1. Introduction to Computer Science – Chapter 9 CSc 2010 Spring 2011 Marco Valero

2. Overview Review image basics Making pictures Image processing Shrinking and enlarging Blurring and sharpening Negative and embossing Robot vision

3. Image basics We used takePicture and show to respectively take and show pictures already We’ve also seen savePicture as a means to save a snapshot to disk makePicture( ) will load a picture from disk and return a picture object ▫myPic = makePicture(pickAFile())

4. Image basics Height and width can be retrieved from a picture ▫getHeight( ) and getWidth( ) show(, ) ▫We can call show(myPic, ‘my title’) to create a window with a title

5. Making pictures Rather than taking pictures, we can create our own ▫width = height = 100 ▫newPic = makePicture(width, height, black) RGB ▫Each is a byte, 0-255 We can loop through each pixel just like a matrix and change the value

6. Image processing We can think of the bitmap as a matrix then any transformation from one picture to another is a matrix transformation 500x500 pixel bitmap ▫250k pixels ▫If 10 operations per transformation that’s 2.5 mil ops! Image processing is intensive

7. Shrinking & enlarging If we wanted to shrink a given n x n image by a factor of f ▫Result size is n/f x n/f ▫Bitmap[x*f, y*f] -> NewBitmap[x, y] Enlarging is the inverse ▫Result size is n*f x n*f ▫Bitmap[x/f, y/f] -> NewBitmap[x, y]

8. Blurring & sharpening Pixel transformation as a result of its local neighbors’ values Blurring is done by setting a pixels value to the averages of its neighbors ▫V = sum([getRed(up),getRed(left),…]) / 5 Sharpening is done by subtracting the sum of its neighbors ▫V = 5*getRed(self) – sum([neighborvalues])

9. Negative & embossing To create a negative of an image we simply subtract 255 from the current value ▫V = 255 – getRed(pixel) Creating an embossed effect is done by subtracting a neighbors value from a pixel ▫V = getRed(pixel) – getRed(neighbor)

10. Robot vision Are computers good at recognizing objects? Are _we_ good at recognizing objects? What would a simple tracking code look like?

11. Robot vision What if we only focused on the object? ▫We can use high contrast filter ▫What are the issues with this? Blob filtering ▫takePicture(‘blob’) ▫Compare to older program