"""
Before the first run, you need to have all necessary Python packages installed. For
that we highly recommend firstly creating Virtual Environment, to have your
development environment seperated from other projects (https://docs.python.org/3/tutorial/venv.html).

In system terminal then run: "pip install numpy opencv-python matplotlib Pillow"
"""

import numpy as np
import cv2 as cv2
from matplotlib import pyplot as plt
from PIL import Image
from typing import Union
import numpy.typing as npt


def imread(path: str) -> npt.NDArray[np.float64]:
    """
    Reads an image in RGB order. Image type is transformed from uint8 to float, and
    range of values is reduced from [0, 255] to [0, 1].
    """
    I = Image.open(path).convert('RGB')  # PIL image.
    I = np.asarray(I)  # Converting to Numpy array.
    I = I.astype(np.float64) / 255
    return I


def imread_gray(path: str) -> npt.NDArray[np.float64]:
    """
    Reads an image in gray. Image type is transformed from uint8 to float, and
    range of values is reduced from [0, 255] to [0, 1].
    """
    I = Image.open(path).convert('L')  # PIL image opening and converting to gray.
    I = np.asarray(I)  # Converting to Numpy array.
    I = I.astype(np.float64) / 255
    return I


def imshow(img: Union[npt.NDArray[np.float64], npt.NDArray[np.uint8],], title=None) -> None:
    """
    Shows an image. Image can be of types:
    - type uint8, in range [0, 255]
    - type float, in range [0, 1]
    """

    if len(img.shape) == 3:
        plt.imshow(img)  # if type of data is "float", then values have to be in [0, 1]
    else:
        plt.imshow(img)
        plt.set_cmap('gray')  # also "hot", "nipy_spectral"
        plt.colorbar()
    if title is not None:
        plt.title(title)

    plt.show()


def signal_show(*signals):
    """
    Plots all given 1D signals in the same plot.
    Signals can be Python lists or 1D numpy array.
    """
    for s in signals:
        if type(s) == np.ndarray:
            s = s.squeeze()
        plt.plot(s)
    plt.show()


def convolve(I: np.ndarray, *ks):
    """
    Convolves input image I with all given kernels.

    :param I: Image, should be of type float64 and scaled from 0 to 1.
    :param ks: 2D Kernels
    :return: Image convolved with all kernels.
    """
    for k in ks:
        k = np.flip(k)  # filter2D performs correlation, so flipping is necessary
        I = cv2.filter2D(I, cv2.CV_64F, k)
    return I


if __name__ == '__main__':  # False if this file is imported to another file and executed from there.

    # For this to work you need to put an image "image.jpg" to where you run the script.

    # Read an image in rgb (check the code in "UZ_utils" for details).
    I = imread('image.jpg')  # see the definition above
    imshow(I, 'rgb')
    print(I.shape, I.dtype, np.max(I))

    # gray
    I = imread_gray('image.jpg')  # see the definition above
    imshow(I, 'gray')
    print(I.shape, I.dtype, np.max(I))

    # Image filtering.
    I = np.zeros((20, 20), dtype=float)
    I[5, 5] = 1
    k = np.array([[1, 2, 3, 4, 5]], dtype=float)
    I = convolve(I, k, k.T)  # see the definition above
    imshow(I)

    # Plotting 1D signal.
    x = np.arange(0, 10, .1)  # From 0 to 10 with 0.1 step.
    y = np.sin(x)
    signal_show(y, 2 * y)  # see the definition above