Metadata-Version: 2.1
Name: abraia
Version: 0.16.0
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)
        
        ## Installation
        
        Abraia is a Python SDK and CLI which can be installed on Windows, Mac, and Linux:
        
        ```sh
        python -m pip install -U abraia
        ```
        
        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
        ```
        
        ## Object detection
        
        ### Load and run a custom model
        
        You can easily train your custom models from [DeepLab](https://abraia.me/deeplab/), and run then later from the edge.
        
        ```python
        from abraia import detect
        
        model_uri = f"https://api.abraia.me/files/multiple/camera/yolov8n.onnx"
        
        model = detect.load_model(model_uri)
        
        im = detect.load_image('people-walking.png').convert('RGB')
        results = model.run(im, confidence=0.5, iou_threshold=0.5)
        im = detect.render_results(im, results)
        im.show()
        ```
        
        ![people detected](https://github.com/abraia/abraia-multiple/raw/master/images/people-detected.png)
        
        You can even run a multi-object detector on video or directly on a camera stream.
        
        ```python
        import numpy as np
        from PIL import Image
        from abraia import detect
        
        
        model_uri = f"https://api.abraia.me/files/multiple/camera/yolov8n.onnx"
        
        model = detect.load_model(model_uri)
        
        video = detect.Video('people-walking.mp4')
        for frame in video:
            im = Image.fromarray(frame)
            results = model.run(im, confidence=0.5, iou_threshold=0.5)
            im = detect.render_results(im, results)
            frame = np.array(im)
            video.show(frame)
        ```
        
        
        ## Image analysis toolbox
        
        Abraia provides a direct interface to load and save images. You can easily load and show the image, load the file metadata, or save the image as a new one.
        
        ```python
        from abraia import Abraia
        
        abraia = Abraia()
        
        im = abraia.load_image('usain.jpg')
        abraia.save_image('usain.png', im)
        im.show()
        ```
        
        ![plot image](https://github.com/abraia/abraia-multiple/raw/master/images/bolt.png)
        
        Read the image metadata and save it as a JSON file.
        
        ```python
        metadata = abraia.load_metadata('usain.jpg')
        abraia.save_json('usain.json', metadata)
        ```
        
            {'FileType': 'JPEG',
            'MIMEType': 'image/jpeg',
            'JFIFVersion': 1.01,
            'ResolutionUnit': 'None',
            'XResolution': 1,
            'YResolution': 1,
            'Comment': 'CREATOR: gd-jpeg v1.0 (using IJG JPEG v62), quality = 80\n',
            'ImageWidth': 640,
            'ImageHeight': 426,
            'EncodingProcess': 'Baseline DCT, Huffman coding',
            'BitsPerSample': 8,
            'ColorComponents': 3,
            'YCbCrSubSampling': 'YCbCr4:2:0 (2 2)',
            'ImageSize': '640x426',
             'Megapixels': 0.273}
        
        ### Upload and list files
        
        Upload a local `src` file to the cloud `path` and return the list of `files` and `folders` on the specified cloud `folder`.
        
        ```python
        import pandas as pd
        
        folder = 'test/'
        abraia.upload_file('images/usain-bolt.jpeg', folder)
        files, folders = abraia.list_files(folder)
        
        pd.DataFrame(files)
        ```
        
        ![files](https://github.com/abraia/abraia-multiple/raw/master/images/files.png)
        
        To list the root folder just omit the folder value.
        
        ### Download and remove files
        
        You can download or remove an stored file just specifying its `path`.
        
        ```python
        path = 'test/birds.jpg'
        dest = 'images/birds.jpg'
        abraia.download_file(path, dest)
        abraia.remove_file(path)
        ```
        
        ## Command line interface
        
        The Abraia CLI provides access to the Abraia Cloud Platform through the command line. It provides a simple way to manage your files and enables the resize and conversion of different image formats. It is an easy way to compress your images for web - JPEG, WebP, or PNG -, and get then ready to publish on the web. 
        
        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/usaint-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]
        ```
        
        ## Hyperspectral image analysis toolbox
        
        The Multiple class provides seamless integration of multispectral and hyperspectral images. ou just need to click on the open in Colab button to start with one of the available Abraia-Multiple notebooks:
        
        * [![](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
        
        The Multiple extension 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.
        
        ![classification](https://github.com/abraia/abraia-multiple/raw/master/images/classification.png)
        
        For instance, you can directly load and save ENVI files, and their metadata.
        
        ```python
        from abraia 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 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).
        
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