//
// This file is auto-generated. Please don't modify it!
//
package org.opencv.dnn;
import org.opencv.core.Mat;
import org.opencv.core.MatOfByte;
import org.opencv.core.MatOfFloat;
import org.opencv.core.MatOfInt;
import org.opencv.core.MatOfRect;
import org.opencv.core.MatOfRect2d;
import org.opencv.core.MatOfRotatedRect;
import org.opencv.core.Scalar;
import org.opencv.core.Size;
import org.opencv.utils.Converters;
import java.util.List;
// C++: class Dnn
public class Dnn {
// C++: enum Backend (cv.dnn.Backend)
public static final int
DNN_BACKEND_DEFAULT = 0,
DNN_BACKEND_HALIDE = 0+1,
DNN_BACKEND_INFERENCE_ENGINE = 0+2,
DNN_BACKEND_OPENCV = 0+3,
DNN_BACKEND_VKCOM = 0+4,
DNN_BACKEND_CUDA = 0+5,
DNN_BACKEND_WEBNN = 0+6,
DNN_BACKEND_TIMVX = 0+7,
DNN_BACKEND_CANN = 0+8;
// C++: enum DataLayout (cv.dnn.DataLayout)
public static final int
DNN_LAYOUT_UNKNOWN = 0,
DNN_LAYOUT_ND = 1,
DNN_LAYOUT_NCHW = 2,
DNN_LAYOUT_NCDHW = 3,
DNN_LAYOUT_NHWC = 4,
DNN_LAYOUT_NDHWC = 5,
DNN_LAYOUT_PLANAR = 6;
// C++: enum ImagePaddingMode (cv.dnn.ImagePaddingMode)
public static final int
DNN_PMODE_NULL = 0,
DNN_PMODE_CROP_CENTER = 1,
DNN_PMODE_LETTERBOX = 2;
// C++: enum SoftNMSMethod (cv.dnn.SoftNMSMethod)
public static final int
SoftNMSMethod_SOFTNMS_LINEAR = 1,
SoftNMSMethod_SOFTNMS_GAUSSIAN = 2;
// C++: enum Target (cv.dnn.Target)
public static final int
DNN_TARGET_CPU = 0,
DNN_TARGET_OPENCL = 0+1,
DNN_TARGET_OPENCL_FP16 = 0+2,
DNN_TARGET_MYRIAD = 0+3,
DNN_TARGET_VULKAN = 0+4,
DNN_TARGET_FPGA = 0+5,
DNN_TARGET_CUDA = 0+6,
DNN_TARGET_CUDA_FP16 = 0+7,
DNN_TARGET_HDDL = 0+8,
DNN_TARGET_NPU = 0+9,
DNN_TARGET_CPU_FP16 = 0+10;
//
// C++: vector_Target cv::dnn::getAvailableTargets(dnn_Backend be)
//
public static List<Integer> getAvailableTargets(int be) {
return getAvailableTargets_0(be);
}
//
// C++: Net cv::dnn::readNetFromDarknet(String cfgFile, String darknetModel = String())
//
/**
* Reads a network model stored in <a href="https://pjreddie.com/darknet/">Darknet</a> model files.
* @param cfgFile path to the .cfg file with text description of the network architecture.
* @param darknetModel path to the .weights file with learned network.
* @return Network object that ready to do forward, throw an exception in failure cases.
*/
public static Net readNetFromDarknet(String cfgFile, String darknetModel) {
return new Net(readNetFromDarknet_0(cfgFile, darknetModel));
}
/**
* Reads a network model stored in <a href="https://pjreddie.com/darknet/">Darknet</a> model files.
* @param cfgFile path to the .cfg file with text description of the network architecture.
* @return Network object that ready to do forward, throw an exception in failure cases.
*/
public static Net readNetFromDarknet(String cfgFile) {
return new Net(readNetFromDarknet_1(cfgFile));
}
//
// C++: Net cv::dnn::readNetFromDarknet(vector_uchar bufferCfg, vector_uchar bufferModel = std::vector<uchar>())
//
/**
* Reads a network model stored in <a href="https://pjreddie.com/darknet/">Darknet</a> model files.
* @param bufferCfg A buffer contains a content of .cfg file with text description of the network architecture.
* @param bufferModel A buffer contains a content of .weights file with learned network.
* @return Net object.
*/
public static Net readNetFromDarknet(MatOfByte bufferCfg, MatOfByte bufferModel) {
Mat bufferCfg_mat = bufferCfg;
Mat bufferModel_mat = bufferModel;
return new Net(readNetFromDarknet_2(bufferCfg_mat.nativeObj, bufferModel_mat.nativeObj));
}
/**
* Reads a network model stored in <a href="https://pjreddie.com/darknet/">Darknet</a> model files.
* @param bufferCfg A buffer contains a content of .cfg file with text description of the network architecture.
* @return Net object.
*/
public static Net readNetFromDarknet(MatOfByte bufferCfg) {
Mat bufferCfg_mat = bufferCfg;
return new Net(readNetFromDarknet_3(bufferCfg_mat.nativeObj));
}
//
// C++: Net cv::dnn::readNetFromCaffe(String prototxt, String caffeModel = String())
//
/**
* Reads a network model stored in <a href="http://caffe.berkeleyvision.org">Caffe</a> framework's format.
* @param prototxt path to the .prototxt file with text description of the network architecture.
* @param caffeModel path to the .caffemodel file with learned network.
* @return Net object.
*/
public static Net readNetFromCaffe(String prototxt, String caffeModel) {
return new Net(readNetFromCaffe_0(prototxt, caffeModel));
}
/**
* Reads a network model stored in <a href="http://caffe.berkeleyvision.org">Caffe</a> framework's format.
* @param prototxt path to the .prototxt file with text description of the network architecture.
* @return Net object.
*/
public static Net readNetFromCaffe(String prototxt) {
return new Net(readNetFromCaffe_1(prototxt));
}
//
// C++: Net cv::dnn::readNetFromCaffe(vector_uchar bufferProto, vector_uchar bufferModel = std::vector<uchar>())
//
/**
* Reads a network model stored in Caffe model in memory.
* @param bufferProto buffer containing the content of the .prototxt file
* @param bufferModel buffer containing the content of the .caffemodel file
* @return Net object.
*/
public static Net readNetFromCaffe(MatOfByte bufferProto, MatOfByte bufferModel) {
Mat bufferProto_mat = bufferProto;
Mat bufferModel_mat = bufferModel;
return new Net(readNetFromCaffe_2(bufferProto_mat.nativeObj, bufferModel_mat.nativeObj));
}
/**
* Reads a network model stored in Caffe model in memory.
* @param bufferProto buffer containing the content of the .prototxt file
* @return Net object.
*/
public static Net readNetFromCaffe(MatOfByte bufferProto) {
Mat bufferProto_mat = bufferProto;
return new Net(readNetFromCaffe_3(bufferProto_mat.nativeObj));
}
//
// C++: Net cv::dnn::readNetFromTensorflow(String model, String config = String())
//
/**
* Reads a network model stored in <a href="https://www.tensorflow.org/">TensorFlow</a> framework's format.
* @param model path to the .pb file with binary protobuf description of the network architecture
* @param config path to the .pbtxt file that contains text graph definition in protobuf format.
* Resulting Net object is built by text graph using weights from a binary one that
* let us make it more flexible.
* @return Net object.
*/
public static Net readNetFromTensorflow(String model, String config) {
return new Net(readNetFromTensorflow_0(model, config));
}
/**
* Reads a network model stored in <a href="https://www.tensorflow.org/">TensorFlow</a> framework's format.
* @param model path to the .pb file with binary protobuf description of the network architecture
* Resulting Net object is built by text graph using weights from a binary one that
* let us make it more flexible.
* @return Net object.
*/
public static Net readNetFromTensorflow(String model) {
return new Net(readNetFromTensorflow_1(model));
}
//
// C++: Net cv::dnn::readNetFromTensorflow(vector_uchar bufferModel, vector_uchar bufferConfig = std::vector<uchar>())
//
/**
* Reads a network model stored in <a href="https://www.tensorflow.org/">TensorFlow</a> framework's format.
* @param bufferModel buffer containing the content of the pb file
* @param bufferConfig buffer containing the content of the pbtxt file
* @return Net object.
*/
public static Net readNetFromTensorflow(MatOfByte bufferModel, MatOfByte bufferConfig) {
Mat bufferModel_mat = bufferModel;
Mat bufferConfig_mat = bufferConfig;
return new Net(readNetFromTensorflow_2(bufferModel_mat.nativeObj, bufferConfig_mat.nativeObj));
}
/**
* Reads a network model stored in <a href="https://www.tensorflow.org/">TensorFlow</a> framework's format.
* @param bufferModel buffer containing the content of the pb file
* @return Net object.
*/
public static Net readNetFromTensorflow(MatOfByte bufferModel) {
Mat bufferModel_mat = bufferModel;
return new Net(readNetFromTensorflow_3(bufferModel_mat.nativeObj));
}
//
// C++: Net cv::dnn::readNetFromTFLite(String model)
//
/**
* Reads a network model stored in <a href="https://www.tensorflow.org/lite">TFLite</a> framework's format.
* @param model path to the .tflite file with binary flatbuffers description of the network architecture
* @return Net object.
