Computer Science 111 Fundamentals of Programming I Introduction to Digital Image Processing

Computer Science 111

Fundamentals of Programming I

Introduction to Digital Image Processing

Digital Images

• Input devices: – scanners – cameras – camcorders

• Output devices: – display screens – printers

• Processing: – file compression– various transformations

Transformations

Convert from color to grayscale

Adjust the brightness

Adjust the contrast

Adjust the size

Rotate

Morph into another image

Morph of the Day

Representing Images

• An image can be represented as a two-dimensional grid of RGB values (pixels)

• To capture an image (via camera or scanner), a continuous range of color info is sampled as a set of discrete color values

• All processing works with the grid of RGB values

• Output maps the grid to a display or a printer

The images Module

• A non-standard, open source module that includes a set of classes and methods for processing images

• Can edit scripts in IDLE, then run them from IDLE or a terminal prompt

python testimages.py

The Image ClassImage(fileName)

Image(width, height)

draw()

clone()

getWidth()

getHeight()

getPixel(x, y)

setPixel(x, y, (r, g, b))

save(<optional file name>)

Represents a grid of pixels

Methods for display, examining the dimensions, examining or resetting pixels, and saving changes to a file

A pixel is just a tuple of 3 integers

A Simple Session

Uses a terminal prompt to launch python

Import the relevant class from the module

The images library must be in the current working directory

>>> from images import Image

A Simple Session

The image file must be in the current working directory

>>> from images import Image

>>> image = Image('smokey.gif')

A Simple Session

draw must be run to display the image

>>> from images import Image

>>> image = Image('smokey.gif')

>>> image.draw()

A Simple Session

>>> from images import Image

>>> image = Image('smokey.gif')

>>> image.draw()

>>> image.getWidth(), image.getHeight()(300, 225)

The image window must be closed to continue testing

The comma creates a tuple of results

A Simple Session

> python

>>> from images import Image

>>> image = Image('smokey.gif')

>>> image.draw()

>>> image.getWidth(), image.getHeight()(300, 225)

>>> image.getPixel(0, 0)(206, 224, 122)

Transformations: Black and White

• Compute the average of the three color components in a pixel

• If the average is less than 128, then set the pixel’s three color components to 0s (black)

• Otherwise, set them to 255s (white)

blackPixel = (0, 0, 0) whitePixel = (255, 255, 255) for y in xrange(image.getHeight()): for x in xrange(image.getWidth()): (r, g, b) = image.getPixel(x, y) average = (r + g + b) / 3 if average < 128: image.setPixel(x, y, blackPixel) else: image.setPixel(x, y, whitePixel)

Transformations: Black and White

Transformations: Inversion

• Should turn black into white or white into black

• Reset each color component of a pixel to 255 minus that component’s value

for each pixel in the image: subtract each color component from 255

Transformations: Inversion

Transformations: Grayscale

• Compute the average of the three color components in a pixel

• Reset each color component of the pixel to this average value

Transformations: Grayscale

for y in xrange(image.getHeight ()) for x in xrange(image.getWidth()): (r, g, b) = image.getPixel(x, y) ave = (r + g + b) / 3 image.setPixel(x, y, (ave, ave, ave))

A Better Grayscale Algorithm

• The simple average of the RGB values does not take account of the human retina’s different sensitivities to the luminance of those values

• The human eye is more sensitive to green, then red, and finally blue

• Psychologists have determined the exact sensitivities

• Multiply each value by a weight factor and then add them up

A Better Grayscale Algorithm

red = int(red * 0.299)green = int(green * 0.587)blue = int(blue * 0.114)gray = red + green + blueimage.setPixel(x, y, (gray, gray, gray))

Old

New

Package Code in a Function

def grayScale(image): for y in xrange(image.getHeight ()): for x in xrange(image.getWidth()): (r, g, b) = image.getPixel(x, y) ave = (r + g + b) / 3 image.setPixel(x, y, (ave, ave, ave))

Note that this function does not return a new image, but modifies its argument

Until now, functions did not modify arguments

This is the most efficient way of modifying large data objects

For Wednesday

Finish the reading the handout on image processing