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--- |
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license: cc-by-nc-4.0 |
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pretty_name: imagenet3d |
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extra_gated_fields: |
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Name: text |
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Affiliation: text |
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--- |
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## ImageNet3D |
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Refer to [github.com/wufeim/imagenet3d](https://github.com/wufeim/imagenet3d) for the full documentation and sample preprocessing code for ImageNet3D. |
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### Download Data |
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Directly download from the HuggingFace WebUI, or on a server, run |
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```py |
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from huggingface_hub import hf_hub_download |
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local_path = '/your/local/directory' |
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hf_hub_download(repo_id='ccvl/ImageNet3D', repo_type='dataset', filename='imagenet3d_0409.zip', local_dir=local_path, local_dir_use_symlinks=False) |
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``` |
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### Example Usage |
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```py |
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from PIL import Image |
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import numpy as np |
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img_path = 'imagenet3d/bed/n02818832_13.JPEG' |
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annot_path = 'imagenet3d/bed/n02818832_13.npz' |
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img = np.array(Image.open(img_path).convert('RGB')) |
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annot = dict(np.load(annot_path, allow_pickle=True))['annotations'] |
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# Number of objects |
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num_objects = len(annot) |
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# Annotation of the first object |
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azimuth = annot[0]['azimuth'] # float, [0, 2*pi] |
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elevation = annot[0]['elevation'] # float, [0, 2*pi] |
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theta = annot[0]['theta'] # float, [0, 2*pi] |
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cad_index = annot[0]['cad_index'] # int |
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distance = annot[0]['distance'] # float |
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viewport = annot[0]['viewport'] # int |
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img_height = annot[0]['height'] # numpy.uint16 |
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img_width = annot[0]['width'] # numpy.uint16 |
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bbox = annot[0]['bbox'] # numpy.ndarray, (x1, y1, x2, y2) |
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category = annot[0]['class'] # str |
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principal_x = annot[0]['px'] # float |
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principal_y = annot[0]['py'] # float |
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# label indicating the quality of the object, occluded or low quality |
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object_status = annot[0]['object_status'] # str, one of ('status_good', 'status_partially', 'status_barely', 'status_bad') |
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# label indicating if multiple objects from same category very close to each other |
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dense = annot[0]['dense'] # str, one of ('dense_yes', 'dense_no') |
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``` |
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