Metadata-Version: 2.1
Name: aeronet
Version: 0.0.4
Summary: Deep learning with remote sensing data.
Home-page: UNKNOWN
Author: Pavel Yakubovskiy
Author-email: qubvel@gmail.com
License: MIT
Description: 
        # Aeroenet
        Python library to work with geospatial raster and vector data.
        
        ### Modules
        #### .backend
        Keras losses (tensorflow backend)
         - .losses  
         -- `jaccard_loss`  
         -- `bce_jaccard_loss`  
         -- `cce_jaccard_loss`  
         -- `custom_bce_jaccard_loss`
         - .metrics  
         -- `iou_score`  
         -- `f_score`  
         -- `f1_score`  
         
         #### .criterions
         Metrics to work with spatial data
         - .raster  
         -- `IoU`  
         -- `mIoU`  
         - .vector  
         -- `mAP50`/`mAP5095`/`mAPxx` - instance-wise metric  
         -- `area_iou`  
         
         #### .dataset
         - .raster  
         -- `Band`/`BandCollection`  
         -- `BandSample`/`BandSampleCollection`
         - .vector  
         -- `Feature`/`FeatureCollection`
         - .transforms  
         -- `polygonize`  
         -- `rasterize`
         - .io  
         -- `Predictor`  
         -- `WindowReader`  
         -- `SampleWindowWriter`  
         -- `SampleCollectionWindowWriter`  
         - .visualization  
         -- `add_mask`
         
        
        ### Quick start
        
        ```python
        import os
        import matpoltib.pyplpot as plt 
        
        from aeronet.dataset import BandCollection
        from aeronet.dataset import RandomDataset
        
        from aeronet.dataset.utils import parse_directory
        from aeronet.dataset.visualization import add_mask
        
        # configuration
        SRC_DIR = '/path/to/elements/'
        channels = ['RED', 'GRN', 'BLU']
        labels = ['100']
        
        # directories of dataset elements
        dirs = [os.path.join(SRC_DIR, x) for x in os.listdir(SRC_DIR)]
        print('Found collections: ', len(dirs), end='\n\n')
        
        # parse channels in directories
        band_paths = [parse_direcotry(x, channels + labels) for x in dirs]
        print('BandCollection 0 paths:\n', band_paths[0], end='\n\n')
        
        # convert to `BandCollection` objects
        band_collections = [BandCollection(fps) for fps in band_paths]
        print('BandCollection 0 object:\n', repr(band_collections[0]))
        
        
        # create random dataset sampler
        dataset = RandomDataset(band_collections, 
                                sample_size=(512, 512), 
                                input_channels=channels, 
                                output_labels=labels,
                                transform=None) # pre-processing function
                                
        # get random sample
        generated_sample = dataset[0]
        image = generated_sample['image']
        mask = generated_sample['mask']
        
        #visualize
        masked_image = add_mask(image, mask)
        
        plt.figure(figsize=(10,10))
        plt.imshow(masked_image)
        plt.show()                    
        
        ```
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Requires-Python: >=3.6.0
Description-Content-Type: text/markdown
