from drawtool import DrawTool
import numpy as np

dt = DrawTool()
dt.set_XY_range(0,10, 0,10)
dt.set_aspect('equal')
dt.set_axes_off()

def is_approx_grey(r, g, b):
    d = 30
    # Copy over your code for this function from neuro1.py

def nongrey_to_blue(pixels):
    pixels2 = np.copy(pixels)

    # Copy over your code for this function from neuro1.py

    return pixels2


def to_grey(pixels, i, j):
    # We've written this for you. Notice how a potential float
    # is converted into an integer.
    return int ( (pixels[i,j,0] + pixels[i,j,1] + pixels[i,j,2])/3 )

def count_blue(pixels):
    # Write code to count the number of pure-blue pixels, that is,
    # pixels for which r=0, g=0, b=255.

    return num_blue

def make_common(pixels1, pixels2, pixels3):
    # m is the minimum amongst # rows in each of the three images.
    # n is ithe minimum # columns amongst the three
    m = min(pixels1.shape[0], pixels2.shape[0], pixels3.shape[0])
    n = min(pixels1.shape[1], pixels2.shape[1], pixels3.shape[1])

    # We'll make a pixel array that's the minimal size
    pixels4 = dt.make_pixel_array(m, n)

    # For each i and j in pixels4, if at least 2 of the three images
    # have a blue pixel in the i,j location, set pixels4[i,j] to blue.
    # Otherwise, set it to a grey value that's the average of the
    # grey values of the three pixels at that location. For this
    # purpose, convert each r,g,b to grey and use that. Remember
    # to convert to an integer. 

    return pixels4
    
def contrast(pixels1, pixels2):
    m = min(pixels1.shape[0], pixels2.shape[0])
    n = min(pixels1.shape[1], pixels2.shape[1])
    pixels3 = dt.make_pixel_array(m, n)

    # For every i,j pixel in pixels3:
    #   if pixels1[i,j] and pixels2[i,j] are both blue
    #      make pixels3[i,j] blue
    #   Otherwise, if pixels1[i,j] is blue but pixels2[i,j] is not, then
    #      set pixels3[i,j] to GREEN
    #   Otherwise, if pixels2[i,j] is blue but pixels1[i,j] is not, then
    #      set pixels3[i,j] to RED
    #   If neither are blue, set pixels3[i,j] to the average of the
    #   corresponding R,G,B values of pixels1[i,j] and pixels2[i,j]

    return pixels3


pixels1 = dt.read_imagefile('task1-subject1-sagittal.jpg')
pixels1 = nongrey_to_blue(pixels1)
pixels2 = dt.read_imagefile('task1-subject2-sagittal.jpg')
pixels2 = nongrey_to_blue(pixels2)
pixels3 = dt.read_imagefile('task1-subject3-sagittal.jpg')
pixels3 = nongrey_to_blue(pixels3)

# pixels_task1 will have the subject-averaged task1 image:
pixels_task1 = make_common(pixels1, pixels2, pixels3)
num_blue = count_blue(pixels_task1)
print('Common volume for task 1 =', num_blue)
# Should print 985 (approximately)
# Note: on some Windows versions you get 993

pixels1 = dt.read_imagefile('task2-subject1-sagittal.jpg')
pixels1 = nongrey_to_blue(pixels1)
pixels2 = dt.read_imagefile('task2-subject2-sagittal.jpg')
pixels2 = nongrey_to_blue(pixels2)
pixels3 = dt.read_imagefile('task2-subject3-sagittal.jpg')
pixels3 = nongrey_to_blue(pixels3)

# pixels_task2 will have the subject-averaged task2 image:
pixels_task2 = make_common(pixels1, pixels2, pixels3)
num_blue = count_blue(pixels_task2)
print('Common volume for task 2 =', num_blue)
# Should print 705 (approximately)

# The contrast() function should compute the contrast image:
pixels_contrast = contrast(pixels_task1, pixels_task2)
num_blue = count_blue(pixels_contrast)
print('Common volume for tasks 1 and 2 =', num_blue)
# Should print 121 (approximately)

dt.draw_image(pixels_contrast)

# NOTE: you can use dt.draw_image() to draw any of the earlier
# images as you put together your module pdf.

dt.display()