// This file is part of OpenCV project. // It is subject to the license terms in the LICENSE file found in the top-level directory // of this distribution and at http://opencv.org/license.html. #include "test_precomp.hpp" #include #include "npy_blob.hpp" namespace opencv_test { namespace { template static std::string _tf(TString filename, bool required = true) { String rootFolder = "dnn/"; return findDataFile(rootFolder + filename, required); } class Test_Model : public DNNTestLayer { public: void testDetectModel(const std::string& weights, const std::string& cfg, const std::string& imgPath, const std::vector& refClassIds, const std::vector& refConfidences, const std::vector& refBoxes, double scoreDiff, double iouDiff, double confThreshold = 0.24, double nmsThreshold = 0.0, const Size& size = {-1, -1}, Scalar mean = Scalar(), double scale = 1.0, bool swapRB = false, bool crop = false, bool nmsAcrossClasses = false) { checkBackend(); Mat frame = imread(imgPath); DetectionModel model(weights, cfg); model.setInputSize(size).setInputMean(mean).setInputScale(scale) .setInputSwapRB(swapRB).setInputCrop(crop); model.setPreferableBackend(backend); model.setPreferableTarget(target); model.setNmsAcrossClasses(nmsAcrossClasses); std::vector classIds; std::vector confidences; std::vector boxes; model.detect(frame, classIds, confidences, boxes, confThreshold, nmsThreshold); std::vector boxesDouble(boxes.size()); for (int i = 0; i < boxes.size(); i++) { boxesDouble[i] = boxes[i]; } normAssertDetections(refClassIds, refConfidences, refBoxes, classIds, confidences, boxesDouble, "", confThreshold, scoreDiff, iouDiff); } void testClassifyModel(const std::string& weights, const std::string& cfg, const std::string& imgPath, std::pair ref, float norm, const Size& size = {-1, -1}, Scalar mean = Scalar(), double scale = 1.0, bool swapRB = false, bool crop = false) { checkBackend(); Mat frame = imread(imgPath); ClassificationModel model(weights, cfg); model.setInputSize(size).setInputMean(mean).setInputScale(scale) .setInputSwapRB(swapRB).setInputCrop(crop); std::pair prediction = model.classify(frame); EXPECT_EQ(prediction.first, ref.first); ASSERT_NEAR(prediction.second, ref.second, norm); } void testKeypointsModel(const std::string& weights, const std::string& cfg, const Mat& frame, const Mat& exp, float norm, const Size& size = {-1, -1}, Scalar mean = Scalar(), double scale = 1.0, bool swapRB = false, bool crop = false) { checkBackend(); std::vector points; KeypointsModel model(weights, cfg); model.setInputSize(size).setInputMean(mean).setInputScale(scale) .setInputSwapRB(swapRB).setInputCrop(crop); model.setPreferableBackend(backend); model.setPreferableTarget(target); points = model.estimate(frame, 0.5); Mat out = Mat(points).reshape(1); normAssert(exp, out, "", norm, norm); } void testSegmentationModel(const std::string& weights_file, const std::string& config_file, const std::string& inImgPath, const std::string& outImgPath, float norm, const Size& size = {-1, -1}, Scalar mean = Scalar(), double scale = 1.0, bool swapRB = false, bool crop = false) { checkBackend(); Mat frame = imread(inImgPath); Mat mask; Mat exp = imread(outImgPath, 0); SegmentationModel model(weights_file, config_file); model.setInputSize(size).setInputMean(mean).setInputScale(scale) .setInputSwapRB(swapRB).setInputCrop(crop); model.setPreferableBackend(backend); model.setPreferableTarget(target); model.segment(frame, mask); normAssert(mask, exp, "", norm, norm); } void testTextRecognitionModel(const std::string& weights, const std::string& cfg, const std::string& imgPath, const std::string& seq, const std::string& decodeType, const std::vector& vocabulary, const Size& size = {-1, -1}, Scalar mean = Scalar(), double scale = 1.0, bool swapRB = false, bool crop = false) { checkBackend(); Mat frame = imread(imgPath, IMREAD_GRAYSCALE); TextRecognitionModel model(weights, cfg); model.setDecodeType(decodeType) .setVocabulary(vocabulary) .setInputSize(size).setInputMean(mean).setInputScale(scale) .setInputSwapRB(swapRB).setInputCrop(crop); model.setPreferableBackend(backend); model.setPreferableTarget(target); std::string