[datasets] Add MJSynth (Synth90K)

docs/source/index.rst

@@ -62,6 +62,7 @@ Supported datasets
 * IC03 from `ICDAR 2003 <http://www.iapr-tc11.org/mediawiki/index.php?title=ICDAR_2003_Robust_Reading_Competitions>`_.
 * IC13 from `ICDAR 2013 <http://dagdata.cvc.uab.es/icdar2013competition/>`_.
 * IMGUR5K from `"TextStyleBrush: Transfer of Text Aesthetics from a Single Example" <https://github.com/facebookresearch/IMGUR5K-Handwriting-Dataset>`_.
+* MJSynth from `"Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition" <https://www.robots.ox.ac.uk/~vgg/data/text/>`_.
 
 
 .. toctree::

docs/source/modules/datasets.rst

@@ -27,6 +27,7 @@ Public datasets
 .. autoclass:: IC03
 .. autoclass:: IC13
 .. autoclass:: IMGUR5K
+.. autoclass:: MJSynth
 
 docTR synthetic datasets
 ^^^^^^^^^^^^^^^^^^^^^^^^

doctr/datasets/__init__.py

@@ -9,6 +9,7 @@
 from .ic13 import *
 from .iiit5k import *
 from .imgur5k import *
+from .mjsynth import *
 from .ocr import *
 from .recognition import *
 from .sroie import *

doctr/datasets/mjsynth.py

@@ -0,0 +1,69 @@
+# Copyright (C) 2021-2022, Mindee.
+
+# This program is licensed under the Apache License version 2.
+# See LICENSE or go to <https://www.apache.org/licenses/LICENSE-2.0.txt> for full license details.
+
+import os
+from typing import Any, Dict, List, Tuple
+
+from tqdm import tqdm
+
+from .datasets import AbstractDataset
+
+__all__ = ["MJSynth"]
+
+
+class MJSynth(AbstractDataset):
+    """MJSynth dataset from `"Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition"
+    <https://www.robots.ox.ac.uk/~vgg/data/text/>`_.
+
+    >>> # NOTE: This is a pure recognition dataset without bounding box labels.
+    >>> # NOTE: You need to download the dataset.
+    >>> from doctr.datasets import MJSynth
+    >>> train_set = MJSynth(img_folder="/path/to/mjsynth/mnt/ramdisk/max/90kDICT32px",
+    >>>                     label_path="/path/to/mjsynth/mnt/ramdisk/max/90kDICT32px/imlist.txt",
+    >>>                     train=True)
+    >>> img, target = train_set[0]
+    >>> test_set = MJSynth(img_folder="/path/to/mjsynth/mnt/ramdisk/max/90kDICT32px",
+    >>>                    label_path="/path/to/mjsynth/mnt/ramdisk/max/90kDICT32px/imlist.txt")
+    >>>                    train=False)
+    >>> img, target = test_set[0]
+
+    Args:
+        img_folder: folder with all the images of the dataset
+        label_path: path to the file with the labels
+        train: whether the subset should be the training one
+        **kwargs: keyword arguments from `AbstractDataset`.
+    """
+
+    def __init__(
+        self,
+        img_folder: str,
+        label_path: str,
+        train: bool = True,
+        **kwargs: Any,
+    ) -> None:
+        super().__init__(img_folder, **kwargs)
+
+        # File existence check
+        if not os.path.exists(label_path) or not os.path.exists(img_folder):
+            raise FileNotFoundError(
+                f"unable to locate {label_path if not os.path.exists(label_path) else img_folder}")
+
+        self.data: List[Tuple[str, Dict[str, Any]]] = []
+        self.train = train
+
+        with open(label_path) as f:
+            img_paths = f.readlines()
+
+        train_samples = int(len(img_paths) * 0.9)
+        set_slice = slice(train_samples) if self.train else slice(train_samples, None)
+
+        for path in tqdm(iterable=img_paths[set_slice], desc='Unpacking MJSynth', total=len(img_paths[set_slice])):
+            label = [path.split('_')[1]]
+            img_path = os.path.join(img_folder, path[2:]).strip()
+
+            self.data.append((img_path, dict(labels=label)))
+
+    def extra_repr(self) -> str:
+        return f"train={self.train}"

tests/conftest.py

@@ -599,3 +599,29 @@ def mock_ic03_dataset(tmpdir_factory, mock_image_stream):
     archive_path = root.join('ic03_train.zip')
     shutil.make_archive(root.join('ic03_train'), 'zip', str(ic03_root))
     return str(archive_path)
+
+
+@pytest.fixture(scope="session")
+def mock_mjsynth_dataset(tmpdir_factory, mock_image_stream):
+    root = tmpdir_factory.mktemp('datasets')
+    mjsynth_root = root.mkdir('mjsynth')
+    image_folder = mjsynth_root.mkdir("images")
+    label_file = mjsynth_root.join("imlist.txt")
+    labels = [
+        "./mjsynth/images/12_I_34.jpg\n",
+        "./mjsynth/images/12_am_34.jpg\n",
+        "./mjsynth/images/12_a_34.jpg\n",
+        "./mjsynth/images/12_Jedi_34.jpg\n",
+        "./mjsynth/images/12_!_34.jpg\n",
+    ]
+
+    with open(label_file, "w") as f:
+        for label in labels:
+            f.write(label)
+
+    file = BytesIO(mock_image_stream)
+    for i in ['I', 'am', 'a', 'Jedi', '!']:
+        fn = image_folder.join(f"12_{i}_34.jpg")
+        with open(fn, 'wb') as f:
+            f.write(file.getbuffer())
+    return str(root), str(label_file)

tests/pytorch/test_datasets_pt.py

@@ -505,3 +505,17 @@ def test_ic03(input_size, num_samples, rotate, recognition, mock_ic03_dataset):
         _validate_dataset_recognition_part(ds, input_size)
     else:
         _validate_dataset(ds, input_size, is_polygons=rotate)
+
+
+# NOTE: following datasets are only for recognition task
+
+def test_mjsynth_dataset(mock_mjsynth_dataset):
+    input_size = (32, 128)
+    ds = datasets.MJSynth(
+        *mock_mjsynth_dataset,
+        img_transforms=Resize(input_size, preserve_aspect_ratio=True),
+    )
+
+    assert len(ds) == 4  # Actual set has 7581382 train and 1337891 test samples
+    assert repr(ds) == f"MJSynth(train={True})"
+    _validate_dataset_recognition_part(ds, input_size)

tests/tensorflow/test_datasets_tf.py

@@ -490,3 +490,17 @@ def test_ic03(input_size, num_samples, rotate, recognition, mock_ic03_dataset):
         _validate_dataset_recognition_part(ds, input_size)
     else:
         _validate_dataset(ds, input_size, is_polygons=rotate)
+
+
+# NOTE: following datasets are only for recognition task
+
+def test_mjsynth_dataset(mock_mjsynth_dataset):
+    input_size = (32, 128)
+    ds = datasets.MJSynth(
+        *mock_mjsynth_dataset,
+        img_transforms=Resize(input_size, preserve_aspect_ratio=True),
+    )
+
+    assert len(ds) == 4  # Actual set has 7581382 train and 1337891 test samples
+    assert repr(ds) == f"MJSynth(train={True})"
+    _validate_dataset_recognition_part(ds, input_size)