*/
public static Net readNetFromTFLite(String model) {
return new Net(readNetFromTFLite_0(model));
}
//
// C++: Net cv::dnn::readNetFromTFLite(vector_uchar bufferModel)
//
/**
* Reads a network model stored in <a href="https://www.tensorflow.org/lite">TFLite</a> framework's format.
* @param bufferModel buffer containing the content of the tflite file
* @return Net object.
*/
public static Net readNetFromTFLite(MatOfByte bufferModel) {
Mat bufferModel_mat = bufferModel;
return new Net(readNetFromTFLite_1(bufferModel_mat.nativeObj));
}
//
// C++: Net cv::dnn::readNetFromTorch(String model, bool isBinary = true, bool evaluate = true)
//
/**
* Reads a network model stored in <a href="http://torch.ch">Torch7</a> framework's format.
* @param model path to the file, dumped from Torch by using torch.save() function.
* @param isBinary specifies whether the network was serialized in ascii mode or binary.
* @param evaluate specifies testing phase of network. If true, it's similar to evaluate() method in Torch.
* @return Net object.
*
* <b>Note:</b> Ascii mode of Torch serializer is more preferable, because binary mode extensively use {@code long} type of C language,
* which has various bit-length on different systems.
*
* The loading file must contain serialized <a href="https://github.com/torch/nn/blob/master/doc/module.md">nn.Module</a> object
* with importing network. Try to eliminate a custom objects from serialazing data to avoid importing errors.
*
* List of supported layers (i.e. object instances derived from Torch nn.Module class):
* - nn.Sequential
* - nn.Parallel
* - nn.Concat
* - nn.Linear
* - nn.SpatialConvolution
* - nn.SpatialMaxPooling, nn.SpatialAveragePooling
* - nn.ReLU, nn.TanH, nn.Sigmoid
* - nn.Reshape
* - nn.SoftMax, nn.LogSoftMax
*
* Also some equivalents of these classes from cunn, cudnn, and fbcunn may be successfully imported.
*/
public static Net readNetFromTorch(String model, boolean isBinary, boolean evaluate) {
return new Net(readNetFromTorch_0(model, isBinary, evaluate));
}
/**
* Reads a network model stored in <a href="http://torch.ch">Torch7</a> framework's format.
* @param model path to the file, dumped from Torch by using torch.save() function.
* @param isBinary specifies whether the network was serialized in ascii mode or binary.
* @return Net object.
*
* <b>Note:</b> Ascii mode of Torch serializer is more preferable, because binary mode extensively use {@code long} type of C language,
* which has various bit-length on different systems.
*
* The loading file must contain serialized <a href="https://github.com/torch/nn/blob/master/doc/module.md">nn.Module</a> object
* with importing network. Try to eliminate a custom objects from serialazing data to avoid importing errors.
*
* List of supported layers (i.e. object instances derived from Torch nn.Module class):
* - nn.Sequential
* - nn.Parallel
* - nn.Concat
* - nn.Linear
* - nn.SpatialConvolution
* - nn.SpatialMaxPooling, nn.SpatialAveragePooling
* - nn.ReLU, nn.TanH, nn.Sigmoid
* - nn.Reshape
* - nn.SoftMax, nn.LogSoftMax
*
* Also some equivalents of these classes from cunn, cudnn, and fbcunn may be successfully imported.
*/
public static Net readNetFromTorch(String model, boolean isBinary) {
return new Net(readNetFromTorch_1(model, isBinary));
}
/**
* Reads a network model stored in <a href="http://torch.ch">Torch7</a> framework's format.
* @param model path to the file, dumped from Torch by using torch.save() function.
* @return Net object.
*
* <b>Note:</b> Ascii mode of Torch serializer is more preferable, because binary mode extensively use {@code long} type of C language,
* which has various bit-length on different systems.
*
* The loading file must contain serialized <a href="https://github.com/torch/nn/blob/master/doc/module.md">nn.Module</a> object
* with importing network. Try to eliminate a custom objects from serialazing data to avoid importing errors.
*
* List of supported layers (i.e. object instances derived from Torch nn.Module class):
* - nn.Sequential
* - nn.Parallel
* - nn.Concat
* - nn.Linear
* - nn.SpatialConvolution
* - nn.SpatialMaxPooling, nn.SpatialAveragePooling
* - nn.ReLU, nn.TanH, nn.Sigmoid
* - nn.Reshape
* - nn.SoftMax, nn.LogSoftMax
*
* Also some equivalents of these classes from cunn, cudnn, and fbcunn may be successfully imported.
*/
public static Net readNetFromTorch(String model) {
return new Net(readNetFromTorch_2(model));
}
//
// C++: Net cv::dnn::readNet(String model, String config = "", String framework = "")
//
/**
* Read deep learning network represented in one of the supported formats.
* @param model Binary file contains trained weights. The following file
* extensions are expected for models from different frameworks:
* * {@code *.caffemodel} (Caffe, http://caffe.berkeleyvision.org/)
* * {@code *.pb} (TensorFlow, https://www.tensorflow.org/)
* * {@code *.t7} | {@code *.net} (Torch, http://torch.ch/)
* * {@code *.weights} (Darknet, https://pjreddie.com/darknet/)
* * {@code *.bin} | {@code *.onnx} (OpenVINO, https://software.intel.com/openvino-toolkit)
* * {@code *.onnx} (ONNX, https://onnx.ai/)
* @param config Text file contains network configuration. It could be a
* file with the following extensions:
* * {@code *.prototxt} (Caffe, http://caffe.berkeleyvision.org/)
* * {@code *.pbtxt} (TensorFlow, https://www.tensorflow.org/)
* * {@code *.cfg} (Darknet, https://pjreddie.com/darknet/)
* * {@code *.xml} (OpenVINO, https://software.intel.com/openvino-toolkit)
* @param framework Explicit framework name tag to determine a format.
* @return Net object.
*
* This function automatically detects an origin framework of trained model
* and calls an appropriate function such REF: readNetFromCaffe, REF: readNetFromTensorflow,
* REF: readNetFromTorch or REF: readNetFromDarknet. An order of {@code model} and {@code config}
* arguments does not matter.
*/
public static Net readNet(String model, String config, String framework) {
return new Net(readNet_0(model, config, framework));
}
/**
* Read deep learning network represented in one of the supported formats.
* @param model Binary file contains trained weights. The following file
* extensions are expected for models from different frameworks:
* * {@code *.caffemodel} (Caffe, http://caffe.berkeleyvision.org/)
* * {@code *.pb} (TensorFlow, https://www.tensorflow.org/)
* * {@code *.t7} | {@code *.net} (Torch, http://torch.ch/)
* * {@code *.weights} (Darknet, https://pjreddie.com/darknet/)
* * {@code *.bin} | {@code *.onnx} (OpenVINO, https://software.intel.com/openvino-toolkit)
* * {@code *.onnx} (ONNX, https://onnx.ai/)
* @param config Text file contains network configuration. It could be a
* file with the following extensions:
* * {@code *.prototxt} (Caffe, http://caffe.berkeleyvision.org/)
* * {@code *.pbtxt} (TensorFlow, https://www.tensorflow.org/)
* * {@code *.cfg} (Darknet, https://pjreddie.com/darknet/)
* * {@code *.xml} (OpenVINO, https://software.intel.com/openvino-toolkit)
* @return Net object.
*
* This function automatically detects an origin framework of trained model
* and calls an appropriate function such REF: readNetFromCaffe, REF: readNetFromTensorflow,
* REF: readNetFromTorch or REF: readNetFromDarknet. An order of {@code model} and {@code config}
* arguments does not matter.
*/
public static Net readNet(String model, String config) {
return new Net(readNet_1(model, config));
}
/**
* Read deep learning network represented in one of the supported formats.
* @param model Binary file contains trained weights. The following file
* extensions are expected for models from different frameworks:
* * {@code *.caffemodel} (Caffe, http://caffe.berkeleyvision.org/)
* * {@code *.pb} (TensorFlow, https://www.tensorflow.org/)
* * {@code *.t7} | {@code *.net} (Torch, http://torch.ch/)
* * {@code *.weights} (Darknet, https://pjreddie.com/darknet/)
* * {@code *.bin} | {@code *.onnx} (OpenVINO, https://software.intel.com/openvino-toolkit)
* * {@code *.onnx} (ONNX, https://onnx.ai/)
* file with the following extensions:
* * {@code *.prototxt} (Caffe, http://caffe.berkeleyvision.org/)
* * {@code *.pbtxt} (TensorFlow, https://www.tensorflow.org/)
* * {@code *.cfg} (Darknet, https://pjreddie.com/darknet/)
* * {@code *.xml} (OpenVINO, https://software.intel.com/openvino-toolkit)
* @return Net object.
*
* This function automatically detects an origin framework of trained model
* and calls an appropriate function such REF: readNetFromCaffe, REF: readNetFromTensorflow,
* REF: readNetFromTorch or REF: readNetFromDarknet. An order of {@code model} and {@code config}
* arguments does not matter.
*/
public static Net readNet(String model) {
return new Net(readNet_2(model));
}
//
// C++: Net cv::dnn::readNet(String framework, vector_uchar bufferModel, vector_uchar bufferConfig = std::vector<uchar>())
//
/**
* Read deep learning network represented in one of the supported formats.
