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README.md
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- split: test
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path: data/test-*
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---
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- split: test
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path: data/test-*
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---
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Processed data from the Soccernet 2023 dataset. Processing notebook is included in this repo.
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To see an example:
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```python
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def show_item(item):
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fig, axs = plt.subplots(nrows = 1, ncols = 4, figsize = (20, 4))
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axs[0].imshow(item['image'])
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axs[0].set_title("Image")
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axs[0].axis('off')
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axs[1].imshow(overlay_mask(item['image'], item['outlines']))
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axs[1].set_title("Outlines")
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axs[1].axis('off')
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axs[2].imshow(show_segments(item['segments']))
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axs[2].set_title("Segments")
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axs[2].axis('off')
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# PART 3: GET MASK OUTLINES
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kernel = np.array([[0, 1, 0],
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[1, -4, 1],
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[0, 1, 0]])
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segments = np.array(item['segments']).astype(np.uint8)
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class_edges = np.zeros(segments.shape[1:], dtype=int)
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for i in range(segments.shape[0]):
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edge = convolve(segments[i], kernel, mode='constant', cval=0)
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edge_detected = edge != 0
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class_edges[edge_detected] = i
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axs[3].imshow(overlay_mask(item['image'], class_edges))
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axs[3].set_title("Segments Outlines")
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axs[3].axis('off')
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if item['is_bad']:
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s = f"Bad ID: {item['id']}"
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else:
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s = f"ID: {item['id']}"
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fig.suptitle(s, fontsize = 8)
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plt.subplots_adjust(hspace = -0.2, wspace = -0.05)
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plt.show()
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show_item(dataset['train'][99])
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```
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