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Merge pull request #24773 from tailsu:sd/pathlike
python: accept path-like objects wherever file names are expected #24773 Merry Christmas, all 🎄 Implements #15731 Support is enabled for all arguments named `filename` or `filepath` (case-insensitive), or annotated with `CV_WRAP_FILE_PATH`. Support is based on `PyOS_FSPath`, which is available in Python 3.6+. When running on older Python versions the arguments must have a `str` value as before. ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [ ] The feature is well documented and sample code can be built with the project CMake
This commit is contained in:
Stefan Dragnev
GitHub
@@ -484,7 +484,7 @@ CV__DNN_INLINE_NS_BEGIN
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* Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine
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* backend.
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*/
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CV_WRAP static Net readFromModelOptimizer(const String& xml, const String& bin);
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CV_WRAP static Net readFromModelOptimizer(CV_WRAP_FILE_PATH const String& xml, CV_WRAP_FILE_PATH const String& bin);
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/** @brief Create a network from Intel's Model Optimizer in-memory buffers with intermediate representation (IR).
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* @param[in] bufferModelConfig buffer with model's configuration.
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@@ -517,7 +517,7 @@ CV__DNN_INLINE_NS_BEGIN
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* @param path path to output file with .dot extension
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* @see dump()
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*/
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CV_WRAP void dumpToFile(const String& path);
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CV_WRAP void dumpToFile(CV_WRAP_FILE_PATH const String& path);
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/** @brief Adds new layer to the net.
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* @param name unique name of the adding layer.
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* @param type typename of the adding layer (type must be registered in LayerRegister).
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@@ -890,7 +890,7 @@ CV__DNN_INLINE_NS_BEGIN
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* @param darknetModel path to the .weights file with learned network.
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* @returns Network object that ready to do forward, throw an exception in failure cases.
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*/
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CV_EXPORTS_W Net readNetFromDarknet(const String &cfgFile, const String &darknetModel = String());
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CV_EXPORTS_W Net readNetFromDarknet(CV_WRAP_FILE_PATH const String &cfgFile, CV_WRAP_FILE_PATH const String &darknetModel = String());
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/** @brief Reads a network model stored in <a href="https://pjreddie.com/darknet/">Darknet</a> model files.
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* @param bufferCfg A buffer contains a content of .cfg file with text description of the network architecture.
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@@ -915,7 +915,7 @@ CV__DNN_INLINE_NS_BEGIN
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* @param caffeModel path to the .caffemodel file with learned network.
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* @returns Net object.
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*/
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CV_EXPORTS_W Net readNetFromCaffe(const String &prototxt, const String &caffeModel = String());
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CV_EXPORTS_W Net readNetFromCaffe(CV_WRAP_FILE_PATH const String &prototxt, CV_WRAP_FILE_PATH const String &caffeModel = String());
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/** @brief Reads a network model stored in Caffe model in memory.
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* @param bufferProto buffer containing the content of the .prototxt file
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@@ -944,7 +944,7 @@ CV__DNN_INLINE_NS_BEGIN
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* let us make it more flexible.
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* @returns Net object.
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*/
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CV_EXPORTS_W Net readNetFromTensorflow(const String &model, const String &config = String());
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CV_EXPORTS_W Net readNetFromTensorflow(CV_WRAP_FILE_PATH const String &model, CV_WRAP_FILE_PATH const String &config = String());
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/** @brief Reads a network model stored in <a href="https://www.tensorflow.org/">TensorFlow</a> framework's format.
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* @param bufferModel buffer containing the content of the pb file
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@@ -969,7 +969,7 @@ CV__DNN_INLINE_NS_BEGIN
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* @param model path to the .tflite file with binary flatbuffers description of the network architecture
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* @returns Net object.
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*/
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CV_EXPORTS_W Net readNetFromTFLite(const String &model);
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CV_EXPORTS_W Net readNetFromTFLite(CV_WRAP_FILE_PATH const String &model);
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/** @brief Reads a network model stored in <a href="https://www.tensorflow.org/lite">TFLite</a> framework's format.
