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ags_grabber
Commit bae8c1bc authored by Larkin Heintzman's avatar Larkin Heintzman
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solving edge case errors for large river features

parent f5408558
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Showing with 158 additions and 61 deletions
feature_set.py
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......@@ -19,6 +19,7 @@ import json
import sys
import csv
import os
import glob
import matlab.engine
......@@ -33,16 +34,16 @@ def grab_features(anchor_point, extent, sample_dist = 10, heading = 0, save_file
# adding water_url twice, once for boundaries and once for linear features
# the layer named 'lakes' gets boundary treatment
url_list = [trails_url, river_url, riverw_url, roads_url, water_url, powerlines_url, railroads_url]
# url_list = [trails_url]
name_list = ['trails', 'rivers', 'rivers_bdd', 'roads', 'lakes', 'powerlines', 'railroads']
# name_list = ['trails']
url_list = [riverw_url, river_url, roads_url, water_url, powerlines_url, railroads_url, trails_url]
name_list = ['rivers_bdd', 'rivers', 'roads', 'lakes', 'powerlines', 'railroads', 'trails']
inac_layers = ['rivers_bdd', 'lakes']
inac_layers = []
# url_list = [riverw_url, river_url]
# name_list = ['rivers_bdd', 'rivers','bleh','bleh']
# inac_layers = ['rivers_bdd']
gis = GIS(username="larkinheintzman",password="Meepp97#26640") # linked my arcgis pro account
ap_meters = lat_lon2meters(anchor_point[0], anchor_point[1])
# print(ap_meters)
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)
......@@ -69,7 +70,7 @@ def grab_features(anchor_point, extent, sample_dist = 10, heading = 0, save_file
q = []
query_cnt = 0
while type(q)==list and query_cnt <= 5: # have to do this because arcgis is sketchy as hell and doesnt always come back
while type(q)==list and query_cnt <= 2: # have to do this because arcgis is sketchy as hell and doesnt always come back
try:
print("querying {} layer...".format(name_list[i]))
q = lyr.query(return_count_only=False, return_ids_only=False, return_geometry=True,
......@@ -78,12 +79,12 @@ def grab_features(anchor_point, extent, sample_dist = 10, heading = 0, save_file
query_cnt = query_cnt + 1
print("error on query: {}".format(e))
print("{} layer failed on query, trying again ...".format(name_list[i]))
if query_cnt > 5 and not q:
print("{} layer failed too many times, breaking!".format(name_list[i]))
if query_cnt > 2 and not q:
print("{} layer failed too many times, leaving empty".format(name_list[i]))
if save_files:
fn = "map_layers/"+name_list[i]+"_data_"+file_id+".csv"
np.savetxt(fn,bin_map,delimiter=",", fmt='%f')
continue
# if save_files:
# fn = "map_layers/"+name_list[i]+"_data_"+file_id+".csv"
# np.savetxt(fn,bin_map,delimiter=",", fmt='%f')
print("{} layer sucessfully queried".format(name_list[i]))
# re-build into list of x-y values
......@@ -108,21 +109,33 @@ def grab_features(anchor_point, extent, sample_dist = 10, heading = 0, save_file
# rotate points about origin to establish heading
[x_pts, y_pts] = point_rotation(origin = [scaled_extent/2,scaled_extent/2],pt = [x_pts, y_pts],ang = heading)
# x_uinterp = x_pts # untrimmed uninterpolated points (but scaled)
# y_uinterp = y_pts
# trim data to only within extents (because arcgis cant fuckin' do this)
rm_mask = np.logical_or(x_pts < 0, x_pts > scaled_extent) # mask to remove points (x axis)
rm_mask = np.logical_or(rm_mask,np.logical_or(y_pts < 0, y_pts > scaled_extent)) # other axis mask
x_pts = x_pts[np.invert(rm_mask)]
y_pts = y_pts[np.invert(rm_mask)] # trim interpolated points
y_pts = y_pts[np.invert(rm_mask)] # trim points
if x_pts.shape[0] <= 2:
continue
# treat each section of a feature intersecting the extent as separate
dists = np.sqrt(np.sum(np.diff(np.array([x_pts, y_pts]).T, axis = 0)**2, axis=1))
breaks = list(np.where(dists >= dists.mean() + 5*dists.std())[0])
x_pts_full = x_pts # save full coordinate set
y_pts_full = y_pts
breaks = [-1] + breaks + [x_pts.shape[0]-1]
if x_pts.shape[0] > 1: # if there are still points after trimming
for br in range(len(breaks) - 1):
x_pts = x_pts_full[(breaks[br]+1):(breaks[br+1]+1)]
y_pts = y_pts_full[(breaks[br]+1):(breaks[br+1]+1)]
if x_pts.shape[0] <= 1: # ignore tiny chops
continue
# if data is too short, add some points in the middle
while x_pts.shape[0] < 4:
x_pt = (x_pts[0] + x_pts[1])/2 # average between first and second point
