--- a
+++ b/utils/utils.py
@@ -0,0 +1,75 @@
+import calendar
+import csv
+import datetime
+import pickle
+import time
+
+import cv2
+import matplotlib.pyplot
+import numpy as np
+from PIL import Image
+from matplotlib.backends.backend_pdf import PdfPages
+
+COLORS = ['b', 'r', 'g', 'c']
+
+
+def timestamp():
+    ts = calendar.timegm(time.gmtime())
+    return datetime.datetime.fromtimestamp(ts).isoformat()
+
+
+def apply_colormap(img, colormap_handle):
+    img = img - img.min()
+    if img.max() != 0:
+        img = img / img.max()
+    img = Image.fromarray(np.uint8(colormap_handle(img) * 255))
+    return img
+
+
+def plot_histogram(data, bins, range, title, exportPDF=None):
+    f = matplotlib.pyplot.figure()
+    matplotlib.pyplot.hist(data.flatten(), bins=bins, range=range, density=True)  # arguments are passed to np.histogram
+    matplotlib.pyplot.title(title)
+    matplotlib.pyplot.show(block=False)
+
+    # save a pdf to disk
+    if exportPDF:
+        pp = PdfPages(exportPDF)
+        pp.savefig(f)
+        pp.close()
+
+    return f
+
+
+def plot_histogram_with_labels(data, labels, bins, _range, title, exportPDF=None):
+    classes = np.unique(labels)
+    f = matplotlib.pyplot.figure()
+
+    for i in range(classes.size):
+        data_with_current_label = data[labels == classes[i]]
+        n, bins, patches = matplotlib.pyplot.hist(data_with_current_label.flatten(), bins=bins, range=_range, color=COLORS[i])
+
+        with open(f'{exportPDF.split(".")[0]}.{i}.npy', 'wb') as file:
+            pickle.dump({'n': n, 'bins': bins, 'mean': np.mean(data_with_current_label), 'var': np.var(data_with_current_label)}, file)
+
+        with open(exportPDF + ".{}.csv".format(i), mode="w") as csv_file:
+            fieldnames = ["Bin", "Count"]
+            writer = csv.DictWriter(csv_file, fieldnames=fieldnames)
+            writer.writeheader()
+            for k in range(len(n)):
+                writer.writerow({"Bin": bins[k], "Count": n[k]})
+
+    matplotlib.pyplot.title(title)
+    matplotlib.pyplot.show(block=False)
+
+    # save a pdf to disk
+    if exportPDF:
+        pp = PdfPages(exportPDF)
+        pp.savefig(f)
+        pp.close()
+
+    return f
+
+
+def normalize(x):
+    return np.expand_dims(cv2.normalize(x, None, 0, 1, cv2.NORM_MINMAX), -1)