The document describes a machine learning project to classify contaminated drinking water samples using images taken with a Raspberry Pi embedded system. A KNNMPCAF3 algorithm is used to extract features from the images and classify them using k-nearest neighbors. The system is integrated with a Blynk IoT app to allow remote monitoring and control via a mobile phone. Accuracy of over 76% was achieved in classifying four levels of contamination. The overall aim is to create a low-cost solution to help monitor water quality.

1. Python for Image Processing
Applications
One Week Faculty Development Program on
Research Challenges in Computer Science & Engineering – RCCSE-2020
01.06.2020 to 05.06.2020
RAMACHANDRA College of Engineering
Eluru, Andra Pradesh
Presented by,
Dr.R.Senthilkumar,
Assistant Professor,
Dept. of ECE,
Institute of Road and Transport Technology,
Erode, Tamil Nadu-638316
Email: rsenthil.optical@gmail.com,
Contact no: 9940882605 Tuesday, July 7, 2020

2. Python Libraries
Python 3.7 and above
Matplotlib
Numpy
Scipy
Pillow
OpenCV
Scikit-learn

3. Software Weblinks
Python 3.7 - https://www.python.org/
Matplotlib - https://matplotlib.org/
Numpy - https://numpy.org/
Scipy - https://www.scipy.org/
Pillow - https://pypi.org/project/Pillow/
OpenCV - https://pypi.org/project/opencv-python/
Scikit-learn - https://scikit-learn.org/ (

4. Software Installation
Python 3.7
* Download Python latest version from Python.org
* Install the Python in windows in any drive
* After installation check whether it is properly installed or not
in your system using the command
D:Program FilesPython37>python
Python 3.7.0 (v3.7.0:1bf9cc5093, Jun 27 2018,
04:06:47) [MSC v.1914 32 bit (Inte
l)] on win32
Type "help", "copyright", "credits" or "license" for more
information.
>>>

5. Software Installation
Numpy, Scipy and Matplotlib installation
D:Program FilesPython37>python
Python 3.7.0 (v3.7.0:1bf9cc5093, Jun 27 2018, 04:06:47) [MSC
v.1914 32 bit (Inte
l)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy
>>> import scipy
>>> import matplotlib
>>>
1. python -m pip install numpy
2. python -m pip install scipy
3. python -m pip install matplotlib

6.  Run: pip install opencv-python
- if you need only main modules
 Run: pip install opencv-contrib-python
- if you need both main and contrib modules

7. Basic Image Processing using
matplotlib & pillow Python libraries
Exercises:
1. Image read and display
2. Pseudo color Image
3. Pseudo color Image color bar
4. Image Resizing
5. Image Interpolation
6. RGB to Gray Image
7. Histogram Plot
8. Cropping a Portion of an Image
9. Shape of an Image and gray scale conversion
10.Image transform
11.Image Filtering
12.Image Details and Changing Image File Format

8. Results
Copyright
Image

9. Output:
Exercise 1

10. Output:
Exercise 2

11. Output:
Exercise 3

12. Output:
Exercise 4

13. Output: Exercise 5

14. Output: Exercise 6

15. Output:
Exercise 7

16. Output:
Exercise 8

17. Output: Exercise 9

18. Output:
Exercise 10-1

19. Output:
Exercise 10-2

20. Output:
Exercise 10-3

21. Output:
Exercise 10-4

22. Output:
Exercise 10-5

23. Output: Exercise 11-1

24. Output: Exercise 11-2

25. Output: Exercise 11-3

26. Output: Exercise 11-4

27. Output: Exercise 12

28. More practice- refer the youtube link
https://www.youtube.com/watch?v=Me2OWBstBNg&t=21s

29. Image Processing using OpenCV
Exercises:
1. Read a colour and display an image
2. Read a colour image and display the size of an image
3. Convert a colout image into Gray image
4. Vertical and Horizontal stack more than one image
5. Image transform (rotation)
6. Image resize

30. Output: Exercise 1
Image size (183, 275)
Output: Exercise 2

31. Output: Exercise 3

32. Output: Exercise 4

33. Output: Exercise 5

34. Output: Exercise 6
Orignal Image Resized Image

35. More practice- Refer the youtube
video link
https://www.youtube.com/watch?v=CCkDS-fo-eQ

36. Video Processing using OpenCV
Exercises:
1. Capture a colour video from web camera
2. Capture a colour video from web camera and covert into
gray video
3. Capture a colour vido and get its frame width and height
4. Set the user specified frame width and height
5. Play a already recorded video
6. Capture a video using webcamera and flip that video

