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import os |
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import pandas as pd |
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import datasets |
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_DESCRIPTION = """\ |
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Demo. |
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""" |
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_CITATION = """\ |
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""" |
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_HOMEPAGE = "xyz" |
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_LICENSE = "BSD 3-Clause License" |
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_URL="https://huggingface.co/datasets/mukesh3444/manual-window-detect/resolve/main/dataset.zip?download=true" |
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_SCENE_CATEGORIES = ["bow_window_indoor"] |
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class ManualWindowDetect (datasets.GeneratorBasedBuilder): |
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"""MIT Scene Parsing Benchmark dataset.""" |
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VERSION = datasets.Version("1.0.0") |
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BUILDER_CONFIGS = [datasets.BuilderConfig(name="manual-window-detect", version=VERSION, description="Detect Windows inside an image")] |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"image": datasets.Image(), |
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"annotation": datasets.Image(), |
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"scene_category": datasets.ClassLabel(names=_SCENE_CATEGORIES), |
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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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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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url = _URL |
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data_dirs = dl_manager.download_and_extract(url) |
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print(data_dirs) |
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train_data = val_data= os.path.join(data_dirs) |
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test_data = os.path.join(data_dirs) |
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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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"data": train_data, |
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"split": "training", |
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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={"data": test_data, "split": "testing"}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"data": val_data, |
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"split": "validation", |
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}, |
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), |
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] |
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def _generate_examples(self, data, split): |
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if split == "testing": |
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image_dir = os.path.join(data,"images",split) |
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for idx, image_file in enumerate(os.listdir(image_dir)): |
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yield idx, { |
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"image": os.path.join(image_dir, image_file), |
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"annotation": None, |
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"scene_category": None, |
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} |
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else: |
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image_id2cat = pd.read_csv( |
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os.path.join(data, "sceneCategories.txt"), sep=" ", names=["image_id", "scene_category"] |
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) |
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image_id2cat = image_id2cat.set_index("image_id") |
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images_dir = os.path.join(data, "images", split) |
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annotations_dir = os.path.join(data, "annotations", split) |
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for idx, image_file in enumerate(os.listdir(images_dir)): |
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image_id = image_file.split(".")[0] |
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yield idx, { |
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"image": os.path.join(images_dir, image_file), |
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"annotation": os.path.join(annotations_dir, image_id + ".png"), |
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"scene_category": 8 |
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} |
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