y_pt = (y_pts[0] + y_pts[1])/2
x_pt = (x_pts[0] + x_pts[1]) / 2 # average between first and second point
y_pt = (y_pts[0] + y_pts[1]) / 2
x_pts = np.insert(x_pts, 1, x_pt)
y_pts = np.insert(y_pts, 1, y_pt)
......@@ -138,35 +151,111 @@ def grab_features(anchor_point, extent, sample_dist = 10, heading = 0, save_file
if name_list[i] in inac_layers:
s_time = time.time()
# do boundary calculation for binary matrix (slow for large bounaries but whatever)
ring = path.Path(np.array([x_pts, y_pts]).T)
# TODO: try cv2 here, test for speeeeeeeeeeeeeeeeeeeeeeed
# 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 = ring.contains_points(test_pts, radius=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[mask,0]
y_pts_inac = test_pts[mask,1]
pts_inac = np.stack([x_pts_inac,y_pts_inac]).T
# remove points being used as linear features
for pt in np.stack([x_pts,y_pts]).T:
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("inac calculation time = {} sec".format(time.time() - s_time))
# loop check
if np.sqrt(np.square(x_pts[0] - x_pts[-1]) + np.square(y_pts[0] - y_pts[-1])) <= 10:
# print('points are looped')
# do boundary calculation for binary matrix (slow for large bounaries but whatever)
ring = path.Path(np.array([x_pts, y_pts]).T)
# 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 = ring.contains_points(test_pts, radius=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[mask,0]
y_pts_inac = test_pts[mask,1]
pts_inac = np.stack([x_pts_inac,y_pts_inac]).T
# remove points being used as linear features
for pt in np.stack([x_pts,y_pts]).T:
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("looped inac calculation time = {} sec".format(time.time() - s_time))
else:
# force it to be a loop
x_pts = np.concatenate([x_pts,3*np.random.random(x_pts.shape)+np.flipud(x_pts)])
y_pts = np.concatenate([y_pts,3*np.random.random(y_pts.shape)+np.flipud(y_pts)])
# do boundary calculation for binary matrix (slow for large bounaries but whatever)
ring = path.Path(np.array([x_pts, y_pts]).T)
# 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 = ring.contains_points(test_pts, radius=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[mask,0]
y_pts_inac = test_pts[mask,1]
pts_inac = np.stack([x_pts_inac,y_pts_inac]).T
# remove points being used as linear features
for pt in np.stack([x_pts,y_pts]).T:
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("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)
......@@ -214,22 +303,25 @@ def grab_features(anchor_point, extent, sample_dist = 10, heading = 0, save_file
np.savetxt(elv_filename,e_interp,delimiter=",", fmt='%f')
if plot_data:
terr_fig = px.imshow(e_interp)
terr_fig.show()
plt.imshow(e_interp)
plt.show()
plt_list = []
# plt_list = []
# fix stupid names
for nme in inac_layers:
name_list.insert(name_list.index(nme), nme+' inac')
# for nme in inac_layers:
# name_list.insert(name_list.index(nme), nme+' inac')
for i in range(viz_map.shape[-1]):
row_idx, col_idx = np.where(viz_map[:,:,i] != 0)
# row_idx, col_idx = np.where(viz_map[:,:,i] != 0)
# flip y values
col_idx = viz_map.shape[0] - col_idx
plt_list.append(go.Scatter(x=row_idx, y=col_idx, mode='markers', name=name_list[i]))
# col_idx = viz_map.shape[0] - col_idx
# plt_list.append(go.Scatter(x=row_idx, y=col_idx, mode='markers', name=name_list[i]))
plt.imshow(viz_map[:,:,i])
# plt.title(name_list[i])
plt.show()
fig = go.Figure(data=plt_list)
fig.show()
# fig = go.Figure(data=plt_list)
# fig.show()
if __name__ == "__main__":
......@@ -240,12 +332,17 @@ if __name__ == "__main__":
# [37.99092, -78.52798, 'BiscuitRun_hikers'],
# [37.519485, -79.651315, 'temp'],
# [38.24969, -78.39555, 'Quinque_dementia'],
[38.55209, -78.32099, 'OldRag'],
# [42.15495, -74.20523, '2149'],
[42.17965, -74.21362, '2129'],
# [38.20656, -78.67878, 'BrownsCove'],
# [38.02723, -78.45076, 'Charlottesville_dementia'],
# [34.12751, -116.93247, 'SanBernardinoPeak'] ,
]
# 42.17965 -74.21362
# 42.15495 -74.20523
base_dir = 'C:/Users/Larkin/ags_grabber'
eng = matlab.engine.start_matlab() # engine for running matlab
......@@ -253,9 +350,9 @@ if __name__ == "__main__":
for i,ics_pt in enumerate(ics):
anchor_point = [float(ics_pt[0]), float(ics_pt[1])]
extent = 15e3
extent = 20e3
save_flag = True
plot_flag = False
plot_flag = True
file_extension = 'temp'
sample_dist = int(extent/100)
......
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