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* @param bufferModel buffer containing the content of the tflite file
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@@ -1011,7 +1011,7 @@ CV__DNN_INLINE_NS_BEGIN
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*
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* Also some equivalents of these classes from cunn, cudnn, and fbcunn may be successfully imported.
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*/
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CV_EXPORTS_W Net readNetFromTorch(const String &model, bool isBinary = true, bool evaluate = true);
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CV_EXPORTS_W Net readNetFromTorch(CV_WRAP_FILE_PATH const String &model, bool isBinary = true, bool evaluate = true);
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/**
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* @brief Read deep learning network represented in one of the supported formats.
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@@ -1037,7 +1037,7 @@ CV__DNN_INLINE_NS_BEGIN
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* @ref readNetFromTorch or @ref readNetFromDarknet. An order of @p model and @p config
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* arguments does not matter.
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*/
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CV_EXPORTS_W Net readNet(const String& model, const String& config = "", const String& framework = "");
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CV_EXPORTS_W Net readNet(CV_WRAP_FILE_PATH const String& model, CV_WRAP_FILE_PATH const String& config = "", const String& framework = "");
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/**
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* @brief Read deep learning network represented in one of the supported formats.
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@@ -1064,7 +1064,7 @@ CV__DNN_INLINE_NS_BEGIN
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* backend.
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*/
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CV_EXPORTS_W
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Net readNetFromModelOptimizer(const String &xml, const String &bin = "");
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Net readNetFromModelOptimizer(CV_WRAP_FILE_PATH const String &xml, CV_WRAP_FILE_PATH const String &bin = "");
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/** @brief Load a network from Intel's Model Optimizer intermediate representation.
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* @param[in] bufferModelConfig Buffer contains XML configuration with network's topology.
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@@ -1093,7 +1093,7 @@ CV__DNN_INLINE_NS_BEGIN
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* @param onnxFile path to the .onnx file with text description of the network architecture.
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* @returns Network object that ready to do forward, throw an exception in failure cases.
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*/
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CV_EXPORTS_W Net readNetFromONNX(const String &onnxFile);
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CV_EXPORTS_W Net readNetFromONNX(CV_WRAP_FILE_PATH const String &onnxFile);
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/** @brief Reads a network model from <a href="https://onnx.ai/">ONNX</a>
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* in-memory buffer.
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@@ -1116,7 +1116,7 @@ CV__DNN_INLINE_NS_BEGIN
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* @param path to the .pb file with input tensor.
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* @returns Mat.
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*/
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CV_EXPORTS_W Mat readTensorFromONNX(const String& path);
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CV_EXPORTS_W Mat readTensorFromONNX(CV_WRAP_FILE_PATH const String& path);
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/** @brief Creates 4-dimensional blob from image. Optionally resizes and crops @p image from center,
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* subtract @p mean values, scales values by @p scalefactor, swap Blue and Red channels.
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@@ -1289,7 +1289,7 @@ CV__DNN_INLINE_NS_BEGIN
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* is taken from NVidia's Caffe fork: https://github.com/NVIDIA/caffe.
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* So the resulting model may be used there.
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*/
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CV_EXPORTS_W void shrinkCaffeModel(const String& src, const String& dst,
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CV_EXPORTS_W void shrinkCaffeModel(CV_WRAP_FILE_PATH const String& src, CV_WRAP_FILE_PATH const String& dst,
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const std::vector<String>& layersTypes = std::vector<String>());
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/** @brief Create a text representation for a binary network stored in protocol buffer format.
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@@ -1298,7 +1298,7 @@ CV__DNN_INLINE_NS_BEGIN
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*
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* @note To reduce output file size, trained weights are not included.
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*/
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CV_EXPORTS_W void writeTextGraph(const String& model, const String& output);
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CV_EXPORTS_W void writeTextGraph(CV_WRAP_FILE_PATH const String& model, CV_WRAP_FILE_PATH const String& output);
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/** @brief Performs non maximum suppression given boxes and corresponding scores.
