Thirst part of the Python Master class including Image Processing using Python-OpenCV and Scikit-image

1. Python Master Class
Part 3
By
Chathuranga Bandara - www.chathuranga.com

2. Agenda
● Intro to Image Processing
● Usage of Opencv
● Usage of Scikit - Image

3. What is it?

4. the analysis and manipulation of a digitized image, especially in order to
improve its quality.

5. It’s basically 3 steps
1. Import the Image
2. Analyse and manipulate the Image
3. Output the Image

6. Purpose of Image processing
Visualization - Observe the objects that are not visible.
Image sharpening and restoration - To create a better image.
Image retrieval - Seek for the image of interest.
Measurement of pattern – Measures various objects in an image.
Image Recognition – Distinguish the objects in an image.

8. OpenCV
OpenCV (Open Source Computer Vision) is a library of programming
functions mainly aimed at real-time computer vision,
developed by Intel's research center in Nizhny Novgorod (Russia),
later supported by Willow Garage and now maintained by Itseez.

9. How to install : First Download
https://raw.githubusercontent.com/milq/scripts-ubuntu-debian/master/install-opencv.sh

10. How to install
bash install-opencv.sh

16. Combine The Two

18. scikit - image
As per scikit-image.org:
“scikit-image is a collection of algorithms for image processing. It is
available free of charge and free of restriction. We pride ourselves on
high-quality, peer-reviewed code, written by an active community of
volunteers.”

19. How to install
Pip install -u scikit-image

20. Science Kit - Image

21. First steps.
1. Create a file called: hello-image.py
2. Open with notepad
3. Try this code.

22. # imports the thing.
from scikit import data, io, filters
image = data.coins()
edges = filters.sobel(image)
io.imshow(edges)
io.show()

23. Another Example
# imports the thing.
from scikit import data, io, filters
import matplotlib.pyplot as plt
image = data.camera()
val = filters.threshold_otsu(image)
mask = camera - val
# save to disk
plt.imsave('mask.jpg', mask)

24. How about some cool stuff?

25. from skimage import data, draw
from skimage import transform, util
import numpy as np
from skimage import filters, color
from matplotlib import pyplot as plt
hl_color = np.array([0, 1, 0])
img = data.rocket()
img = util.img_as_float(img)
eimg = filters.sobel(color.rgb2gray(img))
plt.title('Original Image')
plt.imshow(img)

26. resized = transform.resize(img, (img.shape[0], img.shape[1] - 200))
plt.figure()
plt.title('Resized Image')
plt.imshow(resized)

27. out = transform.seam_carve(img, eimg, 'vertical', 200)
plt.figure()
plt.title('Resized using Seam Carving')
plt.imshow(out)

28. masked_img = img.copy()
poly = [(404, 281), (404, 360), (359, 364), (338, 337), (145, 337), (120, 322),
(145, 304), (340, 306), (362, 284)]
pr = np.array([p[0] for p in poly])
pc = np.array([p[1] for p in poly])
rr, cc = draw.polygon(pr, pc)
masked_img[rr, cc, :] = masked_img[rr, cc, :]*0.5 + hl_color*.5
plt.figure()
plt.title('Object Marked')
plt.imshow(masked_img)

29. eimg[rr, cc] -= 1000
plt.figure()
plt.title('Object Removed')
out = transform.seam_carve(img, eimg, 'vertical', 90)
resized = transform.resize(img, out.shape)
plt.imshow(out)
plt.show()

30. https://github.com/DrunkenCub/intro2pyimage

31. It’s your time to shine!

32. Thank You.