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

modules/dnn/include/opencv2/dnn/dnn.hpp

@@ -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().

modules/dnn/misc/java/test/DnnTensorFlowTest.java

 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);
-
-        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);
+    }
 }

modules/dnn/src/caffe/caffe_importer.cpp

@@ -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

modules/dnn/src/darknet/darknet_importer.cpp

@@ -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

modules/dnn/src/dnn.cpp

@@ -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")

modules/dnn/src/tensorflow/tf_importer.cpp

@@ -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