Deep learning with remote sensing data.
Project description
Aeronet
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
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()
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