* This is an overloaded member function, provided for convenience.
* It differs from the above function only in what argument(s) it accepts.
* @param framework Name of origin framework.
* @param bufferModel A buffer with a content of binary file with weights
* @param bufferConfig A buffer with a content of text file contains network configuration.
* @return Net object.
*/
public static Net readNet(String framework, MatOfByte bufferModel, MatOfByte bufferConfig) {
Mat bufferModel_mat = bufferModel;
Mat bufferConfig_mat = bufferConfig;
return new Net(readNet_3(framework, bufferModel_mat.nativeObj, bufferConfig_mat.nativeObj));
}
/**
* Read deep learning network represented in one of the supported formats.
* This is an overloaded member function, provided for convenience.
* It differs from the above function only in what argument(s) it accepts.
* @param framework Name of origin framework.
* @param bufferModel A buffer with a content of binary file with weights
* @return Net object.
*/
public static Net readNet(String framework, MatOfByte bufferModel) {
Mat bufferModel_mat = bufferModel;
return new Net(readNet_4(framework, bufferModel_mat.nativeObj));
}
//
// C++: Mat cv::dnn::readTorchBlob(String filename, bool isBinary = true)
//
/**
* Loads blob which was serialized as torch.Tensor object of Torch7 framework.
* WARNING: This function has the same limitations as readNetFromTorch().
* @param filename automatically generated
* @param isBinary automatically generated
* @return automatically generated
*/
public static Mat readTorchBlob(String filename, boolean isBinary) {
return new Mat(readTorchBlob_0(filename, isBinary));
}
/**
* Loads blob which was serialized as torch.Tensor object of Torch7 framework.
* WARNING: This function has the same limitations as readNetFromTorch().
* @param filename automatically generated
* @return automatically generated
*/
public static Mat readTorchBlob(String filename) {
return new Mat(readTorchBlob_1(filename));
}
//
// C++: Net cv::dnn::readNetFromModelOptimizer(String xml, String bin = "")
//
/**
* Load a network from Intel's Model Optimizer intermediate representation.
* @param xml XML configuration file with network's topology.
* @param bin Binary file with trained weights.
* @return Net object.
* Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine
* backend.
*/
public static Net readNetFromModelOptimizer(String xml, String bin) {
return new Net(readNetFromModelOptimizer_0(xml, bin));
}
/**
* Load a network from Intel's Model Optimizer intermediate representation.
* @param xml XML configuration file with network's topology.
* @return Net object.
* Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine
* backend.
*/
public static Net readNetFromModelOptimizer(String xml) {
return new Net(readNetFromModelOptimizer_1(xml));
}
//
// C++: Net cv::dnn::readNetFromModelOptimizer(vector_uchar bufferModelConfig, vector_uchar bufferWeights)
//
/**
* Load a network from Intel's Model Optimizer intermediate representation.
* @param bufferModelConfig Buffer contains XML configuration with network's topology.
* @param bufferWeights Buffer contains binary data with trained weights.
* @return Net object.
* Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine
* backend.
*/
public static Net readNetFromModelOptimizer(MatOfByte bufferModelConfig, MatOfByte bufferWeights) {
Mat bufferModelConfig_mat = bufferModelConfig;
Mat bufferWeights_mat = bufferWeights;
return new Net(readNetFromModelOptimizer_2(bufferModelConfig_mat.nativeObj, bufferWeights_mat.nativeObj));
}
//
// C++: Net cv::dnn::readNetFromONNX(String onnxFile)
//
/**
* Reads a network model <a href="https://onnx.ai/">ONNX</a>.
* @param onnxFile path to the .onnx file with text description of the network architecture.
* @return Network object that ready to do forward, throw an exception in failure cases.
*/
public static Net readNetFromONNX(String onnxFile) {
return new Net(readNetFromONNX_0(onnxFile));
}
//
// C++: Net cv::dnn::readNetFromONNX(vector_uchar buffer)
//
/**
* Reads a network model from <a href="https://onnx.ai/">ONNX</a>
* in-memory buffer.
* @param buffer in-memory buffer that stores the ONNX model bytes.
* @return Network object that ready to do forward, throw an exception
* in failure cases.
*/
public static Net readNetFromONNX(MatOfByte buffer) {
Mat buffer_mat = buffer;
return new Net(readNetFromONNX_1(buffer_mat.nativeObj));
}
//
// C++: Mat cv::dnn::readTensorFromONNX(String path)
//
/**
* Creates blob from .pb file.
* @param path to the .pb file with input tensor.
* @return Mat.
*/
public static Mat readTensorFromONNX(String path) {
return new Mat(readTensorFromONNX_0(path));
}
//
// C++: Mat cv::dnn::blobFromImage(Mat image, double scalefactor = 1.0, Size size = Size(), Scalar mean = Scalar(), bool swapRB = false, bool crop = false, int ddepth = CV_32F)
//
/**
* Creates 4-dimensional blob from image. Optionally resizes and crops {@code image} from center,
* subtract {@code mean} values, scales values by {@code scalefactor}, swap Blue and Red channels.
* @param image input image (with 1-, 3- or 4-channels).
* @param scalefactor multiplier for {@code images} values.
* @param size spatial size for output image
* @param mean scalar with mean values which are subtracted from channels. Values are intended
* to be in (mean-R, mean-G, mean-B) order if {@code image} has BGR ordering and {@code swapRB} is true.
* @param swapRB flag which indicates that swap first and last channels
* in 3-channel image is necessary.
* @param crop flag which indicates whether image will be cropped after resize or not
* @param ddepth Depth of output blob. Choose CV_32F or CV_8U.
* if {@code crop} is true, input image is resized so one side after resize is equal to corresponding
* dimension in {@code size} and another one is equal or larger. Then, crop from the center is performed.
* If {@code crop} is false, direct resize without cropping and preserving aspect ratio is performed.
* @return 4-dimensional Mat with NCHW dimensions order.
*
* <b>Note:</b>
* The order and usage of {@code scalefactor} and {@code mean} are (input - mean) * scalefactor.
*/
public static Mat blobFromImage(Mat image, double scalefactor, Size size, Scalar mean, boolean swapRB, boolean crop, int ddepth) {
return new Mat(blobFromImage_0(image.nativeObj, scalefactor, size.width, size.height, mean.val[0], mean.val[1], mean.val[2], mean.val[3], swapRB, crop, ddepth));
}
/**
* Creates 4-dimensional blob from image. Optionally resizes and crops {@code image} from center,
* subtract {@code mean} values, scales values by {@code scalefactor}, swap Blue and Red channels.
* @param image input image (with 1-, 3- or 4-channels).
* @param scalefactor multiplier for {@code images} values.
* @param size spatial size for output image
* @param mean scalar with mean values which are subtracted from channels. Values are intended
* to be in (mean-R, mean-G, mean-B) order if {@code image} has BGR ordering and {@code swapRB} is true.
* @param swapRB flag which indicates that swap first and last channels
* in 3-channel image is necessary.
* @param crop flag which indicates whether image will be cropped after resize or not
* if {@code crop} is true, input image is resized so one side after resize is equal to corresponding
* dimension in {@code size} and another one is equal or larger. Then, crop from the center is performed.
* If {@code crop} is false, direct resize without cropping and preserving aspect ratio is performed.
* @return 4-dimensional Mat with NCHW dimensions order.
*
* <b>Note:</b>
* The order and usage of {@code scalefactor} and {@code mean} are (input - mean) * scalefactor.
*/
public static Mat blobFromImage(Mat image, double scalefactor, Size size, Scalar mean, boolean swapRB, boolean crop) {
return new Mat(blobFromImage_1(image.nativeObj, scalefactor, size.width, size.height, mean.val[0], mean.val[1], mean.val[2], mean.val[3], swapRB, crop));
}
/**
* Creates 4-dimensional blob from image. Optionally resizes and crops {@code image} from center,
* subtract {@code mean} values, scales values by {@code scalefactor}, swap Blue and Red channels.
* @param image input image (with 1-, 3- or 4-channels).
* @param scalefactor multiplier for {@code images} values.
* @param size spatial size for output image
* @param mean scalar with mean values which are subtracted from channels. Values are intended
* to be in (mean-R, mean-G, mean-B) order if {@code image} has BGR ordering and {@code swapRB} is true.
* @param swapRB flag which indicates that swap first and last channels
* in 3-channel image is necessary.
* if {@code crop} is true, input image is resized so one side after resize is equal to corresponding
* dimension in {@code size} and another one is equal or larger. Then, crop from the center is performed.
* If {@code crop} is false, direct resize without cropping and preserving aspect ratio is performed.
* @return 4-dimensional Mat with NCHW dimensions order.
*
* <b>Note:</b>
* The order and usage of {@code scalefactor} and {@code mean} are (input - mean) * scalefactor.
*/
public static Mat blobFromImage(Mat image, double scalefactor, Size size, Scalar mean, boolean swapRB) {
return new Mat(blobFromImage_2(image.nativeObj, scalefactor, size.width, size.height, mean.val[0], mean.val[1], mean.val[2], mean.val[3], swapRB));
}
/**
* Creates 4-dimensional blob from image. Optionally resizes and crops {@code image} from center,
* subtract {@code mean} values, scales values by {@code scalefactor}, swap Blue and Red channels.
