Metadata-Version: 2.1
Name: abraia
Version: 0.11.7
Summary: Abraia Python SDK
Home-page: https://github.com/abraia/abraia-python
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/)
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        # Abraia-Multiple image analysis toolbox
        
        The Abraia-Multiple image analysis toolbox provides and easy and practical way to analyze and classify multispectral and hyperspectral images directly from your browser. You 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 Abraia-Multiple SDK has being developed by [ABRAIA](https://abraia.me/about) in the [Multiple project](https://multipleproject.eu/) to extend the Abraia Cloud Platform providing support for straightforward HyperSpectral Image (HSI) analysis and classification.
        
        ![classification](https://github.com/abraia/abraia-multiple/raw/master/images/classification.png)
        
        ## Configuration
        
        Installed the package, 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
        ```
        
        ## Hyperspectral image analysis toolbox
        
        MULTIPLE provides seamless integration of multiple HyperSpectral Image (HSI) processing and analysis tools, integrating starte-of-the-art image manipulation libraries to provide ready to go scalable multispectral solutions.
        
        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/console/gallery) 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)
        ```
        
        ## Image analysis toolbox
        
        Abraia provides a direct interface to load and save images as numpy arrays. You can easily load the image data and the file metadata, show the image, or save the image data as a new one.
        
        ```python
        from abraia import Multiple
        from abraia.plot import plot_image
        
        multiple = Multiple()
        
        img = multiple.load_image('usain.jpg')
        multiple.save_image('usain.png', img)
        
        plot_image(img, 'Image')
        ```
        
        ![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
        import json
        
        metadata = multiple.load_metadata('usain.jpg')
        multiple.save_file('usain.json', json.dumps(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/'
        multiple.upload_file('images/usain-bolt.jpeg', folder)
        files, folders = multiple.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'
        multiple.download_file(path, dest)
        multiple.remove_file(path)
        ```
        
        ## Command line interface
        
        The Abraia CLI tool provides a simple way to bulk resize, convert, and optimize your images and photos for web. Enabling the conversion from different input formats to get images in the right formats to be used in the web - JPEG, WebP, or PNG -. Moreover, it supports a number of transformations that can be applied to image batches. So you can easily convert your images to be directly published on the web.
        
        ### Installation
        
        The Abraia CLI is a Python tool which can be installed on Windows, Mac, and Linux:
        
        ```sh
        python -m pip install -U abraia
        ```
        
        The first time you run Abraia CLI you need to configure your API key, just write the command bellow and paste your key.
        
        ```sh
        abraia configure
        ```
        
        ### Resize images
        
        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]
        ```
        
        ## License
        
        This software is licensed under the MIT License. [View the license](LICENSE).
        
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