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@ -71,4 +71,63 @@ TEST(SoftCascade, detect) |
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// });
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} |
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class SCSpecific : public ::testing::TestWithParam<std::tr1::tuple<std::string, int> > { |
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}; |
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namespace { |
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std::string itoa(long i) |
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{ |
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static char s[65]; |
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sprintf(s, "%ld", i); |
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return std::string(s); |
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} |
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} |
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TEST_P(SCSpecific, detect) |
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{ |
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std::string xml = cvtest::TS::ptr()->get_data_path() + "../cv/cascadeandhog/sc_cvpr_2012_to_opencv.xml"; |
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cv::gpu::SoftCascade cascade; |
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ASSERT_TRUE(cascade.load(xml)); |
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std::string path = GET_PARAM(0); |
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cv::Mat coloredCpu = cv::imread(cvtest::TS::ptr()->get_data_path() + path); |
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ASSERT_FALSE(coloredCpu.empty()); |
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GpuMat colored(coloredCpu), objectBoxes(1, 1000, CV_8UC1), rois; |
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int level = GET_PARAM(1); |
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cascade.detectMultiScale(colored, rois, objectBoxes, 1, level); |
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cv::Mat dt(objectBoxes); |
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typedef cv::gpu::SoftCascade::Detection detection_t; |
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detection_t* dts = (detection_t*)dt.data; |
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cv::Mat result(coloredCpu); |
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std::cout << "Total detections " << (dt.cols / sizeof(detection_t)) << std::endl; |
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for(int i = 0; i < (int)(dt.cols / sizeof(detection_t)); ++i) |
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{ |
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detection_t d = dts[i]; |
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std::cout << "detection: [" << std::setw(4) << d.x << " " << std::setw(4) << d.y |
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<< "] [" << std::setw(4) << d.w << " " << std::setw(4) << d.h << "] " |
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<< std::setw(12) << d.confidence << std::endl; |
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cv::rectangle(result, cv::Rect(d.x, d.y, d.w, d.h), cv::Scalar(255, 0, 0, 255), 1); |
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} |
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std::cout << "Result stored in " << "/home/kellan/gpu_res_1_oct_" + itoa(level) << "_" |
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+ itoa((dt.cols / sizeof(detection_t))) + ".png" << std::endl; |
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cv::imwrite("/home/kellan/gpu_res_1_oct_" + itoa(level) + "_" + itoa((dt.cols / sizeof(detection_t))) + ".png", |
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result); |
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cv::imshow("res", result); |
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cv::waitKey(0); |
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} |
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INSTANTIATE_TEST_CASE_P(inLevel, SCSpecific, |
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testing::Combine( |
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testing::Values(std::string("../cv/cascadeandhog/bahnhof/image_00000000_0.png")), |
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testing::Range(0, 47) |
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)); |
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#endif |