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

Constants

INPAINT_NS	Use Navier-Stokes based method
INPAINT_TELEA	Use the algorithm proposed by Alexandru Telea Telea04
LDR_SIZE
MIXED_CLONE
MONOCHROME_TRANSFER
NORMAL_CLONE
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
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.
CalibrateCRF	The base class for camera response calibration algorithms.
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.
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.
MergeDebevec	The resulting HDR image is calculated as weighted average of the exposures considering exposure values and camera response.
MergeExposures	The base class algorithms that can merge exposure sequence to a single image.
MergeMertens	Pixels are weighted using contrast, saturation and well-exposedness measures, than images are combined using laplacian pyramids.
MergeRobertson	The resulting HDR image is calculated as weighted average of the exposures considering exposure values and camera response.
Tonemap	Base class for tonemapping algorithms - tools that are used to map HDR image to 8-bit range.
TonemapDrago	Adaptive logarithmic mapping is a fast global tonemapping algorithm that scales the image in logarithmic domain.
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.
TonemapReinhard	This is a global tonemapping operator that models human visual system.

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_colored	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_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.
pencil_sketch	Pencil-like non-photorealistic line drawing
seamless_clone	Image editing tasks concern either global changes (color/intensity corrections, filters, deformations) or local changes concerned to a selection. Here we are interested in achieving local changes, ones that are restricted to a region manually selected (ROI), in a seamless and effortless manner. The extent of the changes ranges from slight distortions to complete replacement by novel content PM03 .
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.