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Merge remote-tracking branch 'upstream/3.4' into merge-3.4

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Revert "documentation: avoid links to 'master' branch from 3.4 maintenance branch"
This reverts commit 9ba9358ecb.

Revert "documentation: avoid links to 'master' branch from 3.4 maintenance branch (2)"
This reverts commit f185802489.
This commit is contained in:
Alexander Alekhin
2018-06-04 19:24:09 +03:00
parent 80709697e5 b2cff44084
commit 45dd575ed2
147 changed files with 5707 additions and 2831 deletions
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modules/dnn/include/opencv2/dnn.hpp
+3 -3
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@@ -42,7 +42,7 @@
#ifndef OPENCV_DNN_HPP
#define OPENCV_DNN_HPP
// This is an umbrealla header to include into you project.
// This is an umbrella header to include into you project.
// We are free to change headers layout in dnn subfolder, so please include
// this header for future compatibility
@@ -52,10 +52,10 @@
This module contains:
- API for new layers creation, layers are building bricks of neural networks;
- set of built-in most-useful Layers;
- API to constuct and modify comprehensive neural networks from 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).
Functionality of this module is designed only for forward pass computations (i.e. network testing).
A network training is in principle not supported.
@}
*/
modules/dnn/include/opencv2/dnn/all_layers.hpp
+14 -8
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@@ -58,7 +58,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
You can use both API, but factory API is less convenient for native C++ programming and basically designed for use inside importers (see @ref readNetFromCaffe(), @ref readNetFromTorch(), @ref readNetFromTensorflow()).
Built-in layers partially reproduce functionality of corresponding Caffe and Torch7 layers.
In partuclar, the following layers and Caffe importer were tested to reproduce <a href="http://caffe.berkeleyvision.org/tutorial/layers.html">Caffe</a> functionality:
In particular, the following layers and Caffe importer were tested to reproduce <a href="http://caffe.berkeleyvision.org/tutorial/layers.html">Caffe</a> functionality:
- Convolution
- Deconvolution
- Pooling
@@ -108,13 +108,13 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
@f$W_{x?} \in R^{N_h \times N_x}@f$, @f$W_{h?} \in R^{N_h \times N_h}@f$, @f$b_? \in R^{N_h}@f$.
For simplicity and performance purposes we use @f$ W_x = [W_{xi}; W_{xf}; W_{xo}, W_{xg}] @f$
(i.e. @f$W_x@f$ is vertical contacentaion of @f$ W_{x?} @f$), @f$ W_x \in R^{4N_h \times N_x} @f$.
(i.e. @f$W_x@f$ is vertical concatenation of @f$ W_{x?} @f$), @f$ W_x \in R^{4N_h \times N_x} @f$.
The same for @f$ W_h = [W_{hi}; W_{hf}; W_{ho}, W_{hg}], W_h \in R^{4N_h \times N_h} @f$
and for @f$ b = [b_i; b_f, b_o, b_g]@f$, @f$b \in R^{4N_h} @f$.
@param Wh is matrix defining how previous output is transformed to internal gates (i.e. according to abovemtioned notation is @f$ W_h @f$)
@param Wx is matrix defining how current input is transformed to internal gates (i.e. according to abovemtioned notation is @f$ W_x @f$)
@param b is bias vector (i.e. according to abovemtioned notation is @f$ b @f$)
@param Wh is matrix defining how previous output is transformed to internal gates (i.e. according to above mentioned notation is @f$ W_h @f$)
@param Wx is matrix defining how current input is transformed to internal gates (i.e. according to above mentioned notation is @f$ W_x @f$)
@param b is bias vector (i.e. according to above mentioned notation is @f$ b @f$)
*/
CV_DEPRECATED virtual void setWeights(const Mat &Wh, const Mat &Wx, const Mat &b) = 0;
@@ -148,7 +148,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
* If setUseTimstampsDim() is set to true then @p input[0] should has at least two dimensions with the following shape: [`T`, `N`, `[data dims]`],
* where `T` specifies number of timestamps, `N` is number of independent streams (i.e. @f$ x_{t_0 + t}^{stream} @f$ is stored inside @p input[0][t, stream, ...]).
*
* If setUseTimstampsDim() is set to fase then @p input[0] should contain single timestamp, its shape should has form [`N`, `[data dims]`] with at least one dimension.
* If setUseTimstampsDim() is set to false then @p input[0] should contain single timestamp, its shape should has form [`N`, `[data dims]`] with at least one dimension.
* (i.e. @f$ x_{t}^{stream} @f$ is stored inside @p input[0][stream, ...]).
*/
@@ -550,7 +550,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
* dst(x, y, c) = \frac{ src(x, y, c) }{norm(c)}
* @f]
*
* Where `x, y` - spatial cooridnates, `c` - channel.
* Where `x, y` - spatial coordinates, `c` - channel.
*
* An every sample in the batch is normalized separately. Optionally,
* output is scaled by the trained parameters.
