ImageTools¶

class ImageTools¶

Bases: pybind11_object

Various image tools; sub-image extraction, modification of the look up table, binarisation…

Methods

`__init__`
`changeLUT`	Change the look up table (LUT) of an image.
`columnMean`	Compute the column-wise mean intensities.
`crop`	Overloaded function.
`extract`	Overloaded function.
`imageAdd`	Compute the image addition: \(Ires = I1 + I2\) .
`imageDifference`	Overloaded function.
`imageDifferenceAbsolute`	Overloaded function.
`imageSubtract`	Compute the image addition: \(Ires = I1 - I2\) .
`inMask`	Overloaded function.
`initUndistortMap`	Compute the undistortion transformation map.
`integralImage`	Compute the integral images:
`interpolate`
`normalize`	Normalize the image intensities.
`normalizedCorrelation`	Compute a correlation between 2 images.
`remap`	Overloaded function.
`templateMatching`	Match a template image into another image using zero-mean normalized cross-correlation:
`warpImage`	Overloaded function.

Inherited Methods

Operators

`__doc__`
`__init__`
`__module__`

Attributes

`INTERPOLATION_AREA`
`INTERPOLATION_CUBIC`
`INTERPOLATION_LINEAR`
`INTERPOLATION_NEAREST`
`__annotations__`

class ImageInterpolationType(self, value: int)¶

Bases: pybind11_object

Values:

INTERPOLATION_NEAREST: Nearest neighbor interpolation.
INTERPOLATION_LINEAR: Bi-linear interpolation (optimized by SIMD lib if enabled).
INTERPOLATION_CUBIC: Bi-cubic interpolation.
INTERPOLATION_AREA: Area interpolation (optimized by SIMD lib if enabled).

__and__(self, other: object) → object¶

__eq__(self, other: object) → bool¶

__ge__(self, other: object) → bool¶

__getstate__(self) → int¶

__gt__(self, other: object) → bool¶

__hash__(self) → int¶

__index__(self) → int¶

__init__(self, value: int)¶

__int__(self) → int¶

__invert__(self) → object¶

__le__(self, other: object) → bool¶

__lt__(self, other: object) → bool¶

__ne__(self, other: object) → bool¶

__or__(self, other: object) → object¶

__rand__(self, other: object) → object¶

__ror__(self, other: object) → object¶

__rxor__(self, other: object) → object¶

__setstate__(self, state: int) → None¶

__xor__(self, other: object) → object¶

property name : str¶

__init__(*args, **kwargs)¶

static changeLUT(I: visp._visp.core.ImageGray, A: int, newA: int, B: int, newB: int) → None¶

Change the look up table (LUT) of an image. Considering pixel gray level values \(l\) in the range \([A, B]\) , this method allows to rescale these values in \([A^*, B^*]\) by linear interpolation:

\(\left\{ \begin{array}{ll} l \in ]-\infty, A] \mbox{, } & l = A^* \\l \in [B, \infty[ \mbox{, } & l = B^* \\l \in ]A, B[ \mbox{, } & l = A^* + (l-A) * \frac{B^*-A^*}{B-A} \end{array} \right.\)

As shown in the example below, this method can be used to binarize an image. For an unsigned char image (in the range 0-255), thresholding this image at level 127 can be done by:

#include <visp3/core/vpImage.h>
#include <visp3/core/vpImageTools.h>
#include <visp3/io/vpImageIo.h>

int main()
{
  vpImage<unsigned char> I;
#ifdef _WIN32
  std::string filename("C:/Temp/visp-images/Klimt/Klimt.ppm");
#else
  std::string filename("/local/soft/ViSP/ViSP-images/Klimt/Klimt.ppm");
#endif

  // Read an image from the disk
  vpImageIo::read(I, filename);

  // Binarize image I:
  // - gray level values less than or equal to 127 are set to 0,
  // - gray level values greater than 128 are set to 255
  vpImageTools::changeLUT(I, 127, 0, 128, 255);

  vpImageIo::write(I, "Klimt.pgm"); // Write the image in a PGM P5 image file format
}

Parameters:

I: visp._visp.core.ImageGray¶: Image to process.
A: int¶: Low gray level value of the range to consider.
B: int¶: Height gray level value of the range to consider.

static columnMean(I: visp._visp.core.ImageDouble, result: visp._visp.core.RowVector) → None¶

Compute the column-wise mean intensities.

