Set swapRB to false in Caffe tests and examples.

modules/dnn/test/test_caffe_importer.cpp

@@ -80,12 +80,7 @@ TEST(Reproducibility_AlexNet, Accuracy)
     Mat sample = imread(_tf("grace_hopper_227.png"));
     ASSERT_TRUE(!sample.empty());
 
-    Size inputSize(227, 227);
-
-    if (sample.size() != inputSize)
-        resize(sample, sample, inputSize);
-
-    net.setInput(blobFromImage(sample), "data");
+    net.setInput(blobFromImage(sample, 1.0f, Size(227, 227), Scalar(), false), "data");
     Mat out = net.forward("prob");
     Mat ref = blobFromNPY(_tf("caffe_alexnet_prob.npy"));
     normAssert(ref, out);
@@ -105,17 +100,17 @@ TEST(Reproducibility_FCN, Accuracy)
     Mat sample = imread(_tf("street.png"));
     ASSERT_TRUE(!sample.empty());
 
-    Size inputSize(500, 500);
-    if (sample.size() != inputSize)
-        resize(sample, sample, inputSize);
-
     std::vector<int> layerIds;
     std::vector<size_t> weights, blobs;
     net.getMemoryConsumption(shape(1,3,227,227), layerIds, weights, blobs);
 
-    net.setInput(blobFromImage(sample), "data");
+    net.setInput(blobFromImage(sample, 1.0f, Size(500, 500), Scalar(), false), "data");
     Mat out = net.forward("score");
-    Mat ref = blobFromNPY(_tf("caffe_fcn8s_prob.npy"));
+
+    Mat refData = imread(_tf("caffe_fcn8s_prob.png"), IMREAD_ANYDEPTH);
+    int shape[] = {1, 21, 500, 500};
+    Mat ref(4, shape, CV_32FC1, refData.data);
+
     normAssert(ref, out);
 }
 #endif
@@ -136,10 +131,7 @@ TEST(Reproducibility_SSD, Accuracy)
     if (sample.channels() == 4)
         cvtColor(sample, sample, COLOR_BGRA2BGR);
 
-    sample.convertTo(sample, CV_32F);
-    resize(sample, sample, Size(300, 300));
-
-    Mat in_blob = blobFromImage(sample);
+    Mat in_blob = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
     net.setInput(in_blob, "data");
     Mat out = net.forward("detection_out");
 
@@ -152,7 +144,7 @@ TEST(Reproducibility_ResNet50, Accuracy)
     Net net = readNetFromCaffe(findDataFile("dnn/ResNet-50-deploy.prototxt", false),
                                findDataFile("dnn/ResNet-50-model.caffemodel", false));
 
-    Mat input = blobFromImage(imread(_tf("googlenet_0.png")), 1, Size(224,224));
+    Mat input = blobFromImage(imread(_tf("googlenet_0.png")), 1.0f, Size(224,224), Scalar(), false);
     ASSERT_TRUE(!input.empty());
 
     net.setInput(input);
@@ -167,7 +159,7 @@ TEST(Reproducibility_SqueezeNet_v1_1, Accuracy)
     Net net = readNetFromCaffe(findDataFile("dnn/squeezenet_v1.1.prototxt", false),
                                findDataFile("dnn/squeezenet_v1.1.caffemodel", false));
 
-    Mat input = blobFromImage(imread(_tf("googlenet_0.png")), 1, Size(227,227));
+    Mat input = blobFromImage(imread(_tf("googlenet_0.png")), 1.0f, Size(227,227), Scalar(), false);
     ASSERT_TRUE(!input.empty());
 
     net.setInput(input);
@@ -180,7 +172,7 @@ TEST(Reproducibility_SqueezeNet_v1_1, Accuracy)
 TEST(Reproducibility_AlexNet_fp16, Accuracy)
 {
     const float l1 = 1e-5;
-    const float lInf = 2e-4;
+    const float lInf = 3e-3;
 
     const string proto = findDataFile("dnn/bvlc_alexnet.prototxt", false);
     const string model = findDataFile("dnn/bvlc_alexnet.caffemodel", false);
@@ -190,7 +182,7 @@ TEST(Reproducibility_AlexNet_fp16, Accuracy)
 
     Mat sample = imread(findDataFile("dnn/grace_hopper_227.png", false));
 
-    net.setInput(blobFromImage(sample, 1, Size(227, 227)));
+    net.setInput(blobFromImage(sample, 1.0f, Size(227, 227), Scalar(), false));
     Mat out = net.forward();
     Mat ref = blobFromNPY(findDataFile("dnn/caffe_alexnet_prob.npy", false));
     normAssert(ref, out, "", l1, lInf);
@@ -212,7 +204,7 @@ TEST(Reproducibility_GoogLeNet_fp16, Accuracy)
     inpMats.push_back( imread(_tf("googlenet_1.png")) );
     ASSERT_TRUE(!inpMats[0].empty() && !inpMats[1].empty());
 
