--- a
+++ b/scripts/test/evaluate.py
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+#==============================================================================#
+#  Author:       Dominik Müller                                                #
+#  Copyright:    2020 IT-Infrastructure for Translational Medical Research,    #
+#                University of Augsburg                                        #
+#                                                                              #
+#  This program is free software: you can redistribute it and/or modify        #
+#  it under the terms of the GNU General Public License as published by        #
+#  the Free Software Foundation, either version 3 of the License, or           #
+#  (at your option) any later version.                                         #
+#                                                                              #
+#  This program is distributed in the hope that it will be useful,             #
+#  but WITHOUT ANY WARRANTY; without even the implied warranty of              #
+#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the               #
+#  GNU General Public License for more details.                                #
+#                                                                              #
+#  You should have received a copy of the GNU General Public License           #
+#  along with this program.  If not, see <http://www.gnu.org/licenses/>.       #
+#==============================================================================#
+#-----------------------------------------------------#
+#                   Library imports                   #
+#-----------------------------------------------------#
+import matplotlib.pyplot as plt
+import matplotlib.animation as animation
+import numpy as np
+import pandas as pd
+import os
+from tqdm import tqdm
+from miscnn.data_loading.interfaces import NIFTI_interface
+from miscnn import Data_IO
+from miscnn.evaluation.cross_validation import load_disk2fold
+from plotnine import *
+import argparse
+
+#-----------------------------------------------------#
+#                      Argparser                      #
+#-----------------------------------------------------#
+parser = argparse.ArgumentParser(description="Automated COVID-19 Segmentation")
+parser.add_argument("-p", "--predictions", help="Path to predictions directory",
+                    required=True, type=str, dest="pred")
+args = parser.parse_args()
+pred_path = args.pred
+os.environ["CUDA_VISIBLE_DEVICES"] = "1"
+eval_path = "evaluation.testing"
+
+#-----------------------------------------------------#
+#                  Score Calculations                 #
+#-----------------------------------------------------#
+def calc_DSC(truth, pred, classes):
+    dice_scores = []
+    # Iterate over each class
+    for i in range(classes):
+        try:
+            gt = np.equal(truth, i)
+            pd = np.equal(pred, i)
+            # Calculate Dice
+            dice = 2*np.logical_and(pd, gt).sum() / (pd.sum() + gt.sum())
+            dice_scores.append(dice)
+        except ZeroDivisionError:
+            dice_scores.append(0.0)
+    # Return computed Dice Similarity Coefficients
+    return dice_scores
+
+def calc_IoU(truth, pred, classes):
+    iou_scores = []
+    # Iterate over each class
+    for i in range(classes):
+        try:
+            gt = np.equal(truth, i)
+            pd = np.equal(pred, i)
+            # Calculate iou
+            iou = np.logical_and(pd, gt).sum() / (pd.sum() + gt.sum() - np.logical_and(pd, gt).sum())
+            iou_scores.append(iou)
+        except ZeroDivisionError:
+            iou_scores.append(0.0)
+    # Return computed IoU
+    return iou_scores
+
+def calc_Sensitivity(truth, pred, classes):
+    sens_scores = []
+    # Iterate over each class
+    for i in range(classes):
+        try:
+            gt = np.equal(truth, i)
+            pd = np.equal(pred, i)
+            # Calculate sensitivity
+            sens = np.logical_and(pd, gt).sum() / gt.sum()
+            sens_scores.append(sens)
+        except ZeroDivisionError:
+            sens_scores.append(0.0)
+    # Return computed sensitivity scores
+    return sens_scores
+
+def calc_Specificity(truth, pred, classes):
+    spec_scores = []
+    # Iterate over each class
+    for i in range(classes):
+        try:
+            not_gt = np.logical_not(np.equal(truth, i))
