"""
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
import enum

class ImageType(enum.Enum):
    uint8 = 0
    float64 = 1


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, type: ImageType) -> npt.NDArray[np.float64] or npt.NDArray[np.uint8]:
    """
    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.

    if type == ImageType.float64:
        I = I.astype(np.float64) / 255
        return I
    elif type == ImageType.uint8:
        return I

    raise Exception("Wrong image format picked!")


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

def convert_float64_array_to_uint8_array(a: npt.NDArray[np.float64]) -> npt.NDArray[np.uint8]:
    return a.astype(np.uint8)