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Add high level API (Merge pull request #14780)
* Add high level API * Fix Model * Add DetectionModel * Add ClassificationModel * Fix classify * Add python test * Fix pytest * Fix comments to review * Fix detect * Fix docs * Modify DetectionOutput postprocessing * Fix test * Extract ref boxes * Fix draw rect * fix test * Add rect wrap * Fix wrap * Fix detect * Fix Rect wrap * Fix OCL_FP16 * Fix MyriadX * Fix nms * Fix NMS * Fix coords
This commit is contained in:
Lubov Batanina
committed by
Maksim Shabunin
Maksim Shabunin
parent
f482050f9a
commit
778f42ad34
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// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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#include "test_precomp.hpp"
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#include <opencv2/dnn/shape_utils.hpp>
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#include "npy_blob.hpp"
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namespace opencv_test { namespace {
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template<typename TString>
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static std::string _tf(TString filename)
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{
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String rootFolder = "dnn/";
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return findDataFile(rootFolder + filename);
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}
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class Test_Model : public DNNTestLayer
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{
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public:
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void testDetectModel(const std::string& weights, const std::string& cfg,
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const std::string& imgPath, const std::vector<int>& refClassIds,
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const std::vector<float>& refConfidences,
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const std::vector<Rect2d>& refBoxes,
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double scoreDiff, double iouDiff,
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double confThreshold = 0.24, double nmsThreshold = 0.0,
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const Size& size = {-1, -1}, Scalar mean = Scalar(),
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double scale = 1.0, bool swapRB = false, bool crop = false)
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{
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checkBackend();
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Mat frame = imread(imgPath);
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DetectionModel model(weights, cfg);
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model.setInputSize(size).setInputMean(mean).setInputScale(scale)
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.setInputSwapRB(swapRB).setInputCrop(crop);
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model.setPreferableBackend(backend);
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model.setPreferableTarget(target);
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std::vector<int> classIds;
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std::vector<float> confidences;
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std::vector<Rect> boxes;
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model.detect(frame, classIds, confidences, boxes, confThreshold, nmsThreshold);
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std::vector<Rect2d> boxesDouble(boxes.size());
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for (int i = 0; i < boxes.size(); i++) {
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boxesDouble[i] = boxes[i];
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}
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normAssertDetections(refClassIds, refConfidences, refBoxes, classIds,
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confidences, boxesDouble, "",
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confThreshold, scoreDiff, iouDiff);
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}
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void testClassifyModel(const std::string& weights, const std::string& cfg,
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const std::string& imgPath, std::pair<int, float> ref, float norm,
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const Size& size = {-1, -1}, Scalar mean = Scalar(),
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double scale = 1.0, bool swapRB = false, bool crop = false)
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{
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checkBackend();
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Mat frame = imread(imgPath);
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ClassificationModel model(weights, cfg);
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model.setInputSize(size).setInputMean(mean).setInputScale(scale)
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.setInputSwapRB(swapRB).setInputCrop(crop);
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std::pair<int, float> prediction = model.classify(frame);
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EXPECT_EQ(prediction.first, ref.first);
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ASSERT_NEAR(prediction.second, ref.second, norm);
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}
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};
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TEST_P(Test_Model, Classify)
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{
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std::pair<int, float> ref(652, 0.641789);
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std::string img_path = _tf("grace_hopper_227.png");
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std::string config_file = _tf("bvlc_alexnet.prototxt");
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std::string weights_file = _tf("bvlc_alexnet.caffemodel");
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Size size{227, 227};
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float norm = 1e-4;
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testClassifyModel(weights_file, config_file, img_path, ref, norm, size);
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}
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TEST_P(Test_Model, DetectRegion)
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{
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applyTestTag(CV_TEST_TAG_LONG, CV_TEST_TAG_MEMORY_1GB);
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
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if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
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#endif
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#if defined(INF_ENGINE_RELEASE)
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if (target == DNN_TARGET_MYRIAD
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&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
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#endif
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std::vector<int> refClassIds = {6, 1, 11};
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std::vector<float> refConfidences = {0.750469f, 0.780879f, 0.901615f};