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@@ -1403,7 +1403,7 @@ CV__DNN_INLINE_NS_BEGIN
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* @param[in] model Binary file contains trained weights.
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* @param[in] config Text file contains network configuration.
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*/
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CV_WRAP Model(const String& model, const String& config = "");
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CV_WRAP Model(CV_WRAP_FILE_PATH const String& model, CV_WRAP_FILE_PATH const String& config = "");
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/**
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* @brief Create model from deep learning network.
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@@ -1508,7 +1508,7 @@ CV__DNN_INLINE_NS_BEGIN
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* @param[in] model Binary file contains trained weights.
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* @param[in] config Text file contains network configuration.
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*/
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CV_WRAP ClassificationModel(const String& model, const String& config = "");
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CV_WRAP ClassificationModel(CV_WRAP_FILE_PATH const String& model, CV_WRAP_FILE_PATH const String& config = "");
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/**
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* @brief Create model from deep learning network.
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@@ -1558,7 +1558,7 @@ CV__DNN_INLINE_NS_BEGIN
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* @param[in] model Binary file contains trained weights.
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* @param[in] config Text file contains network configuration.
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*/
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CV_WRAP KeypointsModel(const String& model, const String& config = "");
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CV_WRAP KeypointsModel(CV_WRAP_FILE_PATH const String& model, CV_WRAP_FILE_PATH const String& config = "");
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/**
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* @brief Create model from deep learning network.
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@@ -1590,7 +1590,7 @@ CV__DNN_INLINE_NS_BEGIN
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* @param[in] model Binary file contains trained weights.
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* @param[in] config Text file contains network configuration.
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*/
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CV_WRAP SegmentationModel(const String& model, const String& config = "");
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CV_WRAP SegmentationModel(CV_WRAP_FILE_PATH const String& model, CV_WRAP_FILE_PATH const String& config = "");
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/**
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* @brief Create model from deep learning network.
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@@ -1621,7 +1621,7 @@ CV__DNN_INLINE_NS_BEGIN
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* @param[in] model Binary file contains trained weights.
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* @param[in] config Text file contains network configuration.
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*/
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CV_WRAP DetectionModel(const String& model, const String& config = "");
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CV_WRAP DetectionModel(CV_WRAP_FILE_PATH const String& model, CV_WRAP_FILE_PATH const String& config = "");
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/**
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* @brief Create model from deep learning network.
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@@ -1687,7 +1687,7 @@ public:
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* @param[in] config Text file contains network configuration
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*/
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CV_WRAP inline
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TextRecognitionModel(const std::string& model, const std::string& config = "")
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TextRecognitionModel(CV_WRAP_FILE_PATH const std::string& model, CV_WRAP_FILE_PATH const std::string& config = "")
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: TextRecognitionModel(readNet(model, config)) { /* nothing */ }
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/**
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@@ -1842,7 +1842,7 @@ public:
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* @param[in] config Text file contains network configuration.
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*/
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CV_WRAP inline
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TextDetectionModel_EAST(const std::string& model, const std::string& config = "")
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TextDetectionModel_EAST(CV_WRAP_FILE_PATH const std::string& model, CV_WRAP_FILE_PATH const std::string& config = "")
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: TextDetectionModel_EAST(readNet(model, config)) { /* nothing */ }
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/**
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@@ -1903,7 +1903,7 @@ public:
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* @param[in] config Text file contains network configuration.
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*/
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CV_WRAP inline
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TextDetectionModel_DB(const std::string& model, const std::string& config = "")
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TextDetectionModel_DB(CV_WRAP_FILE_PATH const std::string& model, CV_WRAP_FILE_PATH const std::string& config = "")
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: TextDetectionModel_DB(readNet(model, config)) { /* nothing */ }
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CV_WRAP TextDetectionModel_DB& setBinaryThreshold(float binaryThreshold);
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