multiview_ball_calib

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multiview_ball_calib

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A python tutorial to use ball calibrating multiview camera system.

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# Multiview Ball Calibration
Here is a python + opencv toolbox that can help you calibrate your multi-camera extrinsic parameters. All 2D and 3D position data are `numpy array` based.

![ball move](https://raw.githubusercontent.com/chenxinfeng4/multiview_ball_calib/main/src_images/ball_move.gif)
![bundle ajust](https://raw.githubusercontent.com/chenxinfeng4/multiview_ball_calib/main/src_images/bundle_adjust.jpg)
![3D camera space](https://raw.githubusercontent.com/chenxinfeng4/multiview_ball_calib/main/src_images/cameras_3d_show.jpg)
![transform](https://raw.githubusercontent.com/chenxinfeng4/multiview_ball_calib/main/src_images/transform_2D_3D.jpg)

# Bilibili video link (B站视频链接)
singleball + global registion: [Video#1](https://www.bilibili.com/video/BV15i421v7xo/)

doubleball: [Video#2](https://www.bilibili.com/video/BV1e1421b7cv/)

## Python package install
I've assume that you've installed basic libs, like `opencv`, `numpy`, `scipy`. Here is the multive calib package, just install it.
The `multiview_calib` is modified from `https://github.com/cvlab-epfl/multiview_calib`, take care use my version.
```bash
pip install -U git+https://github.com/chenxinfeng4/multiview_calib
```

## Ball define and detect
You can use `single-ball`, `double-ball` or `triple-ball`.

Routine:
![routine](https://raw.githubusercontent.com/chenxinfeng4/multiview_ball_calib/main/src_images/routine.jpg)

!!! **The ball detector is not contain in this code**, so you should manually give me the position of ball in each view. You can use YOLO-v8 to detect.

> The `single-ball`, `double-ball` or `triple-ball` are most the some pipeline. Except the double/triple-ball can restore the scale information. It will be helpful when you don't care about the world origin point and axes direction. Other wise I recommand you to use `single-ball` plus `checkboard`, which can onstep register `world axes`.

![ball_define](https://raw.githubusercontent.com/chenxinfeng4/multiview_ball_calib/main/src_images/ball_define.jpg)
![ball_define](https://raw.githubusercontent.com/chenxinfeng4/multiview_ball_calib/main/src_images/doubleball.jpg)

## Tutorial
The test data is contained in the tutorial.

### Step1: Intrinsic Calibration
See the `1_intrinsic_calib/intrinsic_calib.ipynb`. You will get the `intrinsic_calib.json`.

### Step2: Ball Extrinsic Calibration
See the `2A_ball_extrinsic_calib/ball_extrinsic_calib.ipynb` and `2B_triball_extrinsic_calib/tripleball_extrinsic_calib.ipynb`. You will get the `*calibpkl`.

The calibpkl contains the all intrinsic & extrinsic parameters. 
> `K` (3, 3);  
> `dist` [K1, K2, T1, T2, K3];  
> `t` (3,);  
> `R` (3, 3);

### Step3: World Axes Register
See the `3_worldaxes_register/worldaxes_register.ipynb`. You will get the refined calibration model `*.recalibpkl`.

### Step4: Mutural Transform 2D ⇌ 3D
See the `4_transform_between2D_and_3D/mutual_transform_2D_3D.ipynb`

### Step5: Visualize the Camera model
See the `5_plot_camera3D/plot_camera3D.ipynb`

### Step6: Convert file format for `Anipose/Deeplabcut/DANNCE`
See the `6_export_ANIPOSE_DEEPLABCUT_DANNCE/convert_model_file.ipynb`

## Performance and Precision
All 2D and 3D position data are `numpy array` based, so the calibration and reconstruction would be fast.
The calibration error is `0.6 mm` in 3D construction, and `1.6 pixel` in 2D image projection. Only 50-100 time slice ball-allview samples are enough for calibration.

![performance](https://raw.githubusercontent.com/chenxinfeng4/multiview_ball_calib/main/src_images/performance.jpg)

## Adapt to your own data
1. Your multiview videos, sure. All cameras should be synchronized. I recommend your to use OBS Studio grid arange the cameras and record into one file.
2. One `checkboard`. It's for `intrinsic` calibration. Also, it's for `world axes` registration.
3. One `single-ball` or one `triple-ball`. And also a ball detection software. In my case, I use `open-mmlab/mmpose` and `YOLO-v8` to detect the ball and extract the center position. They offer 1000 images/sec ball detection. The config of mmpose/YOLO-v8 is not release in this turorial yet.
4. No ball detection in you case? Manually label 50 time slice ball-all-view samples. And extract the positions into a numpy array as (NVIEW, NSAMPLE, 1, XYP).

## Aknowledgement
- Great work of https://github.com/cvlab-epfl/multiview_calib .

## Extent readings
- [Single-ball calibration] Perez-Cortes, Juan-Carlos, et al. "A system for in-line 3D inspection without hidden surfaces." Sensors 18.9 (2018): 2993.
- [Multi-ball calibration] Perez, Alberto J., Juan-Carlos Perez-Cortes, and Jose-Luis Guardiola. "Simple and precise multi-view camera calibration for 3D reconstruction." Computers in Industry 123 (2020): 103256.
- [Triple-ball calibration] 吴福朝，王亮，胡占义. “一种对多相机系统的标定方法及装置” . 发明专利 (2008): CN 101226638A

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