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Replace std::vector<char> to std::vector<uchar> for Java bindings of dnn importers

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This commit is contained in:
Dmitry Kurtaev 2018-07-11 12:48:34 +03:00
parent d57e5406f0
commit 8b5f061dae
6 changed files with 82 additions and 36 deletions
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16
modules/dnn/include/opencv2/dnn/dnn.hpp
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@ -649,8 +649,8 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
* @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.
@ -674,8 +674,8 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
* @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.
@ -703,8 +703,8 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
* @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.
@ -778,8 +778,8 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
* @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().

71
modules/dnn/misc/java/test/DnnTensorFlowTest.java
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@ -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);
}
}

9
modules/dnn/src/caffe/caffe_importer.cpp
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@ -453,10 +453,13 @@ Net readNetFromCaffe(const char *bufferProto, size_t lenProto,
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

9
modules/dnn/src/darknet/darknet_importer.cpp
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@ -242,10 +242,13 @@ Net readNetFromDarknet(const char *bufferCfg, size_t lenCfg, const char *bufferM
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

4
modules/dnn/src/dnn.cpp
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@ -3047,8 +3047,8 @@ Net readNet(const String& _model, const String& _config, const String& _framewor
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")

9
modules/dnn/src/tensorflow/tf_importer.cpp
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@ -1856,10 +1856,13 @@ Net readNetFromTensorflow(const char* bufferModel, size_t lenModel,
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