result = model.recognize(frame); EXPECT_EQ(result, seq) << "Full frame: " << imgPath; std::vector rois; rois.push_back(Rect(0, 0, frame.cols, frame.rows)); rois.push_back(Rect(0, 0, frame.cols, frame.rows)); // twice std::vector results; model.recognize(frame, rois, results); EXPECT_EQ((size_t)2u, results.size()) << "ROI: " << imgPath; EXPECT_EQ(results[0], seq) << "ROI[0]: " << imgPath; EXPECT_EQ(results[1], seq) << "ROI[1]: " << imgPath; } void testTextDetectionModelByDB(const std::string& weights, const std::string& cfg, const std::string& imgPath, const std::vector>& gt, float binThresh, float polyThresh, uint maxCandidates, double unclipRatio, const Size& size = {-1, -1}, Scalar mean = Scalar(), Scalar scale = Scalar::all(1.0), double boxes_iou_diff = 0.05, bool swapRB = false, bool crop = false) { checkBackend(); Mat frame = imread(imgPath); TextDetectionModel_DB model(weights, cfg); model.setBinaryThreshold(binThresh) .setPolygonThreshold(polyThresh) .setUnclipRatio(unclipRatio) .setMaxCandidates(maxCandidates) .setInputSize(size).setInputMean(mean).setInputScale(scale) .setInputSwapRB(swapRB).setInputCrop(crop); model.setPreferableBackend(backend); model.setPreferableTarget(target); // 1. Check common TextDetectionModel API through RotatedRect std::vector results; model.detectTextRectangles(frame, results); EXPECT_GT(results.size(), (size_t)0); std::vector< std::vector > contours; for (size_t i = 0; i < results.size(); i++) { const RotatedRect& box = results[i]; Mat contour; boxPoints(box, contour); std::vector contour2i(4); for (int i = 0; i < 4; i++) { contour2i[i].x = cvRound(contour.at(i, 0)); contour2i[i].y = cvRound(contour.at(i, 1)); } contours.push_back(contour2i); } #if 0 // test debug Mat result = frame.clone(); drawContours(result, contours, -1, Scalar(0, 0, 255), 1); imshow("result", result); // imwrite("result.png", result); waitKey(0); #endif normAssertTextDetections(gt, contours, "", boxes_iou_diff); // 2. Check quadrangle-based API // std::vector< std::vector > contours; model.detect(frame, contours); #if 0 // test debug Mat result = frame.clone(); drawContours(result, contours, -1, Scalar(0, 0, 255), 1); imshow("result_contours", result); // imwrite("result_contours.png", result); waitKey(0); #endif normAssertTextDetections(gt, contours, "", boxes_iou_diff); } void testTextDetectionModelByEAST( const std::string& weights, const std::string& cfg, const std::string& imgPath, const std::vector& gt, float confThresh, float nmsThresh, const Size& size = {-1, -1}, Scalar mean = Scalar(), double scale = 1.0, bool swapRB = false, bool crop = false, double eps_center = 5/*pixels*/, double eps_size = 5/*pixels*/, double eps_angle = 1 ) { checkBackend(); Mat frame = imread(imgPath); TextDetectionModel_EAST model(weights, cfg); model.setConfidenceThreshold(confThresh) .setNMSThreshold(nmsThresh) .setInputSize(size).setInputMean(mean).setInputScale(scale) .setInputSwapRB(swapRB).setInputCrop(crop); model.setPreferableBackend(backend); model.setPreferableTarget(target); std::vector results; model.detectTextRectangles(frame, results); EXPECT_EQ(results.size(), (size_t)1); for (size_t i = 0; i < results.size(); i++) { const RotatedRect& box = results[i]; #if 0 // test debug Mat contour; boxPoints(box, contour); std::vector contour2i(4); for (int i = 0; i < 4; i++) { contour2i[i].x = cvRound(contour.at(i, 0)); contour2i[i].y = cvRound(contour.at(i, 1)); } std::vector< std::vector > contours; contours.push_back(contour2i); Mat result = frame.clone(); drawContours(result, contours, -1, Scalar(0, 0, 255), 1); imshow("result", result); //imwrite("result.png", result); waitKey(0); #endif const RotatedRect& gtBox = gt[i]; EXPECT_NEAR(box.center.x, gtBox.center.x, eps_center); EXPECT_NEAR(box.center.y, gtBox.center.y, eps_center); EXPECT_NEAR(box.size.width, gtBox.size.width, eps_size); EXPECT_NEAR(box.size.height, gtBox.size.height, eps_size); EXPECT_NEAR(box.angle, gtBox.angle, eps_angle); } } }; TEST_P(Test_Model, Classify) { std::pair