utils.py

import os
import random
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import cv2
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
from tensorflow.keras import backend as K

plt.style.use("ggplot")

sns.set_theme(style="ticks")


def plot_from_img_path(rows, columns, list_img_path, list_mask_path):
    fig = plt.figure(figsize=(12, 12))
    for i in range(1, rows * columns + 1):
        fig.add_subplot(rows, columns, i)
        img_path = list_img_path[i]
        mask_path = list_mask_path[i]

        image = cv2.imread(img_path)
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        mask = cv2.imread(mask_path)
        mask = cv2.cvtColor(mask, cv2.COLOR_BGR2RGB)
        plt.imshow(image)
        plt.imshow(mask, alpha=0.4)
    plt.show()


def dice_coefficients(y_true, y_pred, smooth=100):
    y_true_flatten = K.flatten(y_true)
    y_pred_flatten = K.flatten(y_pred)

    intersection = K.sum(y_true_flatten * y_pred_flatten)
    union = K.sum(y_true_flatten) + K.sum(y_pred_flatten)
    return (2 * intersection + smooth) / (union + smooth)


def dice_coefficient_loss(y_true, y_pred, smooth=100):
    return -(dice_coefficients(y_true, y_pred, smooth))


def iou(y_true, y_pred, smooth=100):
    intersection = K.sum(y_true * y_pred)
    sum = K.sum(y_true * y_pred)
    iou = (intersection + smooth) / (sum - intersection + smooth)
    return iou


def jaccard_distance(y_true, y_pred, smooth=100):
    y_true_flatten = K.flatten(y_true)
    y_pred_flatten = K.flatten(y_pred)
    return 1 - iou(y_true_flatten, y_pred_flatten, smooth)