Metadata-Version: 2.1
Name: abraia
Version: 0.19.0
Summary: Abraia Python SDK
Home-page: https://github.com/abraia/abraia-multiple
Author: Jorge Rodriguez Araujo
Author-email: jorge@abraiasoftware.com
License: MIT
Description: [![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 Python SDK image analysis toolbox
        
        The Abraia Python SDK provides and easy and practical way to develop and deploy Machine Learning image applications on the edge. You can easily annotate and train your custom deep learning model with [DeepLab](https://abraia.me/deeplab/), and deploy the model with this Python SDK.
        
        ![people walking](https://github.com/abraia/abraia-multiple/raw/master/images/people-walking.gif)
        
        Just install the Abraia Python SDK and CLI on Windows, Mac, or Linux:
        
        ```sh
        python -m pip install -U abraia
        ```
        
        And start working with deep learning models ready to work on your local devices.
        
        ## Load and run custom models
        
        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
        
        Detect objects with a pre-trained YOLOv8 model on images, videos, or even camera streams.
        
        ```python
        from abraia import detect
        
        model_uri = f"multiple/models/yolov8n.onnx"
        model = detect.load_model(model_uri)
        
        img = detect.load_image('people-walking.png')
        results = model.run(img, conf_threshold=0.5, iou_threshold=0.5)
        img = detect.render_results(img, results)
        detect.show_image(img)
        ```
        
        ![people detected](https://github.com/abraia/abraia-multiple/raw/master/images/people-detected.png)
        
        To run a multi-object detector on video or directly on a camera stream, you just need to use the Video class to process every frame as is done for images.
        
        ```python
        from abraia import detect
        
        model_uri = f"multiple/models/yolov8n.onnx"
        model = detect.load_model(model_uri)
        
        video = detect.Video('people-walking.mp4')
        for frame in video:
            results = model.run(frame, conf_threshold=0.5, iou_threshold=0.5)
            frame = detect.render_results(frame, results)
            video.show(frame)
        ```
        
        ### Face recognition
        
        Identify people on images with face recognition as shown bellow. 
        
        ```python
        import os
        
        from abraia.faces import Recognition
        from abraia.utils import load_image, save_image
        from abraia.draw import render_results
        
        img = load_image('images/rolling-stones.jpg')
        out = img.copy()
        
        recognition = Recognition()
        results = recognition.represent_faces(img)
        
        index = []
        for src in ['mick-jagger.jpg', 'keith-richards.jpg', 'ronnie-wood.jpg', 'charlie-watts.jpg']:
            img = load_image(f"images/{src}")
            rslt = recognition.represent_faces(img)[0]
            index.append({'name': os.path.splitext(src)[0], 'embeddings': rslt['embeddings']})
        
        result = 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 blurring
        
        Automatically blur car license plates in videos with just a few lines of code.
        
        ```python
        import numpy as np
        
        from abraia import detect
        from abraia import draw
        
        model_uri = 'multiple/models/alpd-seg.onnx'
        model = detect.load_model(model_uri)
        
        src = 'images/cars.mp4'
        video = detect.Video(src, output='images/blur.mp4')
        for k, frame in enumerate(video):
            results = model.run(frame, approx=0.02)
            mask = np.zeros(frame.shape[:2], np.uint8)
            [draw.draw_filled_polygon(mask, result['polygon'], 255) for result in results]
            frame = draw.draw_blurred_mask(frame, mask)
            video.write(frame)
            video.show(frame)
        ```
        
        ![car license plate blurred](https://github.com/abraia/abraia-multiple/raw/master/images/blur.jpg)
        
        ### License plates recognition
        
        Automatically recognize car license plates in images and video streams.
        
        ```python
        from abraia import draw
        from abraia.alpr import ALPR
        from abraia.utils import load_image, show_image
        
        alpr = ALPR()
        
        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['confidence']
        frame = draw.render_results(img, results)
        show_image(img)
        ```
        
        ![car license plate recognition](https://github.com/abraia/abraia-multiple/raw/master/images/car-plate.jpg)
        
        ## Command line interface
        
        The Abraia CLI provides access to the Abraia Cloud Platform through the command line. It makes simple to manage your files and enables bulk image editing capabilities. It provides and easy way to resize, convert, and compress your images - JPEG, WebP, or PNG -, and get them ready to publish on the web. Moreover, you can automatically remove the background, upscale, or anonymize your images in bulk.
        
