__init__.py

Go to the documentation of this file.

"""!
@brief
@defgroup ids_peak_icv_python Python
@ingroup ids_peak_icv_library
 
@brief
The Python interface for the IDS peak ICV library.
 
The interface is delivered as a wheel via the Python Package Index (PyPI).
It can be installed using `pip`:
 
```bash
pip install ids_peak_icv
```
 
To use @htmlonly IDS peak ICV @endhtmlonly in your project import ids_peak_icv by calling:
 
```python
import ids_peak_icv
```
 
@defgroup ids_peak_icv_python_calibration Calibration
@ingroup ids_peak_icv_python
 
@brief
Geometric camera calibration.
 
Provides tools to determine camera parameters, correct lens distortion, and map image pixels to physical real-world coordinates.
 
@defgroup ids_peak_icv_python_experimental Experimental
@ingroup ids_peak_icv_python
 
@brief
Preliminary features under development.
 
Next to its officially released functions the IDS peak ICV library contains preliminary functions
that are being prepared by our software development team but are still under development.
These preliminary functions are marked with a warning.
All preliminary functionality is located in the experimental Python module (ids_peak_icv.experimental).
Please use these functions and classes for testing purposes only.
We welcome any feedback or suggestions that can help us improve them.
Do not use these functions in production environments until they have been officially released.
 
@defgroup ids_peak_icv_python_filters Filters
@ingroup ids_peak_icv_python
 
@brief
Image filtering.
 
@defgroup ids_peak_icv_python_painting Painting
@ingroup ids_peak_icv_python
 
@brief
Image painting.
 
Provides tools and styling options to draw shapes and overlays onto images.
 
@defgroup ids_peak_icv_python_pipeline Image Pipeline
@ingroup ids_peak_icv_python
 
@brief
Comprehensive image processing pipeline system for real-time image transformation and enhancement.
 
The Image Pipeline provides a complete, modular framework for processing raw or pre-processed image data
through a series of configurable transformation stages. It supports a wide range of image processing
operations from basic format conversions to advanced enhancement algorithms.
 
## Architecture Overview
 
The pipeline follows a modular design with two main component types:
- **Modules**: Core processing units that perform specific image transformations
- **Features**: High-level interfaces that provide user-friendly access to module functionality
 
## Processing Flow
 
The pipeline processes images through sequential stages:
 
- **Data Unpacking** - Converts packed sensor formats to standard representations
- **Defect Correction** - Removes hot pixels and other sensor artifacts
- **Geometric Transformations** - Handles binning, decimation, rotation, and mirroring
- **Gain Control** - Applies brightness and white balance adjustments
- **Color Processing** - Performs debayering and color space transformations
- **Enhancement** - Applies sharpening, gamma correction, and tone mapping
- **Format Conversion** - Converts to the desired output pixel format
 
For more information refer to the @ref guide_pipeline Guide.
 
@defgroup ids_peak_icv_python_selectors Selectors
@ingroup ids_peak_icv_python
 
@brief
Object filtering.
 
A Selector encapsulates a filtering operation
applied to a given set of objects.
Instead of modifying the original set,
each selection function returns a new Selector object
that represents the filtered subset.
 
This design enables the chaining of multiple selection criteria.
 
To access the currently selected objects,
use the respective accessor functions,
such as `RegionSelector.regions`.
 
@defgroup ids_peak_icv_python_serialization Serialization
@ingroup ids_peak_icv_python
 
@brief
Object serialization.
 
Serialization refers to the process of transforming
an object’s state or a data structure
into a format suitable for storage or transmission
(e.g., files, databases, or network streams).
This allows data to be persisted
and later reconstructed through deserialization,
restoring the original structure and values.
 
Typical use cases include:
- Saving application state to disk
- Transferring data between processes or systems
 
@defgroup ids_peak_icv_python_thresholds Thresholds
@ingroup ids_peak_icv_python
 
@brief
Image thresholding.
 
@defgroup ids_peak_icv_python_transformations Transformations
@ingroup ids_peak_icv_python
 
@brief
Geometric image transformations.
 
@defgroup ids_peak_icv_python_types Types
@ingroup ids_peak_icv_python
 
@brief
Fundamental data types.
 
@defgroup ids_peak_icv_python_types_geometry Geometry
@ingroup ids_peak_icv_python_types
 
@brief Geometric data types.
 
"""
 
from .backend.utils import Loader
 
lib_loader = Loader()
 
from .datatypes import (BinningFactor, Image, Region)
from . import pipeline
from . import serialization
from .exceptions import (CorruptedException, IOException, MismatchException, NotSupportedException,
                         NotPossibleException, TargetNotFoundException, OutOfRangeException, InternalErrorException,
                         MathErrorException, NullPointerException, InvalidConfigurationException,
                         ICVException)
from . import (selectors, thresholds, calibration, transformations, filters)
from .datatypes import (PointCloud, XYZImage, Polygon, PointXYZ, PointXYZI, CoordinateSystem)