* @param image input image (with 1-, 3- or 4-channels).
* @param scalefactor multiplier for {@code images} values.
* @param size spatial size for output image
* @param mean scalar with mean values which are subtracted from channels. Values are intended
* to be in (mean-R, mean-G, mean-B) order if {@code image} has BGR ordering and {@code swapRB} is true.
* in 3-channel image is necessary.
* if {@code crop} is true, input image is resized so one side after resize is equal to corresponding
* dimension in {@code size} and another one is equal or larger. Then, crop from the center is performed.
* If {@code crop} is false, direct resize without cropping and preserving aspect ratio is performed.
* @return 4-dimensional Mat with NCHW dimensions order.
*
* <b>Note:</b>
* The order and usage of {@code scalefactor} and {@code mean} are (input - mean) * scalefactor.
*/
public static Mat blobFromImage(Mat image, double scalefactor, Size size, Scalar mean) {
return new Mat(blobFromImage_3(image.nativeObj, scalefactor, size.width, size.height, mean.val[0], mean.val[1], mean.val[2], mean.val[3]));
}
/**
* Creates 4-dimensional blob from image. Optionally resizes and crops {@code image} from center,
* subtract {@code mean} values, scales values by {@code scalefactor}, swap Blue and Red channels.
* @param image input image (with 1-, 3- or 4-channels).
* @param scalefactor multiplier for {@code images} values.
* @param size spatial size for output image
* to be in (mean-R, mean-G, mean-B) order if {@code image} has BGR ordering and {@code swapRB} is true.
* in 3-channel image is necessary.
* if {@code crop} is true, input image is resized so one side after resize is equal to corresponding
* dimension in {@code size} and another one is equal or larger. Then, crop from the center is performed.
* If {@code crop} is false, direct resize without cropping and preserving aspect ratio is performed.
* @return 4-dimensional Mat with NCHW dimensions order.
*
* <b>Note:</b>
* The order and usage of {@code scalefactor} and {@code mean} are (input - mean) * scalefactor.
*/
public static Mat blobFromImage(Mat image, double scalefactor, Size size) {
return new Mat(blobFromImage_4(image.nativeObj, scalefactor, size.width, size.height));
}
/**
* Creates 4-dimensional blob from image. Optionally resizes and crops {@code image} from center,
* subtract {@code mean} values, scales values by {@code scalefactor}, swap Blue and Red channels.
* @param image input image (with 1-, 3- or 4-channels).
* @param scalefactor multiplier for {@code images} values.
* to be in (mean-R, mean-G, mean-B) order if {@code image} has BGR ordering and {@code swapRB} is true.
* in 3-channel image is necessary.
* if {@code crop} is true, input image is resized so one side after resize is equal to corresponding
* dimension in {@code size} and another one is equal or larger. Then, crop from the center is performed.
* If {@code crop} is false, direct resize without cropping and preserving aspect ratio is performed.
* @return 4-dimensional Mat with NCHW dimensions order.
*
* <b>Note:</b>
* The order and usage of {@code scalefactor} and {@code mean} are (input - mean) * scalefactor.
*/
public static Mat blobFromImage(Mat image, double scalefactor) {
return new Mat(blobFromImage_5(image.nativeObj, scalefactor));
}
/**
* Creates 4-dimensional blob from image. Optionally resizes and crops {@code image} from center,
* subtract {@code mean} values, scales values by {@code scalefactor}, swap Blue and Red channels.
* @param image input image (with 1-, 3- or 4-channels).
* to be in (mean-R, mean-G, mean-B) order if {@code image} has BGR ordering and {@code swapRB} is true.
* in 3-channel image is necessary.
* if {@code crop} is true, input image is resized so one side after resize is equal to corresponding
* dimension in {@code size} and another one is equal or larger. Then, crop from the center is performed.
* If {@code crop} is false, direct resize without cropping and preserving aspect ratio is performed.
* @return 4-dimensional Mat with NCHW dimensions order.
*
* <b>Note:</b>
* The order and usage of {@code scalefactor} and {@code mean} are (input - mean) * scalefactor.
*/
public static Mat blobFromImage(Mat image) {
return new Mat(blobFromImage_6(image.nativeObj));
}
//
// C++: Mat cv::dnn::blobFromImages(vector_Mat images, double scalefactor = 1.0, Size size = Size(), Scalar mean = Scalar(), bool swapRB = false, bool crop = false, int ddepth = CV_32F)
//
/**
* Creates 4-dimensional blob from series of images. Optionally resizes and
* crops {@code images} from center, subtract {@code mean} values, scales values by {@code scalefactor},
* swap Blue and Red channels.
* @param images input images (all with 1-, 3- or 4-channels).
* @param size spatial size for output image
* @param mean scalar with mean values which are subtracted from channels. Values are intended
* to be in (mean-R, mean-G, mean-B) order if {@code image} has BGR ordering and {@code swapRB} is true.
* @param scalefactor multiplier for {@code images} values.
* @param swapRB flag which indicates that swap first and last channels
* in 3-channel image is necessary.
* @param crop flag which indicates whether image will be cropped after resize or not
* @param ddepth Depth of output blob. Choose CV_32F or CV_8U.
* if {@code crop} is true, input image is resized so one side after resize is equal to corresponding
* dimension in {@code size} and another one is equal or larger. Then, crop from the center is performed.
* If {@code crop} is false, direct resize without cropping and preserving aspect ratio is performed.
* @return 4-dimensional Mat with NCHW dimensions order.
*
* <b>Note:</b>
* The order and usage of {@code scalefactor} and {@code mean} are (input - mean) * scalefactor.
*/
public static Mat blobFromImages(List<Mat> images, double scalefactor, Size size, Scalar mean, boolean swapRB, boolean crop, int ddepth) {
Mat images_mat = Converters.vector_Mat_to_Mat(images);
return new Mat(blobFromImages_0(images_mat.nativeObj, scalefactor, size.width, size.height, mean.val[0], mean.val[1], mean.val[2], mean.val[3], swapRB, crop, ddepth));
}
/**
* Creates 4-dimensional blob from series of images. Optionally resizes and
* crops {@code images} from center, subtract {@code mean} values, scales values by {@code scalefactor},
* swap Blue and Red channels.
* @param images input images (all with 1-, 3- or 4-channels).
* @param size spatial size for output image
* @param mean scalar with mean values which are subtracted from channels. Values are intended
* to be in (mean-R, mean-G, mean-B) order if {@code image} has BGR ordering and {@code swapRB} is true.
* @param scalefactor multiplier for {@code images} values.
* @param swapRB flag which indicates that swap first and last channels
* in 3-channel image is necessary.
* @param crop flag which indicates whether image will be cropped after resize or not
* if {@code crop} is true, input image is resized so one side after resize is equal to corresponding
* dimension in {@code size} and another one is equal or larger. Then, crop from the center is performed.
* If {@code crop} is false, direct resize without cropping and preserving aspect ratio is performed.
* @return 4-dimensional Mat with NCHW dimensions order.
*
* <b>Note:</b>
* The order and usage of {@code scalefactor} and {@code mean} are (input - mean) * scalefactor.
*/
public static Mat blobFromImages(List<Mat> images, double scalefactor, Size size, Scalar mean, boolean swapRB, boolean crop) {
Mat images_mat = Converters.vector_Mat_to_Mat(images);
return new Mat(blobFromImages_1(images_mat.nativeObj, scalefactor, size.width, size.height, mean.val[0], mean.val[1], mean.val[2], mean.val[3], swapRB, crop));
}
/**
* Creates 4-dimensional blob from series of images. Optionally resizes and
* crops {@code images} from center, subtract {@code mean} values, scales values by {@code scalefactor},
* swap Blue and Red channels.
* @param images input images (all with 1-, 3- or 4-channels).
* @param size spatial size for output image
* @param mean scalar with mean values which are subtracted from channels. Values are intended
* to be in (mean-R, mean-G, mean-B) order if {@code image} has BGR ordering and {@code swapRB} is true.
* @param scalefactor multiplier for {@code images} values.
* @param swapRB flag which indicates that swap first and last channels
* in 3-channel image is necessary.
* if {@code crop} is true, input image is resized so one side after resize is equal to corresponding
* dimension in {@code size} and another one is equal or larger. Then, crop from the center is performed.
* If {@code crop} is false, direct resize without cropping and preserving aspect ratio is performed.
* @return 4-dimensional Mat with NCHW dimensions order.
*
* <b>Note:</b>
* The order and usage of {@code scalefactor} and {@code mean} are (input - mean) * scalefactor.
*/
public static Mat blobFromImages(List<Mat> images, double scalefactor, Size size, Scalar mean, boolean swapRB) {
Mat images_mat = Converters.vector_Mat_to_Mat(images);
return new Mat(blobFromImages_2(images_mat.nativeObj, scalefactor, size.width, size.height, mean.val[0], mean.val[1], mean.val[2], mean.val[3], swapRB));
}
/**
* Creates 4-dimensional blob from series of images. Optionally resizes and
* crops {@code images} from center, subtract {@code mean} values, scales values by {@code scalefactor},
* swap Blue and Red channels.
* @param images input images (all with 1-, 3- or 4-channels).