Parameters:

I: visp._visp.core.ImageDouble¶: The image.

static crop(*args, **kwargs)¶

Overloaded function.

crop(I: visp._visp.core.ImageGray, roi_top: float, roi_left: float, roi_height: int, roi_width: int, crop: visp._visp.core.ImageGray, v_scale: int = 1, h_scale: int = 1) -> None

Crop a region of interest (ROI) in an image. The ROI coordinates and dimension are defined in the original image.

Setting v_scale and h_scale to values different from 1 allows also to subsample the cropped image.

Note

See crop(const vpImage<Type> &, const vpRect &, vpImage<Type> &)

Parameters:

I: Input image from which a sub image will be extracted.
roi_top: ROI vertical position of the upper/left corner in the input image.
roi_left: ROI horizontal position of the upper/left corner in the input image.
roi_height: Cropped image height corresponding to the ROI height.
roi_width: Cropped image width corresponding to the ROI height.
crop: Cropped image.
v_scale: [in] : Vertical subsampling factor applied to the ROI.
h_scale: [in] : Horizontal subsampling factor applied to the ROI.

crop(I: visp._visp.core.ImageUInt16, roi_top: float, roi_left: float, roi_height: int, roi_width: int, crop: visp._visp.core.ImageUInt16, v_scale: int = 1, h_scale: int = 1) -> None

Crop a region of interest (ROI) in an image. The ROI coordinates and dimension are defined in the original image.

Setting v_scale and h_scale to values different from 1 allows also to subsample the cropped image.

Note

See crop(const vpImage<Type> &, const vpRect &, vpImage<Type> &)

Parameters:

I: Input image from which a sub image will be extracted.
roi_top: ROI vertical position of the upper/left corner in the input image.
roi_left: ROI horizontal position of the upper/left corner in the input image.
roi_height: Cropped image height corresponding to the ROI height.
roi_width: Cropped image width corresponding to the ROI height.
crop: Cropped image.
v_scale: [in] : Vertical subsampling factor applied to the ROI.
h_scale: [in] : Horizontal subsampling factor applied to the ROI.

crop(I: visp._visp.core.ImageFloat, roi_top: float, roi_left: float, roi_height: int, roi_width: int, crop: visp._visp.core.ImageFloat, v_scale: int = 1, h_scale: int = 1) -> None

Crop a region of interest (ROI) in an image. The ROI coordinates and dimension are defined in the original image.

Setting v_scale and h_scale to values different from 1 allows also to subsample the cropped image.

Note

See crop(const vpImage<Type> &, const vpRect &, vpImage<Type> &)

Parameters:

I: Input image from which a sub image will be extracted.
roi_top: ROI vertical position of the upper/left corner in the input image.
roi_left: ROI horizontal position of the upper/left corner in the input image.
roi_height: Cropped image height corresponding to the ROI height.
roi_width: Cropped image width corresponding to the ROI height.
crop: Cropped image.
v_scale: [in] : Vertical subsampling factor applied to the ROI.
h_scale: [in] : Horizontal subsampling factor applied to the ROI.

crop(I: visp._visp.core.ImageDouble, roi_top: float, roi_left: float, roi_height: int, roi_width: int, crop: visp._visp.core.ImageDouble, v_scale: int = 1, h_scale: int = 1) -> None

Crop a region of interest (ROI) in an image. The ROI coordinates and dimension are defined in the original image.

Setting v_scale and h_scale to values different from 1 allows also to subsample the cropped image.

Note

See crop(const vpImage<Type> &, const vpRect &, vpImage<Type> &)

Parameters:

I: Input image from which a sub image will be extracted.
roi_top: ROI vertical position of the upper/left corner in the input image.
roi_left: ROI horizontal position of the upper/left corner in the input image.
roi_height: Cropped image height corresponding to the ROI height.
roi_width: Cropped image width corresponding to the ROI height.
crop: Cropped image.
v_scale: [in] : Vertical subsampling factor applied to the ROI.
h_scale: [in] : Horizontal subsampling factor applied to the ROI.

crop(I: vpImage<bool>, roi_top: float, roi_left: float, roi_height: int, roi_width: int, crop: vpImage<bool>, v_scale: int = 1, h_scale: int = 1) -> None

Crop a region of interest (ROI) in an image. The ROI coordinates and dimension are defined in the original image.

