@techstark/opencv-js/src/types/opencv/dnn.ts

OpenCV JavaScript version for node.js or browser

import type {
  bool,
  double,
  InputArray,
  InputArrayOfArrays,
  int,
  Mat,
  Net,
  OutputArray,
  OutputArrayOfArrays,
  Size,
  size_t,
  uchar,
} from "./_types";
/*
 * # Deep Neural Network module
 * This module contains:
 *
 *
 *
 *  * API for new layers creation, layers are building bricks of neural networks;
 *  * set of built-in most-useful Layers;
 *  * API to construct and modify comprehensive neural networks from layers;
 *  * functionality for loading serialized networks models from different frameworks.
 *
 *
 * Functionality of this module is designed only for forward pass computations (i.e. network testing). A network training is in principle not supported.
 */
/**
 * if `crop` is true, input image is resized so one side after resize is equal to corresponding
 * dimension in `size` and another one is equal or larger. Then, crop from the center is performed. If
 * `crop` is false, direct resize without cropping and preserving aspect ratio is performed.
 *
 * 4-dimensional [Mat] with NCHW dimensions order.
 *
 * @param image input image (with 1-, 3- or 4-channels).
 *
 * @param scalefactor multiplier for image 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 image has BGR ordering and 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.
 */
export declare function blobFromImage(
  image: InputArray,
  scalefactor?: double,
  size?: any,
  mean?: any,
  swapRB?: bool,
  crop?: bool,
  ddepth?: int,
): Mat;

/**
 * This is an overloaded member function, provided for convenience. It differs from the above function
 * only in what argument(s) it accepts.
 */
export declare function blobFromImage(
  image: InputArray,
  blob: OutputArray,
  scalefactor?: double,
  size?: any,
  mean?: any,
  swapRB?: bool,
  crop?: bool,
  ddepth?: int,
): void;

/**
 * if `crop` is true, input image is resized so one side after resize is equal to corresponding
 * dimension in `size` and another one is equal or larger. Then, crop from the center is performed. If
 * `crop` is false, direct resize without cropping and preserving aspect ratio is performed.
 *
 * 4-dimensional [Mat] with NCHW dimensions order.
 *
 * @param images input images (all with 1-, 3- or 4-channels).
 *
 * @param scalefactor multiplier for 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 image has BGR ordering and 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.
 */
export declare function blobFromImages(
  images: InputArrayOfArrays,
  scalefactor?: double,
  size?: Size,
  mean?: any,
  swapRB?: bool,
  crop?: bool,
  ddepth?: int,
): Mat;

/**
 * This is an overloaded member function, provided for convenience. It differs from the above function
 * only in what argument(s) it accepts.
 */
export declare function blobFromImages(
  images: InputArrayOfArrays,
  blob: OutputArray,
  scalefactor?: double,
  size?: Size,
  mean?: any,
  swapRB?: bool,
  crop?: bool,
  ddepth?: int,
): void;

export declare function getAvailableBackends(): any;

export declare function getAvailableTargets(be: Backend): any;

/**
 * @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).
 */
export declare function imagesFromBlob(
  blob_: any,
  images_: OutputArrayOfArrays,
): any;

/**
 * @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 >0, keep at most top_k picked indices.
 */
export declare function NMSBoxes(
  bboxes: any,
  scores: any,
  score_threshold: any,
  nms_threshold: any,
  indices: any,
  eta?: any,
  top_k?: any,
): void;

export declare function NMSBoxes(
  bboxes: any,
  scores: any,
  score_threshold: any,
  nms_threshold: any,
  indices: any,
  eta?: any,
  top_k?: any,
): void;

export declare function NMSBoxes(
  bboxes: any,
  scores: any,
  score_threshold: any,
  nms_threshold: any,
  indices: any,
  eta?: any,
  top_k?: any,
): void;

