def runtest():
# input_raster = "./data/input_raster.tif"
input_raster = "./data/h10_1005_3-2/h10_1005_3-2.tif"
out_csv = "/tmp/out_xyz_2.csv"
rtxyz = Raster2xyz()
rtxyz.translate(input_raster, out_csv)
def raster_to_xyz(raster, csv):
input_raster = raster
out_csv = csv
rtxyz = Raster2xyz()
rtxyz.translate(input_raster, out_csv)
def main():
input_raster = "input.tif"
out_csv = "output.csv"
rtxyz = Raster2xyz()
rtxyz.translate(input_raster, out_csv)
df = pd.read_csv(out_csv)
print(df.head(5))
print(df.columns)
df.loc[df['z'] < 3, 'z'] = 0
df.loc[df['z'] > 200, 'z'] = 0
print(df.head(20))
minx = min(df.x)
print(minx)
miny = min(df.y)
df['x'] = df['x'] - minx
df['y'] = df['y'] - miny
print(df.head(20))
df.to_csv(out_csv, index=False, header=False)
# -*- coding: utf-8 -*-
"""
Created on Mon Nov 16 14:30:24 2020
@author: vincenzomadaghiele
"""
# geoTiff libraries
import rasterio as rs
from rasterio.plot import show
from raster2xyz.raster2xyz import Raster2xyz
# general purpose
import glob
# Instanciate Raster2xyz()
rtxyz = Raster2xyz()
# convert to csv all the geoTiff files in NDVI300
source_path = 'data/copernicus_land/NDVI300/*.tiff'
csv_path = 'data/copernicus_land/NDVI300/'
source_files = glob.glob(source_path)
for i in range(len(source_files)):
file_name = source_files[i][29:-5]
out_path = csv_path + file_name + '.csv'
print('----------------')
print('Converting: ' + file_name)
print('----------------')
rtxyz.translate(source_files[i], out_path)
# plot dataset
dataset = rs.open(source_files[i])
def get_SwissAlti3D(DEM_dir, file, gdf, dhm_ind, check_distance):
"""Read SwissAlti*d for glacier, reproject, convert to xyz, find closest value,
reads out elevation
Params:
DEM_dir: dircetory containing SwissAlti3D glacier wide tiles
file: filename of SA3D tile that contains glacier (based on Gl-ID)
gdf: geopandas dataframe containing all point measurememts for this m_type and glacier
dhm_ind: list of index of all points in gdf for which we need to check elevation
check_distance: if true, check if current z-val is more than 150 m away from SWA3D,
if not, just replace all NaN z-values
"""
dst_crs = 'EPSG:21781'
# Read file, reproject to LV03
with rasterio.open(os.path.join(DEM_dir, "swissAlti3D", 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 rasterio.open('RGB.tif', 'w', **kwargs) as dst:
for i in range(1, src.count + 1):
reproject(source=rasterio.band(src, i),
destination=rasterio.band(dst, i),
src_transform=src.transform,
src_crs=src.crs,
dst_transform=transform,
dst_crs=dst_crs,
resampling=Resampling.nearest)
# Read file, convert to xyz
# with rasterio.open('RGB.tif') as src:
# image = src.read()
# # transform image
# bands, rows, cols = np.shape(image)
# image1 = image.reshape(rows * cols, bands)
# print(image1.shape)
# # bounding box of image
# l, b, r, t = src.bounds
# # resolution of image
# res = src.res
# res = src.res
# if (res[0]< 10):
# res = list(res)
# res[0]=10
# # meshgrid of X and Y
# x = np.arange(l, r, res[0])
# y = np.arange(t, b, -res[0])
# X, Y = np.meshgrid(x, y)
# # flatten X and Y
# newX = np.array(X.flatten('C'))
# newY = np.array(Y.flatten('C'))
# print(newX.shape, newY.shape)
# # join XY and Z information
# try:
# dem = np.column_stack((newX, newY, image1))
# except ValueError:
# print("What is wrong here?")
rtxyz = Raster2xyz()
rtxyz.translate("RGB.tif", "out_xyz.csv")
dem = pd.read_csv("out_xyz.csv", delimiter=",", engine="python")
dem = dem[dem["z"] > 0]
dem = dem.fillna(0).to_numpy()
A = dem[:, :2]
for f in dhm_ind:
# find index of point in dem that is closest to our point pt for which we need to
# add the z-component:
pt = [gdf["x-pos"][f], gdf["y-pos"][f]]
print(f)
# distance and index of clostest point:
distance, ind = spatial.KDTree(A).query(pt)
# get z-pos of closest point:
z_val = dem[ind, 2]
if check_distance:
# Check if z_val and original z_val are more than 150 m apart - if so, replace, if not, leave
dis = abs(z_val - gdf["z-pos"][f])
if dis > 150:
if dis > 10000000000000:
print("Discrepancy bigger only because of NaN val ")
else:
print("Discrepancy bigger than 150 meter, ", z_val,
gdf["z-pos"][f])
gdf.loc[[f], ['z-pos']] = z_val
print("Z-Val and index to be replaced: ", z_val,
gdf["x-pos"][f], f)
# Find index of all gdf entries with same position (x-pos) and replace
# z-pos there as well:
for ix in gdf[gdf["x-pos"] == gdf["x-pos"][f]
and gdf["y-pos"] == gdf["y-pos"]
[f]].index.tolist():
if len(gdf[gdf["x-pos"] == gdf["x-pos"]
[f]].index.tolist()) > 1:
gdf.loc[[ix], ['z-pos']] = z_val
print("Also adjusted z-val for index ", z_val,
gdf["x-pos"][ix], ix)
else:
# write to gdf:
print("Replacing ", z_val, gdf["z-pos"][f])
gdf.loc[[f], ['z-pos']] = z_val
return gdf
def convert_tif_to_raster(tif, out='temp.csv'):
print('Convert to csv: ', tif)
rtxyz = Raster2xyz()
rtxyz.translate(tif, out)
return out