mirror of
https://github.com/rasterio/rasterio.git
synced 2025-12-08 17:36:12 +00:00
3898c3273b
This lets you exclude background pixels if you're only interested in foreground shapes and saves a significant amount of time. Closes #26.
37 lines
1.0 KiB
Python
37 lines
1.0 KiB
Python
#!/usr/bin/env python
|
|
#
|
|
# sieve: demonstrate sieving and polygonizing of raster features.
|
|
|
|
import subprocess
|
|
|
|
import numpy
|
|
import rasterio
|
|
from rasterio.features import sieve, shapes
|
|
|
|
|
|
# Register GDAL and OGR drivers.
|
|
with rasterio.drivers():
|
|
|
|
# Read a raster to be sieved.
|
|
with rasterio.open('rasterio/tests/data/shade.tif') as src:
|
|
shade = src.read_band(1)
|
|
|
|
# Print the number of shapes in the source raster.
|
|
print "Slope shapes: %d" % len(list(shapes(shade)))
|
|
|
|
# Sieve out features 13 pixels or smaller.
|
|
sieved = sieve(shade, 13)
|
|
|
|
# Print the number of shapes in the sieved raster.
|
|
print "Sieved (13) shapes: %d" % len(list(shapes(sieved)))
|
|
|
|
# Write out the sieved raster.
|
|
with rasterio.open('example-sieved.tif', 'w', **src.meta) as dst:
|
|
dst.write_band(1, sieved)
|
|
|
|
# Dump out gdalinfo's report card and open (or "eog") the TIFF.
|
|
print subprocess.check_output(
|
|
['gdalinfo', '-stats', 'example-sieved.tif'])
|
|
subprocess.call(['open', 'example-sieved.tif'])
|
|
|