/**
 * [Net] object.
 * This function automatically detects an origin framework of trained model and calls an appropriate
 * function such [readNetFromCaffe], [readNetFromTensorflow], [readNetFromTorch] or
 * [readNetFromDarknet]. An order of `model` and `config` arguments does not matter.
 *
 * @param model Binary file contains trained weights. The following file extensions are expected for
 * models from different frameworks:
 * .caffemodel (Caffe, http://caffe.berkeleyvision.org/)*.pb (TensorFlow,
 * https://www.tensorflow.org/)*.t7 | *.net (Torch, http://torch.ch/)*.weights (Darknet,
 * https://pjreddie.com/darknet/)*.bin (DLDT, https://software.intel.com/openvino-toolkit)*.onnx (ONNX,
 * https://onnx.ai/)
 *
 * @param config Text file contains network configuration. It could be a file with the following
 * extensions:
 * .prototxt (Caffe, http://caffe.berkeleyvision.org/)*.pbtxt (TensorFlow,
 * https://www.tensorflow.org/)*.cfg (Darknet, https://pjreddie.com/darknet/)*.xml (DLDT,
 * https://software.intel.com/openvino-toolkit)
 *
 * @param framework Explicit framework name tag to determine a format.
 */
export declare function readNet(model: any, config?: any, framework?: any): Net;

/**
 * This is an overloaded member function, provided for convenience. It differs from the above function
 * only in what argument(s) it accepts.
 *
 * [Net] object.
 *
 * @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.
 */
export declare function readNet(
  framework: any,
  bufferModel: uchar,
  bufferConfig?: uchar,
): uchar;

/**
 * [Net] object.
 *
 * @param prototxt path to the .prototxt file with text description of the network architecture.
 *
 * @param caffeModel path to the .caffemodel file with learned network.
 */
export declare function readNetFromCaffe(prototxt: any, caffeModel?: any): Net;

/**
 * [Net] object.
 *
 * @param bufferProto buffer containing the content of the .prototxt file
 *
 * @param bufferModel buffer containing the content of the .caffemodel file
 */
export declare function readNetFromCaffe(
  bufferProto: uchar,
  bufferModel?: uchar,
): uchar;

/**
 * This is an overloaded member function, provided for convenience. It differs from the above function
 * only in what argument(s) it accepts.
 *
 * [Net] object.
 *
 * @param bufferProto buffer containing the content of the .prototxt file
 *
 * @param lenProto length of bufferProto
 *
 * @param bufferModel buffer containing the content of the .caffemodel file
 *
 * @param lenModel length of bufferModel
 */
export declare function readNetFromCaffe(
  bufferProto: any,
  lenProto: size_t,
  bufferModel?: any,
  lenModel?: size_t,
): Net;

/**
 * Network object that ready to do forward, throw an exception in failure cases.
 *
 * [Net] object.
 *
 * @param cfgFile path to the .cfg file with text description of the network architecture.
 *
 * @param darknetModel path to the .weights file with learned network.
 */
export declare function readNetFromDarknet(
  cfgFile: any,
  darknetModel?: any,
): Net;

/**
 * [Net] object.
 *
 * @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.
 */
export declare function readNetFromDarknet(
  bufferCfg: uchar,
  bufferModel?: uchar,
): uchar;

/**
 * [Net] object.
 *
 * @param bufferCfg A buffer contains a content of .cfg file with text description of the network
 * architecture.
 *
 * @param lenCfg Number of bytes to read from bufferCfg
 *
 * @param bufferModel A buffer contains a content of .weights file with learned network.
 *
 * @param lenModel Number of bytes to read from bufferModel
 */
export declare function readNetFromDarknet(
  bufferCfg: any,
  lenCfg: size_t,
  bufferModel?: any,
  lenModel?: size_t,
): Net;

/**
 * [Net] object. Networks imported from Intel's [Model] Optimizer are launched in Intel's Inference
 * Engine backend.
 *
 * @param xml XML configuration file with network's topology.
 *
 * @param bin Binary file with trained weights.
 */
export declare function readNetFromModelOptimizer(xml: any, bin: any): Net;