Setting v_scale and h_scale to values different from 1 allows also to subsample the cropped image.

Note

See crop(const vpImage<Type> &, const vpRect &, vpImage<Type> &)

Parameters:

I: Input image from which a sub image will be extracted.
roi_top: ROI vertical position of the upper/left corner in the input image.
roi_left: ROI horizontal position of the upper/left corner in the input image.
roi_height: Cropped image height corresponding to the ROI height.
roi_width: Cropped image width corresponding to the ROI height.
crop: Cropped image.
v_scale: [in] : Vertical subsampling factor applied to the ROI.
h_scale: [in] : Horizontal subsampling factor applied to the ROI.

crop(I: visp._visp.core.ImageRGBa, roi_top: float, roi_left: float, roi_height: int, roi_width: int, crop: visp._visp.core.ImageRGBa, v_scale: int = 1, h_scale: int = 1) -> None

Crop a region of interest (ROI) in an image. The ROI coordinates and dimension are defined in the original image.

Setting v_scale and h_scale to values different from 1 allows also to subsample the cropped image.

Note

See crop(const vpImage<Type> &, const vpRect &, vpImage<Type> &)

Parameters:

I: Input image from which a sub image will be extracted.
roi_top: ROI vertical position of the upper/left corner in the input image.
roi_left: ROI horizontal position of the upper/left corner in the input image.
roi_height: Cropped image height corresponding to the ROI height.
roi_width: Cropped image width corresponding to the ROI height.
crop: Cropped image.
v_scale: [in] : Vertical subsampling factor applied to the ROI.
h_scale: [in] : Horizontal subsampling factor applied to the ROI.

crop(I: visp._visp.core.ImageRGBf, roi_top: float, roi_left: float, roi_height: int, roi_width: int, crop: visp._visp.core.ImageRGBf, v_scale: int = 1, h_scale: int = 1) -> None

Crop a region of interest (ROI) in an image. The ROI coordinates and dimension are defined in the original image.

Setting v_scale and h_scale to values different from 1 allows also to subsample the cropped image.

Note

See crop(const vpImage<Type> &, const vpRect &, vpImage<Type> &)

Parameters:

I: Input image from which a sub image will be extracted.
roi_top: ROI vertical position of the upper/left corner in the input image.
roi_left: ROI horizontal position of the upper/left corner in the input image.
roi_height: Cropped image height corresponding to the ROI height.
roi_width: Cropped image width corresponding to the ROI height.
crop: Cropped image.
v_scale: [in] : Vertical subsampling factor applied to the ROI.
h_scale: [in] : Horizontal subsampling factor applied to the ROI.

crop(I: visp._visp.core.ImageGray, topLeft: visp._visp.core.ImagePoint, roi_height: int, roi_width: int, crop: visp._visp.core.ImageGray, v_scale: int = 1, h_scale: int = 1) -> None

Crop a region of interest (ROI) in an image. The ROI coordinates and dimension are defined in the original image.

Setting v_scale and h_scale to values different from 1 allows also to subsample the cropped image.

Note

See crop(const vpImage<Type> &, const vpRect &, vpImage<Type> &)

Parameters:

I: Input image from which a sub image will be extracted.
topLeft: ROI position of the upper/left corner in the input image.
roi_height: Cropped image height corresponding to the ROI height.
roi_width: Cropped image width corresponding to the ROI height.
crop: Cropped image.
v_scale: [in] : Vertical subsampling factor applied to the ROI.
h_scale: [in] : Horizontal subsampling factor applied to the ROI.

crop(I: visp._visp.core.ImageUInt16, topLeft: visp._visp.core.ImagePoint, roi_height: int, roi_width: int, crop: visp._visp.core.ImageUInt16, v_scale: int = 1, h_scale: int = 1) -> None

Crop a region of interest (ROI) in an image. The ROI coordinates and dimension are defined in the original image.

Setting v_scale and h_scale to values different from 1 allows also to subsample the cropped image.

