Delete natural_disaster_dataset.py

natural_disaster_dataset.py

@@ -1,107 +0,0 @@
-from PIL import Image
-import os
-from torch.utils.data import DataLoader, Dataset
-import torch
-from skimage import transform
-import matplotlib.pyplot as plt
-import numpy as np
-import torchvision.transforms as transforms
-import torchvision.transforms.functional as TF
-import streamlit as st
-
-class NaturalDisasterDataset(Dataset):
-    """
-    A custom PyTorch Dataset that contains images of several types of natural disasters, 
-    including earthquakes, fires, and floods.
-    """
-    def __init__(self, root:str, transform:any=None) -> None:
-        """
-        Creates a custom PyTorch dataset of natural disasters.
-
-        Args:
-            root (str): A path containing the images.
-            transform (any): A type of transformation from the scikit-image library.
-
-        Returns:
-            None
-        """
-        self.root = root
-        self.transform = transform
-
-        self.image_paths = []
-        self.labels = []
-
-        for label in os.listdir(root):
-            folder = os.path.join(root, label)
-            for file in os.listdir(folder):
-                self.image_paths.append(os.path.join(folder, file))
-                self.labels.append(label)
-
-    def __len__(self) -> int:
-        """
-        Returns the length/size of the dataset.
-
-        Args:
-            None
-
-        Returns:
-            length (int): The length of the dataset.
-        """
-        return len(self.image_paths)
-
-    def __getitem__(self, idx:int) -> dict:
-        """
-        Iterates through the dataset and returns a sample image.
-
-        Args:
-            idx (int): An index to the dataset.
-
-        Returns:
-            sample (dict): A dictionary containing the image and its label.
-        """
-        img_path = self.image_paths[idx]
-        label = self.labels[idx]
-        image = Image.open(img_path).convert("RGB")
-
-        if self.transform:
-            image = self.transform(image) 
-
-        image = transforms.PILToTensor()(image)
-        sample = {"image": image, "category": label}
-        return sample
-
-    
-    def load_sample(self) -> None:
-        """
-        Displays four sample images, one of each type of disaster.
-
-        Args:
-            None
-
-        Returns:
-            None
-        """
-
-        categories_needed = {"Normal", "Earthquake", "Fire", "Flood"}
-        shown = {}
-
-        fig = plt.figure(figsize=(10, 3))
-
-        for sample in self:
-            category = sample["category"]
-
-            # If we still need this category
-            if category in categories_needed and category not in shown:
-                shown[category] = sample["image"]
-
-            # Stop if we have all 4 categories
-            if len(shown) == len(categories_needed):
-                break
-
-        for i, (category, image) in enumerate(shown.items()):
-            ax = plt.subplot(1, 4, i + 1)
-            ax.imshow(image)
-            ax.set_title(category)
-            ax.axis("off")
-
-        plt.tight_layout()