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Merge pull request #9676 from jrobble:fix_caffe_swaprb

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This commit is contained in:
Alexander Alekhin 2017-09-25 16:05:11 +00:00
commit aea25e7f90
10 changed files with 47 additions and 51 deletions
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34
modules/dnn/test/test_caffe_importer.cpp
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@ -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"));

8
modules/dnn/test/test_googlenet.cpp
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@ -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());

2
samples/android/mobilenet-objdetect/src/org/opencv/samples/opencv_mobilenet/MainActivity.java
View File

@ -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();

39
samples/dnn/caffe_googlenet.cpp
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@ -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;

4
samples/dnn/fcn_semsegm.cpp
View File

@ -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]

2
samples/dnn/googlenet_python.py
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@ -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')

2
samples/dnn/mobilenet_ssd_python.py
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@ -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()

3
samples/dnn/resnet_ssd_face_python.py
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@ -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()

2
samples/dnn/ssd_mobilenet_object_detection.cpp
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@ -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]

2
samples/dnn/ssd_object_detection.cpp
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@ -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]