Note

See crop(const vpImage<Type> &, const vpRect &, vpImage<Type> &)

Parameters:

I: Input image from which a sub image will be extracted.
topLeft: ROI position of the upper/left corner in the input image.
roi_height: Cropped image height corresponding to the ROI height.
roi_width: Cropped image width corresponding to the ROI height.
crop: Cropped image.
v_scale: [in] : Vertical subsampling factor applied to the ROI.
h_scale: [in] : Horizontal subsampling factor applied to the ROI.

crop(I: visp._visp.core.ImageFloat, topLeft: visp._visp.core.ImagePoint, roi_height: int, roi_width: int, crop: visp._visp.core.ImageFloat, v_scale: int = 1, h_scale: int = 1) -> None

Crop a region of interest (ROI) in an image. The ROI coordinates and dimension are defined in the original image.

Setting v_scale and h_scale to values different from 1 allows also to subsample the cropped image.

Note

See crop(const vpImage<Type> &, const vpRect &, vpImage<Type> &)

Parameters:

I: Input image from which a sub image will be extracted.
topLeft: ROI position of the upper/left corner in the input image.
roi_height: Cropped image height corresponding to the ROI height.
roi_width: Cropped image width corresponding to the ROI height.
crop: Cropped image.
v_scale: [in] : Vertical subsampling factor applied to the ROI.
h_scale: [in] : Horizontal subsampling factor applied to the ROI.

crop(I: visp._visp.core.ImageDouble, topLeft: visp._visp.core.ImagePoint, roi_height: int, roi_width: int, crop: visp._visp.core.ImageDouble, v_scale: int = 1, h_scale: int = 1) -> None

Crop a region of interest (ROI) in an image. The ROI coordinates and dimension are defined in the original image.

Setting v_scale and h_scale to values different from 1 allows also to subsample the cropped image.

Note

See crop(const vpImage<Type> &, const vpRect &, vpImage<Type> &)

Parameters:

I: Input image from which a sub image will be extracted.
topLeft: ROI position of the upper/left corner in the input image.
roi_height: Cropped image height corresponding to the ROI height.
roi_width: Cropped image width corresponding to the ROI height.
crop: Cropped image.
v_scale: [in] : Vertical subsampling factor applied to the ROI.
h_scale: [in] : Horizontal subsampling factor applied to the ROI.

crop(I: vpImage<bool>, topLeft: visp._visp.core.ImagePoint, roi_height: int, roi_width: int, crop: vpImage<bool>, v_scale: int = 1, h_scale: int = 1) -> None

Crop a region of interest (ROI) in an image. The ROI coordinates and dimension are defined in the original image.

Setting v_scale and h_scale to values different from 1 allows also to subsample the cropped image.

Note

See crop(const vpImage<Type> &, const vpRect &, vpImage<Type> &)

Parameters:

I: Input image from which a sub image will be extracted.
topLeft: ROI position of the upper/left corner in the input image.
roi_height: Cropped image height corresponding to the ROI height.
roi_width: Cropped image width corresponding to the ROI height.
crop: Cropped image.
v_scale: [in] : Vertical subsampling factor applied to the ROI.
h_scale: [in] : Horizontal subsampling factor applied to the ROI.

crop(I: visp._visp.core.ImageRGBa, topLeft: visp._visp.core.ImagePoint, roi_height: int, roi_width: int, crop: visp._visp.core.ImageRGBa, v_scale: int = 1, h_scale: int = 1) -> None

Crop a region of interest (ROI) in an image. The ROI coordinates and dimension are defined in the original image.

Setting v_scale and h_scale to values different from 1 allows also to subsample the cropped image.

Note

See crop(const vpImage<Type> &, const vpRect &, vpImage<Type> &)

Parameters:

I: Input image from which a sub image will be extracted.
topLeft: ROI position of the upper/left corner in the input image.
roi_height: Cropped image height corresponding to the ROI height.
roi_width: Cropped image width corresponding to the ROI height.
crop: Cropped image.
v_scale: [in] : Vertical subsampling factor applied to the ROI.
h_scale: [in] : Horizontal subsampling factor applied to the ROI.

crop(I: visp._visp.core.ImageRGBf, topLeft: visp._visp.core.ImagePoint, roi_height: int, roi_width: int, crop: visp._visp.core.ImageRGBf, v_scale: int = 1, h_scale: int = 1) -> None

Crop a region of interest (ROI) in an image. The ROI coordinates and dimension are defined in the original image.

Setting v_scale and h_scale to values different from 1 allows also to subsample the cropped image.

