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 Class Image(fileName) Image(width, height) draw() clone() getWidth() getHeight() getPixel(x, y) setPixel(x, y, (r, g, b)) save( ) 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 + blue image.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