Class cv::dnn::DetectionModel#

This class represents high-level API for object detection networks. View details

#include <opencv2/dnn/dnn.hpp>

Collaboration diagram for cv::dnn::DetectionModel:

Public Member Functions#

Public Member Functions inherited from cv::dnn::Model

Return	Name	Description
	`Model()`
	`Model(const Model &)`
	`Model(const Net & network)`	Create model from deep learning network.
	`Model(` `CV_WRAP_FILE_PATH const String & model,` `CV_WRAP_FILE_PATH const String & config = "" )`	Create model from deep learning network represented in one of the supported formats. An order of model and config arguments does not matter.
	`Model(Model &&)`
`Model &`	`enableWinograd(bool useWinograd)`
`Impl *`	`getImpl()`
`Impl &`	`getImplRef()`
`Net &`	`getNetwork_()`
`Net &`	`getNetwork_()`
	`operator Net &()`
`Model &`	`operator=(const Model &)`
`Model &`	`operator=(Model &&)`
`void`	`predict(` `InputArray frame,` `OutputArrayOfArrays outs )`	Given the input frame, create input blob, run net and return the output blobs.
`Model &`	`setInputCrop(bool crop)`	Set flag crop for frame.
`Model &`	`setInputMean(const Scalar & mean)`	Set mean value for frame.
`void`	`setInputParams(` `double scale = 1.0,` `const Size & size = Size(),` `const Scalar & mean = Scalar(),` `bool swapRB = false,` `bool crop = false )`	Set preprocessing parameters for frame.
`Model &`	`setInputScale(const Scalar & scale)`	Set scalefactor value for frame.
`Model &`	`setInputSize(const Size & size)`	Set input size for frame.
`Model &`	`setInputSize(` `int width,` `int height )`
`Model &`	`setInputSwapRB(bool swapRB)`	Set flag swapRB for frame.
`Model &`	`setOutputNames(const std::vector< String > & outNames)`	Set output names for frame.
`Model &`	`setPreferableBackend(dnn::Backend backendId)`
`Model &`	`setPreferableTarget(dnn::Target targetId)`

Additional Inherited Members#

Detailed Description#

This class represents high-level API for object detection networks.

DetectionModel allows to set params for preprocessing input image. DetectionModel creates net from file with trained weights and config, sets preprocessing input, runs forward pass and return result detections. For DetectionModel SSD, Faster R-CNN, YOLO topologies are supported.

Constructor & Destructor Documentation#

DetectionModel()#

cv::dnn::DetectionModel::DetectionModel()

Python:

cv.dnn.DetectionModel(model[, config]) -> <dnn_DetectionModel object>

cv.dnn.DetectionModel(network) -> <dnn_DetectionModel object>

DetectionModel()#

cv::dnn::DetectionModel::DetectionModel(const Net & network)

Python:

cv.dnn.DetectionModel(model[, config]) -> <dnn_DetectionModel object>

cv.dnn.DetectionModel(network) -> <dnn_DetectionModel object>

Create model from deep learning network.

Parameters

network — Net object.

DetectionModel()#

cv::dnn::DetectionModel::DetectionModel(
CV_WRAP_FILE_PATH const String & model,
CV_WRAP_FILE_PATH const String & config = “” )

Python:

cv.dnn.DetectionModel(model[, config]) -> <dnn_DetectionModel object>

cv.dnn.DetectionModel(network) -> <dnn_DetectionModel object>

Create detection model from network represented in one of the supported formats. An order of model and config arguments does not matter.

Parameters

model — Binary file contains trained weights.
config — Text file contains network configuration.

Member Function Documentation#

detect()#

void cv::dnn::DetectionModel::detect(
InputArray frame,
std::vector< int > & classIds,
std::vector< float > & confidences,
std::vector< Rect > & boxes,
float confThreshold = 0.5f,
float nmsThreshold = 0.0f )

Python:

cv.dnn.DetectionModel.detect(frame[, confThreshold[, nmsThreshold]]) -> classIds, confidences, boxes

Given the input frame, create input blob, run net and return result detections.

Parameters

frame — The input image.
classIds — Class indexes in result detection.
confidences — A set of corresponding confidences.
boxes — A set of bounding boxes.
confThreshold — A threshold used to filter boxes by confidences.
nmsThreshold — A threshold used in non maximum suppression.

getNmsAcrossClasses()#

bool cv::dnn::DetectionModel::getNmsAcrossClasses()

Python:

cv.dnn.DetectionModel.getNmsAcrossClasses() -> retval

Getter for nmsAcrossClasses. This variable defaults to false, such that when non max suppression is used during the detect() function, it will do so only per-class.

setNmsAcrossClasses()#

DetectionModel & cv::dnn::DetectionModel::setNmsAcrossClasses(bool value)

Python:

cv.dnn.DetectionModel.setNmsAcrossClasses(value) -> retval

nmsAcrossClasses defaults to false, such that when non max suppression is used during the detect() function, it will do so per-class. This function allows you to toggle this behaviour.

Parameters

value — The new value for nmsAcrossClasses

Source file#

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

opencv2/dnn/dnn.hpp