ref(652, 0.641789); std::string img_path = _tf("grace_hopper_227.png"); std::string config_file = _tf("bvlc_alexnet.prototxt"); std::string weights_file = _tf("bvlc_alexnet.caffemodel", false); Size size{227, 227}; float norm = 1e-4; testClassifyModel(weights_file, config_file, img_path, ref, norm, size); } TEST_P(Test_Model, DetectRegion) { applyTestTag( CV_TEST_TAG_LONG, CV_TEST_TAG_MEMORY_2GB ); #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) // accuracy if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) // accuracy if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2020040000) // nGraph compilation failure if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_VERSION); if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000) // FIXIT DNN_BACKEND_INFERENCE_ENGINE is misused if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16); #endif #if defined(INF_ENGINE_RELEASE) if (target == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X); #endif std::vector refClassIds = {6, 1, 11}; std::vector refConfidences = {0.750469f, 0.780879f, 0.901615f}; std::vector refBoxes = {Rect2d(240, 53, 135, 72), Rect2d(112, 109, 192, 200), Rect2d(58, 141, 117, 249)}; std::string img_path = _tf("dog416.png"); std::string weights_file = _tf("yolo-voc.weights", false); std::string config_file = _tf("yolo-voc.cfg"); double scale = 1.0 / 255.0; Size size{416, 416}; bool swapRB = true; double confThreshold = 0.24; double nmsThreshold = (target == DNN_TARGET_MYRIAD) ? 0.397 : 0.4; double scoreDiff = 8e-5, iouDiff = 1e-5; if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || target == DNN_TARGET_CUDA_FP16 || target == DNN_TARGET_CPU_FP16) { scoreDiff = 1e-2; iouDiff = 1.6e-2; } testDetectModel(weights_file, config_file, img_path, refClassIds, refConfidences, refBoxes, scoreDiff, iouDiff, confThreshold, nmsThreshold, size, Scalar(), scale, swapRB); } TEST_P(Test_Model, DetectRegionWithNmsAcrossClasses) { applyTestTag( CV_TEST_TAG_LONG, CV_TEST_TAG_MEMORY_2GB ); #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) // accuracy if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) // accuracy if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2020040000) // nGraph compilation failure if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_VERSION); if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000) if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16); #endif #if defined(INF_ENGINE_RELEASE) if (target == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X); #endif std::vector refClassIds = { 6, 11 }; std::vector refConfidences = { 0.750469f, 0.901615f }; std::vector refBoxes = { Rect2d(240, 53, 135, 72), Rect2d(58, 141, 117, 249) }; std::string img_path = _tf("dog416.png"); std::string weights_file = _tf("yolo-voc.weights", false); std::string config_file = _tf("yolo-voc.cfg"); double scale = 1.0 / 255.0; Size size{ 416, 416 }; bool swapRB = true; bool crop = false; bool nmsAcrossClasses = true; double confThreshold = 0.24; double nmsThreshold = (target == DNN_TARGET_MYRIAD) ? 0.15: 0.15; double scoreDiff = 8e-5, iouDiff = 1e-5; if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || target == DNN_TARGET_CUDA_FP16 || target == DNN_TARGET_CPU_FP16) { scoreDiff = 1e-2; iouDiff = 1.6e-2; } testDetectModel(weights_file, config_file, img_path, refClassIds, refConfidences, refBoxes, scoreDiff, iouDiff, confThreshold, nmsThreshold, size, Scalar(), scale, swapRB, crop, nmsAcrossClasses); } TEST_P(Test_Model, DetectionOutput) { #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) // Check 'backward_compatible_check || in_out_elements_equal' failed at core/src/op/reshape.cpp:427: // While validating node 'v1::Reshape bbox_pred_reshape (ave_bbox_pred_rois[0]:f32{1,8,1,1}, Constant_388[0]:i64{4}) -> (f32{?,?,?,?})' with friendly_name 'bbox_pred_reshape': // Requested output shape {1,300,8,1} is incompatible with input shape {1, 8, 1, 1} if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) // Exception: Function contains several inputs and outputs with one friendly name! (HETERO bug?) