mirror/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2024-11-25 19:50:38 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity

Merge pull request #10730 from dkurt:dnn_faster_rcnn_rfcn_tests

Browse Source
This commit is contained in:
Vadim Pisarevsky 2018-02-01 13:28:10 +00:00
commit 36222c9eed
1 changed files with 42 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

42
modules/dnn/test/test_caffe_importer.cpp
View File

@ -483,4 +483,46 @@ TEST(Test_Caffe, opencv_face_detector)
normAssert(out.reshape(1, out.total() / 7).rowRange(0, 6).colRange(2, 7), ref);
}
TEST(Test_Caffe, FasterRCNN_and_RFCN)
{
std::string models[] = {"VGG16_faster_rcnn_final.caffemodel", "ZF_faster_rcnn_final.caffemodel",
"resnet50_rfcn_final.caffemodel"};
std::string protos[] = {"faster_rcnn_vgg16.prototxt", "faster_rcnn_zf.prototxt",
"rfcn_pascal_voc_resnet50.prototxt"};
Mat refs[] = {(Mat_<float>(3, 6) << 2, 0.949398, 99.2454, 210.141, 601.205, 462.849,
7, 0.997022, 481.841, 92.3218, 722.685, 175.953,
12, 0.993028, 133.221, 189.377, 350.994, 563.166),
(Mat_<float>(3, 6) << 2, 0.90121, 120.407, 115.83, 570.586, 528.395,
7, 0.988779, 469.849, 75.1756, 718.64, 186.762,
12, 0.967198, 138.588, 206.843, 329.766, 553.176),
(Mat_<float>(2, 6) << 7, 0.991359, 491.822, 81.1668, 702.573, 178.234,
12, 0.94786, 132.093, 223.903, 338.077, 566.16)};
for (int i = 0; i < 3; ++i)
{
std::string proto = findDataFile("dnn/" + protos[i], false);
std::string model = findDataFile("dnn/" + models[i], false);
Net net = readNetFromCaffe(proto, model);
Mat img = imread(findDataFile("dnn/dog416.png", false));
resize(img, img, Size(800, 600));
Mat blob = blobFromImage(img, 1.0, Size(), Scalar(102.9801, 115.9465, 122.7717), false, false);
Mat imInfo = (Mat_<float>(1, 3) << img.rows, img.cols, 1.6f);
net.setInput(blob, "data");
net.setInput(imInfo, "im_info");
// Output has shape 1x1xNx7 where N - number of detections.
// An every detection is a vector of values [id, classId, confidence, left, top, right, bottom]
Mat out = net.forward();
out = out.reshape(1, out.total() / 7);
Mat detections;
for (int j = 0; j < out.rows; ++j)
{
if (out.at<float>(j, 2) > 0.8)
detections.push_back(out.row(j).colRange(1, 7));
}
normAssert(detections, refs[i], ("model name: " + models[i]).c_str(), 2e-4, 6e-4);
}
}
}