Lab01:Introduction and

System Setup
ICT-4202: Digital Image Processing Lab

Prepared by
Prof. Dr. Fahima Tabassum
&
Mst. Sumiya siddika, Teaching Assistant
Course Outline
 OBJECTIVE:

To introduce students with Image processing basics and configure python platform in
Anaconda.

 Lab Contents:
● Computer Vision and Image Processing
● Download and install Anaconda
● Install OpenCV, Numpy and Matplotlib libraries in Anaconda
● Reading an image using opencv and matlpotlib
● Show the image in GUI window and plot it
● Show pixel information
● Gray scale image representation
● Writing an image in same and different directory
The Advantages of Python in Image Processing

 Python is a preferred language for image processing due to:

• Extensive libraries like OpenCV, PIL/Pillow, scikit-image etc. offering
specialized functionality.
• Simple and readable code thanks to its clean syntax. Easy for beginners
to adopt.
• Vibrant developer community providing abundant code examples and
troubleshooting support.
• Interoperability with languages like C++ for performance-critical
operations.
• Rapid prototyping enabled by Python's interpreted nature.
Anaconda

 Anaconda software helps you create an environment for many different

versions of Python and package versions. Anaconda is also used to install, remove,
and upgrade packages in your project environments. Furthermore, you may use
Anaconda to deploy any required project with a few mouse clicks.

 Download and install Anaconda from

https://www.anaconda.com/download
Overview of Python Image Processing Libraries
 Some key image processing libraries in Python include:
• OpenCV: Comprehensive library with over 2500 algorithms ranging from
facial recognition to shape analysis.
• PIL/Pillow: Offers basic image handling and processing functionality.
• scikit-image: Implements algorithms for segmentation, filtering,
feature detection etc.
• Mahotas: Specialized library for computer vision operations.
• SimpleCV: Provides an easy interface to OpenCV for rapid prototyping.
 With these mature libraries, Python makes an excellent choice for
developing image processing and computer vision applications.
OpenCV
 In this article, we’ll try to open an image by using OpenCV (Open Source Computer Vision)
library. Following types of files are supported in OpenCV library:

Windows bitmaps – *.bmp, *.dib

JPEG files – *.jpeg, *.jpg
Portable Network Graphics – *.png
WebP – *.webp
Sun rasters – *.sr, *.ras
TIFF files – *.tiff, *.tif
Raster and Vector geospatial data supported by GDAL(which is a translator library for raster and
geospatial data formats)
Libraries Required for OpenCV

 To use the OpenCV library in python, we need to install these libraries as a prerequisite:
 Numpy Library : The computer processes images in the form of a matrix for which NumPy is used and
OpenCV uses it in the background.
 OpenCV python : OpenCV library previously it was cv but the updated version is cv2. It is used to
manipulate images and videos.
 Matplotlib: Matplotlib is a comprehensive library for creating static, animated, and interactive
visualizations in Python. Matplotlib makes easy things easy and hard things possible.
 Differences between OpenCV and Matplotlib:
 OpenCv uses BGR color format and matplotlib uses RGB color format.
Import Required Libraries

 To install these libraries, we need to run these pip commands in cmd:

 pip install opencv-python
 pip install numpy
 pip install matplotlib
 The steps to read and display an image in OpenCV are:
1. Read an image using imread() function.
2. Create a GUI window and display image using imshow() function.
3. Use function waitkey(0) to hold the image window on the screen by the specified number of seconds,
0 means till the user closes it, it will hold GUI window on the screen.
4. Delete image window from the memory after displaying using destroyAllWindows() function
Task: Let’s start reading an image using cv2

To read the images cv2.imread() method is used. This method loads an image from the specified file.
If the image cannot be read (because of missing file, improper permissions, unsupported or invalid
format) then this method returns an empty matrix.
 Syntax: cv2.imread(path, flag)
 Parameters:
 path: A string representing the path of the image to be read.
 flag: It specifies the way in which image should be read. It’s default value is cv2.IMREAD_COLOR
 [If set, always convert image to the 3 channel BGR color image.]
 Return Value: This method returns an image that is loaded from the specified file.
Task: Let’s start reading an image using cv2

 The image should be in the working directory or a full path of image should be given.
 By default, OpenCV stores colored images in BGR(Blue Green and Red) format.
 All three types of flags are described below:
 cv2.IMREAD_COLOR: It specifies to load a color image. Any transparency of image will be neglected. It is
the default flag. Alternatively, we can pass integer value 1 for this flag.
 cv2.IMREAD_GRAYSCALE: It specifies to load an image in grayscale mode. Alternatively, we can pass
integer value 0 for this flag.
 cv2.IMREAD_UNCHANGED: It specifies to load an image as such including alpha channel. Alternatively, we
can pass integer value -1 for this flag.
 Other flags are mentioned in the given link: Other Flag Links
 Below codes are implementations to read images and display images on the screen using OpenCV and
matplotlib libraries functions.
Python code to read image with OpenCV
import cv2
# To read image from disk, we use
# cv2.imread function, in below method,
img = cv2.imread("Flower.jpeg", cv2.IMREAD_COLOR)
# Creating GUI window to display an image on screen
# first Parameter is windows title (should be in string format)
# Second Parameter is image array
# It doesn’t return anything.
cv2.imshow("image", img)
# To hold the window on screen, we use cv2.waitKey method
# Once it detected the close input, it will release the control
# To the next line
# First Parameter is for holding screen for specified milliseconds
# It should be positive integer. If 0 pass an parameter, then it will
# hold the screen until user close it.
cv2.waitKey(0)
# It is for removing/deleting created GUI window from screen
# and memory
cv2.destroyAllWindows()
Python code to read image with Matplotlib:
from matplotlib import pyplot as plt
import cv2
im = cv2.imread("Flower.jpeg")
#to convert bgr2rgb color format
color = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
plt.imshow(color)
plt.title('Image')
plt.show()
#For showing it in window, however having some runtime error
#cv2.imshow("Flower_Image", im)
Pixel and Channel information:
# shows width, height and channels information
img.shape
Grayscale Image:
import cv2
# path
# R Means ‘Raw String’
An ‘r’ before a string tells the Python interpreter to treat
backslashes as a literal (raw) character. Normally, Python uses
backslashes as escape characters. Prefacing the string definition
with ‘r’ is a useful way to define a string where you need the
backslash to be an actual backslash and not part of an escape code
that means something else in the string.
path = r'Flower.jpeg'
# Using cv2.imread() method
# Using 0 to read image in grayscale mode
img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)

# Displaying the image

cv2.imshow('image', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
Saving image in the current directory
 cv2.imwrite() method is used to save an image to any storage device. This will
save the image according to the specified format in current working directory.

 Syntax: cv2.imwrite(filename, image)

 Parameters:
 filename: A string representing the file name. The filename must include image format like
.jpg, .png, etc.
 image: It is the image that is to be saved.
 Return Value: It returns true if image is saved successfully.