diff --git a/modules/dnn/include/opencv2/dnn/dnn.hpp b/modules/dnn/include/opencv2/dnn/dnn.hpp index f65f503529..7cc95ca0c4 100644 --- a/modules/dnn/include/opencv2/dnn/dnn.hpp +++ b/modules/dnn/include/opencv2/dnn/dnn.hpp @@ -644,6 +644,24 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN */ CV_EXPORTS_W Net readNetFromDarknet(const String &cfgFile, const String &darknetModel = String()); + /** @brief Reads a network model stored in Darknet 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. + * @returns Net object. + */ + CV_EXPORTS_W Net readNetFromDarknet(const std::vector& bufferCfg, + const std::vector& bufferModel = std::vector()); + + /** @brief Reads a network model stored in Darknet model files. + * @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 + * @returns Net object. + */ + CV_EXPORTS Net readNetFromDarknet(const char *bufferCfg, size_t lenCfg, + const char *bufferModel = NULL, size_t lenModel = 0); + /** @brief Reads a network model stored in Caffe 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. @@ -651,6 +669,14 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN */ CV_EXPORTS_W Net readNetFromCaffe(const String &prototxt, const String &caffeModel = String()); + /** @brief 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 + * @returns Net object. + */ + CV_EXPORTS_W Net readNetFromCaffe(const std::vector& bufferProto, + const std::vector& bufferModel = std::vector()); + /** @brief Reads a network model stored in Caffe model in memory. * @details This is an overloaded member function, provided for convenience. * It differs from the above function only in what argument(s) it accepts. @@ -672,6 +698,14 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN */ CV_EXPORTS_W Net readNetFromTensorflow(const String &model, const String &config = String()); + /** @brief Reads a network model stored in TensorFlow framework's format. + * @param bufferModel buffer containing the content of the pb file + * @param bufferConfig buffer containing the content of the pbtxt file + * @returns Net object. + */ + CV_EXPORTS_W Net readNetFromTensorflow(const std::vector& bufferModel, + const std::vector& bufferConfig = std::vector()); + /** @brief Reads a network model stored in TensorFlow framework's format. * @details This is an overloaded member function, provided for convenience. * It differs from the above function only in what argument(s) it accepts. @@ -735,6 +769,18 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN */ CV_EXPORTS_W Net readNet(const String& model, const String& config = "", const String& framework = ""); + /** + * @brief Read deep learning network represented in one of the supported formats. + * @details This is an overloaded member function, provided for convenience. + * It differs from the above function only in what argument(s) it accepts. + * @param[in] framework Name of origin framework. + * @param[in] bufferModel A buffer with a content of binary file with weights + * @param[in] bufferConfig A buffer with a content of text file contains network configuration. + * @returns Net object. + */ + CV_EXPORTS_W Net readNet(const String& framework, const std::vector& bufferModel, + const std::vector& bufferConfig = std::vector()); + /** @brief Loads blob which was serialized as torch.Tensor object of Torch7 framework. * @warning This function has the same limitations as readNetFromTorch(). */ diff --git a/modules/dnn/misc/java/test/DnnTensorFlowTest.java b/modules/dnn/misc/java/test/DnnTensorFlowTest.java index 5dd423649e..4e96c73e28 100644 --- a/modules/dnn/misc/java/test/DnnTensorFlowTest.java +++ b/modules/dnn/misc/java/test/DnnTensorFlowTest.java @@ -1,10 +1,14 @@ package org.opencv.test.dnn; import java.io.File; +import java.io.FileInputStream; +import java.io.IOException; import java.util.ArrayList; import java.util.List; import org.opencv.core.Core; import org.opencv.core.Mat; +import org.opencv.core.MatOfFloat; +import org.opencv.core.MatOfByte; import org.opencv.core.Scalar; import