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Align aerial imagery from O-D points (current use case is mines and industry headquarters.
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import geopandas as gpd | |
import pandas as pd | |
import contextily as ctx | |
from rasterio import open as rioopen | |
from rasterio import band | |
from rasterio.mask import mask | |
from rasterio.plot import reshape_as_image, show | |
from rasterio.transform import Affine | |
from rasterio.warp import calculate_default_transform, reproject, Resampling | |
import rioxarray | |
from numpy.ma import masked_where | |
from numpy import isnan, uint8, array, int16, amin, amax, arange, logical_or | |
from matplotlib.colors import LinearSegmentedColormap | |
import matplotlib.pyplot as plt | |
from scipy.ndimage.filters import gaussian_filter | |
from shapely.geometry import box | |
import os | |
import requests | |
import utm | |
def wgs_to_utm(lat, lon): | |
utm_z = str(utm.from_latlon(lat, lon)[2]) | |
if len(utm_z) == 1: | |
utm_z = '0' + utm_z | |
if lat >= 0: | |
epsg = '326' + utm_z | |
else: | |
epsg = '327' + utm_z | |
return epsg | |
def bounds(row, col, crs, radius): | |
y, x = row[col].y, row[col].x | |
epsg = wgs_to_utm(y, x) | |
buff = gpd.GeoSeries([row[col]]).set_crs(epsg=4326).to_crs(epsg=epsg).buffer(radius).to_crs(epsg=crs) | |
bounds = buff.total_bounds | |
nsew = { | |
'n': bounds[3], | |
's': bounds[1], | |
'e': bounds[2], | |
'w': bounds[0] | |
} | |
return nsew | |
def project_raster(file, crs): | |
dst_crs = f'EPSG:{crs}' | |
with rioopen(file) as src: | |
transform, width, height = calculate_default_transform( | |
src.crs, dst_crs, src.width, src.height, *src.bounds | |
) | |
kwargs = src.meta.copy() | |
kwargs.update( | |
crs = dst_crs, | |
transform = transform, | |
width = width, | |
height = height | |
) | |
with rioopen(file, 'w', **kwargs) as dst: | |
for i in range(1, src.count + 1): | |
reproject( | |
source=band(src, i), | |
destination=band(dst, i), | |
src_transform=src.transform, | |
src_crs=src.crs, | |
dst_transform=transform, | |
dst_crs=dst_crs, | |
resampling=Resampling.nearest | |
) | |
return None | |
def clip_n_scoot(file, buff, crs, base, radius, rnd=False): | |
buff_dst_crs = buff.to_crs(epsg=crs) | |
bounds_dst_crs = buff_dst_crs.total_bounds | |
if rnd: | |
shapes = [f for f in buff_dst_crs] | |
else: | |
shapes = [box(*bounds_dst_crs)] | |
with rioopen(file) as src: | |
img, transform = mask(src, shapes, crop=True) | |
if base is not None: | |
with rioopen(base + '.tif') as base_src: | |
meta = base_src.meta | |
base_tl = base_src.transform * (0, 0) | |
base_br = base_src.transform * (base_src.width, base_src.height) | |
base_radius = abs(base_tl[1] - base_br[1]) / 2 | |
ratio = radius/base_radius | |
scale = Affine.scale(ratio) | |
translate = Affine.translation( | |
(base_src.width / 2), | |
(base_src.height / 2) | |
) | |
meta.update( | |
count = src.count, | |
transform = base_src.transform * translate * scale * ~translate | |
) | |
if src.count > 1: | |
pass | |
else: | |
meta.update( | |
dtype = int16 | |
) | |
else: | |
meta = src.meta | |
meta.update( | |
driver = "GTiff", | |
height = img.shape[1], | |
width = img.shape[2], | |
transform = transform | |
) | |
with rioopen(file, 'w+', **meta) as dst: | |
dst.write(img) | |
def locate_that(row, column, name, radius = 4000, base = None, rnd = False): | |
epsg = wgs_to_utm(row[column].y, row[column].x) | |
buff = gpd.GeoSeries([row[column]]).set_crs(epsg=4326).to_crs(epsg=epsg).buffer(radius).to_crs(epsg=3857) | |
bounds = buff.total_bounds | |
file = name + '.tif' | |
_ = ctx.bounds2raster(*bounds, file, source=ctx.providers.Esri.WorldImagery, zoom = 16) | |
project_raster(file, epsg) | |
clip_n_scoot(file, buff=buff, crs=epsg, rnd=rnd, base=base, radius=radius) | |
return None | |
def align_grids(over_name, base_name): | |
print("Aligning grids.") | |
b = rioxarray.open_rasterio(base_name + '.tif') | |
b.rio.to_raster(base_name + '.tif') | |
o = rioxarray.open_rasterio(over_name + '.tif') | |
o_matched = o.rio.reproject_match(b) | |
o_matched.rio.to_raster(over_name + '.tif') | |
return None | |
def get_dem(n, s, e, w, file = 'dem', upsample_factor = 2): | |
endpoint = 'https://portal.opentopography.org/API/globaldem' | |
q = {'demtype': 'SRTMGL1', 'north': n, 'south': s, 'east': e, 'west': w} | |
try: | |
print("Querying OpenTopography.") | |
response = requests.get(endpoint, params = q) | |