Note

See crop(const vpImage<Type> &, const vpRect &, vpImage<Type> &)

Parameters:

I: Input image from which a sub image will be extracted.
topLeft: ROI position of the upper/left corner in the input image.
roi_height: Cropped image height corresponding to the ROI height.
roi_width: Cropped image width corresponding to the ROI height.
crop: Cropped image.
v_scale: [in] : Vertical subsampling factor applied to the ROI.
h_scale: [in] : Horizontal subsampling factor applied to the ROI.

crop(I: visp._visp.core.ImageGray, roi: visp._visp.core.Rect, crop: visp._visp.core.ImageGray, v_scale: int = 1, h_scale: int = 1) -> None

Crop a region of interest (ROI) in an image. The ROI coordinates and dimension are defined in the original image.

Setting v_scale and h_scale to values different from 1 allows also to subsample the cropped image.

Parameters:

I: Input image from which a sub image will be extracted.
roi: Region of interest in image I corresponding to the cropped part of the image.
crop: Cropped image.
v_scale: [in] : Vertical subsampling factor applied to the ROI.
h_scale: [in] : Horizontal subsampling factor applied to the ROI.

crop(I: visp._visp.core.ImageUInt16, roi: visp._visp.core.Rect, crop: visp._visp.core.ImageUInt16, v_scale: int = 1, h_scale: int = 1) -> None

Crop a region of interest (ROI) in an image. The ROI coordinates and dimension are defined in the original image.

Setting v_scale and h_scale to values different from 1 allows also to subsample the cropped image.

Parameters:

I: Input image from which a sub image will be extracted.
roi: Region of interest in image I corresponding to the cropped part of the image.
crop: Cropped image.
v_scale: [in] : Vertical subsampling factor applied to the ROI.
h_scale: [in] : Horizontal subsampling factor applied to the ROI.

crop(I: visp._visp.core.ImageFloat, roi: visp._visp.core.Rect, crop: visp._visp.core.ImageFloat, v_scale: int = 1, h_scale: int = 1) -> None

Crop a region of interest (ROI) in an image. The ROI coordinates and dimension are defined in the original image.

Setting v_scale and h_scale to values different from 1 allows also to subsample the cropped image.

Parameters:

I: Input image from which a sub image will be extracted.
roi: Region of interest in image I corresponding to the cropped part of the image.
crop: Cropped image.
v_scale: [in] : Vertical subsampling factor applied to the ROI.
h_scale: [in] : Horizontal subsampling factor applied to the ROI.

crop(I: visp._visp.core.ImageDouble, roi: visp._visp.core.Rect, crop: visp._visp.core.ImageDouble, v_scale: int = 1, h_scale: int = 1) -> None

Crop a region of interest (ROI) in an image. The ROI coordinates and dimension are defined in the original image.

Setting v_scale and h_scale to values different from 1 allows also to subsample the cropped image.

Parameters:

I: Input image from which a sub image will be extracted.
roi: Region of interest in image I corresponding to the cropped part of the image.
crop: Cropped image.
v_scale: [in] : Vertical subsampling factor applied to the ROI.
h_scale: [in] : Horizontal subsampling factor applied to the ROI.

crop(I: vpImage<bool>, roi: visp._visp.core.Rect, crop: vpImage<bool>, v_scale: int = 1, h_scale: int = 1) -> None

Crop a region of interest (ROI) in an image. The ROI coordinates and dimension are defined in the original image.

Setting v_scale and h_scale to values different from 1 allows also to subsample the cropped image.

Parameters:

I: Input image from which a sub image will be extracted.
roi: Region of interest in image I corresponding to the cropped part of the image.
crop: Cropped image.
v_scale: [in] : Vertical subsampling factor applied to the ROI.
h_scale: [in] : Horizontal subsampling factor applied to the ROI.

crop(I: visp._visp.core.ImageRGBa, roi: visp._visp.core.Rect, crop: visp._visp.core.ImageRGBa, v_scale: int = 1, h_scale: int = 1) -> None

Crop a region of interest (ROI) in an image. The ROI coordinates and dimension are defined in the original image.

Setting v_scale and h_scale to values different from 1 allows also to subsample the cropped image.

