def statistics_func(raster_folder, raster_filename, output_folder, statistical_functions, statistical_args):
start_time = datetime.now()
input_raster = os.path.join(raster_folder, raster_filename)
hdr_file = "" # input_raster + ".hdr" # only used for ENVI stacks
outname = os.path.join(output_folder, raster_filename)
if outname.find(".tif") != -1:
outname = outname[0:len(outname) - 4]
# arr: full size numpy array 3D XxYxZ 200x300x100
arr = preprocessing.rio_array(input_raster, hdr_file=hdr_file)
# activate to get list of dates from .hdr file (.hdr file needs to be specified above)
# dates = arr[1]
for i, func in enumerate(statistical_functions):
# creating results with calling wanted algorithm in parallel_apply_along_axis for quick runtime
result = apply_along_axis.parallel_apply_along_axis(func1d=func, arr=arr[0], axis=0, cores=mp.cpu_count(),
**statistical_args[i])
# selecting dtype based on result
dtype = type(result[0][0])
func_name_end = str(func).find(" at")
func_name_start = 10
func_name = str(func)[func_name_start:func_name_end]
# exporting result to new raster
export_arr.functions_out_array(outname=outname + "_" + func_name + str(i), arr=result, input_file=input_raster,
dtype=dtype)
# print time to this point
statistics_time = datetime.now()
print("breakpoint-time = ", statistics_time - start_time, "Hr:min:sec")
def filter_func(raster_folder, raster_filename, output_folder, filter_functions, filter_args):
start_time = datetime.now()
input_raster = os.path.join(raster_folder, raster_filename)
hdr_file = "" # only used for ENVI stacks
outname = os.path.join(output_folder, raster_filename)
if outname.find(".tif") != -1:
outname = outname[0:len(outname) - 4]
# arr: full size numpy array 3D XxYxZ 200x300x100
arr = preprocessing.rio_array(input_raster, hdr_file=hdr_file)
# activate to get list of dates from .hdr file (.hdr file needs to be specified above)
# dates = arr[1]
for i, func in enumerate(filter_functions):
kernel_size = str(filter_args[i]['kernel'])
filtered_arr = apply_along_axis.parallel_apply_along_axis(func1d=func, arr=arr[0], axis=0, cores=mp.cpu_count(),
**filter_args[i])
filtered_arr = np.rollaxis(filtered_arr, 2)
filtered_arr = np.rollaxis(filtered_arr, 1)
filtered_arr = np.rollaxis(filtered_arr, 2)
dtype = type(filtered_arr[0][0][0])
func_name_end = str(func).find(" at")
func_name_start = 10
func_name = str(func)[func_name_start:func_name_end]
# exporting result to new raster
export_arr.functions_out_array(outname=outname + "_" + func_name + str(kernel_size), arr=filtered_arr,
input_file=input_raster, dtype=dtype)
# print time to this point
filter_time = datetime.now()
print("filter-time = ", filter_time - start_time, "Hr:min:sec")
def subset_processed_data():
"""
function to subset raster data using polygons from a shapefile and export the subset to a new folder
all paths are given in user_data.py
"""
import rasterio as rio
import rasterio.mask
import numpy as np
### install BanDiTS using: python3.6 -m pip install git+https://github.com/marlinmm/BanDiTS.git ###
from BanDiTS.export_arr import functions_out_array
import shutil
subset_path = os.path.join(Paths.sen_processed_path, "subset/")
if os.path.exists(subset_path):
shutil.rmtree(subset_path)
os.mkdir(subset_path)
processed_file_list = extract_files_to_list(Paths.sen_processed_path,
datatype=".tif")
shapefile = import_polygons()
for k, tifs in enumerate(processed_file_list):
src1 = rio.open(processed_file_list[k])
out_image1, out_transform1 = rio.mask.mask(src1, [shapefile[0][0]],
all_touched=1,
crop=True,
nodata=np.nan)
ras_meta1 = src1.profile
ras_meta1.update({
"driver": "GTiff",
"height": out_image1.shape[1],
"width": out_image1.shape[2],
"transform": out_transform1,
"nodata": -9999
})
tmp1 = processed_file_list[k].index("_dir")
tmp2 = len(processed_file_list[k])
print(subset_path + processed_file_list[k][tmp1 + 5:tmp2 - 4])
functions_out_array(outname=subset_path +
processed_file_list[k][tmp1 + 5:tmp2 - 4] +
"_subset.tif",
arr=out_image1,
input_file=processed_file_list[k],
dtype=np.float32,
ras_meta1=ras_meta1)
def main():
################################### INPUT ########################################
# Input Folder Marlin:
raster_folder = "C:/Users/marli/Desktop/GEO402_Testdaten/Input_Files/Raster/"
# Input Folder Jonas:
# raster_folder = "C:/Users/jz199/Documents/Studium/Master/1. Semester\Vorlesungsmitschriften/GEO402 - Ableitung von Landoberflächenparametern/Subset/"
# Input file name
raster_filename = "S1_A_VH_agulhas_full_study_site_50m"
# raster_filename = "SubsetVH.tif"
################################### OUTPUT ########################################
# Output Folder Marlin:
output_folder = "C:/Users/marli/Desktop/GEO402_Testdaten/AAA_output/"
# Output Folder Jonas:
# output_folder = "C:/Users/jz199/Documents/Studium/Master/1. Semester\Vorlesungsmitschriften/GEO419 - Pythonprogrammierung Habermeyer/GEO402_Output/"
# Output File Name:
# output_file = raster_filename[0:len(raster_filename)-4] + "_median_filtered3.tif"
output_file = raster_filename + "_median_test2.tif"
#simple_edge_detection
###################### NO USER INPUT BEYOND THIS POINT ###############################
input_raster = raster_folder + raster_filename
hdr_file = input_raster + ".hdr"
outname = output_folder + output_file
# arr: full size numpy array 3D XxYxZ 200x300x100
arr = preprocessing.rio_array(input_raster, hdr_file=hdr_file)
dates = arr[1]
#print(dates)
# creating filtered array depending on set filter function
filtered_arr = apply_along_axis.parallel_apply_along_axis(func1d=filter_functions.median_filter11, arr=arr[0], axis=0, cores=mp.cpu_count())
filtered_arr = np.rollaxis(filtered_arr, 2)
filtered_arr = np.rollaxis(filtered_arr, 1)
filtered_arr = np.rollaxis(filtered_arr, 2)
dtype = type(filtered_arr[0][0][0])
#print(type(dtype)
#if type(dtype) == np.float64:
# print("lalala")
# --> change float64 to float32
export_arr.functions_out_array(outname=outname, arr=filtered_arr, dates=dates, input_file=input_raster, dtype=dtype)
# creating results with calling wanted algorithm in parallel_apply_along_axis for quick runtime
#result = apply_along_axis.parallel_apply_along_axis(func1d=function.find_peaks, arr=arr[0], axis=0, cores=mp.cpu_count())
# selecting dtype based on result
dtype = np.int32 #type(result[0][0])
# dtype = type(filtered_arr[0][0])
# float64 to float32
# exporting result to new raster
#export_arr.functions_out_array(outname=outname, arr=result, dates=dates, input_file=input_raster, dtype=dtype)
# export_arr.functions_out_array(outname=outname, arr=filtered_arr, input_file=input_raster, dtype=dtype)
end_time = datetime.now()
print("end-time = ", end_time-start_time, "Hr:min:sec")