def three_seven():
"""
3.7
Obtain the unnormalized and the normalized histograms of the following 8-bit,
MxN image. Give your histogram either in a table or a graph, labeling clearly
the value and location of each histogram component in terms of M and N.
Double-check your answer by making sure that the histogram components add to the
correct value.
"""
figure_image = np.array([
[240, 240, 240, 16, 16, 16, 32, 32, 32, 32, 32, 32, 32],
[240, 240, 240, 16, 16, 16, 228, 32, 32, 32, 32, 32, 255],
[240, 240, 240, 16, 16, 16, 228, 228, 32, 32, 32, 255, 255],
[240, 240, 240, 16, 16, 16, 228, 228, 228, 32, 255, 255, 255],
[240, 240, 240, 16, 16, 16, 228, 228, 228, 0, 255, 255, 255],
[240, 240, 240, 16, 16, 16, 228, 228, 0, 0, 0, 255, 255],
[240, 240, 240, 16, 16, 16, 228, 0, 0, 0, 0, 0, 255],
[240, 240, 240, 16, 16, 16, 0, 0, 0, 0, 0, 0, 0],
[127, 127, 127, 191, 191, 191, 0, 0, 0, 0, 0, 0, 0],
[127, 127, 127, 191, 191, 191, 0, 0, 0, 0, 0, 0, 0],
[127, 127, 127, 191, 191, 191, 0, 0, 0, 0, 0, 0, 0],
])
rows, columns = figure_image.shape
ImageDisplay.display_image(figure_image)
histogram = ImageStatistics.calculate_histogram(figure_image)
# calculate normalized histogram
counts = 0
for value in histogram:
counts += value
normalized_histogram = histogram / (rows * columns)
print(histogram)
plt.hist(figure_image)
plt.show()
plt.plot(histogram, label="unnormalized histogram")
plt.legend()
plt.show()
plt.plot(normalized_histogram, label="normalized histogram")
plt.legend()
plt.show()