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #elif defined(INF_ENGINE_RELEASE) // FIXIT DNN_BACKEND_INFERENCE_ENGINE is misused if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16); if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD); #endif std::vector refClassIds = {7, 12}; std::vector refConfidences = {0.991359f, 0.94786f}; std::vector refBoxes = {Rect2d(491, 81, 212, 98), Rect2d(132, 223, 207, 344)}; std::string img_path = _tf("dog416.png"); std::string weights_file = _tf("resnet50_rfcn_final.caffemodel", false); std::string config_file = _tf("rfcn_pascal_voc_resnet50.prototxt"); Scalar mean = Scalar(102.9801, 115.9465, 122.7717); Size size{800, 600}; double scoreDiff = default_l1, iouDiff = 1e-5; float confThreshold = 0.8; double nmsThreshold = 0.0; if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_CUDA_FP16 || target == DNN_TARGET_CPU_FP16) { if (backend == DNN_BACKEND_OPENCV) scoreDiff = 4e-3; else scoreDiff = 2e-2; iouDiff = 1.8e-1; } #if defined(INF_ENGINE_RELEASE) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) { scoreDiff = 0.05; iouDiff = 0.08; } #endif testDetectModel(weights_file, config_file, img_path, refClassIds, refConfidences, refBoxes, scoreDiff, iouDiff, confThreshold, nmsThreshold, size, mean); } TEST_P(Test_Model, DetectionMobilenetSSD) { Mat ref = blobFromNPY(_tf("mobilenet_ssd_caffe_out.npy")); ref = ref.reshape(1, ref.size[2]); std::string img_path = _tf("street.png"); Mat frame = imread(img_path); int frameWidth = frame.cols; int frameHeight = frame.rows; std::vector refClassIds; std::vector refConfidences; std::vector refBoxes; for (int i = 0; i < ref.rows; i++) { refClassIds.emplace_back(ref.at(i, 1)); refConfidences.emplace_back(ref.at(i, 2)); int left = ref.at(i, 3) * frameWidth; int top = ref.at(i, 4) * frameHeight; int right = ref.at(i, 5) * frameWidth; int bottom = ref.at(i, 6) * frameHeight; int width = right - left + 1; int height = bottom - top + 1; refBoxes.emplace_back(left, top, width, height); } std::string weights_file = _tf("MobileNetSSD_deploy_19e3ec3.caffemodel", false); std::string config_file = _tf("MobileNetSSD_deploy_19e3ec3.prototxt"); Scalar mean = Scalar(127.5, 127.5, 127.5); double scale = 1.0 / 127.5; Size size{300, 300}; double scoreDiff = 1e-5, iouDiff = 1e-5; if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_CPU_FP16) { scoreDiff = 1.7e-2; iouDiff = 6.91e-2; } else if (target == DNN_TARGET_MYRIAD) { scoreDiff = 0.017; if (getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X) iouDiff = 0.1; } else if (target == DNN_TARGET_CUDA_FP16) { scoreDiff = 0.0028; iouDiff = 1e-2; } float confThreshold = FLT_MIN; double nmsThreshold = 0.0; testDetectModel(weights_file, config_file, img_path, refClassIds, refConfidences, refBoxes, scoreDiff, iouDiff, confThreshold, nmsThreshold, size, mean, scale); } TEST_P(Test_Model, Keypoints_pose) { if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16); if (target == DNN_TARGET_CPU_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_CPU_FP16); #ifdef HAVE_INF_ENGINE if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif Mat inp = imread(_tf("pose.png")); std::string weights = _tf("onnx/models/lightweight_pose_estimation_201912.onnx", false); float kpdata[] = { 237.65625f, 78.25f, 237.65625f, 136.9375f, 190.125f, 136.9375f, 142.59375f, 195.625f, 79.21875f, 176.0625f, 285.1875f, 117.375f, 348.5625f, 195.625f, 396.09375f, 176.0625f, 205.96875f, 313.0f, 205.96875f, 430.375f, 205.96875f, 528.1875f, 269.34375f, 293.4375f, 253.5f, 430.375f, 237.65625f, 528.1875f, 221.8125f, 58.6875f, 253.5f, 58.6875f, 205.96875f, 78.25f, 253.5f, 58.6875f }; Mat exp(18, 2, CV_32FC1, kpdata); Size size{256, 256}; float norm = 1e-4; double scale = 1.0/255; Scalar mean = Scalar(128, 128, 128); bool swapRB = false; // Ref. Range: [58.6875, 508.625] if (target == DNN_TARGET_CUDA_FP16) norm = 20; // l1 = 1.5, lInf = 20 testKeypointsModel(weights, "", inp, exp, norm, size, mean, scale, swapRB); } TEST_P(Test_Model, Keypoints_face) { #if