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Support GeoDataFrame as a reference shoreline in BufferShoreline #37

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Support GeoDataFrame as a reference shoreline in BufferShoreline #37

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15 changes: 10 additions & 5 deletions Toolshed/VegetationLine.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,6 +15,7 @@
from shapely.geometry import Point, LineString, Polygon
import geopandas as gpd
from rasterio import features
import rasterio as rio

# machine learning modules
import sklearn
Expand Down Expand Up @@ -166,6 +167,7 @@ def extract_veglines(metadata, settings, polygon, dates, savetifs=True):

# calculate a buffer around the reference shoreline
im_ref_buffer = BufferShoreline(settings,settings['reference_shoreline'],georef,cloud_mask)
assert im_ref_buffer.sum() > 0

# TO DO: figure out way to update refline ONLY if no gaps in previous line exist (length-based? based on number of coords?)
# elif output_shoreline[-1].length < im_ref_buffer_og:
Expand Down Expand Up @@ -1344,13 +1346,16 @@ def BufferShoreline(settings,refline,georef,cloud_mask):
"""
if type(refline) == np.ndarray:
refGS = Toolbox.ArrtoGS(refline, georef)
buffDist = settings['max_dist_ref']/georef[1] # convert from metres to pixels using georef cell size
refLSBuffer = refGS.buffer(buffDist)
refShapes = ((geom,value) for geom, value in zip(refLSBuffer.geometry, np.ones(len(refLSBuffer))))
im_buffer_float = features.rasterize(refShapes,out_shape=cloud_mask.shape)
else: # if refline is read in as shapefile
refGS = gpd.GeoSeries(refline['geometry'])
refline = refline.to_crs(epsg=settings['output_epsg'])
refLSBuffer = refline.buffer(settings['max_dist_ref'])
refShapes = ((geom,value) for geom, value in zip(refLSBuffer.geometry, np.ones(len(refLSBuffer))))
im_buffer_float = features.rasterize(refShapes, out_shape=cloud_mask.shape, transform=rio.Affine.from_gdal(*georef))

buffDist = settings['max_dist_ref']/georef[1] # convert from metres to pixels using georef cell size
refLSBuffer = refGS.buffer(buffDist)
refShapes = ((geom,value) for geom, value in zip(refLSBuffer.geometry, np.ones(len(refLSBuffer))))
im_buffer_float = features.rasterize(refShapes,out_shape=cloud_mask.shape)
# convert to bool
im_buffer = im_buffer_float > 0

Expand Down