Imgur5k dataset integration

docs/source/datasets.rst

@@ -26,6 +26,7 @@ Public datasets
 .. autoclass:: SynthText
 .. autoclass:: IC03
 .. autoclass:: IC13
+.. autoclass:: IMGUR5K
 
 docTR synthetic datasets
 ^^^^^^^^^^^^^^^^^^^^^^^^

doctr/datasets/__init__.py

@@ -8,6 +8,7 @@
 from .ic03 import *
 from .ic13 import *
 from .iiit5k import *
+from .imgur5k import *
 from .ocr import *
 from .recognition import *
 from .sroie import *

doctr/datasets/imgur5k.py

@@ -0,0 +1,99 @@
+# 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 json
+import os
+from pathlib import Path
+from typing import Any, Dict, List, Tuple
+
+import cv2
+import numpy as np
+
+from .datasets import AbstractDataset
+from .utils import convert_target_to_relative
+
+__all__ = ["IMGUR5K"]
+
+
+class IMGUR5K(AbstractDataset):
+    """IMGUR5K dataset from `"TextStyleBrush: Transfer of Text Aesthetics from a Single Example"
+    <https://arxiv.org/abs/2106.08385>`_ |
+    `"repository" <https://github.com/facebookresearch/IMGUR5K-Handwriting-Dataset>`_.
+
+    Example::
+        >>> # NOTE: You need to download/generate the dataset from the repository.
+        >>> from doctr.datasets import IMGUR5K
+        >>> train_set = IMGUR5K(train=True, img_folder="/path/to/IMGUR5K-Handwriting-Dataset/images",
+        >>>                     label_path="/path/to/IMGUR5K-Handwriting-Dataset/dataset_info/imgur5k_annotations.json")
+        >>> img, target = train_set[0]
+        >>> test_set = IMGUR5K(train=False, img_folder="/path/to/IMGUR5K-Handwriting-Dataset/images",
+        >>>                    label_path="/path/to/IMGUR5K-Handwriting-Dataset/dataset_info/imgur5k_annotations.json")
+        >>> img, target = test_set[0]
+    Args:
+        img_folder: folder with all the images of the dataset
+        label_path: path to the annotations file of the dataset
+        train: whether the subset should be the training one
+        use_polygons: whether polygons should be considered as rotated bounding box (instead of straight ones)
+        **kwargs: keyword arguments from `AbstractDataset`.
+    """
+
+    def __init__(
+        self,
+        img_folder: str,
+        label_path: str,
+        train: bool = True,
+        use_polygons: bool = False,
+        **kwargs: Any,
+    ) -> None:
+        super().__init__(img_folder, pre_transforms=convert_target_to_relative, **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[Path, Dict[str, Any]]] = []
+        self.train = train
+        np_dtype = np.float32
+
+        img_names = os.listdir(img_folder)
+        train_samples = int(len(img_names) * 0.9)
+        set_slice = slice(train_samples) if self.train else slice(train_samples, None)
+
+        with open(label_path) as f:
+            annotation_file = json.load(f)
+
+        for img_name in img_names[set_slice]:
+            img_path = Path(img_folder, img_name)
+            img_id = img_name.split(".")[0]
+
+            # File existence check
+            if not os.path.exists(os.path.join(self.root, img_name)):
+                raise FileNotFoundError(f"unable to locate {os.path.join(self.root, img_name)}")
+
+            # some files have no annotations which are marked with only a dot in the 'word' key
+            # ref: https://github.com/facebookresearch/IMGUR5K-Handwriting-Dataset/blob/main/README.md
+            if img_id not in annotation_file['index_to_ann_map'].keys():
+                continue
+            ann_ids = annotation_file['index_to_ann_map'][img_id]
+            annotations = [annotation_file['ann_id'][a_id] for a_id in ann_ids]
+
+            labels = [ann['word'] for ann in annotations if ann['word'] != '.']
+            # x_center, y_center, width, height, angle
+            _boxes = [list(map(float, ann['bounding_box'].strip('[ ]').split(', ')))
+                      for ann in annotations if ann['word'] != '.']
+            # (x, y) coordinates of top left, top right, bottom right, bottom left corners
+            box_targets = [cv2.boxPoints(((box[0], box[1]), (box[2], box[3]), box[4])) for box in _boxes]
+
+            if not use_polygons:
+                # xmin, ymin, xmax, ymax
+                box_targets = [np.concatenate((points.min(0), points.max(0)), axis=-1) for points in box_targets]
+
+            # filter images without boxes
+            if len(box_targets) > 0:
+                self.data.append((img_path, dict(boxes=np.asarray(box_targets, dtype=np_dtype), labels=labels)))
+
+    def extra_repr(self) -> str:
+        return f"train={self.train}"

tests/conftest.py

@@ -170,6 +170,77 @@ def mock_ic13(tmpdir_factory, mock_image_stream):
     return str(image_folder), str(label_folder)
 
