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--- |
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license: mit |
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language: |
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- en |
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task_categories: |
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- depth-estimation |
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- image-segmentation |
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- image-to-3d |
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- robotics |
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- image-feature-extraction |
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tags: |
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- biology |
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- art |
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viewer: false |
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pretty_name: 3DPotatoTwin |
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size_categories: |
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- 1K<n<10K |
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--- |
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# 3DPotatoTwin |
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Potato dataset with paired RGB, RGBD, and 3D reconstructed models, can be used for image to 3D and shape completetion tasks |
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## 1. Downloads |
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It is recommended to using `huggingface-cli` to download this datasets to your local computer. |
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**make sure you have at least 150GB free space and [huggingface-cli](https://huggingface.co/docs/huggingface_hub/guides/cli) installed on your computer** |
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```bash |
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huggingface-cli download HowcanoeWang/3DPotatoTwin --repo-type dataset --local-dir /your/path/to/save/dataset |
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``` |
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Then, you can run the following python code to unzip all zipped files at your local computer, any python version > 3.6 should work. This script will also remove zip file after unzipping to free up disk spaces: |
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```bash |
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$ cd /path/to/this/dataset |
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$ python unzip.py |
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``` |
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After executing the unzip script, the dataset is in the following folder structure |
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## 2. Folder structure |
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### 1_rgbd |
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This folder contains 3 subfolders with the source of RGBD imaging of potato tubers scrolling on the conveyer. Including RGB and detph images and 3D point cloud data. |
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``` |
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1_rgbd |
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|-- 0_camera_intrinsics |
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| |-- realsense_d405_camera_intrinsic.json |
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| |-- realsense_d405_settings_harvester.json |
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|-- 1_image |
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| |-- 2R2-8 |
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| | |-- 2R2-8_depth_098.png |
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| | |-- 2R2-8_rgb_098.png |
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| | |-- ... |
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| |-- ... |
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|-- 2_pcd |
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| |-- 2R2-8 |
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| | |-- 2R2-8_pcd_098.ply |
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| | |-- ... |
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| |-- ... |
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``` |
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#### `0_camera_intrinsics` |
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the Intel RealSense RGBD camera settings and interal parameters |
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#### `1_image` |
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Contains the RGB and depth images. The files are named according to `[potato-id]_[data-type]_[ycoord].[extension]`. Note that the ycoord is the y-coordinate of the center of the bounding box (bbc) of the annotated potato tuber in reversed order: `[img_height - y_bbc]`. |
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The RGB images have an alpha channel with the mask annotation. To extract the RGB and mask channels individually please use this code: |
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```python |
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import cv2 |
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rgba = cv2.imread("./1_rgbd/1_image/2R2-8/2R2-8_rgb_098.png", cv2.IMREAD_UNCHANGED) |
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rgb = rgba[:,:,:-1] |
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mask = rgba[:,:,-1] |
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``` |
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Also, it is a time-series data with potato tuber scrolling from the bottom to the top of converyer. |
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#### `2_pcd` |
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the converted 3D point cloud from RGBD scans on converyer |
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--- |
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### 2_sfm |
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This folder contains 4 subfolders with the source of close-range RGB reconstruction for potato tubers on rotation table and photo studio. |
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``` |
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2_sfm/ |
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|-- 0_image/ |
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| |-- 2R1-1/ |
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| | |-- 000/ |
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| | | |-- DSC_000_20230921_0956229427.jpg |
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| | | |-- ... |
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| | |-- 001/ |
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| | |-- 002/ |
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| |-- ... |
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|-- 0_masks/ |
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| |-- 2R1-1/ |
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| | |-- 000/ |
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| | | |-- DSC_000_20230921_0956229427.png |
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| | | |-- ... |
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| | |-- 001/ |
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| | |-- 002/ |
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| |-- ... |
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|-- 0_metashape.projects |
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| |-- 1R_Group0.psx |
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| |-- 1R_Group0.files |
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| |-- ... |
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| |-- 05_export_models.xml |
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|-- 1_mesh |
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| |-- 2R2-8 |
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| | |-- 2R2-8.jpg |
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| | |-- 2R2-8.mtl |
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| | |-- 2R2-8.obj |
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| |-- ... |
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|-- 2_pcd |
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| |-- 2R2-8 |
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| | |-- 2R2-8_10000.ply |
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| | |-- 2R2-8_20000.ply |
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| | |-- 2R2-8_30000.ply |
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| |-- ... |
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``` |
