https://github.com/lucasvandroux/kittipy
Python functions to import and display the images and labels from the Kitty Object Detection Evaluation 2012 Dataset.
https://github.com/lucasvandroux/kittipy
eth-zurich image-detection kitti-dataset python python-functions self-driving-car tsinghua-university
Last synced: 12 days ago
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Python functions to import and display the images and labels from the Kitty Object Detection Evaluation 2012 Dataset.
- Host: GitHub
- URL: https://github.com/lucasvandroux/kittipy
- Owner: LucasVandroux
- License: mit
- Created: 2017-10-13T08:32:56.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2017-12-08T08:12:51.000Z (over 7 years ago)
- Last Synced: 2025-05-13T01:55:34.592Z (12 days ago)
- Topics: eth-zurich, image-detection, kitti-dataset, python, python-functions, self-driving-car, tsinghua-university
- Language: Python
- Homepage:
- Size: 5.62 MB
- Stars: 6
- Watchers: 2
- Forks: 1
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# KittiPy
**Python functions to import and display the images and labels from the [Kitty Object Detection Evaluation 2012 Dataset](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=2d).**
## Prerequisites
- [python](https://www.python.org/) ≥ _v2.7.6_
- [scipy](https://www.scipy.org/) ≥ _v0.19.1_
- [matplotlib](https://matplotlib.org/) ≥ _v1.5.2_
- [numpy](http://www.numpy.org/) ≥ _v1.13.3_
## Installing
1. Copy the file `kittipy.py` in the same directory as the python script or Jupyter notebook you want to use it with.
2. _(Optional)_ Modify the `kittipy.py` global parameters at the begining
3. At the begining of your file, add the line: `from kittipy import *`
4. Enjoy!
## How to use it
### Explore Folder
To get the **numpy array** containing all the ids of the images contained in a folder, use the function:
```
get_data_list(im_set, db_absolute_path = ABSOLUTE_PATH)
```
| Arguments | Default | Description |
| ----------------------|---------------------------|------------------------------------------------|
| `im_set` | | **[string]** 'train' or 'test' to chose the type of set |
| `db_absolute_path` | _ABSOLUTE_PATH_ | **[string]** absolute path to the Kitti root folder |
### Import Image
To import images as a **numpy array**, use the function:
```
import_im(im_id, im_set, db_absolute_path = ABSOLUTE_PATH)
```
| Arguments | Default | Description |
| ----------------------|---------------------------|------------------------------------------------|
| `im_id` | | **[int]** corresponding to the image id in the kitti dataset |
| `im_set` | | **[string]** 'train' or 'test' to chose the type of set |
| `db_absolute_path` | _ABSOLUTE_PATH_ | **[string]** absolute path to the Kitti root folder |
### Import Labels
To import the labels as a **list of python dictionaries**, use the function:
```
import_labels(im_id, im_set, db_absolute_path = ABSOLUTE_PATH)
```
| Arguments | Default | Description |
| ----------------------|---------------------------|------------------------------------------------|
| `im_id` | | **[int]** corresponding to the image id in the kitti dataset |
| `im_set` | | **[string]** 'train' or 'test' to chose the type of set |
| `db_absolute_path` | _ABSOLUTE_PATH_ | **[string]** absolute path to the Kitti root folder |
#### Description of the fields of the dictionary
| Fields | Description |
| ------------------------------|------------------------------------------------|
| `type` | **[string]** 'Car', 'Van', 'Truck', 'Pedestrian', 'Person_sitting', 'Cyclist', 'Tram', 'Misc' or 'DontCare' |
| `truncated` | **[float]** Float from 0 (non-truncated) to 1 (truncated), where truncated refers to the object leaving the image boundaries |
| `occluded` | **[int]** Integer (0,1,2,3) indicating occlusion state: 0 = fully visible, 1 = partly occluded, 2 = largely occluded, 3 = unknown |
| `alpha` | **[float]** Observation angle of an object [-pi..pi] |
| `bbox` | **[dict][float]** `x_min`, `y_min`, `x_max`, `y_max` pixel coordinates (0-based index) |
| `3D_dim` | **[dict][float]** `height`, `width`, `length` (in meters) |
| `3D_loc` | **[dict][float]** `x`, `y`, `z` in camera coordinates (in meters) |
| `rotation_y` | **[float]** around Y-axis in camera coordinates [-pi..pi] |
| `score` _(Optional)_ | **[float]** Only for results, indicating confidence in detection, needed for p/r curves, higher is better. |
### Display Image + Labels
To display a image and its labels in a **Jupyter Notebook**, use the function:
```
display_im(im, labels = [], display_boxes = True, display_info = True,
types_to_display = DEFAULT_TYPES_TO_DISPLAY,
info_to_display = DEFAULT_INFO_TO_DISPLAY,
db_absolute_path = ABSOLUTE_PATH, im_width = FIG_WIDTH,
im_height = FIG_HEIGHT, display_axis = False,
title = '', display_center_boxes = True, num_cell_grid = 0)
```
| Arguments | Default | Description |
| ----------------------|---------------------------|----------------------------|
| `im` | | **[numpy.array]** Image to display |
| `labels` | _[]_ | **[list][dict]** List of dictionaries containing the labels of the image |
| `display_boxes` | _True_ | **[bool]** Display the boxes around the objects |
| `display_info` | _True_ | **[bool]** Write the `info_to_display` under the image |
| `types_to_display` | _DEFAULT_TYPES_TO_DISPLAY_| **[list]** List of types of object to consider |
| `info_to_display` | _DEFAULT_INFO_TO_DISPLAY_ | **[list]** List of characteritics of object to display |
| `im_width` | _FIG_WIDTH_ | **[int]** Width of the image to display |
| `im_height` | _FIG_HEIGHT_ | **[int]** Height of the image to display |
| `display_axis` | _False_ | **[bool]** Display the axis of the image |
| `title` | _''_ | **[string]** Title to write before the image |
| `display_center_boxes`| _True_ | **[bool]** Display the center of the boxes |
| `num_cell_grid` | _0_ | **[int]** Display a `num_cell_grid` x `num_cell_grid` over the image|
**Remarks**: To change `FIG_FONT_SIZE_TITLE`, `COLOR_GRID` and `COLOR_TYPE` do it in the global variables at the begining of the `kittipy.py` file.
### Example
**Code** _(Jupyter Notebook)_
```python
list_ids = get_data_list('train')
image = import_im(list_ids[3], 'train')
labels = import_labels(list_ids[3], 'train')
display_im(image, labels, num_cell_grid = 10)
```
**Output**
## Author
* **Lucas Vandroux (冯凯)** [【Github】](https://github.com/LucasVandroux) [【LinkedIn】](https://www.linkedin.com/in/lucasvandroux/)
I started this project to help me during my Master Thesis at Tsinghua University in China. If you find it useful, don't hesitate to use it and improve it.
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