Add example code from StackOverflow

this might be a good first approach.

detect.py

@@ -0,0 +1,46 @@
+# sample code from https://stackoverflow.com/questions/43111029/how-to-find-the-average-colour-of-an-image-in-python-with-opencv
+
+import matplotlib
+matplotlib.use('TkAgg')
+
+import matplotlib.pyplot as plt
+import cv2
+import numpy as np
+from skimage import io
+
+img = io.imread('https://i.stack.imgur.com/DNM65.png')[:, :, :-1]
+
+# Average color
+average = img.mean(axis=0).mean(axis=0)
+
+# Dominant color
+pixels = np.float32(img.reshape(-1, 3))
+
+n_colors = 5
+criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 200, .1)
+flags = cv2.KMEANS_RANDOM_CENTERS
+
+_, labels, palette = cv2.kmeans(pixels, n_colors, None, criteria, 10, flags)
+_, counts = np.unique(labels, return_counts=True)
+
+dominant = palette[np.argmax(counts)]
+
+# Graph visualization
+avg_patch = np.ones(shape=img.shape, dtype=np.uint8)*np.uint8(average)
+
+indices = np.argsort(counts)[::-1]
+freqs = np.cumsum(np.hstack([[0], counts[indices]/float(counts.sum())]))
+rows = np.int_(img.shape[0]*freqs)
+
+dom_patch = np.zeros(shape=img.shape, dtype=np.uint8)
+for i in range(len(rows) - 1):
+    dom_patch[rows[i]:rows[i + 1], :, :] += np.uint8(palette[indices[i]])
+
+fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(12,6))
+ax0.imshow(avg_patch)
+ax0.set_title('Average color')
+ax0.axis('off')
+ax1.imshow(dom_patch)
+ax1.set_title('Dominant colors')
+ax1.axis('off')
+plt.show()