Parameters:

I: Input image from which a sub image will be extracted.
roi: Region of interest in image I corresponding to the cropped part of the image.
crop: Cropped image.
v_scale: [in] : Vertical subsampling factor applied to the ROI.
h_scale: [in] : Horizontal subsampling factor applied to the ROI.

crop(I: visp._visp.core.ImageRGBf, roi: visp._visp.core.Rect, crop: visp._visp.core.ImageRGBf, v_scale: int = 1, h_scale: int = 1) -> None

Crop a region of interest (ROI) in an image. The ROI coordinates and dimension are defined in the original image.

Setting v_scale and h_scale to values different from 1 allows also to subsample the cropped image.

Parameters:

I: Input image from which a sub image will be extracted.
roi: Region of interest in image I corresponding to the cropped part of the image.
crop: Cropped image.
v_scale: [in] : Vertical subsampling factor applied to the ROI.
h_scale: [in] : Horizontal subsampling factor applied to the ROI.

static extract(*args, **kwargs)¶

Overloaded function.

extract(src: visp._visp.core.ImageGray, dst: visp._visp.core.ImageGray, r: visp._visp.core.RectOriented) -> None

Extract a rectangular region from an image.

Parameters:

src: The source image.
dst: The resulting image.
r: The rectangle area.

extract(src: visp._visp.core.ImageGray, dst: visp._visp.core.ImageDouble, r: visp._visp.core.RectOriented) -> None

Extract a rectangular region from an image.

Parameters:

src: The source image.
dst: The resulting image.
r: The rectangle area.

static imageAdd(I1: visp._visp.core.ImageGray, I2: visp._visp.core.ImageGray, Ires: visp._visp.core.ImageGray, saturate: bool = false) → None¶

Compute the image addition: \(Ires = I1 + I2\) .

Note

The simd lib is used to accelerate processing on x86 and ARM architecture.

Warning

This function does not work in-place (Ires object must be different from I1 and I2).

Parameters:

I1: visp._visp.core.ImageGray¶: The first image.
I2: visp._visp.core.ImageGray¶: The second image.
Ires: visp._visp.core.ImageGray¶: math:Ires = I1 + I2
saturate: bool = false¶: If true, saturate the result to [0 ; 255] using vpMath::saturate , otherwise overflow may occur.

static imageDifference(*args, **kwargs)¶

Overloaded function.

imageDifference(I1: visp._visp.core.ImageGray, I2: visp._visp.core.ImageGray, Idiff: visp._visp.core.ImageGray) -> None

Compute the signed difference between the two images I1 and I2 for visualization purpose: Idiff = I1-I2

pixels with a null difference are set to 128.
A negative difference implies a pixel value < 128
A positive difference implies a pixel value > 128

Parameters:

I1: The first image.
I2: The second image.
Idiff: The result of the difference.

imageDifference(I1: visp._visp.core.ImageRGBa, I2: visp._visp.core.ImageRGBa, Idiff: visp._visp.core.ImageRGBa) -> None

Compute the signed difference between the two images I1 and I2 RGB components for visualization purpose: Idiff = I1-I2. The fourth component named A is not compared. It is set to 0 in the resulting difference image.

pixels with a null difference are set to R=128, G=128, B=128.
A negative difference implies a pixel R, G, B value < 128
A positive difference implies a pixel R, G, B value > 128

Parameters:

I1: The first image.
I2: The second image.
Idiff: The result of the difference between RGB components.

static imageDifferenceAbsolute(*args, **kwargs)¶

Overloaded function.

imageDifferenceAbsolute(I1: visp._visp.core.ImageGray, I2: visp._visp.core.ImageGray, Idiff: visp._visp.core.ImageGray) -> None

Compute the difference between the two images I1 and I2

Warning

: This is NOT for visualization If you want to visualize difference images during servo, please use vpImageTools::imageDifference (..,..,..) function.

Parameters:

I1: The first image.
I2: The second image.
Idiff: The result of the difference.

imageDifferenceAbsolute(I1: visp._visp.core.ImageDouble, I2: visp._visp.core.ImageDouble, Idiff: visp._visp.core.ImageDouble) -> None

Compute the difference between the two images I1 and I2.