+            not_pd = np.logical_not(np.equal(pred, i))
+            # Calculate specificity
+            spec = np.logical_and(not_pd, not_gt).sum() / (not_gt).sum()
+            spec_scores.append(spec)
+        except ZeroDivisionError:
+            spec_scores.append(0.0)
+    # Return computed specificity scores
+    return spec_scores
+
+def calc_Accuracy(truth, pred, classes):
+    acc_scores = []
+    # Iterate over each class
+    for i in range(classes):
+        try:
+            gt = np.equal(truth, i)
+            pd = np.equal(pred, i)
+            not_gt = np.logical_not(np.equal(truth, i))
+            not_pd = np.logical_not(np.equal(pred, i))
+            # Calculate accuracy
+            acc = (np.logical_and(pd, gt).sum() + \
+                   np.logical_and(not_pd, not_gt).sum()) /  gt.size
+            acc_scores.append(acc)
+        except ZeroDivisionError:
+            acc_scores.append(0.0)
+    # Return computed accuracy scores
+    return acc_scores
+
+def calc_Precision(truth, pred, classes):
+    prec_scores = []
+    # Iterate over each class
+    for i in range(classes):
+        try:
+            gt = np.equal(truth, i)
+            pd = np.equal(pred, i)
+            # Calculate precision
+            prec = np.logical_and(pd, gt).sum() / pd.sum()
+            prec_scores.append(prec)
+        except ZeroDivisionError:
+            prec_scores.append(0.0)
+    # Return computed precision scores
+    return prec_scores
+
+#-----------------------------------------------------#
+#                    Run Evaluation                   #
+#-----------------------------------------------------#
+# Initialize Data IO Interface for NIfTI data
+## We are using 4 classes due to [background, lung_left, lung_right, covid-19]
+interface = NIFTI_interface(channels=1, classes=4)
+
+# Create Data IO object to load and write samples in the file structure
+data_io = Data_IO(interface, input_path="data.testing", output_path=pred_path)
+
+# Access all available samples in our file structure
+sample_list = data_io.get_indiceslist()
+sample_list.sort()
+
+# Initialize dataframe
+cols = ["index", "score", "background", "infection"]
+df = pd.DataFrame(data=[], dtype=np.float64, columns=cols)
+
+# Iterate over each sample
+for index in tqdm(sample_list):
+    # Load a sample including its image, ground truth and prediction
+    sample = data_io.sample_loader(index, load_seg=True, load_pred=True)
+    # Access image, ground truth and prediction data
+    image = sample.img_data
+    truth = sample.seg_data
+    pred = sample.pred_data
+
+    pred = np.where(pred==1, 0, pred)
+    pred = np.where(pred==2, 0, pred)
+    pred = np.where(pred==3, 1, pred)
+
+    # Compute diverse Scores
+    dsc = calc_DSC(truth, pred, classes=2)
+    df = df.append(pd.Series([index, "DSC"] + dsc, index=cols),
+                   ignore_index=True)
+    iou = calc_IoU(truth, pred, classes=2)
+    df = df.append(pd.Series([index, "IoU"] + iou, index=cols),
+                   ignore_index=True)
+    sens = calc_Sensitivity(truth, pred, classes=2)
+    df = df.append(pd.Series([index, "Sens"] + sens, index=cols),
+                   ignore_index=True)
+    spec = calc_Specificity(truth, pred, classes=2)
+    df = df.append(pd.Series([index, "Spec"] + spec, index=cols),
+                   ignore_index=True)
+    prec = calc_Precision(truth, pred, classes=2)
+    df = df.append(pd.Series([index, "Prec"] + prec, index=cols),
+                   ignore_index=True)
+    acc = calc_Accuracy(truth, pred, classes=2)
+    df = df.append(pd.Series([index, "Acc"] + acc, index=cols),
+                   ignore_index=True)
+
+# Output complete dataframe
+print(df)
+# Create evaluation directory
+if not os.path.exists(eval_path) : os.mkdir(eval_path)
+# Identify cv & fold
+id = pred_path.split(".")[-1]
+# Store complete dataframe to disk
+path_res_detailed = os.path.join(eval_path, "results." + id + ".csv")
+df.to_csv(path_res_detailed, index=False)
+
+# Print out average, median std evaluation metrics for the current fold
+df_avg = df.groupby(by="score").mean()
+path_out = os.path.join(eval_path, "results." + id + ".mean.csv")
+df_avg.to_csv(path_out, index=True)
+df_med = df.groupby(by="score").median()
+path_out = os.path.join(eval_path, "results." + id + ".median.csv")
+df_med.to_csv(path_out, index=True)
+df_std = df.groupby(by="score").std()
+path_out = os.path.join(eval_path, "results." + id + ".std.csv")
+df_std.to_csv(path_out, index=True)