ImageFilter¶

class ImageFilter¶

Bases: pybind11_object

Various image filter, convolution, etc…

Methods

`__init__`
`canny`	Overloaded function.
`computePartialDerivatives`
`derivativeFilterX`	Overloaded function.
`derivativeFilterY`	Overloaded function.
`filterGaussXPyramidal`
`filterGaussYPyramidal`
`getGaussPyramidal`
`getGaussXPyramidal`
`getGaussYPyramidal`
`median`	Overloaded function.
`sepFilter`	Apply a filter to an image using two separable kernels.
`vpCannyBackendTypeFromString`	Cast a string into a vpImageFilter::vpCannyBackendTypeToString .
`vpCannyBackendTypeList`	Get the list of available vpCannyBackendType.
`vpCannyBackendTypeToString`
`vpCannyFiltAndGradTypeFromStr`	Cast a string into a vpImageFilter::vpCannyFilteringAndGradientType .
`vpCannyFiltAndGradTypeToStr`
`vpGetCannyFiltAndGradTypes`	Get the list of available vpCannyFilteringAndGradientType.

Inherited Methods

Operators

`__doc__`
`__init__`
`__module__`

Attributes

`CANNY_COUNT_BACKEND`
`CANNY_COUNT_FILTERING`
`CANNY_GBLUR_SCHARR_FILTERING`
`CANNY_GBLUR_SOBEL_FILTERING`
`CANNY_OPENCV_BACKEND`
`CANNY_VISP_BACKEND`
`__annotations__`

class CannyBackendType(self, value: int)¶

Bases: pybind11_object

Canny filter and gradient operators to apply on the image before the edge detection stage.

Values:

CANNY_GBLUR_SOBEL_FILTERING: Apply Gaussian blur + Sobel operator on the input image.
CANNY_GBLUR_SCHARR_FILTERING: Apply Gaussian blur + Scharr operator on the input image.
CANNY_COUNT_FILTERING

__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¶

class CannyFilteringAndGradientType(self, value: int)¶

Bases: pybind11_object

Canny filter and gradient operators to apply on the image before the edge detection stage.

Values:

CANNY_GBLUR_SOBEL_FILTERING: Apply Gaussian blur + Sobel operator on the input image.
CANNY_GBLUR_SCHARR_FILTERING: Apply Gaussian blur + Scharr operator on the input image.
CANNY_COUNT_FILTERING

__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 canny(*args, **kwargs)¶

Overloaded function.

canny(I: visp._visp.core.ImageGray, Ic: visp._visp.core.ImageGray, gaussianFilterSize: int, thresholdCanny: float, apertureSobel: int) -> None

Apply the Canny edge operator on the image Isrc and return the resulting image Ires .

The following example shows how to use the method:

#include <visp3/core/vpImage.h>
#include <visp3/core/vpImageFilter.h>

int main()
{
  // Constants for the Canny operator.
  const unsigned int gaussianFilterSize = 5;
  const double thresholdCanny = 15;
  const unsigned int apertureSobel = 3;

  // Image for the Canny edge operator
  vpImage<unsigned char> Isrc;
  vpImage<unsigned char> Icanny;

  // First grab the source image Isrc.

  // Apply the Canny edge operator and set the Icanny image.
  vpImageFilter::canny(Isrc, Icanny, gaussianFilterSize, thresholdCanny, apertureSobel);
  return (0);
}

Parameters:

gaussianFilterSize: The size of the mask of the Gaussian filter to apply (an odd number).
thresholdCanny: The upper threshold for the Canny operator. Only value greater than this value are marked as an edge. If negative, it will be automatically computed, along with the lower threshold. Otherwise, the lower threshold will be set to one third of the thresholdCanny .
apertureSobel: Size of the mask for the Sobel operator (odd number).

canny(I: visp._visp.core.ImageGray, Ic: visp._visp.core.ImageGray, gaussianFilterSize: int, lowerThresholdCanny: float, higherThresholdCanny: float, apertureSobel: int) -> None

Apply the Canny edge operator on the image Isrc and return the resulting image Ires .

