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""" |
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@Date: 2021/08/02 |
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@description: |
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The 2DIoU for calculating the visible and full boundaries, such as the MP3D dataset, |
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has the following data: {'train': 0.9775843958583535, 'test': 0.9828616219607289, 'val': 0.9883810438132491}, |
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indicating that our best performance is limited to below 98.29% 2DIoU using our approach. |
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""" |
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import numpy as np |
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import matplotlib.pyplot as plt |
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from tqdm import tqdm |
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from evaluation.iou import calc_IoU_2D |
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from visualization.floorplan import draw_iou_floorplan |
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from utils.conversion import depth2xyz, uv2xyz |
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def eval_dataset_visible_IoU(dataset, show=False): |
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bar = tqdm(dataset, total=len(dataset), ncols=100) |
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iou2ds = [] |
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for data in bar: |
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bar.set_description(f"Processing {data['id']}") |
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corners = data['corners'] |
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corners = corners[corners[..., 0] + corners[..., 1] != 0] |
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all_xz = uv2xyz(corners)[..., ::2] |
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visible_xz = depth2xyz(data['depth'])[..., ::2] |
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iou2d = calc_IoU_2D(all_xz, visible_xz) |
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iou2ds.append(iou2d) |
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if show: |
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layout_floorplan = draw_iou_floorplan(all_xz, visible_xz, iou2d=iou2d) |
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plt.imshow(layout_floorplan) |
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plt.show() |
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mean_iou2d = np.array(iou2ds).mean() |
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return mean_iou2d |
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def execute_eval_dataset_visible_IoU(root_dir, dataset, modes=None): |
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if modes is None: |
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modes = ["train", "test", "valid"] |
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iou2d_d = {} |
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for mode in modes: |
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print("mode: {}".format(mode)) |
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iou2d = eval_dataset_visible_IoU(dataset(root_dir, mode, patch_num=1024, |
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keys=['depth', 'visible_corners', 'corners', 'id']), show=False) |
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iou2d_d[mode] = iou2d |
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return iou2d_d |
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if __name__ == '__main__': |
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from dataset.mp3d_dataset import MP3DDataset |
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iou2d_d = execute_eval_dataset_visible_IoU(root_dir='../src/dataset/mp3d', |
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dataset=MP3DDataset, |
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modes=['train', 'test', 'val']) |
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print(iou2d_d) |
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