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