@@ -565,7 +565,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
};
/**
* @brief Resize input 4-dimensional blob by nearest neghbor strategy.
* @brief Resize input 4-dimensional blob by nearest neighbor strategy.
*
* Layer is used to support TensorFlow's resize_nearest_neighbor op.
*/
@@ -581,6 +581,12 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
static Ptr<ProposalLayer> create(const LayerParams& params);
};
class CV_EXPORTS CropAndResizeLayer : public Layer
{
public:
static Ptr<Layer> create(const LayerParams& params);
};
//! @}
//! @}
CV__DNN_EXPERIMENTAL_NS_END
modules/dnn/include/opencv2/dnn/dnn.hpp
+12 -11
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@@ -81,12 +81,13 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
{
DNN_TARGET_CPU,
DNN_TARGET_OPENCL,
DNN_TARGET_OPENCL_FP16
DNN_TARGET_OPENCL_FP16,
DNN_TARGET_MYRIAD
};
/** @brief This class provides all data needed to initialize layer.
*
* It includes dictionary with scalar params (which can be readed by using Dict interface),
* It includes dictionary with scalar params (which can be read by using Dict interface),
* blob params #blobs and optional meta information: #name and #type of layer instance.
*/
class CV_EXPORTS LayerParams : public Dict
@@ -137,7 +138,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
* Initialize wrapper from another one. It'll wrap the same host CPU
* memory and mustn't allocate memory on device(i.e. GPU). It might
* has different shape. Use in case of CPU memory reusing for reuse
* associented memory on device too.
* associated memory on device too.
*/
BackendWrapper(const Ptr<BackendWrapper>& base, const MatShape& shape);
@@ -345,7 +346,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
/** @brief Create a network from Intel's Model Optimizer intermediate representation.
* @param[in] xml XML configuration file with network's topology.
* @param[in] bin Binary file with trained weights.
* Networks imported from Intel's Model Optimizer are lauched in Intel's Inference Engine
* Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine
* backend.
*/
CV_WRAP static Net readFromModelOptimizer(const String& xml, const String& bin);
@@ -401,8 +402,8 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
/** @brief Connects #@p outNum output of the first layer to #@p inNum input of the second layer.
* @param outLayerId identifier of the first layer
* @param inpLayerId identifier of the second layer
* @param outNum number of the first layer output
* @param inpLayerId identifier of the second layer
* @param inpNum number of the second layer input
*/
void connect(int outLayerId, int outNum, int inpLayerId, int inpNum);
@@ -563,7 +564,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
*/
CV_WRAP int getLayersCount(const String& layerType) const;
/** @brief Computes bytes number which are requered to store
/** @brief Computes bytes number which are required to store
* all weights and intermediate blobs for model.
* @param netInputShapes vector of shapes for all net inputs.
* @param weights output parameter to store resulting bytes for weights.
@@ -583,7 +584,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
const MatShape& netInputShape,
CV_OUT size_t& weights, CV_OUT size_t& blobs) const;
/** @brief Computes bytes number which are requered to store
/** @brief Computes bytes number which are required to store
* all weights and intermediate blobs for each layer.
* @param netInputShapes vector of shapes for all net inputs.
* @param layerIds output vector to save layer IDs.
@@ -700,13 +701,13 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
* * `*.pb` (TensorFlow, https://www.tensorflow.org/)
* * `*.t7` | `*.net` (Torch, http://torch.ch/)
* * `*.weights` (Darknet, https://pjreddie.com/darknet/)
* * `*.bin` (DLDT, https://software.seek.intel.com/deep-learning-deployment)
* * `*.bin` (DLDT, https://software.intel.com/openvino-toolkit)
* @param[in] 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.seek.intel.com/deep-learning-deployment)
* * `*.xml` (DLDT, https://software.intel.com/openvino-toolkit)
* @param[in] framework Explicit framework name tag to determine a format.
* @returns Net object.
*
@@ -726,7 +727,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
* @param[in] xml XML configuration file with network's topology.
* @param[in] bin Binary file with trained weights.
* @returns Net object.
* Networks imported from Intel's Model Optimizer are lauched in Intel's Inference Engine
* Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine
* backend.
*/
CV_EXPORTS_W Net readNetFromModelOptimizer(const String &xml, const String &bin);
@@ -744,7 +745,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
* @details if @p crop is true, input image is resized so one side after resize is equal to corresponding
* dimension in @p size and another one is equal or larger. Then, crop from the center is performed.
* If @p crop is false, direct resize without cropping and preserving aspect ratio is performed.
* @returns 4-dimansional Mat with NCHW dimensions order.
* @returns 4-dimensional Mat with NCHW dimensions order.
*/
CV_EXPORTS_W Mat blobFromImage(InputArray image, double scalefactor=1.0, const Size& size = Size(),
const Scalar& mean = Scalar(), bool swapRB=true, bool crop=true);