import os
import random
import time

import cv2
import numpy
import skimage
import torch
from sklearn.metrics import (  # recall = sensitivity, precision = PPV
    accuracy_score,
    f1_score,
    jaccard_score,
    precision_score,
    recall_score,
    roc_auc_score,
)

""" Seeding the randomness. """


def seeding(seed):
    random.seed(seed)
    os.environ["PYTHONHASHSEED"] = str(seed)
    numpy.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.backends.cudnn.deterministic = True


""" Create a directory. """


def create_dir(path):
    if not os.path.exists(path):
        os.makedirs(path)
    else:
        pass


""" Calculate the time taken """


def epoch_time(start_time, end_time):
    elapsed_time = end_time - start_time
    elapsed_mins = int(elapsed_time / 60)
    elapsed_secs = int(elapsed_time - (elapsed_mins * 60))
    return elapsed_mins, elapsed_secs


"""METRICS"""


def metricsCalculator(y_true, y_pred):
    score_jaccard = jaccard_score(y_true, y_pred, pos_label=255)
    score_f1 = f1_score(y_true, y_pred, pos_label=255)
    score_recall = recall_score(y_true, y_pred, pos_label=255)
    score_precision = precision_score(y_true, y_pred, pos_label=255)
    score_acc = accuracy_score(y_true, y_pred)
    score_auc = roc_auc_score(y_true, y_pred)

    scores = [
        score_jaccard,
        score_f1,
        score_recall,
        score_precision,
        score_acc,
        score_auc,
    ]

    print(
        f"\t=>Jaccard: {score_jaccard:1.4f} - F1: {score_f1:1.4f} - Recall: {score_recall:1.4f} - Precision: {score_precision:1.4f} - Acc: {score_acc:1.4f} - AUC: {score_auc:1.4f}\n"
    )

    return scores


def skelEndpoints(maskArray):
    skel = skimage.morphology.skeletonize(maskArray.astype("bool"))
    skel = numpy.uint8(skel > 0)

    # Apply the convolution.
    kernel = numpy.uint8([[1, 1, 1], [1, 10, 1], [1, 1, 1]])
    src_depth = -1
    filtered = cv2.filter2D(skel, src_depth, kernel)

    # Look through to find the value of 11.
    # This returns a mask of the endpoints, but if you
    # just want the coordinates, you could simply
    # return numpy.where(filtered==11)
    out = numpy.zeros_like(skel)
    out[numpy.where(filtered == 11)] = 1
    # endCoords = numpy.where(filtered==11)
    # endCoords = list(zip(*endCoords))
    # startPoint = endCoords[0]
    # endPoint = endCoords[1]

    # print(f"Skel starts at {startPoint} and finishes at {endPoint}")

    # print(sum(out))

    out = out.astype("uint8") * 255

    return out


def crudeMaskGenerator(maskArray):
    skel = skimage.morphology.skeletonize(maskArray.astype("bool"))
    skel = numpy.uint8(skel > 0)
    radius = 15

    crudeMask = numpy.zeros_like(skel)
    skelPoints = numpy.argwhere(skel > 0)

    # Create a circular mask to dilate the skel
    y, x = numpy.ogrid[-radius : radius + 1, -radius : radius + 1]

    circleMask = x**2 + y**2 <= radius**2

    for i, point in enumerate(skelPoints[:-1]):
        yPos = point[0]
        xPos = point[1]

        if yPos < skel.shape[0] - radius and xPos < skel.shape[1] - radius:
            if yPos > radius and xPos > radius:
                crudeMask[
                    int(yPos - radius) : int(yPos + radius + 1),
                    int(xPos - radius) : int(xPos + radius + 1),
                ] += circleMask

    crudeMask = crudeMask > 0

    return crudeMask.astype("uint8") * 255