| OpenCV 3.4.1 Open Source Computer Vision

Classes | Enumerations | Functions
HDR imaging
Computational Photography

## Classes
class | cv::AlignExposures
| The base class for algorithms that align images of the same scene with different exposures. More...
class | cv::AlignMTB
| This algorithm converts images to median threshold bitmaps (1 for pixels brighter than median luminance and 0 otherwise) and than aligns the resulting bitmaps using bit operations. More...
class | cv::CalibrateCRF
| The base class for camera response calibration algorithms. More...
class | cv::CalibrateDebevec
| Inverse camera response function is extracted for each brightness value by minimizing an objective function as linear system. Objective function is constructed using pixel values on the same position in all images, extra term is added to make the result smoother. More...
class | cv::CalibrateRobertson
| Inverse camera response function is extracted for each brightness value by minimizing an objective function as linear system. This algorithm uses all image pixels. More...
class | cv::MergeDebevec
| The resulting HDR image is calculated as weighted average of the exposures considering exposure values and camera response. More...
class | cv::MergeExposures
| The base class algorithms that can merge exposure sequence to a single image. More...
class | cv::MergeMertens
| Pixels are weighted using contrast, saturation and well-exposedness measures, than images are combined using laplacian pyramids. More...
class | cv::MergeRobertson
| The resulting HDR image is calculated as weighted average of the exposures considering exposure values and camera response. More...
class | cv::Tonemap
| Base class for tonemapping algorithms - tools that are used to map HDR image to 8-bit range. More...
class | cv::TonemapDrago
| Adaptive logarithmic mapping is a fast global tonemapping algorithm that scales the image in logarithmic domain. More...
class | cv::TonemapDurand
| This algorithm decomposes image into two layers: base layer and detail layer using bilateral filter and compresses contrast of the base layer thus preserving all the details. More...
class | cv::TonemapMantiuk
| This algorithm transforms image to contrast using gradients on all levels of gaussian pyramid, transforms contrast values to HVS response and scales the response. After this the image is reconstructed from new contrast values. More...
class | cv::TonemapReinhard
| This is a global tonemapping operator that models human visual system. More...

## Enumerations
enum | { cv::LDR_SIZE = 256 }

## Functions
Ptr< AlignMTB > | cv::createAlignMTB (int max_bits=6, int exclude_range=4, bool cut=true)
| Creates AlignMTB object. More...
Ptr< CalibrateDebevec > | cv::createCalibrateDebevec (int samples=70, float lambda=10.0f, bool random=false)
| Creates CalibrateDebevec object. More...
Ptr< CalibrateRobertson > | cv::createCalibrateRobertson (int max_iter=30, float threshold=0.01f)
| Creates CalibrateRobertson object. More...
Ptr< MergeDebevec > | cv::createMergeDebevec ()
| Creates MergeDebevec object. More...
Ptr< MergeMertens > | cv::createMergeMertens (float contrast_weight=1.0f, float saturation_weight=1.0f, float exposure_weight=0.0f)
| Creates MergeMertens object. More...
Ptr< MergeRobertson > | cv::createMergeRobertson ()
| Creates MergeRobertson object. More...
Ptr< Tonemap > | cv::createTonemap (float gamma=1.0f)
| Creates simple linear mapper with gamma correction. More...
Ptr< TonemapDrago > | cv::createTonemapDrago (float gamma=1.0f, float saturation=1.0f, float bias=0.85f)
| Creates TonemapDrago object. More...
Ptr< TonemapDurand > | cv::createTonemapDurand (float gamma=1.0f, float contrast=4.0f, float saturation=1.0f, float sigma_space=2.0f, float sigma_color=2.0f)
| Creates TonemapDurand object. More...
Ptr< TonemapMantiuk > | cv::createTonemapMantiuk (float gamma=1.0f, float scale=0.7f, float saturation=1.0f)
| Creates TonemapMantiuk object. More...
Ptr< TonemapReinhard > | cv::createTonemapReinhard (float gamma=1.0f, float intensity=0.0f, float light_adapt=1.0f, float color_adapt=0.0f)
| Creates TonemapReinhard object. More...

## Detailed Description

This section describes high dynamic range imaging algorithms namely tonemapping, exposure alignment, camera calibration with multiple exposures and exposure fusion.

## Enumeration Type Documentation

## § anonymous enum

anonymous enum

Enumerator
LDR_SIZE Python: cv.LDR_SIZE |

## Function Documentation

## § createAlignMTB()

Ptr<AlignMTB> cv::createAlignMTB | ( | int | max_bits = `6`,
|  | int | exclude_range = `4`,
|  | bool | cut = `true`
| ) |  |

Python:
| retval | = | cv.createAlignMTB( | [, max_bits[, exclude_range[, cut]]] | )

Creates AlignMTB object.

Parameters

  max_bits | logarithm to the base 2 of maximal shift in each dimension. Values of 5 and 6 are usually good enough (31 and 63 pixels shift respectively).
  exclude_range | range for exclusion bitmap that is constructed to suppress noise around the median value.
  cut | if true cuts images, otherwise fills the new regions with zeros.

## § createCalibrateDebevec()

Ptr<CalibrateDebevec> cv::createCalibrateDebevec | ( | int | samples = `70`,
|  | float | lambda = `10.0f`,
|  | bool | random = `false`
| ) |  |

Python:
| retval | = | cv.createCalibrateDebevec( | [, samples[, lambda[, random]]] | )

Creates CalibrateDebevec object.

