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mirror of https://github.com/opencv/opencv.git synced 2026-07-30 07:43:03 +04:00

Merge branch '4.x' into '5.x'

This commit is contained in:
Maksim Shabunin
2024-06-11 19:38:59 +03:00
573 changed files with 72922 additions and 7355 deletions
+18 -5
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@@ -222,7 +222,7 @@ CV__DNN_INLINE_NS_BEGIN
/** @brief This interface class allows to build new Layers - are building blocks of networks.
*
* Each class, derived from Layer, must implement allocate() methods to declare own outputs and forward() to compute outputs.
* Each class, derived from Layer, must implement forward() method to compute outputs.
* Also before using the new layer into networks you must register your layer by using one of @ref dnnLayerFactory "LayerFactory" macros.
*/
class CV_EXPORTS_W Layer : public Algorithm
@@ -237,7 +237,7 @@ CV__DNN_INLINE_NS_BEGIN
* @param[in] input vector of already allocated input blobs
* @param[out] output vector of already allocated output blobs
*
* If this method is called after network has allocated all memory for input and output blobs
* This method is called after network has allocated all memory for input and output blobs
* and before inferencing.
*/
CV_DEPRECATED_EXTERNAL
@@ -247,7 +247,7 @@ CV__DNN_INLINE_NS_BEGIN
* @param[in] inputs vector of already allocated input blobs
* @param[out] outputs vector of already allocated output blobs
*
* If this method is called after network has allocated all memory for input and output blobs
* This method is called after network has allocated all memory for input and output blobs
* and before inferencing.
*/
CV_WRAP virtual void finalize(InputArrayOfArrays inputs, OutputArrayOfArrays outputs);
@@ -484,6 +484,14 @@ CV__DNN_INLINE_NS_BEGIN
* @see dump()
*/
CV_WRAP void dumpToFile(CV_WRAP_FILE_PATH const String& path);
/** @brief Dump net structure, hyperparameters, backend, target and fusion to pbtxt file
* @param path path to output file with .pbtxt extension
*
* Use Netron (https://netron.app) to open the target file to visualize the model.
* Call method after setInput(). To see correct backend, target and fusion run after forward().
*/
CV_WRAP void dumpToPbtxt(CV_WRAP_FILE_PATH const String& path);
/** @brief Adds new layer to the net.
* @param name unique name of the adding layer.
* @param type typename of the adding layer (type must be registered in LayerRegister).
@@ -491,7 +499,7 @@ CV__DNN_INLINE_NS_BEGIN
* @param params parameters which will be used to initialize the creating layer.
* @returns unique identifier of created layer, or -1 if a failure will happen.
*/
int addLayer(const String &name, const String &type, const int &dtype, LayerParams &params);
CV_WRAP int addLayer(const String &name, const String &type, const int &dtype, LayerParams &params);
/** @overload Datatype of output blobs set to default CV_32F */
int addLayer(const String &name, const String &type, LayerParams &params);
@@ -499,7 +507,7 @@ CV__DNN_INLINE_NS_BEGIN
/** @brief Adds new layer and connects its first input to the first output of previously added layer.
* @see addLayer()
*/
int addLayerToPrev(const String &name, const String &type, const int &dtype, LayerParams &params);
CV_WRAP int addLayerToPrev(const String &name, const String &type, const int &dtype, LayerParams &params);
/** @overload */
int addLayerToPrev(const String &name, const String &type, LayerParams &params);
@@ -1363,6 +1371,11 @@ CV__DNN_INLINE_NS_BEGIN
*/
CV_WRAP Model& setInputSwapRB(bool swapRB);
/** @brief Set output names for frame.
* @param[in] outNames Names for output layers.
*/
CV_WRAP Model& setOutputNames(const std::vector<String>& outNames);
/** @brief Set preprocessing parameters for frame.
* @param[in] size New input size.
* @param[in] mean Scalar with mean values which are subtracted from channels.
+1 -1
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@@ -6,7 +6,7 @@
#define OPENCV_DNN_VERSION_HPP
/// Use with major OpenCV version only.
#define OPENCV_DNN_API_VERSION 20231225
#define OPENCV_DNN_API_VERSION 20240521
#if !defined CV_DOXYGEN && !defined CV_STATIC_ANALYSIS && !defined CV_DNN_DONT_ADD_INLINE_NS
#define CV__DNN_INLINE_NS __CV_CAT(dnn5_v, OPENCV_DNN_API_VERSION)