Module opencv::photo

Computational Photography

This module includes photo processing algorithms

Inpainting

Denoising

HDR imaging

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

Contrast Preserving Decolorization

Useful links:

http://www.cse.cuhk.edu.hk/leojia/projects/color2gray/index.html

Seamless Cloning

Useful links:

https://www.learnopencv.com/seamless-cloning-using-opencv-python-cpp

Non-Photorealistic Rendering

Useful links:

http://www.inf.ufrgs.br/~eslgastal/DomainTransform

https://www.learnopencv.com/non-photorealistic-rendering-using-opencv-python-c/

C API

Modules

• prelude

Constants

• INPAINT_NS
  Use Navier-Stokes based method
• INPAINT_TELEA
  Use the algorithm proposed by Alexandru Telea Telea04
• LDR_SIZE
• MIXED_CLONE
  The classic method, color-based selection and alpha masking might be time consuming and often leaves an undesirable halo. Seamless cloning, even averaged with the original image, is not effective. Mixed seamless cloning based on a loose selection proves effective.
• MONOCHROME_TRANSFER
  Monochrome transfer allows the user to easily replace certain features of one object by alternative features.
• NORMAL_CLONE
  The power of the method is fully expressed when inserting objects with complex outlines into a new background
• NORMCONV_FILTER
  Normalized Convolution Filtering
• RECURS_FILTER
  Recursive Filtering

Traits

• AlignExposures
  The base class for algorithms that align images of the same scene with different exposures
• AlignExposuresConst
  Constant methods for crate::photo::AlignExposures
• 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.
• AlignMTBConst
  Constant methods for crate::photo::AlignMTB
• CalibrateCRF
  The base class for camera response calibration algorithms.
• CalibrateCRFConst
  Constant methods for crate::photo::CalibrateCRF
• 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.
• CalibrateDebevecConst
  Constant methods for crate::photo::CalibrateDebevec
• 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.
• CalibrateRobertsonConst
  Constant methods for crate::photo::CalibrateRobertson
• MergeDebevec
  The resulting HDR image is calculated as weighted average of the exposures considering exposure values and camera response.
• MergeDebevecConst
  Constant methods for crate::photo::MergeDebevec
• MergeExposures
  The base class algorithms that can merge exposure sequence to a single image.
• MergeExposuresConst
  Constant methods for crate::photo::MergeExposures
• MergeMertens
  Pixels are weighted using contrast, saturation and well-exposedness measures, than images are combined using laplacian pyramids.
• MergeMertensConst
  Constant methods for crate::photo::MergeMertens
• MergeRobertson
  The resulting HDR image is calculated as weighted average of the exposures considering exposure values and camera response.
• MergeRobertsonConst
  Constant methods for crate::photo::MergeRobertson
• Tonemap
  Base class for tonemapping algorithms - tools that are used to map HDR image to 8-bit range.
• TonemapConst
  Constant methods for crate::photo::Tonemap
• TonemapDrago
  Adaptive logarithmic mapping is a fast global tonemapping algorithm that scales the image in logarithmic domain.
• TonemapDragoConst
  Constant methods for crate::photo::TonemapDrago
• 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.
• TonemapMantiukConst
  Constant methods for crate::photo::TonemapMantiuk
• TonemapReinhard
  This is a global tonemapping operator that models human visual system.
• TonemapReinhardConst
  Constant methods for crate::photo::TonemapReinhard

Functions

• color_change
  Given an original color image, two differently colored versions of this image can be mixed seamlessly.
• create_align_mtb
  Creates AlignMTB object
• create_calibrate_debevec
  Creates CalibrateDebevec object
• create_calibrate_robertson
  Creates CalibrateRobertson object
• create_merge_debevec
  Creates MergeDebevec object
• create_merge_mertens
  Creates MergeMertens object
• create_merge_robertson
  Creates MergeRobertson object
• create_tonemap
  Creates simple linear mapper with gamma correction
• create_tonemap_drago
  Creates TonemapDrago object
• create_tonemap_mantiuk
  Creates TonemapMantiuk object
• create_tonemap_reinhard
  Creates TonemapReinhard object
• decolor
  Transforms a color image to a grayscale image. It is a basic tool in digital printing, stylized black-and-white photograph rendering, and in many single channel image processing applications CL12 .
• denoise_tvl1
  Primal-dual algorithm is an algorithm for solving special types of variational problems (that is, finding a function to minimize some functional). As the image denoising, in particular, may be seen as the variational problem, primal-dual algorithm then can be used to perform denoising and this is exactly what is implemented.
• detail_enhance
  This filter enhances the details of a particular image.
• edge_preserving_filter
  Filtering is the fundamental operation in image and video processing. Edge-preserving smoothing filters are used in many different applications EM11 .
• fast_nl_means_denoising
  Perform image denoising using Non-local Means Denoising algorithm http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/ with several computational optimizations. Noise expected to be a gaussian white noise
• fast_nl_means_denoising_1
  C++ default parameters
• fast_nl_means_denoising_colored
  Modification of fastNlMeansDenoising function for colored images
• fast_nl_means_denoising_colored_1
  C++ default parameters
• fast_nl_means_denoising_colored_cuda
  Modification of fastNlMeansDenoising function for colored images
• fast_nl_means_denoising_colored_multi
  Modification of fastNlMeansDenoisingMulti function for colored images sequences
• fast_nl_means_denoising_cuda
  Perform image denoising using Non-local Means Denoising algorithm http://www.ipol.im/pub/algo/bcm_non_local_means_denoising with several computational optimizations. Noise expected to be a gaussian white noise
• fast_nl_means_denoising_multi
  Modification of fastNlMeansDenoising function for images sequence where consecutive images have been captured in small period of time. For example video. This version of the function is for grayscale images or for manual manipulation with colorspaces. For more details see http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.131.6394
• fast_nl_means_denoising_multi_vec
  Modification of fastNlMeansDenoising function for images sequence where consecutive images have been captured in small period of time. For example video. This version of the function is for grayscale images or for manual manipulation with colorspaces. For more details see http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.131.6394
• fast_nl_means_denoising_vec
  Perform image denoising using Non-local Means Denoising algorithm http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/ with several computational optimizations. Noise expected to be a gaussian white noise
• illumination_change
  Applying an appropriate non-linear transformation to the gradient field inside the selection and then integrating back with a Poisson solver, modifies locally the apparent illumination of an image.
• inpaint
  Restores the selected region in an image using the region neighborhood.
• non_local_means
  Performs pure non local means denoising without any simplification, and thus it is not fast.
• non_local_means_1
  C++ default parameters
• pencil_sketch
  @example samples/cpp/tutorial_code/photo/non_photorealistic_rendering/npr_demo.cpp An example using non-photorealistic line drawing functions
• seamless_clone
  @example samples/cpp/tutorial_code/photo/seamless_cloning/cloning_demo.cpp An example using seamlessClone function
• stylization
  Stylization aims to produce digital imagery with a wide variety of effects not focused on photorealism. Edge-aware filters are ideal for stylization, as they can abstract regions of low contrast while preserving, or enhancing, high-contrast features.
• texture_flattening
  By retaining only the gradients at edge locations, before integrating with the Poisson solver, one washes out the texture of the selected region, giving its contents a flat aspect. Here Canny Edge %Detector is used.