all_experiments = list_folders(exp_folders)[-1:]
else:
all_experiments = list_folders(exp_folders)
for i_exp, exp in enumerate(all_experiments):
print("Curr. exp: ", exp, "(", i_exp + 1, "/", len(all_experiments), ")")
for gt_curr in gt_list:
experiment_folder = jp(exp_folders, exp)
#gt_folder = r'D:\dev\ms_data\Challenges\ISBI2015\ISBI_L\isbi_train'
gt_name = gt_curr
patients = list_folders(gt_folder)
post_processing = True
post_processing_type = 'remove_small'
min_area = 3
folds = list_folders(experiment_folder)
labels_for_df = compute_metrics(None, None, labels_only=True)
global_df = pd.DataFrame(columns=labels_for_df)
cnt_global = 0
for gt_patient in patients: #For every patient
print("Current patient: ", gt_patient)
patient_df = pd.DataFrame(columns=labels_for_df)
gt_timepoints = list_files_with_name_containing(
jp(gt_folder, gt_patient), gt_name, "nii.gz")
cnt_patient = 0
for i_timepoint in range(len(gt_timepoints)):
print("Current timepoint: ", i_timepoint + 1, "/",
len(gt_timepoints))
curr_gt = gt_timepoints[i_timepoint]
curr_gt_img = nib.load(curr_gt).get_fdata().astype(np.uint8)
curr_pred = jp(
experiment_folder, "fold" + gt_patient, "results",
init = 1
for i in range(init,num_backward):
list_inference_slices.append(all_slices[:,i:i+desired_timepoints,:,:,:])
if(num_timepoints%2==0): #even
indexes.append(i+1)
else:
indexes.append(i)
sorted_indexes = np.argsort(indexes)
sorted_list_inference_slices = [list_inference_slices[k] for k in sorted_indexes]
return sorted_list_inference_slices
#Evaluate all images
columns = compute_metrics(None, None, labels_only=True)
df = pd.DataFrame(columns = columns)
test_images = list_folders(path_test)
i_row=0
cnt=0
for case in test_images:
print(cnt+1, "/", len(test_images))
scan_path = jp(path_test, case)
inf_slices = get_inference_slices_time( the_path=path_test,
the_case=case,
input_data=options['input_data'],
normalize=True,
out_size = (160,200),
norm_type = 'zero_one')
import os
from os.path import join as jp
import pandas as pd
import numpy as np
import nibabel as nib
import cc3d
from ms_segmentation.general.general import list_folders, list_files_with_name_containing, list_files_with_extension
from ms_segmentation.evaluation.metrics import compute_metrics
path_experiment = r'D:\dev\ms_data\Challenges\ISBI2015\ISBI_CS\cross_validation\CROSS_VALIDATION_UNet3D_2020-06-25_07_07_15[chi-square_norm_train]'
masks =['mask1', 'mask2']
csv_files = list_files_with_extension(path_experiment, 'csv')
labels_for_df = compute_metrics(None, None, labels_only = True)
df_std = pd.DataFrame(columns = labels_for_df)
cnt = 0
for m in masks:
f = [fi for fi in csv_files if m in fi][0]
df = pd.read_csv(jp(path_experiment,f))[:-1] # ignore last row which is the average
df_std.loc[cnt] = list(df.std())
cnt += 1
df_std.to_csv(jp(path_experiment, 'std_mask1_mask2.csv'), float_format = '%.5f', index = False)
'CROSS_VALIDATION_UNet3D_2020-06-25_07_07_15[chi-square_norm_train]'
]
exp_l = [
'CROSS_VALIDATION_UNetConvLSTM3D_2020-06-23_21_31_39[longitudinal_chisquare_normalization_new]',
'CROSS_VALIDATION_UNetConvLSTM3D_2020-06-13_08_43_21[all_modalities_no_hist_matching_new]'
]
metrics = ["DSC", "LFPR", "LTPR"]
cases = {
"CS (min-max norm.)": jp(path_cs, exp_cs[0]),
"CS (proposed norm.)": jp(path_cs, exp_cs[0]),
"L (min-max norm.)": jp(path_l, exp_l[0]),
"L (proposed norm.)": jp(path_l, exp_l[1])
}
col_names = compute_metrics(None, None, labels_only=True)
#total_df = pd.DataFrame(columns = col_names)
for i_case, (c_name, c_path) in enumerate(cases.items()):
df = pd.read_csv(jp(c_path, "all_results_postprocessed_mask1.csv"))[:-1]
built = dc(df)
for i_m, m in enumerate(metrics):
if i_m == 0:
built["metric"] = m
built["metric value"] = df[m]
else:
temp_df = dc(df)
temp_df["metric"] = m
temp_df["metric value"] = temp_df[m]
built = built.append(temp_df)
built["case"] = c_name
import os
import pandas as pd
import nibabel as nib
import numpy as np
from os.path import join as jp
from ms_segmentation.general.general import list_folders, list_files_with_extension
from ms_segmentation.evaluation.metrics import compute_metrics
reference = r'D:\dev\ms_data\Challenges\ISBI2015\Test_Images\results_cs\qwertz06'
to_compare = r'D:\dev\ms_data\Challenges\ISBI2015\Test_Images\results_cs\qwertz09'
comparison_id = reference.split("\\")[-1] + "__" + to_compare.split("\\")[-1]
save_path = r'D:\dev\ms_data\Challenges\ISBI2015\Test_Images'
columns = compute_metrics(None, None, labels_only=True)
df = pd.DataFrame(columns=columns)
list_ref = list_files_with_extension(reference, "nii")
list_to_compare = list_files_with_extension(to_compare, "nii")
assert len(list_ref) == len(list_to_compare)
i_row = 0
for i, img in enumerate(list_ref):
print(i + 1, "/", len(list_ref))
ref_labels = nib.load(jp(reference, img)).get_fdata().astype(np.uint8)
to_compare_labels = nib.load(jp(to_compare,
img)).get_fdata().astype(np.uint8)
metrics = compute_metrics(ref_labels, to_compare_labels)
df.loc[i_row] = list(metrics.values())
i_row += 1