-    net.setInput(blobFromImages(inpMats), "data");
+    net.setInput(blobFromImages(inpMats, 1.0f, Size(), Scalar(), false), "data");
     Mat out = net.forward("prob");
 
     Mat ref = blobFromNPY(_tf("googlenet_prob.npy"));

modules/dnn/test/test_googlenet.cpp

@@ -66,7 +66,7 @@ TEST(Reproducibility_GoogLeNet, Accuracy)
     inpMats.push_back( imread(_tf("googlenet_1.png")) );
     ASSERT_TRUE(!inpMats[0].empty() && !inpMats[1].empty());
 
-    net.setInput(blobFromImages(inpMats), "data");
+    net.setInput(blobFromImages(inpMats, 1.0f, Size(), Scalar(), false), "data");
     Mat out = net.forward("prob");
 
     Mat ref = blobFromNPY(_tf("googlenet_prob.npy"));
@@ -84,7 +84,7 @@ TEST(IntermediateBlobs_GoogLeNet, Accuracy)
     blobsNames.push_back("inception_4c/1x1");
     blobsNames.push_back("inception_4c/relu_1x1");
     std::vector<Mat> outs;
-    Mat in = blobFromImage(imread(_tf("googlenet_0.png")));
+    Mat in = blobFromImage(imread(_tf("googlenet_0.png")), 1.0f, Size(), Scalar(), false);
     net.setInput(in, "data");
     net.forward(outs, blobsNames);
     CV_Assert(outs.size() == blobsNames.size());
@@ -109,7 +109,7 @@ TEST(SeveralCalls_GoogLeNet, Accuracy)
     inpMats.push_back( imread(_tf("googlenet_1.png")) );
     ASSERT_TRUE(!inpMats[0].empty() && !inpMats[1].empty());
 
-    net.setInput(blobFromImages(inpMats), "data");
+    net.setInput(blobFromImages(inpMats, 1.0f, Size(), Scalar(), false), "data");
     Mat out = net.forward();
 
     Mat ref = blobFromNPY(_tf("googlenet_prob.npy"));
@@ -118,7 +118,7 @@ TEST(SeveralCalls_GoogLeNet, Accuracy)
     std::vector<String> blobsNames;
     blobsNames.push_back("conv1/7x7_s2");
     std::vector<Mat> outs;
-    Mat in = blobFromImage(inpMats[0]);
+    Mat in = blobFromImage(inpMats[0], 1.0f, Size(), Scalar(), false);
     net.setInput(in, "data");
     net.forward(outs, blobsNames);
     CV_Assert(outs.size() == blobsNames.size());

samples/android/mobilenet-objdetect/src/org/opencv/samples/opencv_mobilenet/MainActivity.java

@@ -86,7 +86,7 @@ public class MainActivity extends AppCompatActivity implements CvCameraViewListe
         // Forward image through network.
         Mat blob = Dnn.blobFromImage(frame, IN_SCALE_FACTOR,
                 new Size(IN_WIDTH, IN_HEIGHT),
-                new Scalar(MEAN_VAL, MEAN_VAL, MEAN_VAL), true);
+                new Scalar(MEAN_VAL, MEAN_VAL, MEAN_VAL), false);
         net.setInput(blob);
         Mat detections = net.forward();
 

samples/dnn/caffe_googlenet.cpp

@@ -91,21 +91,26 @@ int main(int argc, char **argv)
     String modelBin = "bvlc_googlenet.caffemodel";
     String imageFile = (argc > 1) ? argv[1] : "space_shuttle.jpg";
 
-    //! [Read and initialize network]
-    Net net = dnn::readNetFromCaffe(modelTxt, modelBin);
-    //! [Read and initialize network]
-
-    //! [Check that network was read successfully]
-    if (net.empty())
-    {
-        std::cerr << "Can't load network by using the following files: " << std::endl;
-        std::cerr << "prototxt:   " << modelTxt << std::endl;
-        std::cerr << "caffemodel: " << modelBin << std::endl;
-        std::cerr << "bvlc_googlenet.caffemodel can be downloaded here:" << std::endl;
-        std::cerr << "http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel" << std::endl;
-        exit(-1);
+    Net net;
+    try {
+        //! [Read and initialize network]
+        net = dnn::readNetFromCaffe(modelTxt, modelBin);
+        //! [Read and initialize network]
+    }
+    catch (cv::Exception& e) {
+        std::cerr << "Exception: " << e.what() << std::endl;
+        //! [Check that network was read successfully]
+        if (net.empty())
+        {
+            std::cerr << "Can't load network by using the following files: " << std::endl;
+            std::cerr << "prototxt:   " << modelTxt << std::endl;
+            std::cerr << "caffemodel: " << modelBin << std::endl;
+            std::cerr << "bvlc_googlenet.caffemodel can be downloaded here:" << std::endl;
+            std::cerr << "http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel" << std::endl;
+            exit(-1);
+        }
+        //! [Check that network was read successfully]
     }
-    //! [Check that network was read successfully]
 
     //! [Prepare blob]
     Mat img = imread(imageFile);
@@ -115,9 +120,9 @@ int main(int argc, char **argv)
         exit(-1);
     }
 
-    //GoogLeNet accepts only 224x224 RGB-images
-    Mat inputBlob = blobFromImage(img, 1, Size(224, 224),
-                                  Scalar(104, 117, 123));   //Convert Mat to batch of images
+    //GoogLeNet accepts only 224x224 BGR-images
+    Mat inputBlob = blobFromImage(img, 1.0f, Size(224, 224),
+                                  Scalar(104, 117, 123), false);   //Convert Mat to batch of images
     //! [Prepare blob]
 
     Mat prob;

samples/dnn/fcn_semsegm.cpp

@@ -113,8 +113,8 @@ int main(int argc, char **argv)
         exit(-1);
     }
 
-    resize(img, img, Size(500, 500));       //FCN accepts 500x500 RGB-images
-    Mat inputBlob = blobFromImage(img);   //Convert Mat to batch of images
+    resize(img, img, Size(500, 500));       //FCN accepts 500x500 BGR-images
+    Mat inputBlob = blobFromImage(img, 1, Size(), Scalar(), false);   //Convert Mat to batch of images
     //! [Prepare blob]
 
     //! [Set input blob]

samples/dnn/googlenet_python.py

@@ -11,7 +11,7 @@ def get_class_list():
     with open('synset_words.txt', 'rt') as f:
         return [x[x.find(" ") + 1:] for x in f]
 
-blob = dnn.blobFromImage(cv2.imread('space_shuttle.jpg'), 1, (224, 224), (104, 117, 123))
+blob = dnn.blobFromImage(cv2.imread('space_shuttle.jpg'), 1, (224, 224), (104, 117, 123), false)
 print("Input:", blob.shape, blob.dtype)
 
 net = dnn.readNetFromCaffe('bvlc_googlenet.prototxt', 'bvlc_googlenet.caffemodel')

samples/dnn/mobilenet_ssd_python.py

@@ -41,7 +41,7 @@ if __name__ == "__main__":
     while True:
         # Capture frame-by-frame
         ret, frame = cap.read()
-        blob = cv.dnn.blobFromImage(frame, inScaleFactor, (inWidth, inHeight), meanVal)
+        blob = cv.dnn.blobFromImage(frame, inScaleFactor, (inWidth, inHeight), meanVal, false)
         net.setInput(blob)
         detections = net.forward()
 

samples/dnn/resnet_ssd_face_python.py

@@ -27,8 +27,7 @@ if __name__ == '__main__':
         cols = frame.shape[1]
         rows = frame.shape[0]
 
-        net.setInput(dnn.blobFromImage(cv.resize(frame, (inWidth, inHeight)),
-                                       1.0, (inWidth, inHeight), (104., 177., 123.)))
+        net.setInput(dnn.blobFromImage(frame, 1.0, (inWidth, inHeight), (104.0, 177.0, 123.0), false))
         detections = net.forward()
 
         perf_stats = net.getPerfProfile()

samples/dnn/ssd_mobilenet_object_detection.cpp

@@ -97,7 +97,7 @@ int main(int argc, char** argv)
         //! [Prepare blob]
 
         Mat inputBlob = blobFromImage(frame, inScaleFactor,
-                                      Size(inWidth, inHeight), meanVal); //Convert Mat to batch of images
+                                      Size(inWidth, inHeight), meanVal, false); //Convert Mat to batch of images
         //! [Prepare blob]
 
         //! [Set input blob]

samples/dnn/ssd_object_detection.cpp

@@ -86,7 +86,7 @@ int main(int argc, char** argv)
     //! [Prepare blob]
     Mat preprocessedFrame = preprocess(frame);
 
-    Mat inputBlob = blobFromImage(preprocessedFrame); //Convert Mat to batch of images
+    Mat inputBlob = blobFromImage(preprocessedFrame, 1.0f, Size(), Scalar(), false); //Convert Mat to batch of images
     //! [Prepare blob]
 
     //! [Set input blob]