Loading plio/geofuncs/geofuncs.py +117 −25 Original line number Diff line number Diff line import json import numpy as np def intersection_to_pixels(latlon_to_pixel, ul, lr, xmax, ymax, buffer=300): def intersection_to_pixels(inverse_affine, ul, ur, lr, ll): """ Given an inverse affine transformation, take four bounding coordinates in lat/lon space and convert them to a minimum bounding rectangle in pixel space. inverse_affine : object An Affine transformation object ul : tuple Upper Left coordinate in the form (x,y) ur : tuple Upper Right coordinate in the form (x,y) lr : tuple Lower Right coordinate in the form (x,y) ll : tuple Lower Left coordinate in the form (x,y) """ x_start, y_start = latlon_to_pixel(ul[1], ul[0]) x_stop, y_stop = latlon_to_pixel(lr[1], lr[0]) if x_start > x_stop: x_start, x_stop = x_stop, x_start if y_start > y_stop: y_start, y_stop = y_stop, y_start if x_start < buffer: x_start = 0 if xmax - x_stop < buffer: x_stop = xmax if y_start < buffer: y_start = 0 if ymax - y_stop < buffer: y_stop = ymax return np.s_[y_start:y_stop, x_start:x_stop] def estimate_mbr(geodata_a, geodata_b): minx = np.inf maxx = -np.inf miny = np.inf maxy = -np.inf for c in [ul, ur, lr, ll]: px, py = map(int, inverse_affine * (c[0], c[1])) if px < minx: minx = px if px > maxx: maxx = px if py < miny: miny = py if py > maxy: maxy = py return minx, maxx, miny, maxy def compute_overlap(geodata_a, geodata_b): p1 = geodata_a.footprint p2 = geodata_b.footprint intersection = json.loads(p1.Intersection(p2).ExportToJson())['coordinates'][0] ul, ur, lr, ll = find_four_corners(intersection) a_intersection = intersection_to_pixels(geodata_a.inverse_affine, ul, ur, lr, ll) b_intersection = intersection_to_pixels(geodata_b.inverse_affine, ul, ur, lr, ll) return (ul, ur, lr, ll), a_intersection, b_intersection def estimate_mbr(geodata_a, geodata_b, bufferd=50): p1 = geodata_a.footprint p2 = geodata_b.footprint minx, maxx, miny, maxy = p1.Intersection(p2).GetEnvelope() ul = (minx, maxy) lr = (maxx, miny) a_slice = intersection_to_pixels(geodata_a.latlon_to_pixel, ul, lr, *geodata_a.xy_extent[1]) ul, lr, *geodata_a.xy_extent[1], bufferd) b_slice = intersection_to_pixels(geodata_b.latlon_to_pixel, ul, lr, *geodata_b.xy_extent[1]) ul, lr, *geodata_b.xy_extent[1], bufferd) return a_slice, b_slice def find_corners(coords, threshold=120): """ Given a list of coordinates in the form [(x, y), (x1, y1), ..., (xn, yn)], attempt to find corners by identifying all angles < the threshold. For a line segment composed of 3 points, the angle between ab and ac is 180 if the segments are colinear. If they are less than threshold, the line segments are considered to be a corner. Parameters ---------- coords : list of coordinates threshold : numeric Angle under which a corner is identified, Default: 120 """ corners = [coords[0]] for i, a in enumerate(coords[:-2]): a = np.asarray(a) b = np.asarray(coords[i+1]) c = np.asarray(coords[i+2]) ba = a - b bc = c - b angle = np.arccos(np.dot(ba, bc) / (np.linalg.norm(ba) * np.linalg.norm(bc))) if np.degrees(angle) < threshold: corners.append(b.tolist()) return corners def find_four_corners(coords, threshold=120): """ Find four corners in a polygon by making a call to the find_corners function and using the first four corners. Parameters ---------- coords: list of coordinates threshold : numeric Anfle under which a corner is identified, Default: 120 See Also -------- plio.geofuncs.geofuncs.find_corners """ corners = find_corners(coords, threshold) corners.sort(key = lambda x:x[1]) upper = corners[2:] lower = corners[:2] key = lambda x:x[0] ul = min(upper, key=key) ur = max(upper, key=key) lr = max(lower, key=key) ll = min(lower, key=key) return ul, ur, lr, ll plio/io/io_gdal.py +55 −37 Original line number Diff line number Diff line import json import math import os import warnings import affine import gdal import numpy as np import osr Loading @@ -11,6 +14,8 @@ from plio.io import extract_metadata from plio.geofuncs import geofuncs from plio.utils.utils import find_in_dict from libpysal.cg import is_clockwise gdal.UseExceptions() NP2GDAL_CONVERSION = { Loading @@ -28,7 +33,8 @@ NP2GDAL_CONVERSION = { GDAL2NP_CONVERSION = {} DEFAULT_PROJECTIONS = {'mars':'GEOGCS["Mars 2000",DATUM["D_Mars_2000",SPHEROID["Mars_2000_IAU_IAG",3396190.0,169.89444722361179]],PRIMEM["Greenwich",0],UNIT["Decimal_Degree",0.0174532925199433]]'} DEFAULT_PROJECTIONS = {'mars':'GEOGCS["Mars 2000",DATUM["D_Mars_2000",SPHEROID["Mars_2000_IAU_IAG",3396190.0,169.89444722361179]],PRIMEM["Greenwich",0],UNIT["Decimal_Degree",0.0174532925199433]]', 'moon':'GEOGCS["Moon 2000",DATUM["D_Moon_2000",SPHEROID["Moon_2000_IAU_IAG",1737400.0,0.0]],PRIMEM["Greenwich",0],UNIT["Decimal_Degree",0.0174532925199433]]'} DEFAULT_RADII = {'mars': 3396190.0} for k, v in iter(NP2GDAL_CONVERSION.items()): Loading Loading @@ -174,22 +180,31 @@ class GeoDataset(object): """ if not getattr(self, '_geotransform', None): if self.footprint: # This is an ISIS3 cube that does not report a valid geotransform ul, ll, lr, ur = self.latlon_extent xs, ys = self.raster_size xres = abs(ul[0] - ur[0]) / xs yres = abs(ul[1] - ll[1]) / ys self._geotransform = [ul[0], xres, 0, ul[1], 0, -yres] if ul[1] > ll[1]: # Image is South-North instead of North-South self._geotransform[-1] *= -1 # Lat increases with image length self._geotransform[3] = ll[1] # Origin is at the LL coords = json.loads(self.footprint.ExportToJson())['coordinates'][0][0] ul, ur, lr, ll = geofuncs.find_four_corners(coords) xsize, ysize = self.raster_size xstart = ul[0] xscale = (ur[0] - xstart) / xsize xskew = (ll[0] - xstart) / ysize ystart = ul[1] yskew = (ur[1] - ystart) / xsize yscale = (ll[1] - ystart) / ysize self._geotransform = [xstart, xscale, xskew, ystart, yskew, yscale] else: self._geotransform = self.dataset.GetGeoTransform() return self._geotransform @property def forward_affine(self): self._fa = affine.Affine.from_gdal(*self.geotransform) return self._fa @property def inverse_affine(self): self._ia = ~self.forward_affine return self._ia @property def standard_parallels(self): if not getattr(self, '_standard_parallels', None): Loading @@ -202,6 +217,13 @@ class GeoDataset(object): self._unit_type = self.dataset.GetRasterBand(1).GetUnitType() return self._unit_type @property def north_up(self): if self.footprint: return is_clockwise(json.loads(self.footprint.ExportToJson())['coordinates'][0][0]) else: return True @property def spatial_reference(self): if not getattr(self, '_srs', None): Loading Loading @@ -397,16 +419,8 @@ class GeoDataset(object): (Latitude, Longitude) corresponding to the given (x,y). """ try: geotransform = self.geotransform x = geotransform[0] + (x * geotransform[1]) + (y * geotransform[2]) y = geotransform[3] + (x * geotransform[4]) + (y * geotransform[5]) lon, lat, _ = self.coordinate_transformation.TransformPoint(x, y) except: lat = lon = None warnings.warn('Unable to compute pixel to geographic conversion without ' 'projection information for {}'.format(self.base_name)) lon, lat = self.forward_affine * (x,y) lon, lat, _ = self.coordinate_transformation.TransformPoint(lon, lat) return lat, lon def latlon_to_pixel(self, lat, lon): Loading @@ -425,11 +439,9 @@ class GeoDataset(object): (Sample, line) position corresponding to the given (latitude, longitude). """ geotransform = self.geotransform upperlat, upperlon, _ = self.inverse_coordinate_transformation.TransformPoint(lon, lat) x = (upperlat - geotransform[0]) / geotransform[1] y = (upperlon - geotransform[3]) / geotransform[5] return int(x), int(y) lon, lat, _ = self.inverse_coordinate_transformation.TransformPoint(lon, lat) px, py = map(int, self.inverse_affine * (lat, lon)) return px, py def read_array(self, band=1, pixels=None, dtype='float32'): """ Loading Loading @@ -459,27 +471,33 @@ class GeoDataset(object): if not pixels: array = band.ReadAsArray().astype(dtype) if self.north_up == False: array = np.flipud(array) else: # Check that the read start is not outside of the image xstart, ystart, xextent, yextent = pixels xstart, ystart, xcount, ycount = pixels xmax, ymax = map(int, self.xy_extent[1]) # If the image is south up, flip the roi if self.north_up == False: ystart = ymax - ystart - ycount if xstart < 0: xstart = 0 if ystart < 0: ystart = 0 xmax, ymax = map(int, self.xy_extent[1]) if xstart + pixels[2] > xmax: xextent = xmax - xstart if xstart + xcount > xmax: xcount = xmax - xstart if ystart + pixels[3] > ymax: yextent = ymax - ystart if ystart + ycount > ymax: ycount = ymax - ystart array = band.ReadAsArray(xstart, ystart, xextent, yextent).astype(dtype) array = band.ReadAsArray(xstart, ystart, xcount, ycount).astype(dtype) return array def estimate_mbr(self, geodata): return geofuncs.estimate_mbr(self, geodata) def compute_overlap(self, geodata, **kwargs): return geofuncs.compute_overlap(self, geodata, **kwargs) def array_to_raster(array, file_name, projection=None, Loading Loading
plio/geofuncs/geofuncs.py +117 −25 Original line number Diff line number Diff line import json import numpy as np def intersection_to_pixels(latlon_to_pixel, ul, lr, xmax, ymax, buffer=300): def intersection_to_pixels(inverse_affine, ul, ur, lr, ll): """ Given an inverse affine transformation, take four bounding coordinates in lat/lon space and convert them to a minimum bounding rectangle in pixel space. inverse_affine : object An Affine transformation object ul : tuple Upper Left coordinate in the form (x,y) ur : tuple Upper Right coordinate in the form (x,y) lr : tuple Lower Right coordinate in the form (x,y) ll : tuple Lower Left coordinate in the form (x,y) """ x_start, y_start = latlon_to_pixel(ul[1], ul[0]) x_stop, y_stop = latlon_to_pixel(lr[1], lr[0]) if x_start > x_stop: x_start, x_stop = x_stop, x_start if y_start > y_stop: y_start, y_stop = y_stop, y_start if x_start < buffer: x_start = 0 if xmax - x_stop < buffer: x_stop = xmax if y_start < buffer: y_start = 0 if ymax - y_stop < buffer: y_stop = ymax return np.s_[y_start:y_stop, x_start:x_stop] def estimate_mbr(geodata_a, geodata_b): minx = np.inf maxx = -np.inf miny = np.inf maxy = -np.inf for c in [ul, ur, lr, ll]: px, py = map(int, inverse_affine * (c[0], c[1])) if px < minx: minx = px if px > maxx: maxx = px if py < miny: miny = py if py > maxy: maxy = py return minx, maxx, miny, maxy def compute_overlap(geodata_a, geodata_b): p1 = geodata_a.footprint p2 = geodata_b.footprint intersection = json.loads(p1.Intersection(p2).ExportToJson())['coordinates'][0] ul, ur, lr, ll = find_four_corners(intersection) a_intersection = intersection_to_pixels(geodata_a.inverse_affine, ul, ur, lr, ll) b_intersection = intersection_to_pixels(geodata_b.inverse_affine, ul, ur, lr, ll) return (ul, ur, lr, ll), a_intersection, b_intersection def estimate_mbr(geodata_a, geodata_b, bufferd=50): p1 = geodata_a.footprint p2 = geodata_b.footprint minx, maxx, miny, maxy = p1.Intersection(p2).GetEnvelope() ul = (minx, maxy) lr = (maxx, miny) a_slice = intersection_to_pixels(geodata_a.latlon_to_pixel, ul, lr, *geodata_a.xy_extent[1]) ul, lr, *geodata_a.xy_extent[1], bufferd) b_slice = intersection_to_pixels(geodata_b.latlon_to_pixel, ul, lr, *geodata_b.xy_extent[1]) ul, lr, *geodata_b.xy_extent[1], bufferd) return a_slice, b_slice def find_corners(coords, threshold=120): """ Given a list of coordinates in the form [(x, y), (x1, y1), ..., (xn, yn)], attempt to find corners by identifying all angles < the threshold. For a line segment composed of 3 points, the angle between ab and ac is 180 if the segments are colinear. If they are less than threshold, the line segments are considered to be a corner. Parameters ---------- coords : list of coordinates threshold : numeric Angle under which a corner is identified, Default: 120 """ corners = [coords[0]] for i, a in enumerate(coords[:-2]): a = np.asarray(a) b = np.asarray(coords[i+1]) c = np.asarray(coords[i+2]) ba = a - b bc = c - b angle = np.arccos(np.dot(ba, bc) / (np.linalg.norm(ba) * np.linalg.norm(bc))) if np.degrees(angle) < threshold: corners.append(b.tolist()) return corners def find_four_corners(coords, threshold=120): """ Find four corners in a polygon by making a call to the find_corners function and using the first four corners. Parameters ---------- coords: list of coordinates threshold : numeric Anfle under which a corner is identified, Default: 120 See Also -------- plio.geofuncs.geofuncs.find_corners """ corners = find_corners(coords, threshold) corners.sort(key = lambda x:x[1]) upper = corners[2:] lower = corners[:2] key = lambda x:x[0] ul = min(upper, key=key) ur = max(upper, key=key) lr = max(lower, key=key) ll = min(lower, key=key) return ul, ur, lr, ll
plio/io/io_gdal.py +55 −37 Original line number Diff line number Diff line import json import math import os import warnings import affine import gdal import numpy as np import osr Loading @@ -11,6 +14,8 @@ from plio.io import extract_metadata from plio.geofuncs import geofuncs from plio.utils.utils import find_in_dict from libpysal.cg import is_clockwise gdal.UseExceptions() NP2GDAL_CONVERSION = { Loading @@ -28,7 +33,8 @@ NP2GDAL_CONVERSION = { GDAL2NP_CONVERSION = {} DEFAULT_PROJECTIONS = {'mars':'GEOGCS["Mars 2000",DATUM["D_Mars_2000",SPHEROID["Mars_2000_IAU_IAG",3396190.0,169.89444722361179]],PRIMEM["Greenwich",0],UNIT["Decimal_Degree",0.0174532925199433]]'} DEFAULT_PROJECTIONS = {'mars':'GEOGCS["Mars 2000",DATUM["D_Mars_2000",SPHEROID["Mars_2000_IAU_IAG",3396190.0,169.89444722361179]],PRIMEM["Greenwich",0],UNIT["Decimal_Degree",0.0174532925199433]]', 'moon':'GEOGCS["Moon 2000",DATUM["D_Moon_2000",SPHEROID["Moon_2000_IAU_IAG",1737400.0,0.0]],PRIMEM["Greenwich",0],UNIT["Decimal_Degree",0.0174532925199433]]'} DEFAULT_RADII = {'mars': 3396190.0} for k, v in iter(NP2GDAL_CONVERSION.items()): Loading Loading @@ -174,22 +180,31 @@ class GeoDataset(object): """ if not getattr(self, '_geotransform', None): if self.footprint: # This is an ISIS3 cube that does not report a valid geotransform ul, ll, lr, ur = self.latlon_extent xs, ys = self.raster_size xres = abs(ul[0] - ur[0]) / xs yres = abs(ul[1] - ll[1]) / ys self._geotransform = [ul[0], xres, 0, ul[1], 0, -yres] if ul[1] > ll[1]: # Image is South-North instead of North-South self._geotransform[-1] *= -1 # Lat increases with image length self._geotransform[3] = ll[1] # Origin