Struct cv::dnn::Image2BlobParams#

Processing params of image to blob. View details

#include <opencv2/dnn/dnn.hpp>

Collaboration diagram for cv::dnn::Image2BlobParams:

cv::dnn::Image2BlobParams Node1 cv::dnn::Image2BlobParams   + Image2BlobParams() + Image2BlobParams() + blobRectsToImageRects() + blobRectToImageRect() Node2 cv::Scalar_< double >   + Scalar_() + Scalar_() + Scalar_() + Scalar_() + Scalar_() + Scalar_() + conj() + isReal() + mul() + operator Scalar_< T2 >() + operator=() + operator=() + all() Node2->Node1 +borderValue +mean +scalefactor Node3 cv::Vec< double, 4 >   + Vec() + Vec() + Vec() + Vec() + Vec() + Vec() + Vec() + Vec() + Vec() + Vec() and 17 more... + all() + diag() + eye() + ones() + randn() + randu() + zeros() Node3->Node2 Node4 cv::Matx< double, cn, 1 >   + Matx() + Matx() + Matx() + Matx() + Matx() + Matx() + Matx() + Matx() + Matx() + Matx() and 33 more... + all() + diag() + eye() + ones() + randn() + randu() + zeros() Node4->Node3 Node5 double     Node5->Node4 +val Node6 cv::Matx< _Tp, m, n >   + Matx() + Matx() + Matx() + Matx() + Matx() + Matx() + Matx() + Matx() + Matx() + Matx() and 33 more... + all() + diag() + eye() + ones() + randn() + randu() + zeros() Node6->Node4 < double, cn, 1 > Node9 cv::Matx< _Tp, cn, 1 >   + Matx() + Matx() + Matx() + Matx() + Matx() + Matx() + Matx() + Matx() + Matx() + Matx() and 33 more... + all() + diag() + eye() + ones() + randn() + randu() + zeros() Node6->Node9 < _Tp, cn, 1 > Node7 _Tp     Node7->Node6 +val Node7->Node9 +val Node12 cv::Size_< _Tp >   + Size_() + Size_() + Size_() + Size_() + Size_() + area() + aspectRatio() + empty() + operator Size_< _Tp2 >() + operator=() + operator=() Node7->Node12 +height +width Node8 cv::Vec< _Tp, cn >   + Vec() + Vec() + Vec() + Vec() + Vec() + Vec() + Vec() + Vec() + Vec() + Vec() and 17 more... + all() + diag() + eye() + ones() + randn() + randu() + zeros() Node8->Node3 < double, 4 > Node11 cv::Vec< _Tp, 4 >   + Vec() + Vec() + Vec() + Vec() + Vec() + Vec() + Vec() + Vec() + Vec() + Vec() and 17 more... + all() + diag() + eye() + ones() + randn() + randu() + zeros() Node8->Node11 < _Tp, 4 > Node9->Node8 Node9->Node11 Node10 cv::Scalar_< _Tp >   + Scalar_() + Scalar_() + Scalar_() + Scalar_() + Scalar_() + Scalar_() + conj() + isReal() + mul() + operator Scalar_< T2 >() + operator=() + operator=() + all() Node10->Node2 < double > Node11->Node10 Node12->Node1 +size Node13 bool     Node13->Node1 +swapRB Node14 int     Node14->Node1 +ddepth Node15 DataLayout     Node15->Node1 +datalayout Node16 ImagePaddingMode     Node16->Node1 +paddingmode

cv::dnn::Image2BlobParams Node1 cv::dnn::Image2BlobParams   + Image2BlobParams() + Image2BlobParams() + blobRectsToImageRects() + blobRectToImageRect() Node2 cv::Scalar_< double >   + Scalar_() + Scalar_() + Scalar_() + Scalar_() + Scalar_() + Scalar_() + conj() + isReal() + mul() + operator Scalar_< T2 >() + operator=() + operator=() + all() Node2->Node1 +borderValue +mean +scalefactor Node3 cv::Vec< double, 4 >   + Vec() + Vec() + Vec() + Vec() + Vec() + Vec() + Vec() + Vec() + Vec() + Vec() and 17 more... + all() + diag() + eye() + ones() + randn() + randu() + zeros() Node3->Node2 Node4 cv::Matx< double, cn, 1 >   + Matx() + Matx() + Matx() + Matx() + Matx() + Matx() + Matx() + Matx() + Matx() + Matx() and 33 more... + all() + diag() + eye() + ones() + randn() + randu() + zeros() Node4->Node3 Node5 double     Node5->Node4 +val Node6 cv::Matx< _Tp, m, n >   + Matx() + Matx() + Matx() + Matx() + Matx() + Matx() + Matx() + Matx() + Matx() + Matx() and 33 more... + all() + diag() + eye() + ones() + randn() + randu() + zeros() Node6->Node4 < double, cn, 1 > Node9 cv::Matx< _Tp, cn, 1 >   + Matx() + Matx() + Matx() + Matx() + Matx() + Matx() + Matx() + Matx() + Matx() + Matx() and 33 more... + all() + diag() + eye() + ones() + randn() + randu() + zeros() Node6->Node9 < _Tp, cn, 1 > Node7 _Tp     Node7->Node6 +val Node7->Node9 +val Node12 cv::Size_< _Tp >   + Size_() + Size_() + Size_() + Size_() + Size_() + area() + aspectRatio() + empty() + operator Size_< _Tp2 >() + operator=() + operator=() Node7->Node12 +height +width Node8 cv::Vec< _Tp, cn >   + Vec() + Vec() + Vec() + Vec() + Vec() + Vec() + Vec() + Vec() + Vec() + Vec() and 17 more... + all() + diag() + eye() + ones() + randn() + randu() + zeros() Node8->Node3 < double, 4 > Node11 cv::Vec< _Tp, 4 >   + Vec() + Vec() + Vec() + Vec() + Vec() + Vec() + Vec() + Vec() + Vec() + Vec() and 17 more... + all() + diag() + eye() + ones() + randn() + randu() + zeros() Node8->Node11 < _Tp, 4 > Node9->Node8 Node9->Node11 Node10 cv::Scalar_< _Tp >   + Scalar_() + Scalar_() + Scalar_() + Scalar_() + Scalar_() + Scalar_() + conj() + isReal() + mul() + operator Scalar_< T2 >() + operator=() + operator=() + all() Node10->Node2 < double > Node11->Node10 Node12->Node1 +size Node13 bool     Node13->Node1 +swapRB Node14 int     Node14->Node1 +ddepth Node15 DataLayout     Node15->Node1 +datalayout Node16 ImagePaddingMode     Node16->Node1 +paddingmode