* @param size spatial size for output image
* @param mean scalar with mean values which are subtracted from channels. Values are intended
* to be in (mean-R, mean-G, mean-B) order if {@code image} has BGR ordering and {@code swapRB} is true.
* @param scalefactor multiplier for {@code images} values.
* in 3-channel image is necessary.
* if {@code crop} is true, input image is resized so one side after resize is equal to corresponding
* dimension in {@code size} and another one is equal or larger. Then, crop from the center is performed.
* If {@code crop} is false, direct resize without cropping and preserving aspect ratio is performed.
* @return 4-dimensional Mat with NCHW dimensions order.
*
* <b>Note:</b>
* The order and usage of {@code scalefactor} and {@code mean} are (input - mean) * scalefactor.
*/
public static Mat blobFromImages(List<Mat> images, double scalefactor, Size size, Scalar mean) {
Mat images_mat = Converters.vector_Mat_to_Mat(images);
return new Mat(blobFromImages_3(images_mat.nativeObj, scalefactor, size.width, size.height, mean.val[0], mean.val[1], mean.val[2], mean.val[3]));
}
/**
* Creates 4-dimensional blob from series of images. Optionally resizes and
* crops {@code images} from center, subtract {@code mean} values, scales values by {@code scalefactor},
* swap Blue and Red channels.
* @param images input images (all with 1-, 3- or 4-channels).
* @param size spatial size for output image
* to be in (mean-R, mean-G, mean-B) order if {@code image} has BGR ordering and {@code swapRB} is true.
* @param scalefactor multiplier for {@code images} values.
* in 3-channel image is necessary.
* if {@code crop} is true, input image is resized so one side after resize is equal to corresponding
* dimension in {@code size} and another one is equal or larger. Then, crop from the center is performed.
* If {@code crop} is false, direct resize without cropping and preserving aspect ratio is performed.
* @return 4-dimensional Mat with NCHW dimensions order.
*
* <b>Note:</b>
* The order and usage of {@code scalefactor} and {@code mean} are (input - mean) * scalefactor.
*/
public static Mat blobFromImages(List<Mat> images, double scalefactor, Size size) {
Mat images_mat = Converters.vector_Mat_to_Mat(images);
return new Mat(blobFromImages_4(images_mat.nativeObj, scalefactor, size.width, size.height));
}
/**
* Creates 4-dimensional blob from series of images. Optionally resizes and
* crops {@code images} from center, subtract {@code mean} values, scales values by {@code scalefactor},
* swap Blue and Red channels.
* @param images input images (all with 1-, 3- or 4-channels).
* to be in (mean-R, mean-G, mean-B) order if {@code image} has BGR ordering and {@code swapRB} is true.
* @param scalefactor multiplier for {@code images} values.
* in 3-channel image is necessary.
* if {@code crop} is true, input image is resized so one side after resize is equal to corresponding
* dimension in {@code size} and another one is equal or larger. Then, crop from the center is performed.
* If {@code crop} is false, direct resize without cropping and preserving aspect ratio is performed.
* @return 4-dimensional Mat with NCHW dimensions order.
*
* <b>Note:</b>
* The order and usage of {@code scalefactor} and {@code mean} are (input - mean) * scalefactor.
*/
public static Mat blobFromImages(List<Mat> images, double scalefactor) {
Mat images_mat = Converters.vector_Mat_to_Mat(images);
return new Mat(blobFromImages_5(images_mat.nativeObj, scalefactor));
}
/**
* Creates 4-dimensional blob from series of images. Optionally resizes and
* crops {@code images} from center, subtract {@code mean} values, scales values by {@code scalefactor},
* swap Blue and Red channels.
* @param images input images (all with 1-, 3- or 4-channels).
* to be in (mean-R, mean-G, mean-B) order if {@code image} has BGR ordering and {@code swapRB} is true.
* in 3-channel image is necessary.
* if {@code crop} is true, input image is resized so one side after resize is equal to corresponding
* dimension in {@code size} and another one is equal or larger. Then, crop from the center is performed.
* If {@code crop} is false, direct resize without cropping and preserving aspect ratio is performed.
* @return 4-dimensional Mat with NCHW dimensions order.
*
* <b>Note:</b>
* The order and usage of {@code scalefactor} and {@code mean} are (input - mean) * scalefactor.
*/
public static Mat blobFromImages(List<Mat> images) {
Mat images_mat = Converters.vector_Mat_to_Mat(images);
return new Mat(blobFromImages_6(images_mat.nativeObj));
}
//
// C++: Mat cv::dnn::blobFromImageWithParams(Mat image, Image2BlobParams param = Image2BlobParams())
//
/**
* Creates 4-dimensional blob from image with given params.
*
* This function is an extension of REF: blobFromImage to meet more image preprocess needs.
* Given input image and preprocessing parameters, and function outputs the blob.
*
* @param image input image (all with 1-, 3- or 4-channels).
* @param param struct of Image2BlobParams, contains all parameters needed by processing of image to blob.
* @return 4-dimensional Mat.
*/
public static Mat blobFromImageWithParams(Mat image, Image2BlobParams param) {
return new Mat(blobFromImageWithParams_0(image.nativeObj, param.nativeObj));
}
/**
* Creates 4-dimensional blob from image with given params.
*
* This function is an extension of REF: blobFromImage to meet more image preprocess needs.
* Given input image and preprocessing parameters, and function outputs the blob.
*
* @param image input image (all with 1-, 3- or 4-channels).
* @return 4-dimensional Mat.
*/
public static Mat blobFromImageWithParams(Mat image) {
return new Mat(blobFromImageWithParams_1(image.nativeObj));
}
//
// C++: void cv::dnn::blobFromImageWithParams(Mat image, Mat& blob, Image2BlobParams param = Image2BlobParams())
//
public static void blobFromImageWithParams(Mat image, Mat blob, Image2BlobParams param) {
blobFromImageWithParams_2(image.nativeObj, blob.nativeObj, param.nativeObj);
}
public static void blobFromImageWithParams(Mat image, Mat blob) {
blobFromImageWithParams_3(image.nativeObj, blob.nativeObj);
}
//
// C++: Mat cv::dnn::blobFromImagesWithParams(vector_Mat images, Image2BlobParams param = Image2BlobParams())
//
/**
* Creates 4-dimensional blob from series of images with given params.
*
* This function is an extension of REF: blobFromImages to meet more image preprocess needs.
* Given input image and preprocessing parameters, and function outputs the blob.
*
* @param images input image (all with 1-, 3- or 4-channels).
* @param param struct of Image2BlobParams, contains all parameters needed by processing of image to blob.
* @return 4-dimensional Mat.
*/
public static Mat blobFromImagesWithParams(List<Mat> images, Image2BlobParams param) {
Mat images_mat = Converters.vector_Mat_to_Mat(images);
return new Mat(blobFromImagesWithParams_0(images_mat.nativeObj, param.nativeObj));
}
/**
* Creates 4-dimensional blob from series of images with given params.
*
* This function is an extension of REF: blobFromImages to meet more image preprocess needs.
* Given input image and preprocessing parameters, and function outputs the blob.
*
* @param images input image (all with 1-, 3- or 4-channels).
* @return 4-dimensional Mat.
*/
public static Mat blobFromImagesWithParams(List<Mat> images) {
Mat images_mat = Converters.vector_Mat_to_Mat(images);
return new Mat(blobFromImagesWithParams_1(images_mat.nativeObj));
}
//
// C++: void cv::dnn::blobFromImagesWithParams(vector_Mat images, Mat& blob, Image2BlobParams param = Image2BlobParams())
//
public static void blobFromImagesWithParams(List<Mat> images, Mat blob, Image2BlobParams param) {
Mat images_mat = Converters.vector_Mat_to_Mat(images);
blobFromImagesWithParams_2(images_mat.nativeObj, blob.nativeObj, param.nativeObj);
}
public static void blobFromImagesWithParams(List<Mat> images, Mat blob) {
Mat images_mat = Converters.vector_Mat_to_Mat(images);
blobFromImagesWithParams_3(images_mat.nativeObj, blob.nativeObj);
}
//
// C++: void cv::dnn::imagesFromBlob(Mat blob_, vector_Mat& images_)
//
/**
* Parse a 4D blob and output the images it contains as 2D arrays through a simpler data structure
* (std::vector<cv::Mat>).
* @param blob_ 4 dimensional array (images, channels, height, width) in floating point precision (CV_32F) from
* which you would like to extract the images.
* @param images_ array of 2D Mat containing the images extracted from the blob in floating point precision
* (CV_32F). They are non normalized neither mean added. The number of returned images equals the first dimension
* of the blob (batch size). Every image has a number of channels equals to the second dimension of the blob (depth).
*/
public static void imagesFromBlob(Mat blob_, List<Mat> images_) {
Mat images__mat = new Mat();
imagesFromBlob_0(blob_.nativeObj, images__mat.nativeObj);
Converters.Mat_to_vector_Mat(images__mat, images_);
images__mat.release();
}
//
// C++: void cv::dnn::shrinkCaffeModel(String src, String dst, vector_String layersTypes = std::vector<String>())
//
/**
* Convert all weights of Caffe network to half precision floating point.
* @param src Path to origin model from Caffe framework contains single
* precision floating point weights (usually has {@code .caffemodel} extension).
* @param dst Path to destination model with updated weights.
* @param layersTypes Set of layers types which parameters will be converted.
* By default, converts only Convolutional and Fully-Connected layers'
* weights.
*
* <b>Note:</b> Shrinked model has no origin float32 weights so it can't be used
* in origin Caffe framework anymore. However the structure of data
* is taken from NVidia's Caffe fork: https://github.com/NVIDIA/caffe.
* So the resulting model may be used there.
*/
public static void shrinkCaffeModel(String src, String dst, List<String> layersTypes) {
shrinkCaffeModel_0(src, dst, layersTypes);
}
/**
* Convert all weights of Caffe network to half precision floating point.
* @param src Path to origin model from Caffe framework contains single
* precision floating point weights (usually has {@code .caffemodel} extension).
* @param dst Path to destination model with updated weights.
* By default, converts only Convolutional and Fully-Connected layers'
* weights.
*
* <b>Note:</b> Shrinked model has no origin float32 weights so it can't be used
* in origin Caffe framework anymore. However the structure of data
* is taken from NVidia's Caffe fork: https://github.com/NVIDIA/caffe.
* So the resulting model may be used there.
*/
public static void shrinkCaffeModel(String src, String dst) {
shrinkCaffeModel_1(src, dst);
}
//
// C++: void cv::dnn::writeTextGraph(String model, String output)
//
/**
* Create a text representation for a binary network stored in protocol buffer format.
* @param model A path to binary network.
* @param output A path to output text file to be created.
*
* <b>Note:</b> To reduce output file size, trained weights are not included.
*/
public static void writeTextGraph(String model, String output) {
writeTextGraph_0(model, output);
}
//
// C++: void cv::dnn::NMSBoxes(vector_Rect2d bboxes, vector_float scores, float score_threshold, float nms_threshold, vector_int& indices, float eta = 1.f, int top_k = 0)
//
/**
* Performs non maximum suppression given boxes and corresponding scores.
*
* @param bboxes a set of bounding boxes to apply NMS.
* @param scores a set of corresponding confidences.
* @param score_threshold a threshold used to filter boxes by score.
* @param nms_threshold a threshold used in non maximum suppression.
* @param indices the kept indices of bboxes after NMS.
* @param eta a coefficient in adaptive threshold formula: \(nms\_threshold_{i+1}=eta\cdot nms\_threshold_i\).
* @param top_k if {@code >0}, keep at most {@code top_k} picked indices.
*/
public static void NMSBoxes(MatOfRect2d bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices, float eta, int top_k) {
Mat bboxes_mat = bboxes;
Mat scores_mat = scores;
Mat indices_mat = indices;
NMSBoxes_0(bboxes_mat.nativeObj, scores_mat.nativeObj, score_threshold, nms_threshold, indices_mat.nativeObj, eta, top_k);
}
/**
* Performs non maximum suppression given boxes and corresponding scores.
*
* @param bboxes a set of bounding boxes to apply NMS.
* @param scores a set of corresponding confidences.
* @param score_threshold a threshold used to filter boxes by score.
* @param nms_threshold a threshold used in non maximum suppression.
* @param indices the kept indices of bboxes after NMS.
* @param eta a coefficient in adaptive threshold formula: \(nms\_threshold_{i+1}=eta\cdot nms\_threshold_i\).
*/
public static void NMSBoxes(MatOfRect2d bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices, float eta) {
Mat bboxes_mat = bboxes;
Mat scores_mat = scores;
Mat indices_mat = indices;
NMSBoxes_1(bboxes_mat.nativeObj, scores_mat.nativeObj, score_threshold, nms_threshold, indices_mat.nativeObj, eta);
}
/**
* Performs non maximum suppression given boxes and corresponding scores.
*
* @param bboxes a set of bounding boxes to apply NMS.
* @param scores a set of corresponding confidences.
* @param score_threshold a threshold used to filter boxes by score.
* @param nms_threshold a threshold used in non maximum suppression.
* @param indices the kept indices of bboxes after NMS.
*/
public static void NMSBoxes(MatOfRect2d bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices) {
Mat bboxes_mat = bboxes;
Mat scores_mat = scores;
Mat indices_mat = indices;
NMSBoxes_2(bboxes_mat.nativeObj, scores_mat.nativeObj, score_threshold, nms_threshold, indices_mat.nativeObj);
}
//
// C++: void cv::dnn::NMSBoxes(vector_RotatedRect bboxes, vector_float scores, float score_threshold, float nms_threshold, vector_int& indices, float eta = 1.f, int top_k = 0)
//
public static void NMSBoxesRotated(MatOfRotatedRect bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices, float eta, int top_k) {
Mat bboxes_mat = bboxes;
Mat scores_mat = scores;
Mat indices_mat = indices;
NMSBoxesRotated_0(bboxes_mat.nativeObj, scores_mat.nativeObj, score_threshold, nms_threshold, indices_mat.nativeObj, eta, top_k);
}
public static void NMSBoxesRotated(MatOfRotatedRect bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices, float eta) {
Mat bboxes_mat = bboxes;
Mat scores_mat = scores;
Mat indices_mat = indices;
NMSBoxesRotated_1(bboxes_mat.nativeObj, scores_mat.nativeObj, score_threshold, nms_threshold, indices_mat.nativeObj, eta);
}
public static void NMSBoxesRotated(MatOfRotatedRect bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices) {
Mat bboxes_mat = bboxes;
Mat scores_mat = scores;
Mat indices_mat = indices;
NMSBoxesRotated_2(bboxes_mat.nativeObj, scores_mat.nativeObj, score_threshold, nms_threshold, indices_mat.nativeObj);
}
//
// C++: void cv::dnn::NMSBoxesBatched(vector_Rect2d bboxes, vector_float scores, vector_int class_ids, float score_threshold, float nms_threshold, vector_int& indices, float eta = 1.f, int top_k = 0)
//
/**
* Performs batched non maximum suppression on given boxes and corresponding scores across different classes.
*
* @param bboxes a set of bounding boxes to apply NMS.
* @param scores a set of corresponding confidences.
* @param class_ids a set of corresponding class ids. Ids are integer and usually start from 0.
* @param score_threshold a threshold used to filter boxes by score.
* @param nms_threshold a threshold used in non maximum suppression.
* @param indices the kept indices of bboxes after NMS.
* @param eta a coefficient in adaptive threshold formula: \(nms\_threshold_{i+1}=eta\cdot nms\_threshold_i\).
* @param top_k if {@code >0}, keep at most {@code top_k} picked indices.
*/
public static void NMSBoxesBatched(MatOfRect2d bboxes, MatOfFloat scores, MatOfInt class_ids, float score_threshold, float nms_threshold, MatOfInt indices, float eta, int top_k) {
Mat bboxes_mat = bboxes;
Mat scores_mat = scores;
Mat class_ids_mat = class_ids;
Mat indices_mat = indices;
NMSBoxesBatched_0(bboxes_mat.nativeObj, scores_mat.nativeObj, class_ids_mat.nativeObj, score_threshold, nms_threshold, indices_mat.nativeObj, eta, top_k);
}
/**
* Performs batched non maximum suppression on given boxes and corresponding scores across different classes.
*
* @param bboxes a set of bounding boxes to apply NMS.
* @param scores a set of corresponding confidences.
* @param class_ids a set of corresponding class ids. Ids are integer and usually start from 0.
* @param score_threshold a threshold used to filter boxes by score.
* @param nms_threshold a threshold used in non maximum suppression.
* @param indices the kept indices of bboxes after NMS.
* @param eta a coefficient in adaptive threshold formula: \(nms\_threshold_{i+1}=eta\cdot nms\_threshold_i\).
*/
public static void NMSBoxesBatched(MatOfRect2d bboxes, MatOfFloat scores, MatOfInt class_ids, float score_threshold, float nms_threshold, MatOfInt indices, float eta) {
Mat bboxes_mat = bboxes;
Mat scores_mat = scores;
Mat class_ids_mat = class_ids;
Mat indices_mat = indices;
NMSBoxesBatched_1(bboxes_mat.nativeObj, scores_mat.nativeObj, class_ids_mat.nativeObj, score_threshold, nms_threshold, indices_mat.nativeObj, eta);
}
/**
* Performs batched non maximum suppression on given boxes and corresponding scores across different classes.
*
* @param bboxes a set of bounding boxes to apply NMS.
* @param scores a set of corresponding confidences.
* @param class_ids a set of corresponding class ids. Ids are integer and usually start from 0.
* @param score_threshold a threshold used to filter boxes by score.
* @param nms_threshold a threshold used in non maximum suppression.
* @param indices the kept indices of bboxes after NMS.
*/
public static void NMSBoxesBatched(MatOfRect2d bboxes, MatOfFloat scores, MatOfInt class_ids, float score_threshold, float nms_threshold, MatOfInt indices) {
Mat bboxes_mat = bboxes;
Mat scores_mat = scores;
Mat class_ids_mat = class_ids;
Mat indices_mat = indices;
NMSBoxesBatched_2(bboxes_mat.nativeObj, scores_mat.nativeObj, class_ids_mat.nativeObj, score_threshold, nms_threshold, indices_mat.nativeObj);
}
//
// C++: void cv::dnn::softNMSBoxes(vector_Rect bboxes, vector_float scores, vector_float& updated_scores, float score_threshold, float nms_threshold, vector_int& indices, size_t top_k = 0, float sigma = 0.5, SoftNMSMethod method = SoftNMSMethod::SOFTNMS_GAUSSIAN)
//
/**
* Performs soft non maximum suppression given boxes and corresponding scores.
* Reference: https://arxiv.org/abs/1704.04503
* @param bboxes a set of bounding boxes to apply Soft NMS.
* @param scores a set of corresponding confidences.
* @param updated_scores a set of corresponding updated confidences.
* @param score_threshold a threshold used to filter boxes by score.
* @param nms_threshold a threshold used in non maximum suppression.
* @param indices the kept indices of bboxes after NMS.
* @param top_k keep at most {@code top_k} picked indices.
* @param sigma parameter of Gaussian weighting.
* SEE: SoftNMSMethod
*/
public static void softNMSBoxes(MatOfRect bboxes, MatOfFloat scores, MatOfFloat updated_scores, float score_threshold, float nms_threshold, MatOfInt indices, long top_k, float sigma) {
Mat bboxes_mat = bboxes;
Mat scores_mat = scores;
Mat updated_scores_mat = updated_scores;
Mat indices_mat = indices;
softNMSBoxes_0(bboxes_mat.nativeObj, scores_mat.nativeObj, updated_scores_mat.nativeObj, score_threshold, nms_threshold, indices_mat.nativeObj, top_k, sigma);
}
/**
* Performs soft non maximum suppression given boxes and corresponding scores.
* Reference: https://arxiv.org/abs/1704.04503
* @param bboxes a set of bounding boxes to apply Soft NMS.
* @param scores a set of corresponding confidences.
* @param updated_scores a set of corresponding updated confidences.
* @param score_threshold a threshold used to filter boxes by score.
* @param nms_threshold a threshold used in non maximum suppression.
* @param indices the kept indices of bboxes after NMS.
* @param top_k keep at most {@code top_k} picked indices.
* SEE: SoftNMSMethod
*/
public static void softNMSBoxes(MatOfRect bboxes, MatOfFloat scores, MatOfFloat updated_scores, float score_threshold, float nms_threshold, MatOfInt indices, long top_k) {
Mat bboxes_mat = bboxes;
Mat scores_mat = scores;
Mat updated_scores_mat = updated_scores;
Mat indices_mat = indices;
softNMSBoxes_2(bboxes_mat.nativeObj, scores_mat.nativeObj, updated_scores_mat.nativeObj, score_threshold, nms_threshold, indices_mat.nativeObj, top_k);
}
/**
* Performs soft non maximum suppression given boxes and corresponding scores.
* Reference: https://arxiv.org/abs/1704.04503
* @param bboxes a set of bounding boxes to apply Soft NMS.
* @param scores a set of corresponding confidences.
* @param updated_scores a set of corresponding updated confidences.
* @param score_threshold a threshold used to filter boxes by score.
* @param nms_threshold a threshold used in non maximum suppression.
* @param indices the kept indices of bboxes after NMS.
* SEE: SoftNMSMethod
*/
public static void softNMSBoxes(MatOfRect bboxes, MatOfFloat scores, MatOfFloat updated_scores, float score_threshold, float nms_threshold, MatOfInt indices) {
Mat bboxes_mat = bboxes;
Mat scores_mat = scores;
Mat updated_scores_mat = updated_scores;
Mat indices_mat = indices;
softNMSBoxes_3(bboxes_mat.nativeObj, scores_mat.nativeObj, updated_scores_mat.nativeObj, score_threshold, nms_threshold, indices_mat.nativeObj);
}
//
// C++: String cv::dnn::getInferenceEngineBackendType()
//
/**
* Returns Inference Engine internal backend API.
*
* See values of {@code CV_DNN_BACKEND_INFERENCE_ENGINE_*} macros.
*
* {@code OPENCV_DNN_BACKEND_INFERENCE_ENGINE_TYPE} runtime parameter (environment variable) is ignored since 4.6.0.
*
* @deprecated
* @return automatically generated
*/
@Deprecated
public static String getInferenceEngineBackendType() {
return getInferenceEngineBackendType_0();
}
//
// C++: String cv::dnn::setInferenceEngineBackendType(String newBackendType)
//
/**
* Specify Inference Engine internal backend API.
*
* See values of {@code CV_DNN_BACKEND_INFERENCE_ENGINE_*} macros.
*
* @return previous value of internal backend API
*
* @deprecated
* @param newBackendType automatically generated
*/
@Deprecated
public static String setInferenceEngineBackendType(String newBackendType) {
return setInferenceEngineBackendType_0(newBackendType);
}
//
// C++: void cv::dnn::resetMyriadDevice()
//
/**
* Release a Myriad device (binded by OpenCV).
*
* Single Myriad device cannot be shared across multiple processes which uses
* Inference Engine's Myriad plugin.
*/
public static void resetMyriadDevice() {
resetMyriadDevice_0();
}
//
// C++: String cv::dnn::getInferenceEngineVPUType()
//
/**
* Returns Inference Engine VPU type.
*
* See values of {@code CV_DNN_INFERENCE_ENGINE_VPU_TYPE_*} macros.
* @return automatically generated
*/
public static String getInferenceEngineVPUType() {
return getInferenceEngineVPUType_0();
}
//
// C++: String cv::dnn::getInferenceEngineCPUType()
//
/**
* Returns Inference Engine CPU type.
*
* Specify OpenVINO plugin: CPU or ARM.
* @return automatically generated
*/
public static String getInferenceEngineCPUType() {
return getInferenceEngineCPUType_0();
}
//
// C++: void cv::dnn::releaseHDDLPlugin()
//
/**
* Release a HDDL plugin.
*/
public static void releaseHDDLPlugin() {
releaseHDDLPlugin_0();
}
// C++: vector_Target cv::dnn::getAvailableTargets(dnn_Backend be)
private static native List<Integer> getAvailableTargets_0(int be);
// C++: Net cv::dnn::readNetFromDarknet(String cfgFile, String darknetModel = String())
private static native long readNetFromDarknet_0(String cfgFile, String darknetModel);
private static native long readNetFromDarknet_1(String cfgFile);
// C++: Net cv::dnn::readNetFromDarknet(vector_uchar bufferCfg, vector_uchar bufferModel = std::vector<uchar>())
private static native long readNetFromDarknet_2(long bufferCfg_mat_nativeObj, long bufferModel_mat_nativeObj);
private static native long readNetFromDarknet_3(long bufferCfg_mat_nativeObj);
// C++: Net cv::dnn::readNetFromCaffe(String prototxt, String caffeModel = String())
private static native long readNetFromCaffe_0(String prototxt, String caffeModel);
private static native long readNetFromCaffe_1(String prototxt);
// C++: Net cv::dnn::readNetFromCaffe(vector_uchar bufferProto, vector_uchar bufferModel = std::vector<uchar>())
private static native long readNetFromCaffe_2(long bufferProto_mat_nativeObj, long bufferModel_mat_nativeObj);
private static native long readNetFromCaffe_3(long bufferProto_mat_nativeObj);
// C++: Net cv::dnn::readNetFromTensorflow(String model, String config = String())
private static native long readNetFromTensorflow_0(String model, String config);
private static native long readNetFromTensorflow_1(String model);
// C++: Net cv::dnn::readNetFromTensorflow(vector_uchar bufferModel, vector_uchar bufferConfig = std::vector<uchar>())
private static native long readNetFromTensorflow_2(long bufferModel_mat_nativeObj, long bufferConfig_mat_nativeObj);
private static native long readNetFromTensorflow_3(long bufferModel_mat_nativeObj);
// C++: Net cv::dnn::readNetFromTFLite(String model)
private static native long readNetFromTFLite_0(String model);
// C++: Net cv::dnn::readNetFromTFLite(vector_uchar bufferModel)
private static native long readNetFromTFLite_1(long bufferModel_mat_nativeObj);
// C++: Net cv::dnn::readNetFromTorch(String model, bool isBinary = true, bool evaluate = true)
private static native long readNetFromTorch_0(String model, boolean isBinary, boolean evaluate);
private static native long readNetFromTorch_1(String model, boolean isBinary);
private static native long readNetFromTorch_2(String model);
// C++: Net cv::dnn::readNet(String model, String config = "", String framework = "")
private static native long readNet_0(String model, String config, String framework);
private static native long readNet_1(String model, String config);
private static native long readNet_2(String model);
// C++: Net cv::dnn::readNet(String framework, vector_uchar bufferModel, vector_uchar bufferConfig = std::vector<uchar>())
private static native long readNet_3(String framework, long bufferModel_mat_nativeObj, long bufferConfig_mat_nativeObj);
private static native long readNet_4(String framework, long bufferModel_mat_nativeObj);
// C++: Mat cv::dnn::readTorchBlob(String filename, bool isBinary = true)
private static native long readTorchBlob_0(String filename, boolean isBinary);
private static native long readTorchBlob_1(String filename);
// C++: Net cv::dnn::readNetFromModelOptimizer(String xml, String bin = "")
private static native long readNetFromModelOptimizer_0(String xml, String bin);
private static native long readNetFromModelOptimizer_1(String xml);
// C++: Net cv::dnn::readNetFromModelOptimizer(vector_uchar bufferModelConfig, vector_uchar bufferWeights)
private static native long readNetFromModelOptimizer_2(long bufferModelConfig_mat_nativeObj, long bufferWeights_mat_nativeObj);
// C++: Net cv::dnn::readNetFromONNX(String onnxFile)
private static native long readNetFromONNX_0(String onnxFile);
// C++: Net cv::dnn::readNetFromONNX(vector_uchar buffer)
private static native long readNetFromONNX_1(long buffer_mat_nativeObj);
// C++: Mat cv::dnn::readTensorFromONNX(String path)
private static native long readTensorFromONNX_0(String path);
// C++: Mat cv::dnn::blobFromImage(Mat image, double scalefactor = 1.0, Size size = Size(), Scalar mean = Scalar(), bool swapRB = false, bool crop = false, int ddepth = CV_32F)
private static native long blobFromImage_0(long image_nativeObj, double scalefactor, double size_width, double size_height, double mean_val0, double mean_val1, double mean_val2, double mean_val3, boolean swapRB, boolean crop, int ddepth);
private static native long blobFromImage_1(long image_nativeObj, double scalefactor, double size_width, double size_height, double mean_val0, double mean_val1, double mean_val2, double mean_val3, boolean swapRB, boolean crop);
private static native long blobFromImage_2(long image_nativeObj, double scalefactor, double size_width, double size_height, double mean_val0, double mean_val1, double mean_val2, double mean_val3, boolean swapRB);
private static native long blobFromImage_3(long image_nativeObj, double scalefactor, double size_width, double size_height, double mean_val0, double mean_val1, double mean_val2, double mean_val3);
private static native long blobFromImage_4(long image_nativeObj, double scalefactor, double size_width, double size_height);
private static native long blobFromImage_5(long image_nativeObj, double scalefactor);
private static native long blobFromImage_6(long image_nativeObj);
// C++: Mat cv::dnn::blobFromImages(vector_Mat images, double scalefactor = 1.0, Size size = Size(), Scalar mean = Scalar(), bool swapRB = false, bool crop = false, int ddepth = CV_32F)
private static native long blobFromImages_0(long images_mat_nativeObj, double scalefactor, double size_width, double size_height, double mean_val0, double mean_val1, double mean_val2, double mean_val3, boolean swapRB, boolean crop, int ddepth);
private static native long blobFromImages_1(long images_mat_nativeObj, double scalefactor, double size_width, double size_height, double mean_val0, double mean_val1, double mean_val2, double mean_val3, boolean swapRB, boolean crop);
private static native long blobFromImages_2(long images_mat_nativeObj, double scalefactor, double size_width, double size_height, double mean_val0, double mean_val1, double mean_val2, double mean_val3, boolean swapRB);
private static native long blobFromImages_3(long images_mat_nativeObj, double scalefactor, double size_width, double size_height, double mean_val0, double mean_val1, double mean_val2, double mean_val3);
private static native long blobFromImages_4(long images_mat_nativeObj, double scalefactor, double size_width, double size_height);
private static native long blobFromImages_5(long images_mat_nativeObj, double scalefactor);
private static native long blobFromImages_6(long images_mat_nativeObj);
// C++: Mat cv::dnn::blobFromImageWithParams(Mat image, Image2BlobParams param = Image2BlobParams())
private static native long blobFromImageWithParams_0(long image_nativeObj, long param_nativeObj);
private static native long blobFromImageWithParams_1(long image_nativeObj);
// C++: void cv::dnn::blobFromImageWithParams(Mat image, Mat& blob, Image2BlobParams param = Image2BlobParams())
private static native void blobFromImageWithParams_2(long image_nativeObj, long blob_nativeObj, long param_nativeObj);
private static native void blobFromImageWithParams_3(long image_nativeObj, long blob_nativeObj);
// C++: Mat cv::dnn::blobFromImagesWithParams(vector_Mat images, Image2BlobParams param = Image2BlobParams())
private static native long blobFromImagesWithParams_0(long images_mat_nativeObj, long param_nativeObj);
private static native long blobFromImagesWithParams_1(long images_mat_nativeObj);
// C++: void cv::dnn::blobFromImagesWithParams(vector_Mat images, Mat& blob, Image2BlobParams param = Image2BlobParams())
private static native void blobFromImagesWithParams_2(long images_mat_nativeObj, long blob_nativeObj, long param_nativeObj);
private static native void blobFromImagesWithParams_3(long images_mat_nativeObj, long blob_nativeObj);
// C++: void cv::dnn::imagesFromBlob(Mat blob_, vector_Mat& images_)
private static native void imagesFromBlob_0(long blob__nativeObj, long images__mat_nativeObj);
// C++: void cv::dnn::shrinkCaffeModel(String src, String dst, vector_String layersTypes = std::vector<String>())
private static native void shrinkCaffeModel_0(String src, String dst, List<String> layersTypes);
private static native void shrinkCaffeModel_1(String src, String dst);
// C++: void cv::dnn::writeTextGraph(String model, String output)
private static native void writeTextGraph_0(String model, String output);
// C++: void cv::dnn::NMSBoxes(vector_Rect2d bboxes, vector_float scores, float score_threshold, float nms_threshold, vector_int& indices, float eta = 1.f, int top_k = 0)
private static native void NMSBoxes_0(long bboxes_mat_nativeObj, long scores_mat_nativeObj, float score_threshold, float nms_threshold, long indices_mat_nativeObj, float eta, int top_k);
private static native void NMSBoxes_1(long bboxes_mat_nativeObj, long scores_mat_nativeObj, float score_threshold, float nms_threshold, long indices_mat_nativeObj, float eta);
private static native void NMSBoxes_2(long bboxes_mat_nativeObj, long scores_mat_nativeObj, float score_threshold, float nms_threshold, long indices_mat_nativeObj);
// C++: void cv::dnn::NMSBoxes(vector_RotatedRect bboxes, vector_float scores, float score_threshold, float nms_threshold, vector_int& indices, float eta = 1.f, int top_k = 0)
private static native void NMSBoxesRotated_0(long bboxes_mat_nativeObj, long scores_mat_nativeObj, float score_threshold, float nms_threshold, long indices_mat_nativeObj, float eta, int top_k);
private static native void NMSBoxesRotated_1(long bboxes_mat_nativeObj, long scores_mat_nativeObj, float score_threshold, float nms_threshold, long indices_mat_nativeObj, float eta);
private static native void NMSBoxesRotated_2(long bboxes_mat_nativeObj, long scores_mat_nativeObj, float score_threshold, float nms_threshold, long indices_mat_nativeObj);
// C++: void cv::dnn::NMSBoxesBatched(vector_Rect2d bboxes, vector_float scores, vector_int class_ids, float score_threshold, float nms_threshold, vector_int& indices, float eta = 1.f, int top_k = 0)
private static native void NMSBoxesBatched_0(long bboxes_mat_nativeObj, long scores_mat_nativeObj, long class_ids_mat_nativeObj, float score_threshold, float nms_threshold, long indices_mat_nativeObj, float eta, int top_k);
private static native void NMSBoxesBatched_1(long bboxes_mat_nativeObj, long scores_mat_nativeObj, long class_ids_mat_nativeObj, float score_threshold, float nms_threshold, long indices_mat_nativeObj, float eta);
private static native void NMSBoxesBatched_2(long bboxes_mat_nativeObj, long scores_mat_nativeObj, long class_ids_mat_nativeObj, float score_threshold, float nms_threshold, long indices_mat_nativeObj);
// C++: void cv::dnn::softNMSBoxes(vector_Rect bboxes, vector_float scores, vector_float& updated_scores, float score_threshold, float nms_threshold, vector_int& indices, size_t top_k = 0, float sigma = 0.5, SoftNMSMethod method = SoftNMSMethod::SOFTNMS_GAUSSIAN)
private static native void softNMSBoxes_0(long bboxes_mat_nativeObj, long scores_mat_nativeObj, long updated_scores_mat_nativeObj, float score_threshold, float nms_threshold, long indices_mat_nativeObj, long top_k, float sigma);
private static native void softNMSBoxes_2(long bboxes_mat_nativeObj, long scores_mat_nativeObj, long updated_scores_mat_nativeObj, float score_threshold, float nms_threshold, long indices_mat_nativeObj, long top_k);
private static native void softNMSBoxes_3(long bboxes_mat_nativeObj, long scores_mat_nativeObj, long updated_scores_mat_nativeObj, float score_threshold, float nms_threshold, long indices_mat_nativeObj);
// C++: String cv::dnn::getInferenceEngineBackendType()
private static native String getInferenceEngineBackendType_0();
// C++: String cv::dnn::setInferenceEngineBackendType(String newBackendType)
private static native String setInferenceEngineBackendType_0(String newBackendType);
// C++: void cv::dnn::resetMyriadDevice()
private static native void resetMyriadDevice_0();
// C++: String cv::dnn::getInferenceEngineVPUType()
private static native String getInferenceEngineVPUType_0();
// C++: String cv::dnn::getInferenceEngineCPUType()
private static native String getInferenceEngineCPUType_0();
// C++: void cv::dnn::releaseHDDLPlugin()
private static native void releaseHDDLPlugin_0();
}