defined(INF_ENGINE_RELEASE) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif Mat inp = imread(_tf("gray_face.png"), 0); std::string weights = _tf("onnx/models/facial_keypoints.onnx", false); Mat exp = blobFromNPY(_tf("facial_keypoints_exp.npy")); Size size{224, 224}; double scale = 1.0/255; Scalar mean = Scalar(); bool swapRB = false; // Ref. Range: [-1.1784188, 1.7758257] float norm = 1e-4; if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_CPU_FP16) norm = 5e-3; if (target == DNN_TARGET_MYRIAD) { // Myriad2: l1 = 0.0004, lInf = 0.002 // MyriadX: l1 = 0.003, lInf = 0.009 norm = 0.009; } if (target == DNN_TARGET_CUDA_FP16) norm = 0.004; // l1 = 0.0006, lInf = 0.004 testKeypointsModel(weights, "", inp, exp, norm, size, mean, scale, swapRB); } TEST_P(Test_Model, Detection_normalized) { std::string img_path = _tf("grace_hopper_227.png"); std::vector refClassIds = {15}; std::vector refConfidences = {0.999222f}; std::vector refBoxes = {Rect2d(0, 4, 227, 222)}; std::string weights_file = _tf("MobileNetSSD_deploy_19e3ec3.caffemodel", false); std::string config_file = _tf("MobileNetSSD_deploy_19e3ec3.prototxt"); Scalar mean = Scalar(127.5, 127.5, 127.5); double scale = 1.0 / 127.5; Size size{300, 300}; double scoreDiff = 1e-5, iouDiff = 1e-5; float confThreshold = FLT_MIN; double nmsThreshold = 0.0; if (target == DNN_TARGET_CUDA) { scoreDiff = 3e-4; iouDiff = 0.018; } if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || target == DNN_TARGET_CUDA_FP16 || target == DNN_TARGET_CPU_FP16) { scoreDiff = 5e-3; iouDiff = 0.09; } #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020040000) if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) { scoreDiff = 0.02; iouDiff = 0.1f; } #endif testDetectModel(weights_file, config_file, img_path, refClassIds, refConfidences, refBoxes, scoreDiff, iouDiff, confThreshold, nmsThreshold, size, mean, scale); } TEST_P(Test_Model, Segmentation) { applyTestTag( CV_TEST_TAG_MEMORY_2GB ); float norm = 0; #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) // Failed to allocate graph: NC_ERROR if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); // accuracy if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) { norm = 25.0f; // depends on OS/OpenCL version } #elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) // Failed to allocate graph: NC_ERROR if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); // cnn_network_ngraph_impl.cpp:104 Function contains several inputs and outputs with one friendly name: 'upscore2'! if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); // cnn_network_ngraph_impl.cpp:104 Function contains several inputs and outputs with one friendly name: 'upscore2'! if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #elif defined(INF_ENGINE_RELEASE) // Failed to allocate graph: NC_ERROR if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); #endif if ((backend == DNN_BACKEND_OPENCV && (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_CPU_FP16)) || (backend == DNN_BACKEND_CUDA && target == DNN_TARGET_CUDA_FP16)) { norm = 2.0f; // l1 = 0.01 lInf = 2 } std::string inp = _tf("dog416.png"); std::string weights_file = _tf("fcn8s-heavy-pascal.prototxt"); std::string config_file = _tf("fcn8s-heavy-pascal.caffemodel", false); std::string exp = _tf("segmentation_exp.png"); Size size{128, 128}; double scale = 1.0; Scalar mean = Scalar(); bool swapRB = false; testSegmentationModel(weights_file, config_file, inp, exp, norm, size, mean, scale, swapRB); } TEST_P(Test_Model, TextRecognition) { #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) // FIXIT: dnn/src/ie_ngraph.cpp:494: error: (-215:Assertion failed) !inps.empty() in function 'createNet' if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); // Node Transpose_79 was not assigned on any pointed device if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION ); #elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) // IE Exception: Ngraph operation Reshape with name 71 has dynamic output shape on 0 port, but CPU