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ags_grabber
Commit d6dee84b authored by Larkin Heintzman's avatar Larkin Heintzman
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changed terrain interpolation method to avoid edge artifacts (hopefully)

parent 54b7dd2e
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Showing with 47 additions and 66 deletions
arcgis_terrain.py
View file @ d6dee84b
......@@ -10,6 +10,7 @@ import math
from pyproj import Transformer, Proj, transform
from scipy.interpolate import griddata
from scipy import interpolate
from scipy import ndimage
import time
# This function computes the factor of the argument passed
......@@ -208,6 +209,16 @@ def get_terrain_map(lat_lon = [0,0], sample_dist = 10, extent = 100, heading = 0
y = np.append(y, y_row, axis=0)
e = np.append(e, e_row, axis=0)
# flip elevation data up to down to match other layers
e = np.flipud(e)
# interpolate terrain to match size/resolution of other layers
c = np.int(extent/sample_dist)
scale_factor = 3/20 # factor to get 6.66667m mapping from 1m mapping (1/6.6667)
scaled_extent = np.ceil(scale_factor*extent).astype(np.int)
factor = scaled_extent/e.shape[0]
e_interp = ndimage.zoom(e, factor, order = 3)
if show_plot:
# Attaching 3D axis to the figure
grid_x, grid_y = np.mgrid[min(x):max(x):100j, min(y):max(y):100j]
......@@ -228,7 +239,7 @@ def get_terrain_map(lat_lon = [0,0], sample_dist = 10, extent = 100, heading = 0
print("y max: {}".format(np.max(y)))
print("y max - min: {}".format(np.max(y)-np.min(y)))
return [e,x,y,data,lat_lon]
return [e,e_interp,x,y,data,lat_lon]
# savedimg = elv_layer.export_image(bbox=elv_layer.extent, size=[3840,2160], f='image', save_folder='.', save_file='testerino.jpg')
if __name__ == "__main__":
......@@ -237,36 +248,51 @@ if __name__ == "__main__":
# anchor_point = [float(ics_pt[0]), float(ics_pt[1])]
anchor_point = [42.17965, -74.21362]
extent = 20e3
sample_dist = int(extent/100)
sample_dist = int(extent/10)
heading = 0
start_time = time.time()
# save terrain as csv file (this method is pretty slow, but can compensate with interp)
[e,x,y,data,ll_pt] = get_terrain_map(lat_lon=anchor_point,
[e,e_interp,x,y,data,ll_pt] = get_terrain_map(lat_lon=anchor_point,
sample_dist = sample_dist,
extent = extent,
heading = -heading) # because flipping
# flip elevation data up to down to match other layers
e = np.flipud(e)
scale_factor = 3/20 # factor to get 6.66667m mapping from 1m mapping (1/6.6667)
scaled_extent = np.ceil(scale_factor*extent).astype(np.int)
plt.imshow(e_interp)
plt.title('e interp')
plt.show()
# interpolate terrain to match size/resolution of other layers
c = np.int(extent/sample_dist)
f = interpolate.interp2d(np.linspace(0, extent, c), np.linspace(0, extent, c), e, kind='cubic')
gdt = np.gradient(e_interp)
plt.imshow(np.sqrt(gdt[0]**2 + gdt[1]**2))
plt.title('e interp grad')
plt.show()
c = np.int(extent / sample_dist)
scale_factor = 3 / 20 # factor to get 6.66667m mapping from 1m mapping (1/6.6667)
scaled_extent = np.ceil(scale_factor * extent).astype(np.int)
x_start = np.linspace(0, extent, c)
y_start = np.linspace(0, extent, c)
X_start, Y_start = np.meshgrid(x_start, y_start)
Z_start = np.zeros_like(X_start)
f = interpolate.Rbf(X_start, Y_start, Z_start, e, function='multiquadric')
x_temp = np.linspace(0,extent,scaled_extent) # get correct size of terrain map
y_temp = np.linspace(0,extent,scaled_extent)
e_interp = f(x_temp, y_temp)
X_temp, Y_temp = np.meshgrid(x_temp, y_temp)
Z_temp = np.zeros_like(X_temp)
e_interp = f(X_temp, Y_temp, Z_temp)
plt.imshow(e_interp)
plt.title('e interp old')
plt.show()
plt.imshow(e)
plt.show()
plt.imshow(x)
plt.show()
plt.imshow(y)
gdt = np.gradient(e_interp)
plt.imshow(np.sqrt(gdt[0]**2 + gdt[1]**2))
plt.title('e interp grad old')
plt.show()
print('done')
# elv_filename = "map_layers\\elv_data_"+file_id+".csv"
# if save_files:
# np.savetxt(elv_filename,e_interp,delimiter=",", fmt='%f')
......
