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folder.py
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from PIL import Image
import paddle
from paddle.io import Dataset
from paddle.utils import try_import
__all__ = []
def has_valid_extension(filename, extensions):
"""Checks if a file is a valid extension.
Args:
filename (str): path to a file
extensions (list[str]|tuple[str]): extensions to consider
Returns:
bool: True if the filename ends with one of given extensions
"""
assert isinstance(
extensions, (list, tuple)
), "`extensions` must be list or tuple."
extensions = tuple([x.lower() for x in extensions])
return filename.lower().endswith(extensions)
def make_dataset(dir, class_to_idx, extensions, is_valid_file=None):
images = []
dir = os.path.expanduser(dir)
if extensions is not None:
def is_valid_file(x):
return has_valid_extension(x, extensions)
for target in sorted(class_to_idx.keys()):
d = os.path.join(dir, target)
if not os.path.isdir(d):
continue
for root, _, fnames in sorted(os.walk(d, followlinks=True)):
for fname in sorted(fnames):
path = os.path.join(root, fname)
if is_valid_file(path):
item = (path, class_to_idx[target])
images.append(item)
return images
class DatasetFolder(Dataset):
"""A generic data loader where the samples are arranged in this way:
.. code-block:: text
root/class_a/1.ext
root/class_a/2.ext
root/class_a/3.ext
root/class_b/123.ext
root/class_b/456.ext
root/class_b/789.ext
Args:
root (str): Root directory path.
loader (Callable, optional): A function to load a sample given its path. Default: None.
extensions (list[str]|tuple[str], optional): A list of allowed extensions.
Both :attr:`extensions` and :attr:`is_valid_file` should not be passed.
If this value is not set, the default is to use ('.jpg', '.jpeg', '.png',
'.ppm', '.bmp', '.pgm', '.tif', '.tiff', '.webp'). Default: None.
transform (Callable, optional): A function/transform that takes in
a sample and returns a transformed version. Default: None.
is_valid_file (Callable, optional): A function that takes path of a file
and check if the file is a valid file. Both :attr:`extensions` and
:attr:`is_valid_file` should not be passed. Default: None.
Returns:
:ref:`api_paddle_io_Dataset`. An instance of DatasetFolder.
Attributes:
classes (list[str]): List of the class names.
class_to_idx (dict[str, int]): Dict with items (class_name, class_index).
samples (list[tuple[str, int]]): List of (sample_path, class_index) tuples.
targets (list[int]): The class_index value for each image in the dataset.
Example:
.. code-block:: python
import shutil
import tempfile
import cv2
import numpy as np
import paddle.vision.transforms as T
from pathlib import Path
from paddle.vision.datasets import DatasetFolder
def make_fake_file(img_path: str):
if img_path.endswith((".jpg", ".png", ".jpeg")):
fake_img = np.random.randint(0, 256, (32, 32, 3), dtype=np.uint8)
cv2.imwrite(img_path, fake_img)
elif img_path.endswith(".txt"):
with open(img_path, "w") as f:
f.write("This is a fake file.")
