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229 lines
6.5 KiB
Markdown
229 lines
6.5 KiB
Markdown
# Camera calibration
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Computer vision is becoming more and more widespread. Often, computer vision algorithms are not precise and obtain distorted images from the camera, which is especially true for fisheye cameras.
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> The image is "rounded" closer to the edge.
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Any computer vision algorithm will perceive the picture incorrectly. To remove such distortion, the camera that receives the image is to be calibrated in accordance with its own peculiarities.
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## Script installation
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First, you have to install the necessary libraries:
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```
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pip install numpy
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pip install opencv-python
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pip install glob
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pip install pyyaml
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pip install urllib.request
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```
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Then download the script from the repository:
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```(bash)
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git clone https://github.com/tinderad/clever_cam_calibration.git
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```
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Go to the downloaded folder and install the script:
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```(bash)
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cd clever_cam_calibration
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sudo python setup.py build
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sudo python setup.py install
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```
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If you are using Windows, download the archive from the [repository](https://github.com/tinderad/clever_cam_calibration/archive/master.zip), unzip it and install:
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```(bash)
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cd path\to\archive\clever_cam_calibration\
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python setup.py build
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python setup.py install
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```
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> path\to\archive – path to unpacked archive.
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## Preparing for calibration
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You will have to prepare a calibration target. It looks like a chessboard. The file is available for downloading [here](https://www.oreilly.com/library/view/learning-opencv-3/9781491937983/assets/lcv3_ac01.png).
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Glue a printed target to any solid surface. Count the number of intersections on the board lengthwise and widthwise, measure the size of a cell (mm).
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Turn on Clover and connect to its Wi-Fi.
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> Navigate to 192.168.11.1:8080 and check whether the computer receives images from the image_raw topic.
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## Calibration
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Run script **_calibrate_cam_**:
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**Windows:**
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```(bash)
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>path\to\python\Scripts\calibrate_cam.exe
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```
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> path\to\Python – path to the Python folder
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**Linux:**
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```(bash)
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>calibrate_cam
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```
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Specify board parameters:
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```(bash)
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>calibrate_cam
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Chessboard width: # Intersections widthwise
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Chessboard height: # Intersections heightwise
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Square size: # Length of cell edge (mm)
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Saving mode (YES - on): # Save mode
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```
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> Save mode: if enabled, all received pictures will be saved in the current folder.
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The script will start running:
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```
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Calibration started!
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Commands:
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help, catch (key: Enter), delete, restart, stop, finish
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```
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To calibrate the camera, make at least 25 photos of the chessboard at various angles.
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To make a photo, enter command **_catch_**.
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```(bash)
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>catch
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```
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The program will inform you about the calibration status.
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```(bash)
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...
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Chessboard not found, now 0 (25 required)
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> # Enter
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---
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Image added, now 1 (25 required)
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```
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> Instead of entering command **_catch_** each time, you can just press **_Enter_** (enter a blank line).
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After you have made a sufficient number of images, enter command **_finish_**.
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```(bash)
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...
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>finish
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Calibration successful!
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```
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### Calibration by the existing images
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If you already have images, you can calibrate the camera by them with the help of script **_calibrate_cam_ex_**.
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```(bash)
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>calibrate_cam_ex
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```
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Specify target characteristics and the path to the folder with images:
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```(bash)
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>calibrate_cam_ex
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Chessboard width: # Intersections widthwise
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Chessboard height: # Intersections heightwise
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Square size: # Length of cell edge (mm)
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Path: # Path to the folder with images
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```
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Apart from that, this script works similarly to **_calibrate_cam_**.
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The program will process all received pictures, and create file **_camera_info_****_._****_yaml_** in the current folder. Using this file, you can equalize distortions in the images obtained from this camera.
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> If you change the resolution of the received image, you will have to re-calibrate the camera.
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## Correcting distortions
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Function **_get_undistorted_image(cv2_image, camera_info)_** is responsible for obtaining a corrected image:
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* **_cv2_image_**: An image encoded into a cv2 array.
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* **_camera_****___****_info_**: The path to the calibration file.¬
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The function returns a cv2 array, into which the corrected image is coded.
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> If you are using a fisheye camera provided with Clover, for processing images with resolution 320x240 or 640x480, you can use the existing calibration settings. To do this, pass parameters **_clever_cam_calibration.clevercamcalib.CLEVER_FISHEYE_CAM_320_** or **_clever_cam_calibration.clevercamcalib.CLEVER_FISHEYE_CAM_640_** as argument **_camera_info_**, respectively.
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## Examples of operation
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Source images:
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Corrected images:
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## An example of usage
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**Processing image stream from the camera**.
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This program receives images from the camera on Clover and displays them on the screen in corrected for, using the existing calibration file.
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```python
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import clevercamcalib.clevercamcalib as ccc
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import cv2
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import urllib.request
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import numpy as np
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while True:
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req = urllib.request.urlopen('http://192.168.11.1:8080/snapshot?topic=/main_camera/image_raw')
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arr = np.asarray(bytearray(req.read()), dtype=np.uint8)
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image = cv2.imdecode(arr, -1)
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undistorted_img = ccc.get_undistorted_image(image, ccc.CLEVER_FISHEYE_CAM_640)
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cv2.imshow("undistort", undistorted_img)
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cv2.waitKey(33)
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cv2.destroyAllWindows()
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```
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## The usage for ArUco
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To apply the calibration parameters to the ArUco navigation system, move the calibration .yaml file to Raspberry Pi of Clover, and initialize it.
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> Don't forget to connect to Wi-Fi of Clover.
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The SFTP protocol is used for transferring the file. This example, WinSCP program is used.
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Connect to Raspberry Pi via SFTP:
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> Password: _**raspberry**_
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Press “Enter”. Go to _**/home/pi/catkin_ws/src/clever/clever/camera_info/**_, and copy the calibration .yaml file to this folder:
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Now we have to select this file in ArUco configuration. Connection via SSH is used for this purpose. This example, PuTTY program is used.
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Connect to Raspberry Pi via SSH:
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Log in with username _**pi**_ and password _**raspberry**_, go to directory _**/home/pi/catkin_ws/src/clever/clever/launch**_ and start editing configuration _**main_camera.launch**_:
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In line _**camera node**_, change parameter _**camera_info**_ to _**camera_info.yaml**_:
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> Don't forget to change camera resolution.
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