Description
Image calibration is an important step to ensure that the images captured by the vehicle's cameras are accurately processed and analyzed by computer vision algorithms for the purpose of the ADAS systems and autonomous vehicles. Hyperspec image calibration tool for this purpose includes the following features:
Distortion correction: This feature corrects any lens distortion in the images captured by the vehicle's cameras, such as radial or tangential distortion, to ensure that objects are accurately represented in the image.
Geometric calibration: This feature corrects any geometric distortions in the images, such as perspective distortion or skew, which may affect the accuracy of object detection and tracking algorithms.
Image scaling: This feature rescales the images to a consistent size and resolution, which may be necessary for efficient processing and analysis by computer vision algorithms.
Batch processing: This feature allows multiple images to be calibrated and processed simultaneously, which is necessary for processing large datasets of images captured by the vehicle's cameras.
Integration with AI/ML frameworks: The image calibration tool may be integrated with popular AI/ML frameworks such as TensorFlow, PyTorch, or Keras, to streamline the workflow and enable seamless integration with other computer vision algorithms.
User-friendly interface: The tool has a user-friendly interface that is easy to use and customizable to the user's preferences.
How to open Image Calibration Tool:
User Interface
The user interface of the Hyperspec image calibration tool for AI/ML autonomous vehicles is intuitive and easy to use, with the following key features:
Image preview: This is the main area where the image is displayed. The tool includes left right and center camera images. The user will be able to preview the original image and the calibrated image to visualize the effects of the calibration process.
Calibration settings: The user will be able to adjust the calibration settings, such as camera parameters and distortion coefficients, to fine-tune the calibration process. Calibration settings are different for all three images. Eg. Fx, Fy, a,b
Batch processing: The user will be able to calibrate multiple images with the help of tool navigation, to save time and increase efficiency.
Calibration results: The tool has detailed calibration results, such as the mean reprojection error and other calibration metrics, to ensure that the calibration process is accurate and reliable.
Integration with other tools: The user should be able to export the calibrated images to other tools or applications for further analysis and processing, such as computer vision algorithms or machine learning frameworks.
User-friendly interface: The user interface should be simple and intuitive, with clear labeling and helpful tooltips to guide the user through the calibration process.
Customizability: The user should be able to customize the interface to suit their preferences, such as changing the layout or color scheme.
In addition, the user interface should be optimized for use in autonomous vehicles, which may have limited computing resources and require real-time processing. The tool should be designed to run efficiently on embedded systems or GPUs and should be able to process images in real-time to meet the requirements of autonomous driving applications.
Process
Here are the general steps for using the Hyperspec image calibration tool:
- Capture images: Capture a set of images using the cameras mounted on the autonomous vehicle.
- Load images: Load the task into the image calibration tool with the help of the Roadmentor dashboard.
- Camera calibration: Calibrate the camera by selecting a calibration pattern, such as a checkerboard, and capturing images of the pattern from different angles. The tool will then use these images to determine the camera parameters and distortion coefficients.
- Image calibration: Apply the calibration parameters to the original images to correct for any distortions, such as lens distortion, geometric distortion, or color distortion.
- Preview and adjust: Preview the calibrated images and adjust the calibration settings as necessary to fine-tune the calibration process.
- Batch processing: If desired, select multiple images and calibrate them in batch mode to save time and increase efficiency.
- Export: Export the calibrated images to other tools or applications for further analysis and processing, such as computer vision algorithms or machine learning frameworks.
- It is important to note that the specific steps and settings for using an image calibration tool may vary depending on the tool and the specific application. It may be necessary to consult the tool's documentation or seek expert guidance to ensure accurate and reliable calibration of the images for AI/ML applications in autonomous vehicles.