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Merge pull request #21854 from opencv-pushbot:dnn_test_update_checks_face_detector_4.x

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OpenCV Pushbot 2022-04-12 17:20:22 +00:00 committed by GitHub
commit 03c9648f2e
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2 changed files with 9 additions and 3 deletions
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10
modules/dnn/test/test_caffe_importer.cpp
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@ -633,6 +633,9 @@ TEST_P(opencv_face_detector, Accuracy)
std::string model = findDataFile(get<0>(GetParam()), false); std::string model = findDataFile(get<0>(GetParam()), false);
dnn::Target targetId = (dnn::Target)(int)get<1>(GetParam()); dnn::Target targetId = (dnn::Target)(int)get<1>(GetParam());
if (targetId == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
Net net = readNetFromCaffe(proto, model); Net net = readNetFromCaffe(proto, model);
Mat img = imread(findDataFile("gpu/lbpcascade/er.png")); Mat img = imread(findDataFile("gpu/lbpcascade/er.png"));
Mat blob = blobFromImage(img, 1.0, Size(), Scalar(104.0, 177.0, 123.0), false, false); Mat blob = blobFromImage(img, 1.0, Size(), Scalar(104.0, 177.0, 123.0), false, false);
@ -660,6 +663,9 @@ TEST_P(opencv_face_detector, issue_15106)
std::string model = findDataFile(get<0>(GetParam()), false); std::string model = findDataFile(get<0>(GetParam()), false);
dnn::Target targetId = (dnn::Target)(int)get<1>(GetParam()); dnn::Target targetId = (dnn::Target)(int)get<1>(GetParam());
if (targetId == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
Net net = readNetFromCaffe(proto, model); Net net = readNetFromCaffe(proto, model);
Mat img = imread(findDataFile("cv/shared/lena.png")); Mat img = imread(findDataFile("cv/shared/lena.png"));
img = img.rowRange(img.rows / 4, 3 * img.rows / 4).colRange(img.cols / 4, 3 * img.cols / 4); img = img.rowRange(img.rows / 4, 3 * img.rows / 4).colRange(img.cols / 4, 3 * img.cols / 4);
@ -673,13 +679,13 @@ TEST_P(opencv_face_detector, issue_15106)
// An every detection is a vector of values [id, classId, confidence, left, top, right, bottom] // An every detection is a vector of values [id, classId, confidence, left, top, right, bottom]
Mat out = net.forward(); Mat out = net.forward();
Mat ref = (Mat_<float>(1, 7) << 0, 1, 0.9149431, 0.30424616, 0.26964942, 0.88733053, 0.99815309); Mat ref = (Mat_<float>(1, 7) << 0, 1, 0.9149431, 0.30424616, 0.26964942, 0.88733053, 0.99815309);
normAssertDetections(ref, out, "", 0.2, 6e-5, 1e-4); normAssertDetections(ref, out, "", 0.89, 6e-5, 1e-4);
} }
INSTANTIATE_TEST_CASE_P(Test_Caffe, opencv_face_detector, INSTANTIATE_TEST_CASE_P(Test_Caffe, opencv_face_detector,
Combine( Combine(
Values("dnn/opencv_face_detector.caffemodel", Values("dnn/opencv_face_detector.caffemodel",
"dnn/opencv_face_detector_fp16.caffemodel"), "dnn/opencv_face_detector_fp16.caffemodel"),
Values(DNN_TARGET_CPU, DNN_TARGET_OPENCL) testing::ValuesIn(getAvailableTargets(DNN_BACKEND_OPENCV))
) )
); );

2
modules/dnn/test/test_int8_layers.cpp
View File

@ -906,7 +906,7 @@ TEST_P(Test_Int8_nets, opencv_face_detector)
0, 1, 0.97203469, 0.67965847, 0.06876482, 0.73999709, 0.1513494, 0, 1, 0.97203469, 0.67965847, 0.06876482, 0.73999709, 0.1513494,
0, 1, 0.95097077, 0.51901293, 0.45863652, 0.5777427, 0.5347801); 0, 1, 0.95097077, 0.51901293, 0.45863652, 0.5777427, 0.5347801);
float confThreshold = 0.5, scoreDiff = 0.002, iouDiff = 0.21; float confThreshold = 0.5, scoreDiff = 0.002, iouDiff = 0.4;
testDetectionNet(net, blob, ref, confThreshold, scoreDiff, iouDiff); testDetectionNet(net, blob, ref, confThreshold, scoreDiff, iouDiff);
} }