plug-in supports only static shape if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION ); #endif std::string imgPath = _tf("text_rec_test.png"); std::string weightPath = _tf("onnx/models/crnn.onnx", false); std::string seq = "welcome"; Size size{100, 32}; double scale = 1.0 / 127.5; Scalar mean = Scalar(127.5); std::string decodeType = "CTC-greedy"; std::vector vocabulary = {"0","1","2","3","4","5","6","7","8","9", "a","b","c","d","e","f","g","h","i","j","k","l","m","n","o","p","q","r","s","t","u","v","w","x","y","z"}; testTextRecognitionModel(weightPath, "", imgPath, seq, decodeType, vocabulary, size, mean, scale); } TEST_P(Test_Model, TextRecognitionWithCTCPrefixBeamSearch) { #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000) // Node Transpose_79 was not assigned on any pointed device if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION ); #elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000) // IE Exception: Ngraph operation Reshape with name 71 has dynamic output shape on 0 port, but CPU plug-in supports only static shape if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)) applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION ); #endif std::string imgPath = _tf("text_rec_test.png"); std::string weightPath = _tf("onnx/models/crnn.onnx", false); std::string seq = "welcome"; Size size{100, 32}; double scale = 1.0 / 127.5; Scalar mean = Scalar(127.5); std::string decodeType = "CTC-prefix-beam-search"; std::vector vocabulary = {"0","1","2","3","4","5","6","7","8","9", "a","b","c","d","e","f","g","h","i","j","k","l","m","n","o","p","q","r","s","t","u","v","w","x","y","z"}; testTextRecognitionModel(weightPath, "", imgPath, seq, decodeType, vocabulary, size, mean, scale); } TEST_P(Test_Model, TextDetectionByDB) { if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16); if (target == DNN_TARGET_CPU_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_CPU_FP16); std::string imgPath = _tf("text_det_test1.png"); std::string weightPathDB = _tf("onnx/models/DB_TD500_resnet50.onnx", false); std::string weightPathPPDB = _tf("onnx/models/PP_OCRv3_DB_text_det.onnx", false); // GroundTruth std::vector> gt = { { Point(142, 193), Point(136, 164), Point(213, 150), Point(219, 178) }, { Point(136, 165), Point(122, 114), Point(319, 71), Point(330, 122) } }; Size size{736, 736}; Scalar scaleDB = Scalar::all(1.0 / 255.0); Scalar meanDB = Scalar(122.67891434, 116.66876762, 104.00698793); // new mean and stddev Scalar meanPPDB = Scalar(123.675, 116.28, 103.53); Scalar stddevPPDB = Scalar(0.229, 0.224, 0.225); Scalar scalePPDB = scaleDB / stddevPPDB; float binThresh = 0.3; float polyThresh = 0.5; uint maxCandidates = 200; double unclipRatio = 2.0; { SCOPED_TRACE("Original DB"); testTextDetectionModelByDB(weightPathDB, "", imgPath, gt, binThresh, polyThresh, maxCandidates, unclipRatio, size, meanDB, scaleDB, 0.05f); } { SCOPED_TRACE("PP-OCRDBv3"); testTextDetectionModelByDB(weightPathPPDB, "", imgPath, gt, binThresh, polyThresh, maxCandidates, unclipRatio, size, meanPPDB, scalePPDB, 0.21f); } } TEST_P(Test_Model, TextDetectionByEAST) { std::string imgPath = _tf("text_det_test2.jpg"); std::string weightPath = _tf("frozen_east_text_detection.pb", false); // GroundTruth std::vector gt = { RotatedRect(Point2f(657.55f, 409.5f), Size2f(316.84f, 62.45f), -4.79) }; // Model parameters Size size{320, 320}; double scale = 1.0; Scalar mean = Scalar(123.68, 116.78, 103.94); bool swapRB = true; // Detection algorithm parameters float confThresh = 0.5; float nmsThresh = 0.4; double eps_center = 5/*pixels*/; double eps_size = 5/*pixels*/; double eps_angle = 1; if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_CUDA_FP16 || target == DNN_TARGET_MYRIAD || target == DNN_TARGET_CPU_FP16) { eps_center = 10; eps_size = 25; eps_angle = 3; } testTextDetectionModelByEAST(weightPath, "", imgPath, gt, confThresh, nmsThresh, size, mean, scale, swapRB, false/*crop*/, eps_center, eps_size, eps_angle ); } INSTANTIATE_TEST_CASE_P(/**/, Test_Model, dnnBackendsAndTargets()); }} // namespace