# Basic Image Operations using Python language (Anaconda) Thank you

Project 1: Basic Image Operations

Write a program to read in an image, apply several image processing operations to it, display and save the results to new image files. Note the extra requirements for graduate students. You may take advantage of pre-existing library functions that implement the algorithms discussed (please note this in program comments); otherwise you must implement the algorithms yourself.

Python, MATLAB, or C++ programs are acceptable; If you would like to use a different language, please get prior approval from the instructor (non-runnable programs will receive a 0)

You are to submit your program source code files, your source images (not the processed images), and if necessary, a brief set of instructions to run your program (for example, if they require a certain add-on, etc.). You may package these together in a .zip folder if convenient.

Program Requirements

Read in an image of your choice (Keep it appropriate, and not too large)

Display the image to the screen, and print its pixel dimensions

If your image is in color, convert it to gray-scale

Perform the following operations, displaying and saving the results to labeled image files (You may use any method you wish to handle off-edge pixels)

Invert the gray-scale image

Translate the gray-scale image one pixel down and one pixel to the right

Stretch/shrink the gray-scale image contrast to an intensity range of 0-255

Threshold the contrast adjusted image: you may experiment to find an interesting value and result

shrink the thresholded region of the binary image

expand the thresholded region of the binary image

Detect edges of the the thresholded region in binary image

Apply Salt & Pepper noise reduction in the binary image

Apply a 3x3 averaging filter to the contrast adjusted image

Compare the results of the binary image edge detection algorithm with edge detection using only thresholding and shrinking/expanding

Compare results of shrinking, expanding, and averaging when you use either 1, 0, or nearest pixel value for the off-image pixels.

Apply the less strict noise reduction algorithm discussed in class (and the textbook) to the binary image.

Demonstrate the result of applying salt and pepper, and less strict noise reduction algorithm to the binary image sequentially (make a copy first).

Skills: Python

( 5 reviews ) Dammam, Saudi Arabia

Project ID: #27317782

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