feature_set.py
View file @ d6dee84b
......@@ -71,8 +71,9 @@ def grab_features(anchor_point, extent, sample_dist = 10, heading = 0, save_file
print("querying {} layer...".format(name_list[i]))
q = lyr.query(return_count_only=False, return_ids_only=False, return_geometry=True,
out_sr='3857', geometry_filter=geom_filter)
except json.decoder.JSONDecodeError as e:
query_cnt = query_cnt + 1
except (json.decoder.JSONDecodeError, TypeError) as e:
if type(e) != TypeError:
query_cnt = query_cnt + 1
print("error on query: {}".format(e))
print("{} layer failed on query, trying again ...".format(name_list[i]))
gis = GIS(username="larkinheintzman",password="Meepp97#26640") # linked my arcgis pro account
......@@ -218,44 +219,6 @@ def grab_features(anchor_point, extent, sample_dist = 10, heading = 0, save_file
inac_bin_map[pts_inac[:,1], pts_inac[:,0]] = 1 # set indices to 1
# print("looped(tm) inac calculation time = {} sec".format(time.time() - s_time))
#-----------------------------
# # print("points not looped")
# # do boundary calculation for binary matrix (slow for large bounaries but whatever)
#
# # test_pts is the rectangular matrix covering ring for boundary calculation
# x_test, y_test = np.meshgrid(np.arange(np.min(x_pts), np.max(x_pts), 1) , np.arange(np.min(y_pts), np.max(y_pts), 1))
# test_pts = np.array([x_test.flatten(), y_test.flatten()]).T
# mask = np.zeros(test_pts.shape[0])
# core_pts = np.stack([x_pts,y_pts]).T
#
# for pt in core_pts:
# test_dists = np.sqrt(np.square(test_pts[:,0] - pt[0]) +
# np.square(test_pts[:,1] - pt[1]))
# mask[test_dists<=1] = 1
#
# # instead of filling gaps, we want to save filled in areas separately
# # so we need to re-create the bin_map here but on inac. points
# x_pts_inac = test_pts[np.where(mask),0].flatten()
# y_pts_inac = test_pts[np.where(mask),1].flatten()
# pts_inac = np.stack([x_pts_inac,y_pts_inac]).T
#
# # remove points being used as linear features
# for pt in core_pts:
# pts_inac = np.delete(pts_inac, np.where(np.equal(pt,pts_inac).all(1)), axis = 0)
#
# # binarization step
# pts_inac = np.round(pts_inac).astype(np.int)
# # flip y axis
# pts_inac[:,1] = inac_bin_map.shape[1] - pts_inac[:,1]
# # remove any points outside limits of binary map (fixes round versus ceil issues)
# rm_mask = np.logical_or(pts_inac[:,0] < 0, pts_inac[:,0] >= inac_bin_map.shape[1])
# rm_mask = np.logical_or(rm_mask, np.logical_or(pts_inac[:,1] < 0, pts_inac[:,1] >= inac_bin_map.shape[0]))
# pts_inac = pts_inac[np.invert(rm_mask),:]
# inac_bin_map[pts_inac[:,1], pts_inac[:,0]] = 1 # set indices to 1
# print("non looped inac calculation time = {} sec".format(time.time() - s_time))
# binarization step
x_pts_idx = np.round(x_pts).astype(np.int)
y_pts_idx = np.round(y_pts).astype(np.int)
......@@ -284,19 +247,11 @@ def grab_features(anchor_point, extent, sample_dist = 10, heading = 0, save_file
np.savetxt(fn,bin_map,delimiter=",", fmt='%f')
# save terrain as csv file (this method is pretty slow, but can compensate with interp)
[e,x,y,data,ll_pt] = get_terrain_map(lat_lon=anchor_point,
[e,e_interp,x,y,data,ll_pt] = get_terrain_map(lat_lon=anchor_point,
sample_dist = sample_dist,
extent = extent,
heading = -heading) # because flipping
# flip elevation data up to down to match other layers
e = np.flipud(e)
# interpolate terrain to match size/resolution of other layers
c = np.int(extent/sample_dist)
f = interpolate.interp2d(np.linspace(0, extent, c), np.linspace(0, extent, c), e, kind='cubic')
x_temp = np.linspace(0,extent,scaled_extent) # get correct size of terrain map
y_temp = np.linspace(0,extent,scaled_extent)
e_interp = f(x_temp, y_temp)
elv_filename = "map_layers\\elv_data_"+file_id+".csv"
if save_files:
......@@ -352,7 +307,7 @@ if __name__ == "__main__":
anchor_point = [float(ics_pt[0]), float(ics_pt[1])]
extent = 20e3
save_flag = True
plot_flag = True
plot_flag = False
file_extension = 'temp'
sample_dist = int(extent/100)
......
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