def make_directory(root, directory_hierarchy, file_maker=make_fake_file):
root = Path(root)
root.mkdir(parents=True, exist_ok=True)
for subpath in directory_hierarchy:
if isinstance(subpath, str):
filepath = root / subpath
file_maker(str(filepath))
else:
dirname = list(subpath.keys())[0]
make_directory(root / dirname, subpath[dirname])
directory_hirerarchy = [
{"class_0": [
"abc.jpg",
"def.png"]},
{"class_1": [
"ghi.jpeg",
"jkl.png",
{"mno": [
"pqr.jpeg",
"stu.jpg"]}]},
"this_will_be_ignored.txt",
]
# You can replace this with any directory to explore the structure
# of generated data. e.g. fake_data_dir = "./temp_dir"
fake_data_dir = tempfile.mkdtemp()
make_directory(fake_data_dir, directory_hirerarchy)
data_folder_1 = DatasetFolder(fake_data_dir)
print(data_folder_1.classes)
# ['class_0', 'class_1']
print(data_folder_1.class_to_idx)
# {'class_0': 0, 'class_1': 1}
print(data_folder_1.samples)
# [('./temp_dir/class_0/abc.jpg', 0), ('./temp_dir/class_0/def.png', 0),
# ('./temp_dir/class_1/ghi.jpeg', 1), ('./temp_dir/class_1/jkl.png', 1),
# ('./temp_dir/class_1/mno/pqr.jpeg', 1), ('./temp_dir/class_1/mno/stu.jpg', 1)]
print(data_folder_1.targets)
# [0, 0, 1, 1, 1, 1]
print(len(data_folder_1))
# 6
for i in range(len(data_folder_1)):
img, label = data_folder_1[i]
# do something with img and label
print(type(img), img.size, label)
# <class 'PIL.Image.Image'> (32, 32) 0
transform = T.Compose(
[
T.Resize(64),
T.ToTensor(),
T.Normalize(
mean=[0.5, 0.5, 0.5],
std=[0.5, 0.5, 0.5],
to_rgb=True,
),
]
)
data_folder_2 = DatasetFolder(
fake_data_dir,
loader=lambda x: cv2.imread(x), # load image with OpenCV
extensions=(".jpg",), # only load *.jpg files
transform=transform, # apply transform to every image
)
print([img_path for img_path, label in data_folder_2.samples])
# ['./temp_dir/class_0/abc.jpg', './temp_dir/class_1/mno/stu.jpg']
print(len(data_folder_2))
# 2
for img, label in iter(data_folder_2):
# do something with img and label
print(type(img), img.shape, label)
# <class 'paddle.Tensor'> [3, 64, 64] 0
shutil.rmtree(fake_data_dir)
"""
def __init__(
self,
root,
loader=None,
extensions=None,
transform=None,
is_valid_file=None,
):
self.root = root
self.transform = transform
if extensions is None:
extensions = IMG_EXTENSIONS
classes, class_to_idx = self._find_classes(self.root)
samples = make_dataset(
self.root, class_to_idx, extensions, is_valid_file
)
if len(samples) == 0:
raise (
RuntimeError(
"Found 0 directories in subfolders of: " + self.root + "\n"
"Supported extensions are: " + ",".join(extensions)
)
)
self.loader = default_loader if loader is None else loader
self.extensions = extensions
self.classes = classes
self.class_to_idx = class_to_idx
self.samples = samples
self.targets = [s[1] for s in samples]
self.dtype = paddle.get_default_dtype()
def _find_classes(self, dir):
"""
Finds the class folders in a dataset.
Args:
dir (string): Root directory path.
Returns:
tuple: (classes, class_to_idx) where classes are relative to (dir),
and class_to_idx is a dictionary.
"""
classes = [d.name for d in os.scandir(dir) if d.is_dir()]
classes.sort()
class_to_idx = {classes[i]: i for i in range(len(classes))}
return classes, class_to_idx
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (sample, target) where target is class_index of the target class.
"""
path, target = self.samples[index]
sample = self.loader(path)
if self.transform is not None:
sample = self.transform(sample)
return sample, target
def __len__(self):
return len(self.samples)
IMG_EXTENSIONS = (
'.jpg',
'.jpeg',
'.png',
'.ppm',
'.bmp',
'.pgm',
'.tif',
'.tiff',
'.webp',
)
def pil_loader(path):
with open(path, 'rb') as f:
img = Image.open(f)
return img.convert('RGB')
def cv2_loader(path):
cv2 = try_import('cv2')
return cv2.cvtColor(cv2.imread(path), cv2.COLOR_BGR2RGB)
def default_loader(path):
from paddle.vision import get_image_backend
if get_image_backend() == 'cv2':
return cv2_loader(path)
else:
return pil_loader(path)
class ImageFolder(Dataset):
"""A generic data loader where the samples are arranged in this way:
.. code-block:: text
root/1.ext
root/2.ext
root/sub_dir/3.ext
Args:
root (str): Root directory path.
loader (Callable, optional): A function to load a sample given its path. Default: None.
extensions (list[str]|tuple[str], optional): A list of allowed extensions.
Both :attr:`extensions` and :attr:`is_valid_file` should not be passed.
