Seamless Cloning

Functions
void	cv::colorChange (InputArray src, InputArray mask, OutputArray dst, float red_mul=1.0f, float green_mul=1.0f, float blue_mul=1.0f)
	Given an original color image, two differently colored versions of this image can be mixed seamlessly. More...
void	cv::illuminationChange (InputArray src, InputArray mask, OutputArray dst, float alpha=0.2f, float beta=0.4f)
	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. More...
void	cv::seamlessClone (InputArray src, InputArray dst, InputArray mask, Point p, OutputArray blend, int flags)
	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 [143] . More...
void	cv::textureFlattening (InputArray src, InputArray mask, OutputArray dst, float low_threshold=30, float high_threshold=45, int kernel_size=3)
	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. More...

Detailed Description

Function Documentation

colorChange()

void cv::colorChange	(	InputArray	src,
		InputArray	mask,
		OutputArray	dst,
		float	red_mul = 1.0f,
		float	green_mul = 1.0f,
		float	blue_mul = 1.0f
	)

Python:
	dst	=	cv.colorChange(	src, mask[, dst[, red_mul[, green_mul[, blue_mul]]]]	)

Given an original color image, two differently colored versions of this image can be mixed seamlessly.

Parameters

src	Input 8-bit 3-channel image.
mask	Input 8-bit 1 or 3-channel image.
dst	Output image with the same size and type as src .
red_mul	R-channel multiply factor.
green_mul	G-channel multiply factor.
blue_mul	B-channel multiply factor.

Multiplication factor is between .5 to 2.5.

Examples:

cloning_demo.cpp.

illuminationChange()

void cv::illuminationChange	(	InputArray	src,
		InputArray	mask,
		OutputArray	dst,
		float	alpha = 0.2f,
		float	beta = 0.4f
	)

Python:
	dst	=	cv.illuminationChange(	src, mask[, dst[, alpha[, beta]]]	)

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.

Parameters

src	Input 8-bit 3-channel image.
mask	Input 8-bit 1 or 3-channel image.
dst	Output image with the same size and type as src.
alpha	Value ranges between 0-2.
beta	Value ranges between 0-2.

This is useful to highlight under-exposed foreground objects or to reduce specular reflections.

Examples:

cloning_demo.cpp.

seamlessClone()

void cv::seamlessClone	(	InputArray	src,
		InputArray	dst,
		InputArray	mask,
		Point	p,
		OutputArray	blend,
		int	flags
	)

Python:
	blend	=	cv.seamlessClone(	src, dst, mask, p, flags[, blend]	)

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 [143] .

Parameters

src	Input 8-bit 3-channel image.
dst	Input 8-bit 3-channel image.
mask	Input 8-bit 1 or 3-channel image.
p	Point in dst image where object is placed.
blend	Output image with the same size and type as dst.
flags	Cloning method that could be one of the following: NORMAL_CLONE The power of the method is fully expressed when inserting objects with complex outlines into a new background 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.

Examples:

cloning_demo.cpp.

textureFlattening()

void cv::textureFlattening	(	InputArray	src,
		InputArray	mask,
		OutputArray	dst,
		float	low_threshold = 30,
		float	high_threshold = 45,
		int	kernel_size = 3
	)

Python:
	dst	=	cv.textureFlattening(	src, mask[, dst[, low_threshold[, high_threshold[, kernel_size]]]]	)

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.

Parameters

src	Input 8-bit 3-channel image.
mask	Input 8-bit 1 or 3-channel image.
dst	Output image with the same size and type as src.
low_threshold	Range from 0 to 100.
high_threshold	Value > 100.
kernel_size	The size of the Sobel kernel to be used.

NOTE:**

The algorithm assumes that the color of the source image is close to that of the destination. This assumption means that when the colors don't match, the source image color gets tinted toward the color of the destination image.

Examples:

cloning_demo.cpp.