Chiebot-Mirror
/
opencv
8
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Set swapRB to false in Caffe tests and examples.

Browse Source
pull/9676/head
jrobble 8 years ago
parent 0624411875
commit c67ad49378
10 changed files with 47 additions and 51 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 34
      modules/dnn/test/test_caffe_importer.cpp
  2. 8
      modules/dnn/test/test_googlenet.cpp
  3. 2
      samples/android/mobilenet-objdetect/src/org/opencv/samples/opencv_mobilenet/MainActivity.java
  4. 39
      samples/dnn/caffe_googlenet.cpp
  5. 4
      samples/dnn/fcn_semsegm.cpp
  6. 2
      samples/dnn/googlenet_python.py
  7. 2
      samples/dnn/mobilenet_ssd_python.py
  8. 3
      samples/dnn/resnet_ssd_face_python.py
  9. 2
      samples/dnn/ssd_mobilenet_object_detection.cpp
  10. 2
      samples/dnn/ssd_object_detection.cpp

34
modules/dnn/test/test_caffe_importer.cpp
Unescape View File

@ -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
Unescape View File

@ -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
Unescape 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
Unescape View File

@ -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
Unescape 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
Unescape View File

@ -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
Unescape View File

@ -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
Unescape View File

@ -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
Unescape View File

@ -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
Unescape View File

@ -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]

Write Preview
Loading…
Cancel
Save