org.opencv.core.Size; import org.opencv.dnn.DictValue; @@ -26,6 +30,15 @@ public class DnnTensorFlowTest extends OpenCVTestCase { Net net; + private static void normAssert(Mat ref, Mat test) { + final double l1 = 1e-5; + final double lInf = 1e-4; + double normL1 = Core.norm(ref, test, Core.NORM_L1) / ref.total(); + double normLInf = Core.norm(ref, test, Core.NORM_INF) / ref.total(); + assertTrue(normL1 < l1); + assertTrue(normLInf < lInf); + } + @Override protected void setUp() throws Exception { super.setUp(); @@ -46,7 +59,7 @@ public class DnnTensorFlowTest extends OpenCVTestCase { File testDataPath = new File(envTestDataPath); - File f = new File(testDataPath, "dnn/space_shuttle.jpg"); + File f = new File(testDataPath, "dnn/grace_hopper_227.png"); sourceImageFile = f.toString(); if(!f.exists()) throw new Exception("Test image is missing: " + sourceImageFile); @@ -77,31 +90,55 @@ public class DnnTensorFlowTest extends OpenCVTestCase { } - public void testTestNetForward() { - Mat rawImage = Imgcodecs.imread(sourceImageFile); + public void checkInceptionNet(Net net) + { + Mat image = Imgcodecs.imread(sourceImageFile); + assertNotNull("Loading image from file failed!", image); - assertNotNull("Loading image from file failed!", rawImage); - - Mat image = new Mat(); - Imgproc.resize(rawImage, image, new Size(224,224)); - - Mat inputBlob = Dnn.blobFromImage(image); + Mat inputBlob = Dnn.blobFromImage(image, 1.0, new Size(224, 224), new Scalar(0), true, true); assertNotNull("Converting image to blob failed!", inputBlob); - Mat inputBlobP = new Mat(); - Core.subtract(inputBlob, new Scalar(117.0), inputBlobP); - - net.setInput(inputBlobP, "input" ); - - Mat result = net.forward(); + net.setInput(inputBlob, "input"); + Mat result = new Mat(); + try { + net.setPreferableBackend(Dnn.DNN_BACKEND_OPENCV); + result = net.forward("softmax2"); + } + catch (Exception e) { + fail("DNN forward failed: " + e.getMessage()); + } assertNotNull("Net returned no result!", result); - Core.MinMaxLocResult minmax = Core.minMaxLoc(result.reshape(1, 1)); + result = result.reshape(1, 1); + Core.MinMaxLocResult minmax = Core.minMaxLoc(result); + assertEquals("Wrong prediction", (int)minmax.maxLoc.x, 866); - assertTrue("No image recognized!", minmax.maxVal > 0.9); + Mat top5RefScores = new MatOfFloat(new float[] { + 0.63032645f, 0.2561979f, 0.032181446f, 0.015721032f, 0.014785315f + }).reshape(1, 1); + Core.sort(result, result, Core.SORT_DESCENDING); + normAssert(result.colRange(0, 5), top5RefScores); } + public void testTestNetForward() { + checkInceptionNet(net); + } + + public void testReadFromBuffer() { + File modelFile = new File(modelFileName); + byte[] modelBuffer = new byte[ (int)modelFile.length() ]; + + try { + FileInputStream fis = new FileInputStream(modelFile); + fis.read(modelBuffer); + fis.close(); + } catch (IOException e) { + fail("Failed to read a model: " + e.getMessage()); + } + net = Dnn.readNetFromTensorflow(new MatOfByte(modelBuffer)); + checkInceptionNet(net); + } } diff --git a/modules/dnn/src/caffe/caffe_importer.cpp b/modules/dnn/src/caffe/caffe_importer.cpp index 37db7f039a..59f47eef1a 100644 --- a/modules/dnn/src/caffe/caffe_importer.cpp +++ b/modules/dnn/src/caffe/caffe_importer.cpp @@ -453,6 +453,15 @@ Net readNetFromCaffe(const char *bufferProto, size_t lenProto, return net; } +Net readNetFromCaffe(const std::vector& bufferProto, const std::vector& bufferModel) +{ + const char* bufferProtoPtr = reinterpret_cast(&bufferProto[0]); + const char* bufferModelPtr = bufferModel.empty() ? NULL : + reinterpret_cast(&bufferModel[0]); + return readNetFromCaffe(bufferProtoPtr, bufferProto.size(), + bufferModelPtr, bufferModel.size()); +} + #endif //HAVE_PROTOBUF CV__DNN_EXPERIMENTAL_NS_END diff --git a/modules/dnn/src/darknet/darknet_importer.cpp b/modules/dnn/src/darknet/darknet_importer.cpp index 8bd64d099c..282b37277c 100644 --- a/modules/dnn/src/darknet/darknet_importer.cpp +++ b/modules/dnn/src/darknet/darknet_importer.cpp @@ -44,6 +44,7 @@ #include "../precomp.hpp" #include +#include #include #include #include @@ -66,14 +67,19 @@ public: DarknetImporter() {} - DarknetImporter(const char *cfgFile, const char *darknetModel) + DarknetImporter(std::istream &cfgStream, std::istream &darknetModelStream) { CV_TRACE_FUNCTION(); - ReadNetParamsFromCfgFileOrDie(cfgFile, &net); + ReadNetParamsFromCfgStreamOrDie(cfgStream, &net); + ReadNetParamsFromBinaryStreamOrDie(darknetModelStream, &net); + } - if (darknetModel && darknetModel[0]) - ReadNetParamsFromBinaryFileOrDie(darknetModel, &net); + DarknetImporter(std::istream &cfgStream) + { + CV_TRACE_FUNCTION(); + + ReadNetParamsFromCfgStreamOrDie(cfgStream, &net); } struct BlobNote @@ -175,15 +181,75 @@ public: } }; -} - -Net readNetFromDarknet(const String &cfgFile, const String &darknetModel /*= String()*/) +static Net readNetFromDarknet(std::istream &cfgFile, std::istream &darknetModel) { - DarknetImporter darknetImporter(cfgFile.c_str(), darknetModel.c_str()); Net net; + DarknetImporter darknetImporter(cfgFile, darknetModel); darknetImporter.populateNet(net); return net; } +static Net readNetFromDarknet(std::istream &cfgFile) +{ + Net net; + DarknetImporter darknetImporter(cfgFile); + darknetImporter.populateNet(net); + return net; +} + +} + +Net readNetFromDarknet(const String &cfgFile, const String &darknetModel /*= String()*/) +{ + std::ifstream cfgStream(cfgFile.c_str()); + if (!cfgStream.is_open()) + { + CV_Error(cv::Error::StsParseError, "Failed to parse NetParameter file: " + std::string(cfgFile)); + } + if (darknetModel != String()) + { + std::ifstream darknetModelStream(darknetModel.c_str(), std::ios::binary); + if (!darknetModelStream.is_open()) + { + CV_Error(cv::Error::StsParseError, "Failed to parse NetParameter file: " + std::string(darknetModel)); + } + return readNetFromDarknet(cfgStream, darknetModelStream); + } + else + return readNetFromDarknet(cfgStream); +} + +struct BufferStream : public std::streambuf +{ + BufferStream(const char* s, std::size_t n) + { + char* ptr = const_cast(s); + setg(ptr, ptr, ptr + n); + } +}; + +Net readNetFromDarknet(const char *bufferCfg, size_t lenCfg, const char *bufferModel, size_t lenModel) +{ + BufferStream cfgBufferStream(bufferCfg, lenCfg); + std::istream cfgStream(&cfgBufferStream); + if (lenModel) + { + BufferStream weightsBufferStream(bufferModel, lenModel); + std::istream weightsStream(&weightsBufferStream); + return readNetFromDarknet(cfgStream, weightsStream); + } + else + return readNetFromDarknet(cfgStream); +} + +Net readNetFromDarknet(const std::vector& bufferCfg, const std::vector& bufferModel) +{ + const char* bufferCfgPtr = reinterpret_cast(&bufferCfg[0]); + const char* bufferModelPtr = bufferModel.empty() ? NULL : + reinterpret_cast(&bufferModel[0]); + return readNetFromDarknet(bufferCfgPtr, bufferCfg.size(), + bufferModelPtr, bufferModel.size()); +} + CV__DNN_EXPERIMENTAL_NS_END }} // namespace diff --git a/modules/dnn/src/darknet/darknet_io.cpp b/modules/dnn/src/darknet/darknet_io.cpp index 03805dd364..815b84f651 100644 --- a/modules/dnn/src/darknet/darknet_io.cpp +++ b/modules/dnn/src/darknet/darknet_io.cpp @@ -476,68 +476,61 @@ namespace cv { return dst; } - bool ReadDarknetFromCfgFile(const char *cfgFile, NetParameter *net) + bool ReadDarknetFromCfgStream(std::istream &ifile, NetParameter *net) { - std::ifstream ifile; - ifile.open(cfgFile); - if (ifile.is_open()) - { - bool read_net = false; - int layers_counter = -1; - for (std::string line; std::getline(ifile, line);) { - line = escapeString(line); - if (line.empty()) continue; - switch (line[0]) { - case '\0': break; - case '#': break; - case ';': break; - case '[': - if (line == "[net]") { - read_net = true; - } - else { - // read section - read_net = false; - ++layers_counter; - const size_t layer_type_size = line.find("]") - 1; - CV_Assert(layer_type_size < line.size()); - std::string layer_type = line.substr(1, layer_type_size); - net->layers_cfg[layers_counter]["type"] = layer_type; - } - break; - default: - // read