diff --git a/README.md b/README.md
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--- a/README.md
+++ b/README.md
@@ -4,4 +4,4 @@
 
 ## Practicals
 
-1. [Color Transfer via Discrete Optimal Transport using the sliced approach](https://codimd.math.cnrs.fr/s/2eRBqV9zl)
+1. [Color Transfer via Discrete Optimal Transport using the sliced approach](https://codimd.math.cnrs.fr/s/s_rh7X9wF), [Material](https://github.com/dcoeurjo/CGDI-Practicals/tree/main/1-SlicedOptimalTransport)
diff --git a/misc/README.md b/misc/README.md
new file mode 100644
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@@ -0,0 +1,3 @@
+# Extra tools
+
+* `computeHistogram.py`: compute and the three RGB histograms from an image.
diff --git a/misc/computeHistogram.py b/misc/computeHistogram.py
new file mode 100755
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+++ b/misc/computeHistogram.py
@@ -0,0 +1,49 @@
+#!/usr/bin/env python3
+ # import the necessary packages
+from matplotlib import pyplot as plt
+import numpy as np
+import argparse
+import cv2
+
+# construct the argument parser and parse the arguments
+ap = argparse.ArgumentParser()
+ap.add_argument("-i", "--image", required = True, help = "Path to the image")
+ap.add_argument("-o", "--outputhist", required = True, help = "Path to the histogram image")
+args = vars(ap.parse_args())
+
+# load the image and show it
+image = cv2.imread(args["image"])
+cv2.imshow("image", image)
+
+
+# grab the image channels, initialize the tuple of colors,
+# the figure and the flattened feature vector
+chans = cv2.split(image)
+colors = ("b", "g", "r")
+plt.figure()
+plt.title("")
+plt.xlabel("Bins")
+plt.ylabel("# of Pixels")
+features = []
+
+# loop over the image channels
+for (chan, color) in zip(chans, colors):
+	# create a histogram for the current channel and
+	# concatenate the resulting histograms for each
+	# channel
+	hist = cv2.calcHist([chan], [0], None, [256], [0, 256])
+	features.extend(hist)
+
+	# plot the histogram
+	plt.plot(hist, color = color)
+	plt.xlim([0, 256])
+
+
+plt.savefig(args["outputhist"])
+# here we are simply showing the dimensionality of the
+# flattened color histogram 256 bins for each channel
+# x 3 channels = 768 total values -- in practice, we would
+# normally not use 256 bins for each channel, a choice
+# between 32-96 bins are normally used, but this tends
+# to be application dependent
+print("flattened feature vector size: %d" % (np.array(features).flatten().shape))