abraia 0.25.4
Abraia Python SDK
License: MIT

[![Build Status](https://github.com/abraia/abraia-multiple/actions/workflows/build.yml/badge.svg)](https://github.com/abraia/abraia-multiple/actions/workflows/build.yml)
        [![Python Package](https://img.shields.io/pypi/v/abraia.svg)](https://pypi.org/project/abraia/)
        ![Package Downloads](https://img.shields.io/pypi/dm/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)
        ```
        
        ![people detected](https://github.com/abraia/abraia-multiple/raw/master/images/people-detected.jpg)
        
        ### 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')
        ```
        
        ![rolling stones identified](https://github.com/abraia/abraia-multiple/raw/master/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)
        ```
        
        ![car license plate recognition](https://github.com/abraia/abraia-multiple/raw/master/images/car-plate.jpg)
        
        ### 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')
        ```
        
        ![blur car license plate](https://github.com/abraia/abraia-multiple/raw/master/images/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/assets/colab-badge.svg)](https://colab.research.google.com/github/abraia/abraia-multiple/blob/master/notebooks/hyperspectral-analysis.ipynb) Hyperspectral image analysis
        
        * [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/abraia/abraia-multiple/blob/master/notebooks/hyperspectral-classification.ipynb) Hyperspectral image classification
        
        ![classification](https://github.com/abraia/abraia-multiple/raw/master/images/classification.png)
        
        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).