aeronet 0.0.3

Deep learning with remote sensing data.

# 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()

```