Python image processing_Python image processing.pptx

ELC Activity Thapar Institute of Engineering and Technology 2024
ELC Activity
Image Processing
Introduction to Python for Image
Processing

Python
• What is Python?
• Python is a high-level, general-purpose programming
language.
• Python in Image Processing:
• Python is one of the widely used programming languages
for processing the Digital Images. Its amazing libraries
and tools help in achieving the task of image processing
very efficiently.

Working With Python
• To write a Python code, many IDEs are available.
• Working with Anaconda: Anaconda is a distribution of the
Python and R programming languages for scientific
computing, that aims to simplify package management and
deployment.
• Working with Google Colab: Colab, or "Colaboratory", allows
you to write and execute Python in your browser, with
– Zero configuration required
– Access to GPUs free of charge
– Easy sharing

Getting Started with Python
• Starting with Google Colab:
– Sign-in into your Gmail account and open the new
notebook in Google Colab.
• You will be redirected to the new
notebook.

• In the new notebook:
Space to upload the data or images for
working within the session
Space to write your code
Output

• To Run the code in notebook:

Importing Libraries
• OpenCV: It is a huge open-source library that includes several
hundreds of computer vision, machine learning, and image
processing algorithms. It provides a comprehensive set of functions
and tools that facilitate the development of applications dealing
with images and videos.
import cv2
• Pillow: The Python Imaging Library adds image processing
capabilities to your Python interpreter. This library provides
extensive file format support, an efficient internal representation,
and fairly powerful image processing capabilities.
import PIL
from PIL import Image

Importing Libraries
• NumPy: NumPy stands for Numerical Python. This library adds the
support for large, multi-dimensional arrays and matrices, along with
a large collection of high-level mathematical functions to operate
on these arrays.
import numpy as np
• Matplotlib: Matplotlib is an amazing visualization library in Python
for 2D plots of arrays. Matplotlib is a multi-platform data
visualization library built on NumPy arrays. Matplotlib consists of
several plots like line, bar, scatter, histogram, etc.
from matplotlib import pyplot as plt

Opening and Reading Image
• PIL.Image.open() Opens and identifies the given image file.
• The plt.imshow() function in pyplot module of matplotlib
library is used to display data as an image; i.e. on a 2D regular
raster.
img = Image.open("9.jpeg")
plt.imshow(img)

Opening and Reading Image
• Printing Image details
print(img.format)
print(img.size)
print(img.mode)
JPEG
(1238, 1280)
RGB

Image Pre-Processing

Converting into Gray scale
• First, convert the image into a numpy array:
img = np.array(img)
• Then convert the image into gray scale and show it:
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
plt.imshow(gray,cmap='gray')

Applying Thresholding
• Apply a thresholding value according to the image and then
convert it into an inverse binary image; where 1 means
contour lines and 0 means background.
_, threshold = cv2.threshold(gray, 110, 255,
cv2.THRESH_BINARY_INV)

Shape Detection

Applying Morphological Operators
• First defining the kernel shape and size to further use in
morphological operators.
• These kernels basically works as filter to either remove or add the
pixels in the contours as per the morphological operator applied.
# kernel for dilation operation
kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(4,4))
# kernel for closing operation
kernelc = cv2.getStructuringElement(cv2.MORPH_RECT,(6,6))
# kernel for opening operation
kernelo = cv2.getStructuringElement(cv2.MORPH_RECT,(3,3))

• Apply different morphological operators, to extract the shape
boundary and discard the remaining background.
# for getting the complete shape
dilated = cv2.dilate(threshold, kernel, iterations=3)
# for connecting the disjoint edges of shapes
closing = cv2.morphologyEx(dilated, cv2.MORPH_CLOSE,
kernelc, iterations = 3)
# for removing the blobs created because of the above two
operations
opening = cv2.morphologyEx(dilated, cv2.MORPH_OPEN,
kernelo, iterations = 8)

• Apply different morphological operators, to extract the shape
boundary and discard the remaining background.
# for skeletonization
thinned = cv2.ximgproc.thinning(opening)
dilated_t = cv2.dilate(thinned, kernel, iterations=6)

• Finally, the complete contours formed and detected.
• Hence, the first step of detecting the shape is completed.
Original Image Detected shapes

Shape Identification

Identifying and Drawing Contours
• Approximately identifying only the closed contour boundaries.
• cv2.approxPloyDP() function approximates the entire shape.
approx = cv2.approxPolyDP(contour, 0.02 *
cv2.arcLength(contour, True), True)
• Draw the identified contours by using drawContours() function
cv2.drawContours(img, [contour], 0, (0, 0, 255), 2))
Note: All the above function will be run within the counted contours of the shape
calculated by findContours() function.
contours,_ = cv2.findContours(dilated_t, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)

Identifying and Drawing Contours
• The identified contours will look like the following:
Original Image Identified shapes

Shape Recognition

Recognizing and Putting name of shapes
• First find the center of the shape and then put name on it.
• To recognize the name of the shape, the length of the closed
contours is calculated, which is earlier calculated by
cv2.approxPloyDP() function.
• To detect the center of the point:
# finding center point of shape
M = cv2.moments(contour)
if M['m00'] != 0.0:
x = int(M['m10']/M['m00'])
y = int(M['m01']/M['m00'])

• Recognize the shape by the number of contours in a closed shape and put
the name in the center of the each shape:
if len(approx) == 3:
cv2.putText(img, 'Triangle', (x, y),
cv2.FONT_HERSHEY_SIMPLEX, 2, (255, 0, 0), 5)
elif len(approx) == 4:
cv2.putText(img, 'Rectangle', (x, y),
cv2.putText(img, 'Pentagon', (x, y),
cv2.putText(img, 'Hexagon', (x, y),
cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 0, 255), 2)
else:
cv2.putText(img, 'circle', (x, y),

• The final output will be like:
Original Image Final recognized shapes

Challenges
• Challenges:
– Identify the optimal Binary threshold value
– Irregular Shape Detection
– Shape detection with hazy and noisy background
– Smoothness and Continuity Improvement

Resources
• The following resources can be referred:
– Gonzalez, Rafael C. Digital image processing. Pearson education India, 3rd
edition, 2009.
– Jähne, Bernd. Digital image processing. Springer Science & Business Media,
2005.
– https://docs.opencv.org/4.x/d6/d00/tutorial_py_root.html
– https://docs.opencv.org/3.4/d2/d96/tutorial_py_table_of_contents_imgproc.
html
– https://www.bogotobogo.com/cplusplus/files/OReilly%20Learning%20OpenC
V.pdf

Thank You

Python image processing_Python image processing.pptx

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