Module earthvision.datasets.resisc45
RESISC45 Dataset.
Expand source code
"""RESISC45 Dataset."""
from PIL import Image
import os
import posixpath
import shutil
import numpy as np
import pandas as pd
from typing import Any, Callable, Optional, Tuple
from .vision import VisionDataset
from earthvision.constants.RESISC45.config import CLASS_ENC, CLASS_DEC
from earthvision.datasets.utils import _urlretrieve, _load_img
class RESISC45(VisionDataset):
"""RESISC45 Dataset.
Args:
root (string): Root directory of dataset.
transform (callable, optional): A function/transform that takes in an PIL image and
returns a transformed version. E.g, transforms.RandomCrop
target_transform (callable, optional): A function/transform that takes in the
target and transforms it.
download (bool, optional): If true, downloads the dataset from the internet and
puts it in root directory. If dataset is already downloaded, it is not
downloaded again.
"""
mirrors = "https://storage.googleapis.com/ossjr"
resources = "NWPU-RESISC45.zip"
def __init__(
self,
root: str,
transform: Optional[Callable] = None,
target_transform: Optional[Callable] = None,
download: bool = False,
) -> None:
super(RESISC45, self).__init__(root, transform=transform, target_transform=target_transform)
self.root = root
self.class_enc = CLASS_ENC
self.class_dec = CLASS_DEC
if download and self._check_exists():
print("file already exists.")
if download and not self._check_exists():
self.download()
self.extract_file()
self.img_labels = self.get_path_and_label()
def __getitem__(self, idx: int) -> Tuple[Any, Any]:
"""
Args:
idx (int): Index
Returns:
tuple: (img, target) where target is index of the target class.
"""
img_path = self.img_labels.iloc[idx, 0]
img = np.array(_load_img(img_path))
target = self.img_labels.iloc[idx, 1]
if self.transform is not None:
img = Image.fromarray(img)
img = self.transform(img)
if self.target_transform is not None:
target = Image.fromarray(target)
target = self.target_transform(target)
return img, target
def __len__(self) -> int:
return len(self.img_labels)
def get_path_and_label(self):
"""Return dataframe type consist of image path and corresponding label."""
DATA_SIZE = 700
category = os.listdir(os.path.join(self.root, "NWPU-RESISC45"))
image_path = []
label = []
for cat in category:
cat_enc = self.class_enc[cat]
label += [cat_enc] * DATA_SIZE
for num in range(1, DATA_SIZE + 1):
filename = cat + "_" + str(num).zfill(3) + ".jpg"
image_path += [os.path.join(self.root, "NWPU-RESISC45", cat, filename)]
df = pd.DataFrame({"image": image_path, "label": label})
return df
def _check_exists(self):
is_exists = os.path.exists(os.path.join(self.root, "NWPU-RESISC45"))
return is_exists
def download(self) -> None:
"""Download and extract file."""
file_url = posixpath.join(self.mirrors, self.resources)
_urlretrieve(file_url, os.path.join(self.root, self.resources))
def extract_file(self) -> None:
"""Extract file from compressed."""
shutil.unpack_archive(os.path.join(self.root, self.resources), f"{self.root}")
os.remove(os.path.join(self.root, self.resources))Classes
class RESISC45 (root: str, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, download: bool = False)-
RESISC45 Dataset.
Args
root:string- Root directory of dataset.
transform:callable, optional- A function/transform that takes in an PIL image and returns a transformed version. E.g, transforms.RandomCrop
target_transform:callable, optional- A function/transform that takes in the target and transforms it.
download:bool, optional- If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again.
Expand source code
class RESISC45(VisionDataset): """RESISC45 Dataset. Args: root (string): Root directory of dataset. transform (callable, optional): A function/transform that takes in an PIL image and returns a transformed version. E.g, transforms.RandomCrop target_transform (callable, optional): A function/transform that takes in the target and transforms it. download (bool, optional): If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again. """ mirrors = "https://storage.googleapis.com/ossjr" resources = "NWPU-RESISC45.zip" def __init__( self, root: str, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, download: bool = False, ) -> None: super(RESISC45, self).__init__(root, transform=transform, target_transform=target_transform) self.root = root self.class_enc = CLASS_ENC self.class_dec = CLASS_DEC if download and self._check_exists(): print("file already exists.") if download and not self._check_exists(): self.download() self.extract_file() self.img_labels = self.get_path_and_label() def __getitem__(self, idx: int) -> Tuple[Any, Any]: """ Args: idx (int): Index Returns: tuple: (img, target) where target is index of the target class. """ img_path = self.img_labels.iloc[idx, 0] img = np.array(_load_img(img_path)) target = self.img_labels.iloc[idx, 1] if self.transform is not None: img = Image.fromarray(img) img = self.transform(img) if self.target_transform is not None: target = Image.fromarray(target) target = self.target_transform(target) return img, target def __len__(self) -> int: return len(self.img_labels) def get_path_and_label(self): """Return dataframe type consist of image path and corresponding label.""" DATA_SIZE = 700 category = os.listdir(os.path.join(self.root, "NWPU-RESISC45")) image_path = [] label = [] for cat in category: cat_enc = self.class_enc[cat] label += [cat_enc] * DATA_SIZE for num in range(1, DATA_SIZE + 1): filename = cat + "_" + str(num).zfill(3) + ".jpg" image_path += [os.path.join(self.root, "NWPU-RESISC45", cat, filename)] df = pd.DataFrame({"image": image_path, "label": label}) return df def _check_exists(self): is_exists = os.path.exists(os.path.join(self.root, "NWPU-RESISC45")) return is_exists def download(self) -> None: """Download and extract file.""" file_url = posixpath.join(self.mirrors, self.resources) _urlretrieve(file_url, os.path.join(self.root, self.resources)) def extract_file(self) -> None: """Extract file from compressed.""" shutil.unpack_archive(os.path.join(self.root, self.resources), f"{self.root}") os.remove(os.path.join(self.root, self.resources))Ancestors
- VisionDataset
- torch.utils.data.dataset.Dataset
- typing.Generic
Class variables
var functions : Dict[str, Callable]var mirrorsvar resources
Methods
def download(self) ‑> None-
Download and extract file.
Expand source code
def download(self) -> None: """Download and extract file.""" file_url = posixpath.join(self.mirrors, self.resources) _urlretrieve(file_url, os.path.join(self.root, self.resources)) def extract_file(self) ‑> None-
Extract file from compressed.
Expand source code
def extract_file(self) -> None: """Extract file from compressed.""" shutil.unpack_archive(os.path.join(self.root, self.resources), f"{self.root}") os.remove(os.path.join(self.root, self.resources)) def get_path_and_label(self)-
Return dataframe type consist of image path and corresponding label.
Expand source code
def get_path_and_label(self): """Return dataframe type consist of image path and corresponding label.""" DATA_SIZE = 700 category = os.listdir(os.path.join(self.root, "NWPU-RESISC45")) image_path = [] label = [] for cat in category: cat_enc = self.class_enc[cat] label += [cat_enc] * DATA_SIZE for num in range(1, DATA_SIZE + 1): filename = cat + "_" + str(num).zfill(3) + ".jpg" image_path += [os.path.join(self.root, "NWPU-RESISC45", cat, filename)] df = pd.DataFrame({"image": image_path, "label": label}) return df