entry - const size_t separator_index = line.find('='); - CV_Assert(separator_index < line.size()); - if (separator_index != std::string::npos) { - std::string name = line.substr(0, separator_index); - std::string value = line.substr(separator_index + 1, line.size() - (separator_index + 1)); - name = escapeString(name); - value = escapeString(value); - if (name.empty() || value.empty()) continue; - if (read_net) - net->net_cfg[name] = value; - else - net->layers_cfg[layers_counter][name] = value; - } + bool read_net = false; + int layers_counter = -1; + for (std::string line; std::getline(ifile, line);) { + line = escapeString(line); + if (line.empty()) continue; + switch (line[0]) { + case '\0': break; + case '#': break; + case ';': break; + case '[': + if (line == "[net]") { + read_net = true; + } + else { + // read section + read_net = false; + ++layers_counter; + const size_t layer_type_size = line.find("]") - 1; + CV_Assert(layer_type_size < line.size()); + std::string layer_type = line.substr(1, layer_type_size); + net->layers_cfg[layers_counter]["type"] = layer_type; + } + break; + default: + // read entry + const size_t separator_index = line.find('='); + CV_Assert(separator_index < line.size()); + if (separator_index != std::string::npos) { + std::string name = line.substr(0, separator_index); + std::string value = line.substr(separator_index + 1, line.size() - (separator_index + 1)); + name = escapeString(name); + value = escapeString(value); + if (name.empty() || value.empty()) continue; + if (read_net) + net->net_cfg[name] = value; + else + net->layers_cfg[layers_counter][name] = value; } } - - std::string anchors = net->layers_cfg[net->layers_cfg.size() - 1]["anchors"]; - std::vector vec = getNumbers(anchors); - std::map &net_params = net->net_cfg; - net->width = getParam(net_params, "width", 416); - net->height = getParam(net_params, "height", 416); - net->channels = getParam(net_params, "channels", 3); - CV_Assert(net->width > 0 && net->height > 0 && net->channels > 0); } - else - return false; + + std::string anchors = net->layers_cfg[net->layers_cfg.size() - 1]["anchors"]; + std::vector vec = getNumbers(anchors); + std::map &net_params = net->net_cfg; + net->width = getParam(net_params, "width", 416); + net->height = getParam(net_params, "height", 416); + net->channels = getParam(net_params, "channels", 3); + CV_Assert(net->width > 0 && net->height > 0 && net->channels > 0); int current_channels = net->channels; net->out_channels_vec.resize(net->layers_cfg.size()); - int layers_counter = -1; + layers_counter = -1; setLayersParams setParams(net); @@ -676,13 +669,8 @@ namespace cv { return true; } - - bool ReadDarknetFromWeightsFile(const char *darknetModel, NetParameter *net) + bool ReadDarknetFromWeightsStream(std::istream &ifile, NetParameter *net) { - std::ifstream ifile; - ifile.open(darknetModel, std::ios::binary); - CV_Assert(ifile.is_open()); - int32_t major_ver, minor_ver, revision; ifile.read(reinterpret_cast(&major_ver), sizeof(int32_t)); ifile.read(reinterpret_cast(&minor_ver), sizeof(int32_t)); @@ -778,19 +766,18 @@ namespace cv { } - void ReadNetParamsFromCfgFileOrDie(const char *cfgFile, darknet::NetParameter *net) + void ReadNetParamsFromCfgStreamOrDie(std::istream &ifile, darknet::NetParameter *net) { - if (!darknet::ReadDarknetFromCfgFile(cfgFile, net)) { - CV_Error(cv::Error::StsParseError, "Failed to parse NetParameter file: " + std::string(cfgFile)); + if (!darknet::ReadDarknetFromCfgStream(ifile, net)) { + CV_Error(cv::Error::StsParseError, "Failed to parse NetParameter stream"); } } - void ReadNetParamsFromBinaryFileOrDie(const char *darknetModel, darknet::NetParameter *net) + void ReadNetParamsFromBinaryStreamOrDie(std::istream &ifile, darknet::NetParameter *net) { - if (!darknet::ReadDarknetFromWeightsFile(darknetModel, net)) { - CV_Error(cv::Error::StsParseError, "Failed to parse NetParameter file: " + std::string(darknetModel)); + if (!darknet::ReadDarknetFromWeightsStream(ifile, net)) { + CV_Error(cv::Error::StsParseError, "Failed