37. Exercises:
7. Converting Colour video to Gray video
8. Converting Colour video to Gray and Gray to Binary video
9. Video Blurring (Low pass filtering)
10. Video resize and interpolation followed video blurring
11. Edge detection
12. Video Masking
13. Histogram Equalization
14. Video image transform
15. Video motion Detection

38. More practice –Refer the youtube
video lecture
https://www.youtube.com/watch?v=bR01_iGx7os

39. Machine Learning based Classification of Contaminated
Drinking Water Using Raspberry Pi Embedded System and
IoT Device
Aim of this research project:
The drinking and human usage fresh water are contaminated and
polluted by wastages released from dyeing and other industries. The
classification of highly contaminated, mildly contaminated, lightly
contaminated and recycled water is important for different level of water
purification. The aim of this project is to develop an Internet of Things
based low cost embedded Raspberry pi model for drinking water image
classification. For image classification, a machine learning based
algorithm proposed here called as KNNMPCAF3 (K-Nearest Neighbour
Machine Learning based Principle Component Analysis First Three).

40. Algorithm
KNNMPCA3 Algorithm
Step 1: Start the program.
Step 2: Collect the water sample images.
Step 3: Derive top 3 principle components after pressing the keypad.
Step 4: PCAs corresponding to each image samples are encoded.
Step 5: The encoded image samples are classified using KNN classifier.
Step 6:The classification accuracies are calculated for different number of neighbours.
Step 7:Results are plotted.

41. Flowchart

42. Proposed Model Block Diagram

43. Collected River Water samples
Non-Contaminated
River Water Sample
Lightly Contaminated
River Water Sample
Moderately
Contaminated River
Water Sample
Severly
Contaminated River
Water Sample

44. Raspberry pi Hardware

45. Blynk IoT App configuration in Mobile
Phone

46. Interface Raspberry pi Embedded kit
Keypad, LEDs and Buzzer

47. Interfacing VNC viewer installed Laptop and
Raspberry pi kit using USB

48. Principle Component Analysis Feature Extraction

49. Table 1: Images captured their respective classes and along
with top 3 principle components are listed here
Class Number
and Class Name
Image Id PCA 1 PCA 2 PCA 3
Class ‘0’
Non-contaminated
water
I1 0.639 0.609 0.594
I2 0.784 0.505 0.476
I3 0.639 0.609 0.594
Class ‘1’
Lightly
contaminated
water
I4 0.874 0.871 0.836
I5 0.877 0.762 0.722
I6 0.167 0.144 0.141
Class ‘2’
Moderately
contaminated
water
I7 0.133 0.133 0.129
I8 0.285 0.285 0.261
I9 0.134 0.132 0.129
Class ‘3’
Severely
contaminated
water
I10 1.048 1.007 9.547
I11 0.487 0.461 0.418
I12 0.134 0.132 0.122
I13 1.611 1.500 1.247

50. Table 2: Raspberry pi general purpose input and output pins
configuration and corresponding interfacing devices
GPIO Pin Number Configured
Input/output
Connected Device
GPIO 8 Input Keypad
GPIO 10 Output BLUE Colour LED
GPIO 16 Output GREEN Colour LED
GPIO 18 Output RED Colour LED
GPIO 22 Output BUZZER
GPIO 6 Ground GND pin 3

51. Manual Control & Operation of Mobile Phont IoT
App by a health inspector or sanitary worker

52. Software display- Python programmed and display the
results in terminal or python shell script

53. Table 3: Recognition accuracy and false alarm for all
the four cases
Number of KNNs
Recognition Accuracy in
Percentage
False Alarm in Percentage
1 100 0
2 76.92 23.08
3 76.92 23.08
4 69.23 30.77

54. Plot between No.of Nearest neighbor vs % Recognition
Accuracy & False alarm

55. More details- refer YouTube video lecture
1. Machine learning using Python+Raspberry pi -
https://www.youtube.com/watch?v=5SrZTyuFpmU&t=13s
2. Image classification using Python+Raspberry pi+Machine learning part 1 –
https://www.youtube.com/watch?v=GpJomlEiJfQ&t=18s
3. Image classification using Python+Raspberry pi+Machine learning part 2–
https://www.youtube.com/watch?v=jvLjRh2bGAI&t=51s
4. Image classification using Python+Raspberry pi+Machine learning part 3 –
https://www.youtube.com/watch?v=YIjGiEWF7IY&t=15s
5. Machine Learning based Classification of Contaminated Drinking Water Using
Raspberry Pi Embedded System and IoT Device
https://www.youtube.com/watch?v=-oONEWCjLTw&t=369s

56. Is it necessary to file copyright or
patent our work
http://copyright.gov.in/frmStatusGenUser.aspx
Diary number:6588/2020-CO/L