        To compress an image you just need to specify the input and output paths for the image:
        
        ```sh
        abraia convert images/birds.jpg images/birds_o.jpg
        ```
        
        ![Image compressed from url](https://github.com/abraia/abraia-multiple/raw/master/images/birds_o.jpg)
        
        To resize and optimize and image maintaining the aspect ratio is enough to specify the `width` or the `height` of the new image:
        
        ```sh
        abraia convert --width 500 images/usain-bolt.jpeg images/usaint-bolt_500.jpeg
        ```
        
        ![Usain Bolt resized](https://github.com/abraia/abraia-multiple/raw/master/images/usain-bolt_500.jpeg)
        
        You can also automatically change the aspect ratio specifying both `width` and `height` parameters and setting the resize `mode` (pad, crop, thumb):
        
        ```sh
        abraia convert --width 333 --height 333 --mode pad images/lion.jpg images/lion_333x333.jpg
        abraia convert --width 333 --height 333 images/lion.jpg images/lion_333x333.jpg
        ```
        
        ![Image lion smart cropped](https://github.com/abraia/abraia-multiple/raw/master/images/lion_333x333_pad.jpg)
        ![Image lion smart cropped](https://github.com/abraia/abraia-multiple/raw/master/images/lion_333x333.jpg)
        
        So, you can automatically resize all the images in a specific folder preserving the aspect ration of each image just specifying the target `width` or `height`:
        
        ```sh
        abraia convert --width 300 [path] [dest]
        ```
        
        Or, automatically pad or crop all the images contained in the folder specifying both `width` and `height`:
        
        ```sh
        abraia convert --width 300 --height 300 --mode crop [path] [dest]
        ```
        
        ### Remove background
        
        Automatically remove images background and make them transparent in bulk.
        
        ```sh
        abraia editing "*.jpg" --mode removebg
        ```
        
        ![bolt transparent background](https://github.com/abraia/abraia-multiple/raw/master/images/usain-bolt.png)
        
        ### Upscale images
        
        Scale up and enhance images in bulk, doubling the size and preserving quality.
        
        ```sh
        abraia editing "*.jpg" --mode upscale
        ```
        
        ![upscaled cat](https://github.com/abraia/abraia-multiple/raw/master/images/cat-upscaled.jpg)
        
        ### Anonymize images
        
        Anonymize images in bulk, automatically blurring faces, car license plates, and removing metadata.
        
        ```sh
        abraia editing "*.jpg" --mode anonymize
        ````
        
        ![people and car anonymized](https://github.com/abraia/abraia-multiple/raw/master/images/people-car-anonymized.jpg)
        
        ## Hyperspectral image analysis toolbox
        
        The Multiple extension provides seamless integration of multispectral and hyperspectral images. It has being developed by [ABRAIA](https://abraia.me/about) in the [Multiple project](https://multipleproject.eu/) to extend the Abraia SDK and Cloud Platform 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)
        
        Or install the multiple extension to use the Abraia-Multiple SDK:
        
        ```sh
        python -m pip install -U "abraia[multiple]"
        ```
        
        To use the SDK you have to configure your [Id and Key](https://abraia.me/console/) 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')
        ```
        
        ### Pseudocolor visualization
        
        A common operation with spectral images is to 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')
        ```
        
        ### Classification model
        
        Two classification models are directly available for automatic identification on hysperspectral images. One is based on support vector machines ('svm') while the other is based on deep image classification ('hsn'). Both models are available under a simple interface like bellow:
        
        ```python
        n_bands, n_classes = 30, 17
        model = hsi.create_model('hsn', (25, 25, n_bands), n_classes)
        model.train(X, y, train_ratio=0.3, epochs=5)
        y_pred = model.predict(X)
        ```
        
        ## License
        
        This software is licensed under the MIT License. [View the license](LICENSE).
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
Provides-Extra: multiple
Provides-Extra: dev