Parameters:

I1: The first image.
I2: The second image.
Idiff: The result of the difference.

imageDifferenceAbsolute(I1: visp._visp.core.ImageRGBa, I2: visp._visp.core.ImageRGBa, Idiff: visp._visp.core.ImageRGBa) -> None

Compute the difference between the two images I1 and I2 RGB components. The fourth component named A is not compared. It is set to 0 in the resulting difference image.

Warning

: This is NOT for visualization. If you want to visualize difference images during servo, please use vpImageTools::imageDifference (..,..,..) function.

Parameters:

I1: The first image.
I2: The second image.
Idiff: The result of the difference between RGB components.

static imageSubtract(I1: visp._visp.core.ImageGray, I2: visp._visp.core.ImageGray, Ires: visp._visp.core.ImageGray, saturate: bool = false) → None¶

Compute the image addition: \(Ires = I1 - I2\) .

Note

The simd lib is used to accelerate processing on x86 and ARM architecture.

Warning

This function does not work in-place (Ires object must be different from I1 and I2).

Parameters:

I1: visp._visp.core.ImageGray¶: The first image.
I2: visp._visp.core.ImageGray¶: The second image.
Ires: visp._visp.core.ImageGray¶: math:Ires = I1 - I2
saturate: bool = false¶: If true, saturate the result to [0 ; 255] using vpMath::saturate , otherwise overflow may occur.

static inMask(*args, **kwargs)¶

Overloaded function.

inMask(I: visp._visp.core.ImageGray, mask: visp._visp.core.ImageGray, I_mask: visp._visp.core.ImageGray) -> int

Parameters:

I: Input image.
mask: Mask where pixels to consider have values that differ from 0.
I_mask: Resulting image where pixels that are in the mask are kept.

Returns:

The number of pixels that are in the mask.

inMask(I: visp._visp.core.ImageRGBa, mask: visp._visp.core.ImageGray, I_mask: visp._visp.core.ImageRGBa) -> int

Parameters:

I: Input image.
mask: Mask where pixels to consider have values that differ from 0.
I_mask: Resulting image where pixels that are in the mask are kept.

Returns:

The number of pixels that are in the mask.

static initUndistortMap(cam: visp._visp.core.CameraParameters, width: int, height: int, mapU: vpArray2D<int>, mapV: vpArray2D<int>, mapDu: vpArray2D<float>, mapDv: vpArray2D<float>) → None¶

Compute the undistortion transformation map.

Parameters:

cam: Camera intrinsic parameters with distortion coefficients.
width: Image width.
height: Image height.
mapU: 2D array that contains at each coordinate the u-coordinate in the distorted image.
mapV: 2D array that contains at each coordinate the v-coordinate in the distorted image.
mapDu: 2D array that contains at each coordinate the \(\Delta u\) for the interpolation.
mapDv: 2D array that contains at each coordinate the \(\Delta v\) for the interpolation.

static integralImage(I: visp._visp.core.ImageGray, II: visp._visp.core.ImageDouble, IIsq: visp._visp.core.ImageDouble) → None¶

Compute the integral images:

\(II(u,v)=\sum_{u^{'}\leq u, v^{'}\leq v}I(u,v)\)

\(IIsq(u,v)=\sum_{u^{'}\leq u, v^{'}\leq v}I(u,v)^2\) .

Parameters:

I: visp._visp.core.ImageGray¶: Input image.
II: visp._visp.core.ImageDouble¶: Integral image II.
IIsq: visp._visp.core.ImageDouble¶: Integral image IIsq.

static interpolate(I: visp._visp.core.ImageGray, point: visp._visp.core.ImagePoint, method: visp._visp.core.ImageTools.ImageInterpolationType = INTERPOLATION_NEAREST) → float¶

static normalize(I: visp._visp.core.ImageDouble) → None¶

Normalize the image intensities.

Parameters:

I: visp._visp.core.ImageDouble¶: The image to normalize.

static normalizedCorrelation(I1: visp._visp.core.ImageDouble, I2: visp._visp.core.ImageDouble, useOptimized: bool = true) → float¶

Compute a correlation between 2 images.

Parameters:

I1: visp._visp.core.ImageDouble¶: The first image.
I2: visp._visp.core.ImageDouble¶: The second image.
useOptimized: bool = true¶: Use SSE if true and available.

static remap(*args, **kwargs)¶

Overloaded function.

remap(I: visp._visp.core.ImageGray, mapU: vpArray2D<int>, mapV: vpArray2D<int>, mapDu: vpArray2D<float>, mapDv: vpArray2D<float>, Iundist: visp._visp.core.ImageGray) -> None

Apply the transformation map to the image.

