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        "author": "Pavel Yakubovskiy",
        "author_email": "qubvel@gmail.com",
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            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: Implementation :: CPython",
            "Programming Language :: Python :: Implementation :: PyPy"
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        "description": "\n# Aeroenet\nPython library to work with geospatial raster and vector data.\n\n### Modules\n#### .backend\nKeras losses (tensorflow backend)\n - .losses  \n -- `jaccard_loss`  \n -- `bce_jaccard_loss`  \n -- `cce_jaccard_loss`  \n -- `custom_bce_jaccard_loss`\n - .metrics  \n -- `iou_score`  \n -- `f_score`  \n -- `f1_score`  \n\n #### .criterions\n Metrics to work with spatial data\n - .raster  \n -- `IoU`  \n -- `mIoU`  \n - .vector  \n -- `mAP50`/`mAP5095`/`mAPxx` - instance-wise metric  \n -- `area_iou`  \n\n #### .dataset\n - .raster  \n -- `Band`/`BandCollection`  \n -- `BandSample`/`BandSampleCollection`\n - .vector  \n -- `Feature`/`FeatureCollection`\n - .transforms  \n -- `polygonize`  \n -- `rasterize`\n - .io  \n -- `Predictor`  \n -- `WindowReader`  \n -- `SampleWindowWriter`  \n -- `SampleCollectionWindowWriter`  \n - .visualization  \n -- `add_mask`\n\n\n### Quick start\n\n```python\nimport os\nimport matpoltib.pyplpot as plt \n\nfrom aeronet.dataset import BandCollection\nfrom aeronet.dataset import RandomDataset\n\nfrom aeronet.dataset.utils import parse_directory\nfrom aeronet.dataset.visualization import add_mask\n\n# configuration\nSRC_DIR = '/path/to/elements/'\nchannels = ['RED', 'GRN', 'BLU']\nlabels = ['100']\n\n# directories of dataset elements\ndirs = [os.path.join(SRC_DIR, x) for x in os.listdir(SRC_DIR)]\nprint('Found collections: ', len(dirs), end='\\n\\n')\n\n# parse channels in directories\nband_paths = [parse_direcotry(x, channels + labels) for x in dirs]\nprint('BandCollection 0 paths:\\n', band_paths[0], end='\\n\\n')\n\n# convert to `BandCollection` objects\nband_collections = [BandCollection(fps) for fps in band_paths]\nprint('BandCollection 0 object:\\n', repr(band_collections[0]))\n\n\n# create random dataset sampler\ndataset = RandomDataset(band_collections, \n                        sample_size=(512, 512), \n                        input_channels=channels, \n                        output_labels=labels,\n                        transform=None) # pre-processing function\n\n# get random sample\ngenerated_sample = dataset[0]\nimage = generated_sample['image']\nmask = generated_sample['mask']\n\n#visualize\nmasked_image = add_mask(image, mask)\n\nplt.figure(figsize=(10,10))\nplt.imshow(masked_image)\nplt.show()                    \n\n```\n\n\n",
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