response.raise_for_status() | |
with open(file + '.tif', 'wb') as dst: | |
dst.write(response.content) | |
except requests.exceptions.HTTPError as errh: | |
print(errh) | |
except requests.exceptions.ConnectionError as errc: | |
print(errc) | |
except requests.exceptions.Timeout as errt: | |
print(errt) | |
except requests.exceptions.RequestException as err: | |
print(err) | |
def convert_gray(img): | |
gray = 0.07 * img[:,:,2] + 0.72 * img[:,:,1] + 0.21 * img[:,:,0] | |
img_gray = gray.astype(uint8) | |
return img_gray | |
def map_plot(file, dims=(24,24), base = 'base', over = 'over', dem = 'dem', contour_smooth = 50): | |
print("Plotting map.") | |
fig, ax = plt.subplots(nrows = 1, ncols = 1, figsize=(24,24)) | |
base_color = LinearSegmentedColormap.from_list('magenta_to_white', ['#ff0160', '#fff']) | |
over_color = LinearSegmentedColormap.from_list('blue_to_white', ['#08306b', '#fff']) | |
with rioopen(base + '.tif') as base_src: | |
base = convert_gray(reshape_as_image(base_src.read())) | |
ax.set_axis_off() | |
show( | |
base, | |
ax=ax, | |
cmap = base_color, | |
transform = base_src.transform | |
) | |
with rioopen(over + '.tif') as over_src: | |
over = over_src.read() | |
over_gray = convert_gray(reshape_as_image(over)) | |
show( | |
masked_where( | |
logical_or( | |
over_gray==0, over_gray==255 | |
), over_gray | |
), | |
ax=ax, | |
cmap = over_color, | |
transform = over_src.transform | |
) | |
with rioopen(dem + '.tif') as dem_src: | |
dem = dem_src.read() | |
masked_dem = masked_where(dem == -32768, dem) | |
ndmin = amin(masked_dem) | |
ndmax = amax(masked_dem) | |
r = arange(ndmin, ndmax, 5) | |
filtered = gaussian_filter(masked_where(masked_dem < ndmin, masked_dem), contour_smooth) | |
ct = show( | |
masked_where( | |
filtered <= ndmin, | |
filtered | |
), | |
ax=ax, | |
transform = dem_src.transform, | |
contour=True, | |
levels=r, | |
colors='#fff', | |
linewidths = 0.8, | |
alpha = 0.6 | |
) | |
fig.savefig(f'{file}.pdf', bbox_inches='tight', dpi=300) | |
def row_process(row, base_geom, base_size, base_id, over_geom, over_size, over_id, rnd=True): | |
for tif in [f for f in os.listdir('./') if f.endswith('.tif')]: | |
os.remove(os.path.join('./', tif)) | |
print("Fetching and processing base imagery.") | |
base = locate_that(row, | |
column = base_geom, | |
radius = base_size, | |
name = 'base' | |
) | |
print("Fetching and processing overlay.") | |
over = locate_that(row, | |
column = over_geom, | |
radius = over_size, | |
base = 'base', | |
rnd = rnd, | |
name = 'over' | |
) | |
align_grids('over', 'base') | |
print("Fetching and processing DEM.") | |
# Download raster, write to dem.tif | |
nsew = bounds(row, crs=4326, col=over_geom, radius=base_size + 500) | |
get_dem(nsew['n'], nsew['s'], nsew['e'], nsew['w'], file='dem') | |
# Project it to appropriate UTM zone. | |
epsg = wgs_to_utm(array(nsew['n'], nsew['s']), array([nsew['w'], nsew['e']])) | |
project_raster('dem.tif', epsg) | |
# Create new buffer for clipping contours. | |
buff = gpd.GeoSeries([row[over_geom]]).set_crs(epsg=4326).to_crs(epsg=epsg).buffer(base_size - ((base_size-over_size) / 2)) | |
clip_n_scoot('dem.tif', buff=buff, crs=epsg, rnd=True, base='base', radius=base_size - ((base_size-over_size) / 2)) | |
align_grids('dem', 'base') | |
map_plot(f'{int(row[base_id])}-{int(row[over_id])}') | |
return None | |
hq_df = gpd.read_file('./data/firms/hq_geography.geojson') | |
prop_df = gpd.read_file('./data/properties/geography.geojson') | |
own_df = pd.read_csv('./data/properties/ownership.csv') | |
overview_df = pd.read_csv('./data/properties/overview.csv') | |
df = hq_df.merge(own_df, left_on='id', right_on='CompanyID') | |
df = df.merge(prop_df, left_on='PropertyID', right_on='id') | |
df = df.merge(overview_df, left_on='PropertyID', right_on='PropertyID') | |
df = df.rename(columns={"geometry_x": "geometry_hq", "geometry_y": "geometry_prop"}) | |
df = df[ | |
((df["Asset Type"] == 'Mine') | (df["Asset Type"] == 'Mine Complex')) | |
& (df["Work Type"] != 'Underground') | |
& ((df["Development Status"] == 'Production') | (df["Asset Type"] == 'Closed') | (df["Asset Type"] == 'Decommissioning')) | |
].reset_index() | |
outputs = df.loc[8:9,:].apply( | |
row_process, | |
axis = 1, | |
base_geom = 'geometry_hq', | |
base_size = 2500, | |
base_id = 'CompanyID', | |
over_geom = 'geometry_prop', | |
over_size = 1500, | |
over_id = 'PropertyID' | |
) |
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