensed under the MIT License. [View the license](LICENSE).