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--- |
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extra_gated_prompt: "Test" |
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extra_gated_fields: |
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Name: text |
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PI/Advisor: text |
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Affiliation: text |
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Purpose: text |
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Country: country |
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I agree to use this dataset for non-commercial use ONLY: checkbox |
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--- |
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--- |
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# Dataset Card for Dataset CrashCar |
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<!-- Provide a quick summary of the dataset. --> |
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This is the dataset proposed in 'CrashCar101: Procedural Generation for Damage Assessment' [WACV24] |
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<!-- Provide the basic links for the dataset. --> |
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- **Project Page:** https://crashcar.compute.dtu.dk |
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- **Repository:** https://github.com/JensPars/CrashCar_procedural_generation |
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- **Paper:** https://openaccess.thecvf.com/content/WACV2024/papers/Parslov_CrashCar101_Procedural_Generation_for_Damage_Assessment_WACV_2024_paper.pdf |
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Example dataset class in pytorch |
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```python |
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import os |
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import torch |
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from glob import glob |
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from PIL import Image |
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import numpy as np |
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from pathlib import Path |
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import pandas as pd |
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class CarDataset(torch.utils.data.Dataset): |
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def __init__(self, root_dir, transform=None, tgt_transform=None): |
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img_root = os.path.join(root_dir, 'img', '*', '*.png') |
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part_root = os.path.join(root_dir, 'parts', '*', '*.png') |
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damage_root = os.path.join(root_dir, 'damage', '*', '*.png') |
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self.img_root = sorted(glob(img_root)) |
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self.part_root = sorted(glob(part_root)) |
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self.damage_root = sorted(glob(damage_root)) |
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self.transform = transform |
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self.tgt_transform = tgt_transform |
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def __len__(self): |
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return len(self.img_root) |
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def __getitem__(self, idx): |
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img = Image.open(self.img_root[idx]) |
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part_img = Image.open(self.part_root[idx]) |
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damage_img = Image.open(self.damage_root[idx]) |
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if self.transform: |
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img = self.transform(img) |
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part_img = self.transform(part_img) |
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damage_img = self.transform(damage_img) |
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return { |
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'image': img, |
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'part': part_img, |
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'damage': damage_img |
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} |
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```` |
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The following code will yield |
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```python |
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import matplotlib.pyplot as plt |
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import numpy as np |
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dataset = CarDataset(root, transform=None) |
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out = dataset[20000] |
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fig, axs = plt.subplots(1, 3, figsize=(15, 5)) |
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axs[0].imshow(out['image']) |
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axs[0].axis('off') |
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axs[1].imshow(out['image']) |
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alpha_map = (np.array(out['damage'])!= 0).astype(float) |
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axs[1].imshow(out['damage'], cmap="jet", alpha=alpha_map) |
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axs[1].axis('off') |
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axs[2].imshow(out['image']) |
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alpha_map = (np.array(out['part'])!= 0).astype(float) |
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axs[2].imshow(out['part'], cmap="jet", alpha=alpha_map) |
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axs[2].axis('off') |
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plt.tight_layout() |
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plt.show() |
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```` |
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