If this value is not set, the default is to use ('.jpg', '.jpeg', '.png',
'.ppm', '.bmp', '.pgm', '.tif', '.tiff', '.webp'). Default: None.
transform (Callable, optional): A function/transform that takes in
a sample and returns a transformed version. Default: None.
is_valid_file (Callable, optional): A function that takes path of a file
and check if the file is a valid file. Both :attr:`extensions` and
:attr:`is_valid_file` should not be passed. Default: None.
Returns:
:ref:`api_paddle_io_Dataset`. An instance of ImageFolder.
Attributes:
samples (list[str]): List of sample path.
Example:
.. code-block:: python
import shutil
import tempfile
import cv2
import numpy as np
import paddle.vision.transforms as T
from pathlib import Path
from paddle.vision.datasets import ImageFolder
def make_fake_file(img_path: str):
if img_path.endswith((".jpg", ".png", ".jpeg")):
fake_img = np.random.randint(0, 256, (32, 32, 3), dtype=np.uint8)
cv2.imwrite(img_path, fake_img)
elif img_path.endswith(".txt"):
with open(img_path, "w") as f:
f.write("This is a fake file.")
def make_directory(root, directory_hierarchy, file_maker=make_fake_file):
root = Path(root)
root.mkdir(parents=True, exist_ok=True)
for subpath in directory_hierarchy:
if isinstance(subpath, str):
filepath = root / subpath
file_maker(str(filepath))
else:
dirname = list(subpath.keys())[0]
make_directory(root / dirname, subpath[dirname])
directory_hierarchy = [
"abc.jpg",
"def.png",
{"ghi": [
"jkl.jpeg",
{"mno": [
"pqr.jpg"]}]},
"this_will_be_ignored.txt",
]
# You can replace this with any directory to explore the structure
# of generated data. e.g. fake_data_dir = "./temp_dir"
fake_data_dir = tempfile.mkdtemp()
make_directory(fake_data_dir, directory_hierarchy)
image_folder_1 = ImageFolder(fake_data_dir)
print(image_folder_1.samples)
# ['./temp_dir/abc.jpg', './temp_dir/def.png',
# './temp_dir/ghi/jkl.jpeg', './temp_dir/ghi/mno/pqr.jpg']
print(len(image_folder_1))
# 4
for i in range(len(image_folder_1)):
(img,) = image_folder_1[i]
# do something with img
print(type(img), img.size)
# <class 'PIL.Image.Image'> (32, 32)
transform = T.Compose(
[
T.Resize(64),
T.ToTensor(),
T.Normalize(
mean=[0.5, 0.5, 0.5],
std=[0.5, 0.5, 0.5],
to_rgb=True,
),
]
)
image_folder_2 = ImageFolder(
fake_data_dir,
loader=lambda x: cv2.imread(x), # load image with OpenCV
extensions=(".jpg",), # only load *.jpg files
transform=transform, # apply transform to every image
)
print(image_folder_2.samples)
# ['./temp_dir/abc.jpg', './temp_dir/ghi/mno/pqr.jpg']
print(len(image_folder_2))
# 2
for (img,) in iter(image_folder_2):
# do something with img
print(type(img), img.shape)
# <class 'paddle.Tensor'> [3, 64, 64]
shutil.rmtree(fake_data_dir)
"""
def __init__(
self,
root,
loader=None,
extensions=None,
transform=None,
is_valid_file=None,
):
self.root = root
if extensions is None:
extensions = IMG_EXTENSIONS
samples = []
path = os.path.expanduser(root)
if extensions is not None:
def is_valid_file(x):
return has_valid_extension(x, extensions)
for root, _, fnames in sorted(os.walk(path, followlinks=True)):
for fname in sorted(fnames):
f = os.path.join(root, fname)
if is_valid_file(f):
samples.append(f)
if len(samples) == 0:
raise (
RuntimeError(
"Found 0 files in subfolders of: " + self.root + "\n"
"Supported extensions are: " + ",".join(extensions)
)
)
self.loader = default_loader if loader is None else loader
self.extensions = extensions
self.samples = samples
self.transform = transform
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
sample of specific index.
"""
path = self.samples[index]
sample = self.loader(path)
if self.transform is not None:
sample = self.transform(sample)
return [sample]
def __len__(self):
return len(self.samples)