The following example shows how to use the method:

#include <visp3/core/vpImage.h>
#include <visp3/core/vpImageFilter.h>

int main()
{
  // Constants for the Canny operator.
  const unsigned int gaussianFilterSize = 5;
  const float upperThresholdCanny = 15;
  const float lowerThresholdCanny = 5;
  const unsigned int apertureSobel = 3;

  // Image for the Canny edge operator
  vpImage<unsigned char> Isrc;
  vpImage<unsigned char> Icanny;

  // First grab the source image Isrc.

  // Apply the Canny edge operator and set the Icanny image.
  vpImageFilter::canny(Isrc, Icanny, gaussianFilterSize, lowerThresholdCanny, upperThresholdCanny, apertureSobel);
  return (0);
}

Parameters:

gaussianFilterSize: The size of the mask of the Gaussian filter to apply (an odd number).
apertureSobel: Size of the mask for the Sobel operator (odd number).

static computePartialDerivatives(cv_I: cv::Mat, cv_dIx: cv::Mat, cv_dIy: cv::Mat, computeDx: bool = true, computeDy: bool = true, normalize: bool = true, gaussianKernelSize: int = 5, gaussianStdev: float = 2.f, apertureGradient: int = 3, filteringType: visp._visp.core.ImageFilter.CannyFilteringAndGradientType = CANNY_GBLUR_SOBEL_FILTERING) → None¶

static derivativeFilterX(*args, **kwargs)¶

Overloaded function.

derivativeFilterX(I: visp._visp.core.ImageGray, r: int, c: int) -> float

Apply a 1x3 derivative filter to an image pixel.

Parameters:

I: Image to filter
r: coordinates (row) of the pixel
c: coordinates (column) of the pixel

derivativeFilterX(I: visp._visp.core.ImageFloat, r: int, c: int) -> float

Apply a 1x3 derivative filter to an image pixel.

Parameters:

I: Image to filter
r: coordinates (row) of the pixel
c: coordinates (column) of the pixel

derivativeFilterX(I: visp._visp.core.ImageDouble, r: int, c: int) -> float

Apply a 1x3 derivative filter to an image pixel.

Parameters:

I: Image to filter
r: coordinates (row) of the pixel
c: coordinates (column) of the pixel

static derivativeFilterY(*args, **kwargs)¶

Overloaded function.

derivativeFilterY(I: visp._visp.core.ImageGray, r: int, c: int) -> float

Apply a 3x1 derivative filter to an image pixel.

Parameters:

I: Image to filter
r: coordinates (row) of the pixel
c: coordinates (column) of the pixel

derivativeFilterY(I: visp._visp.core.ImageFloat, r: int, c: int) -> float

Apply a 3x1 derivative filter to an image pixel.

Parameters:

I: Image to filter
r: coordinates (row) of the pixel
c: coordinates (column) of the pixel

derivativeFilterY(I: visp._visp.core.ImageDouble, r: int, c: int) -> float

Apply a 3x1 derivative filter to an image pixel.

Parameters:

I: Image to filter
r: coordinates (row) of the pixel
c: coordinates (column) of the pixel

static filterGaussXPyramidal(I: visp._visp.core.ImageGray, i: int, j: int) → int¶

static filterGaussYPyramidal(I: visp._visp.core.ImageGray, i: int, j: int) → int¶

static getGaussPyramidal(I: visp._visp.core.ImageGray, GI: visp._visp.core.ImageGray) → None¶

static getGaussXPyramidal(I: visp._visp.core.ImageGray, GI: visp._visp.core.ImageGray) → None¶

static getGaussYPyramidal(I: visp._visp.core.ImageGray, GI: visp._visp.core.ImageGray) → None¶

static median(*args, **kwargs)¶

Overloaded function.

median(cv_I: cv::Mat) -> float

Calculates the median value of a single channel. The algorithm is based on based on https://github.com/arnaudgelas/OpenCVExamples/blob/master/cvMat/Statistics/Median/Median.cpp .

median(Isrc: visp._visp.core.ImageGray) -> float

Calculates the median value of a single channel. The algorithm is based on based on https://github.com/arnaudgelas/OpenCVExamples/blob/master/cvMat/Statistics/Median/Median.cpp .

Note

See vpImageFilter::median(const cv::Mat)

Parameters:

Isrc: Gray-level image in ViSP format.

Returns:

Gray level image median value.

median(Isrc: visp._visp.core.ImageRGBa) -> list[float]

Calculates the median value of a vpRGBa image. The result is ordered in RGB format.

Note

See vpImageFilter::median(const cv::Mat)

Parameters:

Isrc: RGB image in ViSP format. Alpha channel is ignored.

Returns:

std::vector<float> meds such as meds[0] = red-channel-median, meds[1] = green-channel-median and meds[2] = blue-channel-median.

static sepFilter(I: visp._visp.core.ImageGray, If: visp._visp.core.ImageDouble, kernelH: visp._visp.core.ColVector, kernelV: visp._visp.core.ColVector) → None¶

Apply a filter to an image using two separable kernels. For instance, the Sobel kernel can be decomposed to:

\[\begin{split}\left [ \begin{matrix} 1 & 0 & -1 \\2 & 0 & -2 \\1 & 0 & -1 \end{matrix} \right ] = \left [ \begin{matrix} 1 \\2 \\1 \end{matrix} \right ] \ast \left [ \begin{matrix} 1 & 0 & -1 \end{matrix} \right ] \end{split}\]

Thus, the convolution operation can be performed as:

\[\begin{split}G_x = \left [ \begin{matrix} 1 \\2 \\1 \end{matrix} \right ] \ast \left ( \left [ \begin{matrix} 1 & 0 & -1 \end{matrix} \right ] \ast I \right ) \end{split}\]

Using two separable kernels reduce the number of operations and can be faster for large kernels.

Note

Only pixels in the input image fully covered by the kernel are considered.

Parameters:

I: visp._visp.core.ImageGray¶: Image to filter
If: visp._visp.core.ImageDouble¶: Filtered image.
kernelH: visp._visp.core.ColVector¶: Separable kernel (performed first).
kernelV: visp._visp.core.ColVector¶: Separable kernel (performed last).

static vpCannyBackendTypeFromString(name: str) → visp._visp.core.ImageFilter.CannyBackendType¶

Cast a string into a ** vpImageFilter::vpCannyBackendTypeToString ** .

Parameters:

name: str¶: The name of the backend.

Returns:

vpImageFilter::vpCannyBackendTypeToString The corresponding enumeration value.

static vpCannyBackendTypeList(pref: str = <, sep: str =, suf: str = >) → str¶

Get the list of available vpCannyBackendType.

Parameters:

pref: The prefix of the list.
sep: The separator between two elements of the list.
suf: The suffix of the list.

Returns:

std::string The list of available items.

static vpCannyBackendTypeToString(type: visp._visp.core.ImageFilter.CannyBackendType) → str¶

static vpCannyFiltAndGradTypeFromStr(name: str) → visp._visp.core.ImageFilter.CannyFilteringAndGradientType¶

Cast a string into a ** vpImageFilter::vpCannyFilteringAndGradientType ** .

Parameters:

name: str¶: The name of the backend.

Returns:

vpImageFilter::vpCannyFilteringAndGradientType The corresponding enumeration value.

static vpCannyFiltAndGradTypeToStr(type: visp._visp.core.ImageFilter.CannyFilteringAndGradientType) → str¶

static vpGetCannyFiltAndGradTypes(pref: str = <, sep: str =, suf: str = >) → str¶

Get the list of available vpCannyFilteringAndGradientType.

Parameters:

pref: The prefix of the list.
sep: The separator between two elements of the list.
suf: The suffix of the list.

Returns:

std::string The list of available items.