Today: color-logic examples

> Logic-Color Examples

Grayscale (recall)

• Grayscale
• Color is gray when red == green == blue
• e.g. 220 220 220
• e.g. 50 50 50
• e.g. 0 0 0
• Try it in rgb-explorer

(a) Convert Color To Grayscale

• Many techniques, we'll use the "average" technique
• Have pixel 50/100/150 - how to make gray?
• Compute average of red/green/blue values for a pixel
• Set red/green/blue to that number
• e.g. pixel 50/100/150
  average of these is 100
  set red/green/blue all to 100 - medium gray!
• e.g. pixel 25/50/75
  average is 50, set all to 50
  this pixel is dark gray
• Grayscale captures the light/dark of pixel, but without hue

# Convert pixel from color to grayscale
# - parenthesis required here
# - using int div //, regular / works too (float)
average = (pixel.red + pixel.green + pixel.blue) // 3
pixel.red = average
pixel.green = average
pixel.blue = average

Color If Logic

• Say we have this stop sign
• Detect areas of an image based on color, e.g. red

b. Red Detect red > 100

• First try using red value with >=
• Color detection - think about the "hurdle" in the if-test
• e.g. if pixel.red > 100:
• Starts with hurdle value of 100
• Adjust value to get best results
• Q: To make this more selective, make 100 bigger or smaller?
• A: bigger. Later with < need to think for a second to get the direction correct
• Does not work that great:
  Bright areas and red areas both have big red numbers
• redish pixel: 220, 50, 50
• white pixel: 220, 220, 220
• Best can do here is maybe hurdle of 160

c. Red Detect - Average

• Improvement: compare red value to average of red/green/blue values
• "redish pixel"
  the red value is above the average of this pixel
• "blueish pixel"
  the blue value is above the average of this pxiel
• red >= average * 1.0
• Adjust the 1.0 "hurdle" factor by eye - 1.2 looks good
• The average technique works great to select the red stop sign pixels
• Experiment: for detected pixels, reduce only red and green, leaving blue unchanged
  result is blue stop sign
• Experiment: for detected pixels, swap red and green values

d. Curb Repair 1

• Detect the red curb - adjust factor
• Set it to flat gray
• Does not look great
• How to make this look good?

e. Curb Repair 2

• Change the detected curb pixels to grayscale - looks great!
• Use the average value instead of the fixed 180 value - !!!
• Meaning of this code
  Drain the color, but keep the brightness

Bluescreen

• Demo Google search: blue screen movie image
• Also known as Chroma Key (wikipedia)
• Video is just a series of still images, 20-60 per second
• Have image with special color
• Have back background image
• 1. Detect colored area in an image, e.g. the blue area
• 2. Replace colored pixels with pixels from back image

Bluescreen Algorithm Outline

• Two images we'll call image and back
• Detect, say, red pixels in image
• For each red pixel (make a drawing)
  Consider the pixel at the same x,y in back image
  Copy that pixel from back to image
  i.e. copy RGB numbers from back pixel to image pixel
• Result: for red areas, copy over areas from back image
• Adjacent pixels in back are still adjacent in new image, so it looks right

Here is the output we are going for with this bluescreen algorithm:

Before:

After:

Diagram:

Bluescreen Stop Sign

> Bluescreen Stop Sign

Solution code

def stop_leaves(front_filename, back_filename):
    """Implement stop_leaves as above."""
    image = SimpleImage(front_filename)
    back = SimpleImage(back_filename)
    for y in range(image.height):
        for x in range(image.width):
            pixel = image.get_pixel(x, y)
            average = (pixel.red + pixel.green + pixel.blue) // 3
            if pixel.red >= average * 1.6:
                # the key line:
                back_pixel = back.get_pixel(pixel.x, pixel.y)
                pixel.red = back_pixel.red
                pixel.green = back_pixel.green
                pixel.blue = back_pixel.blue
    return image

Bluescreen Monkey

If Sufficient time, in-class exercise/demo. Observe: the bluescreen algorithm depends on the colors in the main image. BUT it does not depend on the colors of the back image - the back image can have any colors it in. Try the different background-cases for the monkey.

> Bluescreen Monkey