Image processing¶

Python provides several great libraries that allow a wide range of operation on images. For further information, please read the tutorials of: * OpenCV * Scikit Image

In this notebook, we just introduce a few classical image processing operations while playing with dices. The goal of the example is to count the total score one the dices, the answer being \(113\).

from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
from scipy import ndimage
import pandas as pd
import os
%matplotlib nbagg

You can download the image used in this example here: dices.jpg. The following code will work if the image is located in the same directory as the notebook itself.

First, let’s check if the file dices.tif is in the current directory.

path = "dices.tif"
files = os.listdir("./")
if path in files:
    print("Ok, the file is in {0}".format(files))
else:
    print("The file is not in {0} , retry !".format(files))

Ok, the file is in ['_data', '02_Image_Processing.ipynb', '06_Practical_Work', '.ipynb_checkpoints', '00_Basics.ipynb', 'dices.tif', '04_Tutorials', '02_Advanced_Examples']

Now let’s read it using Python Image Library (aka PIL):

im = Image.open(path)
Nc, Nl = im.size
im = im.resize((Nc // 2 ,Nl // 2),Image.ANTIALIAS)
fig, ax = plt.subplots()
ax.axis("off")
plt.imshow(im)
plt.show()

<IPython.core.display.Javascript object>

Conversion to grayscale¶

R, G, B = im.split()
R = np.array(R)
G = np.array(G)
B = np.array(B)
R

array([[223, 221, 224, ..., 195, 195, 193],
       [225, 225, 221, ..., 194, 195, 194],
       [223, 223, 223, ..., 195, 195, 195],
       ...,
       [229, 228, 231, ..., 192, 195, 196],
       [230, 229, 229, ..., 192, 194, 195],
       [230, 229, 228, ..., 193, 195, 196]], dtype=uint8)

fig = plt.figure()
ax1 = fig.add_subplot(3, 1, 1)
plt.title("R")
plt.imshow(R, cmap = cm.gray)
ax1.axis("off")
ax1 = fig.add_subplot(3, 1, 2)
plt.title("G")
plt.imshow(G, cmap = cm.gray)
ax1.axis("off")
ax1 = fig.add_subplot(3, 1, 3)
plt.title("B")
plt.imshow(B, cmap = cm.gray)
ax1.axis("off")
plt.show()

<IPython.core.display.Javascript object>

The green channel has a great contrats so we chose to work only on this channel now.

Histogram¶

The histogram shows the repartition of the pixels on the color scale.

plt.figure()
plt.hist(G.flatten(), bins = np.arange(256), histtype = "stepfilled")
plt.grid()
plt.xlabel("Pixel value")
plt.ylabel("Pixel count")
plt.show()

<IPython.core.display.Javascript object>

Thresholding¶

Using the histogram, one can see that there are 3 peaks. The left peak is the darkest one and corresponds to the colors of the dices bodies. We can cut around \(120\) to isolate the dices bodies from the dots on the dices.

Erosion / Dilation¶

struc = np.ones((8,8))
Gtd = ndimage.binary_dilation(Gt, structure = struc)
Gtde = ndimage.binary_erosion(Gtd, structure = struc)
plt.figure()
plt.imshow(Gtde, cmap = cm.gray, interpolation = "nearest")
plt.grid()
plt.show()

<IPython.core.display.Javascript object>

Labeling¶

Gtdel, number = ndimage.measurements.label(Gtde == False)
number

plt.figure()
plt.imshow(Gtde,cmap = cm.gray, interpolation = "nearest")
plt.imshow(np.where(Gtdel > 1, Gtdel, np.nan),
           cmap = cm.jet, interpolation = "nearest")
plt.grid()
plt.show()

<IPython.core.display.Javascript object>

data = Gtdel.flatten()
count = np.bincount(data)
(count < 1000).sum()

So the total score is \(113\), the method works !