def create_spectral_curve_for_ideal_data():
# create_spectral_curve_from_dataloader(test_dataloader())
create_spectral_curve_from_dataloader(indian_pines_dataloader())
create_spectral_curve_from_dataloader(samson_dataloader())
create_spectral_curve_from_dataloader(jasper_ridge_dataloader())
create_spectral_curve_from_dataloader(salinas_dataloader())
create_spectral_curve_from_dataloader(salinas_a_dataloader())
create_spectral_curve_from_dataloader(pavia_dataloader())
def analyse_all_data_separately():
print("Searching for spectral curve and labeled files")
result_directories_with_dataloaders = {
"./results/IndianPines/data/": indian_pines_dataloader(),
"./results/JasperRidge/data/": jasper_ridge_dataloader(),
"./results/Pavia/data/": pavia_dataloader(),
"./results/Salinas/data/": salinas_dataloader(),
"./results/SalinasA/data/": salinas_a_dataloader(),
"./results/Samson/data/": samson_dataloader(),
# "./result/tests/data":test_dataloader(),
}
# name: directory
for path in result_directories_with_dataloaders:
print()
print()
names_and_directories = {}
print("\tPath: ", path)
print("\tDataloader name: ",
result_directories_with_dataloaders[path].get_name(False))
# r=root, d=directories, f = files
for r, d, f in os.walk(path):
for file in f:
if '.txt' in file and "spectral_curve" not in file and "report_" not in file:
names_and_directories[file] = os.path.join(r, file)
print(names_and_directories)
for file_name in names_and_directories:
labels_image_path = names_and_directories[file_name]
dataloader_for_this = result_directories_with_dataloaders[path]
spectral_curve_path = \
dataloader_for_this.get_results_directory(verbal=False) + "data/spectral_curve_" + file_name
if os.path.exists(spectral_curve_path):
print()
print("\t File name: ", file_name)
print("\t Labels image path: ", labels_image_path)
print("\t Spectral curve path: ", spectral_curve_path)
print("\t Dataloader name: ",
dataloader_for_this.get_name(verbal=False))
try:
single_analyse(dataloader_for_this, spectral_curve_path,
labels_image_path)
except NotEnoughLabelsError:
print("NotEnoughLabelsError")
print()
else:
print()
print("FILE DOES NOT EXIST")
print("File: ", spectral_curve_path)
plt.close("all")
def create_spectral_curve_for_existing_data():
import os
print("Searching for result files")
result_directories_with_dataloaders = {
"./results/IndianPines/data/": indian_pines_dataloader(),
"./results/JasperRidge/data/": jasper_ridge_dataloader(),
"./results/Pavia/data/": pavia_dataloader(),
"./results/Salinas/data/": salinas_dataloader(),
"./results/SalinasA/data/": salinas_a_dataloader(),
"./results/Samson/data/": samson_dataloader(),
# "./result/tests/data":test_dataloader(),
}
# name: directory
for path in result_directories_with_dataloaders:
names_and_directories = {}
print("\tPath: ", path)
print("\tDataloader name: ", result_directories_with_dataloaders[path].get_name(False))
# r=root, d=directories, f = files
for r, d, f in os.walk(path):
for file in f:
if '.txt' in file \
and "spectral_curve" not in file\
and "report_" not in file:
names_and_directories[file] = os.path.join(r, file)
print(names_and_directories)
for file_name in names_and_directories:
from dataloader.result_dataloader import Dataloader as ResoultDataloader
dataloader = ResoultDataloader()
image_labels =\
dataloader.get_image_labels_from_file(names_and_directories[file_name], verbal=False)
# import matplotlib.pyplot as plt
# plt.imshow(image)
# plt.show()
if file_name.endswith('.txt'):
file_name = file_name[:-4]
print("\t create_spectral_curve_from_dataloader_plus: ", file_name)
print("\t Dir: ", result_directories_with_dataloaders[path].get_name(False))
create_spectral_curve_from_dataloader_plus(
result_directories_with_dataloaders[path],
image_labels,
output_name=file_name,
show_img=False)