Parameters:

I: Input grayscale image.
mapU: Map that contains at each destination coordinate the u-coordinate in the source image.
mapV: Map that contains at each destination coordinate the v-coordinate in the source image.
mapDu: Map that contains at each destination coordinate the \(\Delta u\) for the interpolation.
mapDv: Map that contains at each destination coordinate the \(\Delta v\) for the interpolation.
Iundist: Output transformed grayscale image.

remap(I: visp._visp.core.ImageRGBa, mapU: vpArray2D<int>, mapV: vpArray2D<int>, mapDu: vpArray2D<float>, mapDv: vpArray2D<float>, Iundist: visp._visp.core.ImageRGBa) -> None

Apply the transformation map to the image.

Parameters:

I: Input color image.
mapU: Map that contains at each destination coordinate the u-coordinate in the source image.
mapV: Map that contains at each destination coordinate the v-coordinate in the source image.
mapDu: Map that contains at each destination coordinate the \(\Delta u\) for the interpolation.
mapDv: Map that contains at each destination coordinate the \(\Delta v\) for the interpolation.
Iundist: Output transformed color image.

static templateMatching(I: visp._visp.core.ImageGray, I_tpl: visp._visp.core.ImageGray, I_score: visp._visp.core.ImageDouble, step_u: int, step_v: int, useOptimized: bool = true) → None¶

Match a template image into another image using zero-mean normalized cross-correlation:

\(\frac{\sum_{u^{'},v^{'}} (I(u+u^{'},v+v^{'})-\bar{I}_{u^{'},v^{'}}) (T(u^{'},v^{'})-\bar{T}_{u^{'},v^{'}})}{\sqrt{\sum_{u^{'},v^{'}} (I(u+u^{'},v+v^{'})-\bar{I}_{u^{'},v^{'}})^2 \sum_{u^{'},v^{'}}(T(u^{'},v^{'})-\bar{T}_{u^{'},v^{'}})^2}}\)

Parameters:

I: visp._visp.core.ImageGray¶: Input image.
I_tpl: visp._visp.core.ImageGray¶: Template image.
I_score: visp._visp.core.ImageDouble¶: Output template matching score.
step_u: int¶: Step in u-direction to speed-up the computation.
step_v: int¶: Step in v-direction to speed-up the computation.
useOptimized: bool = true¶: Use optimized version (SSE, OpenMP, integral images, …) if true and available.

static warpImage(*args, **kwargs)¶

Overloaded function.

warpImage(src: visp._visp.core.ImageGray, T: visp._visp.core.Matrix, dst: visp._visp.core.ImageGray, interpolation: visp._visp.core.ImageTools.ImageInterpolationType = INTERPOLATION_NEAREST, fixedPointArithmetic: bool = true, pixelCenter: bool = false) -> None
warpImage(src: visp._visp.core.ImageFloat, T: visp._visp.core.Matrix, dst: visp._visp.core.ImageFloat, interpolation: visp._visp.core.ImageTools.ImageInterpolationType = INTERPOLATION_NEAREST, fixedPointArithmetic: bool = true, pixelCenter: bool = false) -> None
warpImage(src: visp._visp.core.ImageDouble, T: visp._visp.core.Matrix, dst: visp._visp.core.ImageDouble, interpolation: visp._visp.core.ImageTools.ImageInterpolationType = INTERPOLATION_NEAREST, fixedPointArithmetic: bool = true, pixelCenter: bool = false) -> None
warpImage(src: visp._visp.core.ImageUInt16, T: visp._visp.core.Matrix, dst: visp._visp.core.ImageUInt16, interpolation: visp._visp.core.ImageTools.ImageInterpolationType = INTERPOLATION_NEAREST, fixedPointArithmetic: bool = true, pixelCenter: bool = false) -> None
warpImage(src: visp._visp.core.ImageRGBa, T: visp._visp.core.Matrix, dst: visp._visp.core.ImageRGBa, interpolation: visp._visp.core.ImageTools.ImageInterpolationType = INTERPOLATION_NEAREST, fixedPointArithmetic: bool = true, pixelCenter: bool = false) -> None