 
+@pytest.fixture(scope="session")
+def mock_imgur5k(tmpdir_factory, mock_image_stream):
+    file = BytesIO(mock_image_stream)
+    image_folder = tmpdir_factory.mktemp("images")
+    label_folder = tmpdir_factory.mktemp("dataset_info")
+    labels = {
+        "index_id": {
+            "YsaVkzl": {
+                "image_url": "https://i.imgur.com/YsaVkzl.jpg",
+                "image_path": "/path/to/IMGUR5K-Handwriting-Dataset/images/YsaVkzl.jpg",
+                "image_hash": "993a7cbb04a7c854d1d841b065948369"
+            },
+            "wz3wHhN": {
+                "image_url": "https://i.imgur.com/wz3wHhN.jpg",
+                "image_path": "/path/to/IMGUR5K-Handwriting-Dataset/images/wz3wHhN.jpg",
+                "image_hash": "9157426a98ee52f3e1e8d41fa3a99175"
+            },
+            "BRHSP23": {
+                "image_url": "https://i.imgur.com/BRHSP23.jpg",
+                "image_path": "/path/to/IMGUR5K-Handwriting-Dataset/images/BRHSP23.jpg",
+                "image_hash": "aab01f7ac82ae53845b01674e9e34167"
+            }
+        },
+        "index_to_ann_map": {
+            "YsaVkzl": [
+                "YsaVkzl_0",
+                "YsaVkzl_1",
+                "YsaVkzl_2"],
+            "wz3wHhN": [
+                "wz3wHhN_0",
+                "wz3wHhN_1"],
+            "BRHSP23": [
+                "BRHSP23_0"]
+        },
+        "ann_id": {
+            "YsaVkzl_0": {
+                "word": "I",
+                "bounding_box": "[605.33, 1150.67, 614.33, 226.33, 81.0]"
+            },
+            "YsaVkzl_1": {
+                "word": "am",
+                "bounding_box": "[783.67, 654.67, 521.0, 222.33, 56.67]"
+            },
+            "YsaVkzl_2": {
+                "word": "a",
+                "bounding_box": "[959.0, 437.0, 76.67, 201.0, 38.33]"
+            },
+            "wz3wHhN_0": {
+                "word": "jedi",
+                "bounding_box": "[783.67, 654.67, 521.0, 222.33, 56.67]"
+            },
+            "wz3wHhN_1": {
+                "word": "!",
+                "bounding_box": "[959.0, 437.0, 76.67, 201.0, 38.33]"
+            },
+            "BRHSP23_0": {
+                "word": "jedi",
+                "bounding_box": "[783.67, 654.67, 521.0, 222.33, 56.67]"
+            }
+        }
+    }
+    label_file = label_folder.join("imgur5k_annotations.json")
+    with open(label_file, 'w') as f:
+        json.dump(labels, f)
+    for index_id in ['YsaVkzl', 'wz3wHhN', 'BRHSP23']:
+        fn_i = image_folder.join(f"{index_id}.jpg")
+        with open(fn_i, 'wb') as f:
+            f.write(file.getbuffer())
+    return str(image_folder), str(label_file)
+
+
 @pytest.fixture(scope="session")
 def mock_svhn_dataset(tmpdir_factory, mock_image_stream):
     root = tmpdir_factory.mktemp('datasets')

tests/pytorch/test_datasets_pt.py

@@ -219,6 +219,28 @@ def test_ic13_dataset(num_samples, rotate, mock_ic13):
     _validate_dataset(ds, input_size, is_polygons=rotate)
 
 
+@pytest.mark.parametrize(
+    "num_samples, rotate",
+    [
+        [3, True],  # Actual set has 7149 train and 796 test samples
+        [3, False]
+
+    ],
+)
+def test_imgur5k_dataset(num_samples, rotate, mock_imgur5k):
+    input_size = (512, 512)
+    ds = datasets.IMGUR5K(
+        *mock_imgur5k,
+        train=True,
+        img_transforms=Resize(input_size),
+        use_polygons=rotate,
+    )
+
+    assert len(ds) == num_samples - 1  # -1 because of the test set 90 / 10 split
+    assert repr(ds) == f"IMGUR5K(train={True})"
+    _validate_dataset(ds, input_size, is_polygons=rotate)
+
+
 @pytest.mark.parametrize(
     "input_size, num_samples, rotate",
     [

tests/tensorflow/test_datasets_tf.py

@@ -206,6 +206,28 @@ def test_ic13_dataset(mock_ic13, num_samples, rotate):
     _validate_dataset(ds, input_size, is_polygons=rotate)
 
 
+@pytest.mark.parametrize(
+    "num_samples, rotate",
+    [
+        [3, True],  # Actual set has 7149 train and 796 test samples
+        [3, False]
+
+    ],
+)
+def test_imgur5k_dataset(num_samples, rotate, mock_imgur5k):
+    input_size = (512, 512)
+    ds = datasets.IMGUR5K(
+        *mock_imgur5k,
+        train=True,
+        img_transforms=Resize(input_size),
+        use_polygons=rotate,
+    )
+
+    assert len(ds) == num_samples - 1  # -1 because of the test set 90 / 10 split
+    assert repr(ds) == f"IMGUR5K(train={True})"
+    _validate_dataset(ds, input_size, is_polygons=rotate)
+
+
 @pytest.mark.parametrize(
     "input_size, num_samples, rotate",
     [