to parse NetParameter stream"); } } - } } diff --git a/modules/dnn/src/darknet/darknet_io.hpp b/modules/dnn/src/darknet/darknet_io.hpp index 5859f736b6..f783ca7b49 100644 --- a/modules/dnn/src/darknet/darknet_io.hpp +++ b/modules/dnn/src/darknet/darknet_io.hpp @@ -109,10 +109,9 @@ namespace cv { }; } - // Read parameters from a file into a NetParameter message. - void ReadNetParamsFromCfgFileOrDie(const char *cfgFile, darknet::NetParameter *net); - void ReadNetParamsFromBinaryFileOrDie(const char *darknetModel, darknet::NetParameter *net); - + // Read parameters from a stream into a NetParameter message. + void ReadNetParamsFromCfgStreamOrDie(std::istream &ifile, darknet::NetParameter *net); + void ReadNetParamsFromBinaryStreamOrDie(std::istream &ifile, darknet::NetParameter *net); } } #endif diff --git a/modules/dnn/src/dnn.cpp b/modules/dnn/src/dnn.cpp index db905930b2..994df854b0 100644 --- a/modules/dnn/src/dnn.cpp +++ b/modules/dnn/src/dnn.cpp @@ -3126,6 +3126,23 @@ Net readNet(const String& _model, const String& _config, const String& _framewor model + (config.empty() ? "" : ", " + config)); } +Net readNet(const String& _framework, const std::vector& bufferModel, + const std::vector& bufferConfig) +{ + String framework = _framework.toLowerCase(); + if (framework == "caffe") + return readNetFromCaffe(bufferConfig, bufferModel); + else if (framework == "tensorflow") + return readNetFromTensorflow(bufferModel, bufferConfig); + else if (framework == "darknet") + return readNetFromDarknet(bufferConfig, bufferModel); + else if (framework == "torch") + CV_Error(Error::StsNotImplemented, "Reading Torch models from buffers"); + else if (framework == "dldt") + CV_Error(Error::StsNotImplemented, "Reading Intel's Model Optimizer models from buffers"); + CV_Error(Error::StsError, "Cannot determine an origin framework with a name " + framework); +} + Net readNetFromModelOptimizer(const String &xml, const String &bin) { return Net::readFromModelOptimizer(xml, bin); diff --git a/modules/dnn/src/tensorflow/tf_importer.cpp b/modules/dnn/src/tensorflow/tf_importer.cpp index 7d7d300386..89732b45ad 100644 --- a/modules/dnn/src/tensorflow/tf_importer.cpp +++ b/modules/dnn/src/tensorflow/tf_importer.cpp @@ -1856,5 +1856,14 @@ Net readNetFromTensorflow(const char* bufferModel, size_t lenModel, return net; } +Net readNetFromTensorflow(const std::vector& bufferModel, const std::vector& bufferConfig) +{ + const char* bufferModelPtr = reinterpret_cast(&bufferModel[0]); + const char* bufferConfigPtr = bufferConfig.empty() ? NULL : + reinterpret_cast(&bufferConfig[0]); + return readNetFromTensorflow(bufferModelPtr, bufferModel.size(), + bufferConfigPtr, bufferConfig.size()); +} + CV__DNN_EXPERIMENTAL_NS_END }} // namespace diff --git a/modules/dnn/test/test_darknet_importer.cpp b/modules/dnn/test/test_darknet_importer.cpp index 682213b791..077498d92e 100644 --- a/modules/dnn/test/test_darknet_importer.cpp +++ b/modules/dnn/test/test_darknet_importer.cpp @@ -65,6 +65,34 @@ TEST(Test_Darknet, read_yolo_voc) ASSERT_FALSE(net.empty()); } +TEST(Test_Darknet, read_yolo_voc_stream) +{ + Mat ref; + Mat sample = imread(_tf("dog416.png")); + Mat inp = blobFromImage(sample, 1.0/255, Size(416, 416), Scalar(), true, false); + const std::string cfgFile = findDataFile("dnn/yolo-voc.cfg", false); + const std::string weightsFile = findDataFile("dnn/yolo-voc.weights", false); + // Import by paths. + { + Net net = readNetFromDarknet(cfgFile, weightsFile); + net.setInput(inp); + net.setPreferableBackend(DNN_BACKEND_OPENCV); + ref = net.forward(); + } + // Import from bytes array. + { + std::string cfg, weights; + readFileInMemory(cfgFile, cfg); + readFileInMemory(weightsFile, weights); + + Net net = readNetFromDarknet(&cfg[0], cfg.size(), &weights[0], weights.size()); + net.setInput(inp); + net.setPreferableBackend(DNN_BACKEND_OPENCV); + Mat out = net.forward(); + normAssert(ref, out); + } +} + class Test_Darknet_layers : public DNNTestLayer { public: