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()
def three_five():
"""
3.5
Do the following:
(a) Purpose a method for extracting the bit planes of an image based on
converting the value of its pixels to binary.
(b) Find all the bit planes o the following 4-bit image:
0 1 8 6
2 2 1 1
1 15 14 12
3 6 9 10
"""
four_bit_image = np.array([[0, 1, 8, 6], [2, 2, 1, 1], [1, 15, 14, 12],
[3, 6, 9, 10]])
ImageDisplay.display_image(four_bit_image)
bit_planes = ImageAnalysis.calculate_bit_planes(four_bit_image,
bits_per_pixel=4)
ImageDisplay.display_bit_planes(bit_planes * 256)