Parameters

  samples | number of pixel locations to use
  lambda | smoothness term weight. Greater values produce smoother results, but can alter the response.
  random | if true sample pixel locations are chosen at random, otherwise they form a rectangular grid.

## § createCalibrateRobertson()

Ptr<CalibrateRobertson> cv::createCalibrateRobertson | ( | int | max_iter = `30`,
|  | float | threshold = `0.01f`
| ) |  |

Python:
| retval | = | cv.createCalibrateRobertson( | [, max_iter[, threshold]] | )

Creates CalibrateRobertson object.

Parameters

  max_iter | maximal number of Gauss-Seidel solver iterations.
  threshold | target difference between results of two successive steps of the minimization.

## § createMergeDebevec()

Ptr<MergeDebevec> cv::createMergeDebevec | ( |  | ) |

Python:
| retval | = | cv.createMergeDebevec( |  | )

Creates MergeDebevec object.

## § createMergeMertens()

Ptr<MergeMertens> cv::createMergeMertens | ( | float | contrast_weight = `1.0f`,
|  | float | saturation_weight = `1.0f`,
|  | float | exposure_weight = `0.0f`
| ) |  |

Python:
| retval | = | cv.createMergeMertens( | [, contrast_weight[, saturation_weight[, exposure_weight]]] | )

Creates MergeMertens object.

Parameters

  contrast_weight | contrast measure weight. See MergeMertens.
  saturation_weight | saturation measure weight
  exposure_weight | well-exposedness measure weight

## § createMergeRobertson()

Ptr<MergeRobertson> cv::createMergeRobertson | ( |  | ) |

Python:
| retval | = | cv.createMergeRobertson( |  | )

Creates MergeRobertson object.

## § createTonemap()

Ptr<Tonemap> cv::createTonemap | ( | float | gamma = `1.0f` | ) |

Python:
| retval | = | cv.createTonemap( | [, gamma] | )

Creates simple linear mapper with gamma correction.

Parameters

  gamma | positive value for gamma correction. Gamma value of 1.0 implies no correction, gamma equal to 2.2f is suitable for most displays. Generally gamma > 1 brightens the image and gamma < 1 darkens it.

## § createTonemapDrago()

Ptr<TonemapDrago> cv::createTonemapDrago | ( | float | gamma = `1.0f`,
|  | float | saturation = `1.0f`,
|  | float | bias = `0.85f`
| ) |  |

Python:
| retval | = | cv.createTonemapDrago( | [, gamma[, saturation[, bias]]] | )

Creates TonemapDrago object.

Parameters

  gamma | gamma value for gamma correction. See createTonemap
  saturation | positive saturation enhancement value. 1.0 preserves saturation, values greater than 1 increase saturation and values less than 1 decrease it.
  bias | value for bias function in [0, 1] range. Values from 0.7 to 0.9 usually give best results, default value is 0.85.

## § createTonemapDurand()

Ptr<TonemapDurand> cv::createTonemapDurand | ( | float | gamma = `1.0f`,
|  | float | contrast = `4.0f`,
|  | float | saturation = `1.0f`,
|  | float | sigma_space = `2.0f`,
|  | float | sigma_color = `2.0f`
| ) |  |

Python:
| retval | = | cv.createTonemapDurand( | [, gamma[, contrast[, saturation[, sigma_space[, sigma_color]]]]] | )

Creates TonemapDurand object.

Parameters

  gamma | gamma value for gamma correction. See createTonemap
  contrast | resulting contrast on logarithmic scale, i. e. log(max / min), where max and min are maximum and minimum luminance values of the resulting image.
  saturation | saturation enhancement value. See createTonemapDrago
  sigma_space | bilateral filter sigma in color space
  sigma_color | bilateral filter sigma in coordinate space

## § createTonemapMantiuk()

Ptr<TonemapMantiuk> cv::createTonemapMantiuk | ( | float | gamma = `1.0f`,
|  | float | scale = `0.7f`,
|  | float | saturation = `1.0f`
| ) |  |

Python:
| retval | = | cv.createTonemapMantiuk( | [, gamma[, scale[, saturation]]] | )

Creates TonemapMantiuk object.

Parameters

  gamma | gamma value for gamma correction. See createTonemap
  scale | contrast scale factor. HVS response is multiplied by this parameter, thus compressing dynamic range. Values from 0.6 to 0.9 produce best results.
  saturation | saturation enhancement value. See createTonemapDrago

## § createTonemapReinhard()

Ptr<TonemapReinhard> cv::createTonemapReinhard | ( | float | gamma = `1.0f`,
|  | float | intensity = `0.0f`,
|  | float | light_adapt = `1.0f`,
|  | float | color_adapt = `0.0f`
| ) |  |

Python:
| retval | = | cv.createTonemapReinhard( | [, gamma[, intensity[, light_adapt[, color_adapt]]]] | )

Creates TonemapReinhard object.

Parameters

  gamma | gamma value for gamma correction. See createTonemap
  intensity | result intensity in [-8, 8] range. Greater intensity produces brighter results.
  light_adapt | light adaptation in [0, 1] range. If 1 adaptation is based only on pixel value, if 0 it's global, otherwise it's a weighted mean of this two cases.
  color_adapt | chromatic adaptation in [0, 1] range. If 1 channels are treated independently, if 0 adaptation level is the same for each channel.