Detailed Description#

struct Image2BlobParams#

Processing params of image to blob.

It includes all possible image processing operations and corresponding parameters.

See also

blobFromImageWithParams

Note

The order and usage of scalefactor and mean are (input - mean) * scalefactor. The order and usage of scalefactor, size, mean, swapRB, and ddepth are consistent with the function of blobFromImage.

Examples
samples/dnn/object_detection.cpp.

Constructor & Destructor Documentation#

Image2BlobParams()#

cv::dnn::Image2BlobParams::Image2BlobParams()

Python:

cv.dnn.Image2BlobParams() -> <dnn_Image2BlobParams object>

cv.dnn.Image2BlobParams(scalefactor[, size[, mean[, swapRB[, ddepth[, datalayout[, mode[, borderValue]]]]]]]) -> <dnn_Image2BlobParams object>

Image2BlobParams()#

cv::dnn::Image2BlobParams::Image2BlobParams(
const Scalar & scalefactor,
const Size & size = Size(),
const Scalar & mean = Scalar(),
bool swapRB = false,
int ddepth = CV_32F,
DataLayout datalayout = DNN_LAYOUT_NCHW,
ImagePaddingMode mode = dnn::DNN_PMODE_NULL,
Scalar borderValue = 0.0 )

Python:

cv.dnn.Image2BlobParams() -> <dnn_Image2BlobParams object>

cv.dnn.Image2BlobParams(scalefactor[, size[, mean[, swapRB[, ddepth[, datalayout[, mode[, borderValue]]]]]]]) -> <dnn_Image2BlobParams object>

Member Function Documentation#

blobRectsToImageRects()#

void cv::dnn::Image2BlobParams::blobRectsToImageRects(
const std::vector< Rect > & rBlob,
std::vector< Rect > & rImg,
const Size & size )

Python:

cv.dnn.Image2BlobParams.blobRectsToImageRects(rBlob, size) -> rImg

Get rectangle coordinates in original image system from rectangle in blob coordinates.

Parameters

  • rBlob — rect in blob coordinates.

  • rImg — result rect in image coordinates.

  • size — original input image size.

blobRectToImageRect()#

Rect cv::dnn::Image2BlobParams::blobRectToImageRect(
const Rect & rBlob,
const Size & size )

Python:

cv.dnn.Image2BlobParams.blobRectToImageRect(rBlob, size) -> retval

Get rectangle coordinates in original image system from rectangle in blob coordinates.

Parameters

  • rBlob — rect in blob coordinates.

  • size — original input image size.

Returns

rectangle in original image coordinates.

Member Data Documentation#

borderValue#

Scalar cv::dnn::Image2BlobParams::borderValue

Value used in padding mode for padding.

datalayout#

DataLayout cv::dnn::Image2BlobParams::datalayout

Order of output dimensions. Choose DNN_LAYOUT_NCHW or DNN_LAYOUT_NHWC.

ddepth#

int cv::dnn::Image2BlobParams::ddepth

Depth of output blob. Choose CV_32F or CV_8U.

mean#

Scalar cv::dnn::Image2BlobParams::mean

Scalar with mean values which are subtracted from channels.

paddingmode#

dnn::ImagePaddingMode cv::dnn::Image2BlobParams::paddingmode

Image padding mode.

See also

ImagePaddingMode.

scalefactor#

Scalar cv::dnn::Image2BlobParams::scalefactor

scalefactor multiplier for input image values.

size#

Size cv::dnn::Image2BlobParams::size

Spatial size for output image.

swapRB#

bool cv::dnn::Image2BlobParams::swapRB

Flag which indicates that swap first and last channels.

Source file#

The documentation for this struct was generated from the following file: