* @param bufferModel A buffer contains a content of .weights file with learned network.
* @returns Net object.
*/
- CV_EXPORTS_W Net readNetFromDarknet(const std::vector<char>& bufferCfg,
- const std::vector<char>& bufferModel = std::vector<char>());
+ CV_EXPORTS_W Net readNetFromDarknet(const std::vector<uchar>& bufferCfg,
+ const std::vector<uchar>& bufferModel = std::vector<uchar>());
/** @brief Reads a network model stored in <a href="https://pjreddie.com/darknet/">Darknet</a> model files.
* @param bufferCfg A buffer contains a content of .cfg file with text description of the network architecture.
* @param bufferModel buffer containing the content of the .caffemodel file
* @returns Net object.
*/
- CV_EXPORTS_W Net readNetFromCaffe(const std::vector<char>& bufferProto,
- const std::vector<char>& bufferModel = std::vector<char>());
+ CV_EXPORTS_W Net readNetFromCaffe(const std::vector<uchar>& bufferProto,
+ const std::vector<uchar>& bufferModel = std::vector<uchar>());
/** @brief Reads a network model stored in Caffe model in memory.
* @details This is an overloaded member function, provided for convenience.
* @param bufferConfig buffer containing the content of the pbtxt file
* @returns Net object.
*/
- CV_EXPORTS_W Net readNetFromTensorflow(const std::vector<char>& bufferModel,
- const std::vector<char>& bufferConfig = std::vector<char>());
+ CV_EXPORTS_W Net readNetFromTensorflow(const std::vector<uchar>& bufferModel,
+ const std::vector<uchar>& bufferConfig = std::vector<uchar>());
/** @brief Reads a network model stored in <a href="https://www.tensorflow.org/">TensorFlow</a> framework's format.
* @details This is an overloaded member function, provided for convenience.
* @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<char>& bufferModel,
- const std::vector<char>& bufferConfig = std::vector<char>());
+ CV_EXPORTS_W Net readNet(const String& framework, const std::vector<uchar>& bufferModel,
+ const std::vector<uchar>& bufferConfig = std::vector<uchar>());
/** @brief Loads blob which was serialized as torch.Tensor object of Torch7 framework.
* @warning This function has the same limitations as readNetFromTorch().
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;
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();
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);
}
- public void testTestNetForward() {
- Mat rawImage = Imgcodecs.imread(sourceImageFile);
-
- assertNotNull("Loading image from file failed!", rawImage);
+ public void checkInceptionNet(Net net)
+ {
+ Mat image = Imgcodecs.imread(sourceImageFile);
+ assertNotNull("Loading image from file failed!", image);
- 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);
+
+ Mat top5RefScores = new MatOfFloat(new float[] {
+ 0.63032645f, 0.2561979f, 0.032181446f, 0.015721032f, 0.014785315f
+ }).reshape(1, 1);
- assertTrue("No image recognized!", minmax.maxVal > 0.9);
+ 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);
+ }
}
return net;
}
-Net readNetFromCaffe(const std::vector<char>& bufferProto, const std::vector<char>& bufferModel)
+Net readNetFromCaffe(const std::vector<uchar>& bufferProto, const std::vector<uchar>& bufferModel)
{
- return readNetFromCaffe(&bufferProto[0], bufferProto.size(),
- bufferModel.empty() ? NULL : &bufferModel[0], bufferModel.size());
+ const char* bufferProtoPtr = reinterpret_cast<const char*>(&bufferProto[0]);
+ const char* bufferModelPtr = bufferModel.empty() ? NULL :
+ reinterpret_cast<const char*>(&bufferModel[0]);
+ return readNetFromCaffe(bufferProtoPtr, bufferProto.size(),
+ bufferModelPtr, bufferModel.size());
}
#endif //HAVE_PROTOBUF
return readNetFromDarknet(cfgStream);
}
-Net readNetFromDarknet(const std::vector<char>& bufferCfg, const std::vector<char>& bufferModel)
+Net readNetFromDarknet(const std::vector<uchar>& bufferCfg, const std::vector<uchar>& bufferModel)
{
- return readNetFromDarknet(&bufferCfg[0], bufferCfg.size(),
- bufferModel.empty() ? NULL : &bufferModel[0], bufferModel.size());
+ const char* bufferCfgPtr = reinterpret_cast<const char*>(&bufferCfg[0]);
+ const char* bufferModelPtr = bufferModel.empty() ? NULL :
+ reinterpret_cast<const char*>(&bufferModel[0]);
+ return readNetFromDarknet(bufferCfgPtr, bufferCfg.size(),
+ bufferModelPtr, bufferModel.size());
}
CV__DNN_EXPERIMENTAL_NS_END
model + (config.empty() ? "" : ", " + config));
}
-Net readNet(const String& _framework, const std::vector<char>& bufferModel,
- const std::vector<char>& bufferConfig)
+Net readNet(const String& _framework, const std::vector<uchar>& bufferModel,
+ const std::vector<uchar>& bufferConfig)
{
String framework = _framework.toLowerCase();
if (framework == "caffe")
return net;
}
-Net readNetFromTensorflow(const std::vector<char>& bufferModel, const std::vector<char>& bufferConfig)
+Net readNetFromTensorflow(const std::vector<uchar>& bufferModel, const std::vector<uchar>& bufferConfig)
{
- return readNetFromCaffe(&bufferModel[0], bufferModel.size(),
- bufferConfig.empty() ? NULL : &bufferConfig[0], bufferConfig.size());
+ const char* bufferModelPtr = reinterpret_cast<const char*>(&bufferModel[0]);
+ const char* bufferConfigPtr = bufferConfig.empty() ? NULL :
+ reinterpret_cast<const char*>(&bufferConfig[0]);
+ return readNetFromTensorflow(bufferModelPtr, bufferModel.size(),
+ bufferConfigPtr, bufferConfig.size());
}
CV__DNN_EXPERIMENTAL_NS_END