/**
 * Network object that ready to do forward, throw an exception in failure cases.
 *
 * @param onnxFile path to the .onnx file with text description of the network architecture.
 */
export declare function readNetFromONNX(onnxFile: any): Net;

/**
 * Network object that ready to do forward, throw an exception in failure cases.
 *
 * @param buffer memory address of the first byte of the buffer.
 *
 * @param sizeBuffer size of the buffer.
 */
export declare function readNetFromONNX(buffer: any, sizeBuffer: size_t): Net;

/**
 * Network object that ready to do forward, throw an exception in failure cases.
 *
 * @param buffer in-memory buffer that stores the ONNX model bytes.
 */
export declare function readNetFromONNX(buffer: uchar): uchar;

/**
 * [Net] object.
 *
 * @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.
 */
export declare function readNetFromTensorflow(model: any, config?: any): Net;

/**
 * [Net] object.
 *
 * @param bufferModel buffer containing the content of the pb file
 *
 * @param bufferConfig buffer containing the content of the pbtxt file
 */
export declare function readNetFromTensorflow(
  bufferModel: uchar,
  bufferConfig?: uchar,
): uchar;

/**
 * This is an overloaded member function, provided for convenience. It differs from the above function
 * only in what argument(s) it accepts.
 *
 * @param bufferModel buffer containing the content of the pb file
 *
 * @param lenModel length of bufferModel
 *
 * @param bufferConfig buffer containing the content of the pbtxt file
 *
 * @param lenConfig length of bufferConfig
 */
export declare function readNetFromTensorflow(
  bufferModel: any,
  lenModel: size_t,
  bufferConfig?: any,
  lenConfig?: size_t,
): Net;

/**
 * [Net] object.
 *
 * Ascii mode of Torch serializer is more preferable, because binary mode extensively use `long` type
 * of C language, which has various bit-length on different systems.
 * The loading file must contain serialized  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.
 *
 * @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.
 */
export declare function readNetFromTorch(
  model: any,
  isBinary?: bool,
  evaluate?: bool,
): Net;

/**
 * [Mat].
 *
 * @param path to the .pb file with input tensor.
 */
export declare function readTensorFromONNX(path: any): Mat;

/**
 * This function has the same limitations as [readNetFromTorch()].
 */
export declare function readTorchBlob(filename: any, isBinary?: bool): Mat;

/**
 * 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: . So the resulting model may be
 * used there.
 *
 * @param src Path to origin model from Caffe framework contains single precision floating point
 * weights (usually has .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.
 */
export declare function shrinkCaffeModel(
  src: any,
  dst: any,
  layersTypes?: any,
): void;

/**
 * To reduce output file size, trained weights are not included.
 *
 * @param model A path to binary network.
 *
 * @param output A path to output text file to be created.
 */
export declare function writeTextGraph(model: any, output: any): void;

/**
 * DNN_BACKEND_DEFAULT equals to DNN_BACKEND_INFERENCE_ENGINE if OpenCV is built with Intel's Inference
 * Engine library or DNN_BACKEND_OPENCV otherwise.
 *
 */
export declare const DNN_BACKEND_DEFAULT: Backend; // initializer:

export declare const DNN_BACKEND_HALIDE: Backend; // initializer:

export declare const DNN_BACKEND_INFERENCE_ENGINE: Backend; // initializer:

export declare const DNN_BACKEND_OPENCV: Backend; // initializer:

export declare const DNN_BACKEND_VKCOM: Backend; // initializer:

export declare const DNN_TARGET_CPU: Target; // initializer:

export declare const DNN_TARGET_OPENCL: Target; // initializer:

export declare const DNN_TARGET_OPENCL_FP16: Target; // initializer:

export declare const DNN_TARGET_MYRIAD: Target; // initializer:

export declare const DNN_TARGET_VULKAN: Target; // initializer:

export declare const DNN_TARGET_FPGA: Target; // initializer:

/**
 * [Net::setPreferableBackend]
 *
 */
export type Backend = any;

/**
 * [Net::setPreferableBackend]
 *
 */
export type Target = any;