import numpy as np
import numpy.typing as npt
from matplotlib import pyplot as plt
import random
import cv2
import uz_framework.image as uz_image

#################################################################
# EXCERCISE 1: Exercise 1: Global approach to image description #
#################################################################
def ex1():
    one_a()
    one_b()
    one_c()

def one_a() -> npt.NDArray[np.float64]:
    """
     Firstly, you will implement the function myhist3 that computes a 3-D histogram
    from a three channel image. The images you will use are RGB, but the function
    should also work on other color spaces. The resulting histogram is stored in a 3-D
    matrix. The size of the resulting histogram is determined by the parameter n_bins.
    The bin range calculation is exactly the same as in the previous assignment, except
    now you will get one index for each image channel. Iterate through the image pixels
    and increment the appropriate histogram cells. You can create an empty 3-D numpy
    array with H = np.zeros((n_bins,n_bins,n_bins)). Take care that you normalize
    the resulting histogram.
    """
    image = uz_image.imread('./data/dataset/object_01_1.png', uz_image.ImageType.float64)
    bins = uz_image.get_image_bins_ND(image, 20)
    return bins 

def one_b() -> None:
    """
     In order to perform image comparison using histograms, we need to implement
     some distance measures. These are defined for two input histograms and return a
     single scalar value that represents the similarity (or distance) between the two histograms.
     Implement a function compare_histograms that accepts two histograms
     and a string that identifies the distance measure you wish to calculate
     Implement L2 metric, chi-square distance, intersection and Hellinger distance.
     Function implemented in uz_framework
    """
    return None

def one_c() -> None:
    """
    Test your function
    Compute a 8×8×8-bin 3-D histogram for each image. Reshape each of them into a
    1-D array. Using plt.subplot(), display all three images in the same window as well
    as their corresponding histograms. Compute the L2 distance between histograms of
    object 1 and 2 as well as L2 distance between histograms of objects 1 and 3.

    Question: Which image (object_02_1.png or object_03_1.png) is more similar
    to image object_01_1.png considering the L2 distance? How about the other three
    distances? We can see that all three histograms contain a strongly expressed component (one bin has a much higher value than the others). Which color does this
    bin represent
    Answer:
    """
    IM1 = uz_image.imread('./data/dataset/object_01_1.png', uz_image.ImageType.float64)
    IM2 = uz_image.imread('./data/dataset/object_02_1.png', uz_image.ImageType.float64)
    IM3 = uz_image.imread('./data/dataset/object_03_1.png', uz_image.ImageType.float64)
    N_BINS = 8

    H1 = uz_image.get_image_bins_ND(IM1, N_BINS).reshape(-1)
    H2 = uz_image.get_image_bins_ND(IM2, N_BINS).reshape(-1)
    H3 = uz_image.get_image_bins_ND(IM3, N_BINS).reshape(-1)
    
    fig, axs = plt.subplots(2,3)
    fig.suptitle('Euclidian distance between three images')

    axs[0, 0].imshow(IM1)
    axs[0, 0].set(title='Image1')
    axs[0, 1].imshow(IM2)
    axs[0, 1].set(title='Image2')
    axs[0, 2].imshow(IM3)
    axs[0, 2].set(title='Image3')

    axs[1, 0].bar(np.arange(N_BINS**3), H1, width=3)
    axs[1, 0].set(title=f'L_2(h1, h1) = {np.round(uz_image.compare_two_histograms(H1, H1, uz_image.DistanceMeasure.euclidian_distance), 2)}')
    axs[1, 1].bar(np.arange(N_BINS**3), H2, width=3)
    axs[1, 1].set(title=f'L_2(h1, h2) = {np.round(uz_image.compare_two_histograms(H1, H2, uz_image.DistanceMeasure.euclidian_distance), 2)}')
    axs[1, 2].bar(np.arange(N_BINS**3), H3, width=3)
    axs[1, 2].set(title=f'L_2(h1, h3) = {np.round(uz_image.compare_two_histograms(H1, H3, uz_image.DistanceMeasure.euclidian_distance), 2)}')

    plt.show()

    
# ######## #
# SOLUTION #
# ######## #

def main():
    ex1()

if __name__ == '__main__':
    main()