# Name: *** Enter your name here ***
#
# Student-ID: *** Enter your student ID here ***
#
# Rename the file to <yourname>03.py
#
# Submit your solutions via moodle
#
import numpy as np
import matplotlib.pylab as plt

# Please do not change the code above this line

# Exercise 1: Conversion of RGB to grayscale images
#
def exercise1():

    # close all figures
    plt.close('all')

    # Task 1A: Read the image `FluorescentCells01.jpg` and print some
    # information about it such as the *shape* and *data type* of the image, as
    # well as the *minimum* and *maximum* pixel value. 

    # *** Insert your Python code here ***
    
    # Task 1B: Convert the image to a grayscale image by averaging the pixel
    # values of the individual color layers. The data type of the grayscale
    # image should be identical to the data type of the input image. 

    # *** Insert your Python code here ***
    
    # Task 1C: To account for differences in the sensitivity of the
    # photoreceptors in our retina, a weighted sum of the R, G, and B
    # components should be formed where the weights for the color channels are
    # 0.2989, 0.5870, and 0.1140, respectively. 

    # *** Insert your Python code here ***

    # Task 1D: Show the original RGB image and both grayscale images

    # *** Insert your Python code here ***
            

# Exercise 2: Showing under- and overflow in a color image
#
def exercise2():

    # close all figures
    plt.close('all')

    # Task 2A: Read the image and show the individual layers as grayscale
    # images. 

    # *** Insert your Python code here ***
            
    # Task 2B: For each channel, show underflowing pixels in blue, overflowing
    # pixels in red, and all other pixels in gray

    # *** Insert your Python code here ***
            
    
# Exercise 3: Image histograms 
#
def exercise3():

    # close all figures
    plt.close('all')

    # Task 3A: Read the image and show image histograms for the individual
    # layers using a logarithmic scale for the counts. 

    # *** Insert your Python code here ***

    # Task 3B: Show image histograms for the individual layers excluding the
    # intensities of pixels suffering from under- or overflow

    # *** Insert your Python code here ***


# Test code - you shouldn't change this part of the template

if __name__ == '__main__':

    exercises = {'1': (exercise1, ),
                 '2': (exercise2, ),
                 '3': (exercise3, ), 
                 }

    choice = input('Please enter the number of the exercise: ').upper()
    if choice not in exercises:
        print('Please choose among {0}'.format(
            list(exercises.keys())))
    else:
        func, *args = exercises[choice]
        print('Task {0}, running "{1}"'.format(
            choice, func.__name__))
        result = func(*args)
        if result is not None:
            print('Result:', result)