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"""Street View House Numbers (SVHN) dataset.""" |
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import io |
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import os |
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import h5py |
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import numpy as np |
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import scipy.io as sio |
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import datasets |
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from datasets.tasks import ImageClassification |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = """\ |
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@article{netzer2011reading, |
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title={Reading digits in natural images with unsupervised feature learning}, |
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author={Netzer, Yuval and Wang, Tao and Coates, Adam and Bissacco, Alessandro and Wu, Bo and Ng, Andrew Y}, |
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year={2011} |
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} |
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""" |
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_DESCRIPTION = """\ |
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SVHN is a real-world image dataset for developing machine learning and object recognition algorithms with minimal requirement on data preprocessing and formatting. |
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It can be seen as similar in flavor to MNIST (e.g., the images are of small cropped digits), but incorporates an order of magnitude more labeled data (over 600,000 digit images) |
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and comes from a significantly harder, unsolved, real world problem (recognizing digits and numbers in natural scene images). SVHN is obtained from house numbers in Google Street View images. |
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""" |
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_HOMEPAGE = "http://ufldl.stanford.edu/housenumbers/" |
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_LICENSE = "Custom (non-commercial)" |
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_URLs = { |
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"full_numbers": [ |
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"http://ufldl.stanford.edu/housenumbers/train.tar.gz", |
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"http://ufldl.stanford.edu/housenumbers/test.tar.gz", |
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"http://ufldl.stanford.edu/housenumbers/extra.tar.gz", |
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], |
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"cropped_digits": [ |
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"http://ufldl.stanford.edu/housenumbers/train_32x32.mat", |
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"http://ufldl.stanford.edu/housenumbers/test_32x32.mat", |
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"http://ufldl.stanford.edu/housenumbers/extra_32x32.mat", |
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], |
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} |
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_DIGIT_LABELS = [str(num) for num in range(10)] |
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class SVHN(datasets.GeneratorBasedBuilder): |
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"""Street View House Numbers (SVHN) dataset.""" |
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VERSION = datasets.Version("1.0.0") |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig( |
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name="full_numbers", |
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version=VERSION, |
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description="Contains the original, variable-resolution, color house-number images with character level bounding boxes.", |
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), |
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datasets.BuilderConfig( |
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name="cropped_digits", |
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version=VERSION, |
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description="Character level ground truth in an MNIST-like format. All digits have been resized to a fixed resolution of 32-by-32 pixels. The original character bounding boxes are extended in the appropriate dimension to become square windows, so that resizing them to 32-by-32 pixels does not introduce aspect ratio distortions. Nevertheless this preprocessing introduces some distracting digits to the sides of the digit of interest.", |
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), |
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] |
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def _info(self): |
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if self.config.name == "full_numbers": |
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features = datasets.Features( |
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{ |
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"image": datasets.Image(), |
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"digits": datasets.Sequence( |
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{ |
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"bbox": datasets.Sequence(datasets.Value("int32"), length=4), |
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"label": datasets.ClassLabel(num_classes=10), |
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} |
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), |
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} |
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) |
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else: |
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features = datasets.Features( |
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{ |
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"image": datasets.Image(), |
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"label": datasets.ClassLabel(num_classes=10), |
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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supervised_keys=None, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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task_templates=[ImageClassification(image_column="image", label_column="label")] |
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if self.config.name == "cropped_digits" |
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else None, |
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) |
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def _split_generators(self, dl_manager): |
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if self.config.name == "full_numbers": |
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train_archive, test_archive, extra_archive = dl_manager.download(_URLs[self.config.name]) |
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for path, f in dl_manager.iter_archive(train_archive): |
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if path.endswith("digitStruct.mat"): |
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train_annot_data = f.read() |
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break |
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for path, f in dl_manager.iter_archive(test_archive): |
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if path.endswith("digitStruct.mat"): |
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test_annot_data = f.read() |
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break |
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for path, f in dl_manager.iter_archive(extra_archive): |
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if path.endswith("digitStruct.mat"): |
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extra_annot_data = f.read() |
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break |
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train_archive = dl_manager.iter_archive(train_archive) |
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test_archive = dl_manager.iter_archive(test_archive) |
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extra_archive = dl_manager.iter_archive(extra_archive) |
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train_filepath, test_filepath, extra_filepath = None, None, None |
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else: |
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train_annot_data, test_annot_data, extra_annot_data = None, None, None |
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train_archive, test_archive, extra_archive = None, None, None |
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train_filepath, test_filepath, extra_filepath = dl_manager.download(_URLs[self.config.name]) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"annot_data": train_annot_data, |
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"files": train_archive, |
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"filepath": train_filepath, |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"annot_data": test_annot_data, |
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"files": test_archive, |
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"filepath": test_filepath, |
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}, |
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), |
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datasets.SplitGenerator( |
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name="extra", |
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gen_kwargs={ |
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"annot_data": extra_annot_data, |
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"files": extra_archive, |
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"filepath": extra_filepath, |
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}, |
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), |
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] |
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def _generate_examples(self, annot_data, files, filepath): |
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if self.config.name == "full_numbers": |
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def _get_digits(bboxes, h5_file): |
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def key_to_values(key, bbox): |
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if bbox[key].shape[0] == 1: |
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return [int(bbox[key][0][0])] |
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else: |
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return [int(h5_file[bbox[key][i][0]][()].item()) for i in range(bbox[key].shape[0])] |
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bbox = h5_file[bboxes[0]] |
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assert bbox.keys() == {"height", "left", "top", "width", "label"} |
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bbox_columns = [key_to_values(key, bbox) for key in ["left", "top", "width", "height", "label"]] |
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return [ |
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{"bbox": [left, top, width, height], "label": label % 10} |
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for left, top, width, height, label in zip(*bbox_columns) |
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] |
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with h5py.File(io.BytesIO(annot_data), "r") as h5_file: |
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for path, f in files: |
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root, ext = os.path.splitext(path) |
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if ext != ".png": |
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continue |
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img_idx = int(os.path.basename(root)) - 1 |
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yield img_idx, { |
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"image": {"path": path, "bytes": f.read()}, |
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"digits": _get_digits(h5_file["digitStruct/bbox"][img_idx], h5_file), |
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} |
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else: |
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data = sio.loadmat(filepath) |
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for i, (image_array, label) in enumerate(zip(np.rollaxis(data["X"], -1), data["y"])): |
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yield i, { |
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"image": image_array, |
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"label": label.item() % 10, |
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} |
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