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#### `0_image` |
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The raw RGB images taken by 3 DSLR cameras on rotation table for close-range 3D reconstruction. |
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For each potato tuber, 3 camera views were grouped into subfoders `000`, `001`, `002`. |
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#### `0_masks` |
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The masks to filter out backgrounds for 3D reconstruction, providing faster and more reliable photo alignment and better output quality. |
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These are simple computer vision colorspace threshold segmenetation, just rough masks rather than perfect segmentation masks. |
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### `0_metashape.projects` |
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Agisoft Metashape reconstruction projects, which contain useful SfM-MVS meta information like camera pose and internal parameters. |
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We grouped 50 potatos for each projects, for the ease of data management and batch processing, for example, |
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`05_export_models.xml` is a Metashape batch script for model exporting. |
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For camera pose information, they are availabe at: `*.files/[chunk_id]/chunks.zip/doc.xml`. For camera internal information, they are available at `*.files/[chunk_id]/0/frame.zip/doc.xml`. For more details, pleach check Metashape official documentation. |
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<details> |
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<summary>Considering using <code>EasyIDP</code> for parsing previous camera parameters easily.</summary> |
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Use `$ pip install easyidp` to install this tool to your python environment first. |
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```python |
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>>> import easyidp as idp |
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>>> ms = idp.Metashape(r"./2_SfM/2_metashape.projects/1R_Group0.psx") |
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>>> ms |
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<'1R_Group0.psx' easyidp.Metashape object with 40 active chunks> |
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id label |
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---- ------- |
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-> 0 R1-1 |
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1 R1-10 |
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2 R1-2 |
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... ... |
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38 R4-7 |
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39 R4-9 |
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>>> ms.open_chunk("R4-9") # switch to chunk/potato 'R4-9' |
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>>> ms.photos # show the list of all photos |
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<easyidp.Container> with 72 items |
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[0] 000-DSC_000_2745 |
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<easyidp.reconstruct.Photo object at 0x7a7c1db35df0> |
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[1] 000-DSC_000_2748 |
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<easyidp.reconstruct.Photo object at 0x7a7b04064490> |
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... |
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[70] 002-DSC_002_2812 |
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<easyidp.reconstruct.Photo object at 0x7a7affb1fa00> |
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[71] 002-DSC_002_2814 |
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<easyidp.reconstruct.Photo object at 0x7a7affb1fe80> |
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>>> ms.photos[0].label |
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'000-DSC_000_2745' |
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>>> ms.photos[0].transform |
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array([[ 0.15930206, -0.12569926, 0.97919485, -3.02706453], |
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[-0.46709831, -0.88341351, -0.03741308, -0.01090792], |
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[ 0.86973675, -0.45142028, -0.1994435 , -2.61149946], |
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[ 0. , 0. , 0. , 1. ]]) |
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``` |
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> [!CAUTION] |
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> Abovementioned transform matrix applies to metashape local coordinate, please check Metashape documents for more details. |
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> [!IMPORTANT] |
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> Current EasyIDP version only supports parsing the transformation matrix ([docs](https://easyidp.readthedocs.io/en/latest/python_api/autodoc/easyidp.reconstruct.Photo.html#easyidp.reconstruct.Photo)). In some cases, the rotation, position and location are missing in Metashape xml files thus not implemented this feature. |
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> [!TIP] |
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> To use the transformation from RGBD point cloud to close-range SfM 3D models, please refer to `3_pair/tmatrix` listed below. |
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</details> |
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#### `1_mesh` |
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The reconstructed close-range high-quality 3D meshes for potato tubers. The meshes can be visualized in Open3D: |
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```python |
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>>> import open3d as o3d |
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>>> mesh = o3d.io.read_triangle_mesh("./2_sfm/1_mesh/2R2-8/2R2-8.obj", enable_post_processing=True, print_progress=False) |
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>>> o3d.visualization.draw_geometries([mesh], window_name="mesh") |
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``` |
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#### `2_pcd` |
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The downsampled point clouds of these meshes (containing 10000, 20000, and 30000 points respectively). |
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--- |
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### 3_pair |
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This folder contains 1 subfolder with the transformation matrices to overlay the partial point cloud with the 3D mesh. Please refer to the `transform.py` file for more details. |
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The `ground_truth.csv` is the measured tuber traits, the volumes are measured by drainage method. |
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The `non-perfect.txt` records the not perfect matching, please excluded them to traing any machine learning products. |
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## 3. Citation Information |
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Please cite our publication if this dataset helped your research: |
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``` |
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@article{BLOK2025109673, |
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title = {High-throughput 3D shape completion of potato tubers on a harvester}, |
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author = {Pieter M. Blok and Federico Magistri and Cyrill Stachniss and Haozhou Wang and James Burridge and Wei Guo}, |
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journal = {Computers and Electronics in Agriculture}, |
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volume = {228}, |
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pages = {109673}, |
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year = {2025}, |
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issn = {0168-1699}, |
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doi = {https://doi.org/10.1016/j.compag.2024.109673}, |
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url = {https://www.sciencedirect.com/science/article/pii/S0168169924010640}, |
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keywords = {Potato, Deep learning, RGB-D, 3D shape completion, Structure-from-Motion}, |
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} |
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``` |