is at the LL coords = json.loads(self.footprint.ExportToJson())['coordinates'][0][0] ul, ur, lr, ll = geofuncs.find_four_corners(coords) xsize, ysize = self.raster_size xstart = ul[0] xscale = (ur[0] - xstart) / xsize xskew = (ll[0] - xstart) / ysize ystart = ul[1] yskew = (ur[1] - ystart) / xsize yscale = (ll[1] - ystart) / ysize self._geotransform = [xstart, xscale, xskew, ystart, yskew, yscale] else: self._geotransform = self.dataset.GetGeoTransform() return self._geotransform @property def forward_affine(self): self._fa = affine.Affine.from_gdal(*self.geotransform) return self._fa @property def inverse_affine(self): self._ia = ~self.forward_affine return self._ia @property def standard_parallels(self): if not getattr(self, '_standard_parallels', None): Loading @@ -202,6 +217,13 @@ class GeoDataset(object): self._unit_type = self.dataset.GetRasterBand(1).GetUnitType() return self._unit_type @property def north_up(self): if self.footprint: return is_clockwise(json.loads(self.footprint.ExportToJson())['coordinates'][0][0]) else: return True @property def spatial_reference(self): if not getattr(self, '_srs', None): Loading Loading @@ -397,16 +419,8 @@ class GeoDataset(object): (Latitude, Longitude) corresponding to the given (x,y). """ try: geotransform = self.geotransform x = geotransform[0] + (x * geotransform[1]) + (y * geotransform[2]) y = geotransform[3] + (x * geotransform[4]) + (y * geotransform[5]) lon, lat, _ = self.coordinate_transformation.TransformPoint(x, y) except: lat = lon = None warnings.warn('Unable to compute pixel to geographic conversion without ' 'projection information for {}'.format(self.base_name)) lon, lat = self.forward_affine * (x,y) lon, lat, _ = self.coordinate_transformation.TransformPoint(lon, lat) return lat, lon def latlon_to_pixel(self, lat, lon): Loading @@ -425,11 +439,9 @@ class GeoDataset(object): (Sample, line) position corresponding to the given (latitude, longitude). """ geotransform = self.geotransform upperlat, upperlon, _ = self.inverse_coordinate_transformation.TransformPoint(lon, lat) x = (upperlat - geotransform[0]) / geotransform[1] y = (upperlon - geotransform[3]) / geotransform[5] return int(x), int(y) lon, lat, _ = self.inverse_coordinate_transformation.TransformPoint(lon, lat) px, py = map(int, self.inverse_affine * (lat, lon)) return px, py def read_array(self, band=1, pixels=None, dtype='float32'): """ Loading Loading @@ -459,27 +471,33 @@ class GeoDataset(object): if not pixels: array = band.ReadAsArray().astype(dtype) if self.north_up == False: array = np.flipud(array) else: # Check that the read start is not outside of the image xstart, ystart, xextent, yextent = pixels xstart, ystart, xcount, ycount = pixels xmax, ymax = map(int, self.xy_extent[1]) # If the image is south up, flip the roi if self.north_up == False: ystart = ymax - ystart - ycount if xstart < 0: xstart = 0 if ystart < 0: ystart = 0 xmax, ymax = map(int, self.xy_extent[1]) if xstart + pixels[2] > xmax: xextent = xmax - xstart if xstart + xcount > xmax: xcount = xmax - xstart if ystart + pixels[3] > ymax: yextent = ymax - ystart if ystart + ycount > ymax: ycount = ymax - ystart array = band.ReadAsArray(xstart, ystart, xextent, yextent).astype(dtype) array = band.ReadAsArray(xstart, ystart, xcount, ycount).astype(dtype) return array def estimate_mbr(self, geodata): return geofuncs.estimate_mbr(self, geodata) def compute_overlap(self, geodata, **kwargs): return geofuncs.compute_overlap(self, geodata, **kwargs) def array_to_raster(array, file_name, projection=None, Loading
