Post on 04-Jan-2016
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