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std::vector<Rect2d> refBoxes = {Rect2d(240, 53, 135, 72),
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Rect2d(112, 109, 192, 200),
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Rect2d(58, 141, 117, 249)};
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std::string img_path = _tf("dog416.png");
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std::string weights_file = _tf("yolo-voc.weights");
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std::string config_file = _tf("yolo-voc.cfg");
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double scale = 1.0 / 255.0;
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Size size{416, 416};
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bool swapRB = true;
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double confThreshold = 0.24;
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double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 1e-2 : 8e-5;
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double iouDiff = (target == DNN_TARGET_MYRIAD || target == DNN_TARGET_OPENCL_FP16) ? 1.6e-2 : 1e-5;
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double nmsThreshold = (target == DNN_TARGET_MYRIAD) ? 0.397 : 0.4;
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testDetectModel(weights_file, config_file, img_path, refClassIds, refConfidences,
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refBoxes, scoreDiff, iouDiff, confThreshold, nmsThreshold, size,
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Scalar(), scale, swapRB);
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}
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TEST_P(Test_Model, DetectionOutput)
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{
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#if defined(INF_ENGINE_RELEASE)
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if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
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if (target == DNN_TARGET_MYRIAD)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
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#endif
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std::vector<int> refClassIds = {7, 12};
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std::vector<float> refConfidences = {0.991359f, 0.94786f};
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std::vector<Rect2d> refBoxes = {Rect2d(491, 81, 212, 98),
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Rect2d(132, 223, 207, 344)};
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std::string img_path = _tf("dog416.png");
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std::string weights_file = _tf("resnet50_rfcn_final.caffemodel");
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std::string config_file = _tf("rfcn_pascal_voc_resnet50.prototxt");
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Scalar mean = Scalar(102.9801, 115.9465, 122.7717);
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Size size{800, 600};
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double scoreDiff = (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) ?
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4e-3 : default_l1;
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double iouDiff = (target == DNN_TARGET_OPENCL_FP16) ? 1.8e-1 : 1e-5;
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float confThreshold = 0.8;
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double nmsThreshold = 0.0;
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testDetectModel(weights_file, config_file, img_path, refClassIds, refConfidences, refBoxes,
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scoreDiff, iouDiff, confThreshold, nmsThreshold, size, mean);
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}
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TEST_P(Test_Model, DetectionMobilenetSSD)
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{
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Mat ref = blobFromNPY(_tf("mobilenet_ssd_caffe_out.npy"));
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ref = ref.reshape(1, ref.size[2]);
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std::string img_path = _tf("street.png");
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Mat frame = imread(img_path);
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int frameWidth = frame.cols;
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int frameHeight = frame.rows;
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std::vector<int> refClassIds;
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std::vector<float> refConfidences;
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std::vector<Rect2d> refBoxes;
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for (int i = 0; i < ref.rows; i++)
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{
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refClassIds.emplace_back(ref.at<float>(i, 1));
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refConfidences.emplace_back(ref.at<float>(i, 2));
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int left = ref.at<float>(i, 3) * frameWidth;
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int top = ref.at<float>(i, 4) * frameHeight;
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int right = ref.at<float>(i, 5) * frameWidth;
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int bottom = ref.at<float>(i, 6) * frameHeight;
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int width = right - left + 1;
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int height = bottom - top + 1;
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refBoxes.emplace_back(left, top, width, height);
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}
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std::string weights_file = _tf("MobileNetSSD_deploy.caffemodel");
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std::string config_file = _tf("MobileNetSSD_deploy.prototxt");
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Scalar mean = Scalar(127.5, 127.5, 127.5);
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double scale = 1.0 / 127.5;
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Size size{300, 300};
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double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 1.7e-2 : 1e-5;
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double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || (target == DNN_TARGET_MYRIAD &&
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getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)) ? 6.91e-2 : 1e-5;
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float confThreshold = FLT_MIN;
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double nmsThreshold = 0.0;
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testDetectModel(weights_file, config_file, img_path, refClassIds, refConfidences, refBoxes,
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scoreDiff, iouDiff, confThreshold, nmsThreshold, size, mean, scale);
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}
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INSTANTIATE_TEST_CASE_P(/**/, Test_Model, dnnBackendsAndTargets());
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}} // namespace
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