743 lines
28 KiB
C++
743 lines
28 KiB
C++
// 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, bool required = true)
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{
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String rootFolder = "dnn/";
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return findDataFile(rootFolder + filename, required);
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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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bool nmsAcrossClasses = 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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model.setNmsAcrossClasses(nmsAcrossClasses);
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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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void testKeypointsModel(const std::string& weights, const std::string& cfg,
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const Mat& frame, const Mat& exp, 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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std::vector<Point2f> points;
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KeypointsModel 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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points = model.estimate(frame, 0.5);
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Mat out = Mat(points).reshape(1);
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normAssert(exp, out, "", norm, norm);
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}
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void testSegmentationModel(const std::string& weights_file, const std::string& config_file,
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const std::string& inImgPath, const std::string& outImgPath,
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float norm, 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(inImgPath);
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Mat mask;
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Mat exp = imread(outImgPath, 0);
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SegmentationModel model(weights_file, config_file);
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model.setInputSize(size).setInputMean(mean).setInputScale(scale)
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.setInputSwapRB(swapRB).setInputCrop(crop);
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model.segment(frame, mask);
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normAssert(mask, exp, "", norm, norm);
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}
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void testTextRecognitionModel(const std::string& weights, const std::string& cfg,
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const std::string& imgPath, const std::string& seq,
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const std::string& decodeType, const std::vector<std::string>& vocabulary,
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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, IMREAD_GRAYSCALE);
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TextRecognitionModel model(weights, cfg);
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model.setDecodeType(decodeType)
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.setVocabulary(vocabulary)
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.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::string result = model.recognize(frame);
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EXPECT_EQ(result, seq) << "Full frame: " << imgPath;
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std::vector<Rect> rois;
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rois.push_back(Rect(0, 0, frame.cols, frame.rows));
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rois.push_back(Rect(0, 0, frame.cols, frame.rows)); // twice
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std::vector<std::string> results;
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model.recognize(frame, rois, results);
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EXPECT_EQ((size_t)2u, results.size()) << "ROI: " << imgPath;
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EXPECT_EQ(results[0], seq) << "ROI[0]: " << imgPath;
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EXPECT_EQ(results[1], seq) << "ROI[1]: " << imgPath;
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}
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void testTextDetectionModelByDB(const std::string& weights, const std::string& cfg,
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const std::string& imgPath, const std::vector<std::vector<Point>>& gt,
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float binThresh, float polyThresh,
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uint maxCandidates, double unclipRatio,
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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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TextDetectionModel_DB model(weights, cfg);
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model.setBinaryThreshold(binThresh)
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.setPolygonThreshold(polyThresh)
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.setUnclipRatio(unclipRatio)
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.setMaxCandidates(maxCandidates)
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.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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// 1. Check common TextDetectionModel API through RotatedRect
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std::vector<cv::RotatedRect> results;
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model.detectTextRectangles(frame, results);
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EXPECT_GT(results.size(), (size_t)0);
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std::vector< std::vector<Point> > contours;
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for (size_t i = 0; i < results.size(); i++)
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{
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const RotatedRect& box = results[i];
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Mat contour;
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boxPoints(box, contour);
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std::vector<Point> contour2i(4);
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for (int i = 0; i < 4; i++)
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{
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contour2i[i].x = cvRound(contour.at<float>(i, 0));
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contour2i[i].y = cvRound(contour.at<float>(i, 1));
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}
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contours.push_back(contour2i);
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}
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#if 0 // test debug
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Mat result = frame.clone();
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drawContours(result, contours, -1, Scalar(0, 0, 255), 1);
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imshow("result", result); // imwrite("result.png", result);
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waitKey(0);
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#endif
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normAssertTextDetections(gt, contours, "", 0.05f);
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// 2. Check quadrangle-based API
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// std::vector< std::vector<Point> > contours;
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model.detect(frame, contours);
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#if 0 // test debug
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Mat result = frame.clone();
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drawContours(result, contours, -1, Scalar(0, 0, 255), 1);
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imshow("result_contours", result); // imwrite("result_contours.png", result);
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waitKey(0);
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#endif
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normAssertTextDetections(gt, contours, "", 0.05f);
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}
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void testTextDetectionModelByEAST(
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const std::string& weights, const std::string& cfg,
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const std::string& imgPath, const std::vector<RotatedRect>& gt,
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float confThresh, float nmsThresh,
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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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double eps_center = 5/*pixels*/, double eps_size = 5/*pixels*/, double eps_angle = 1
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)
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{
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checkBackend();
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Mat frame = imread(imgPath);
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TextDetectionModel_EAST model(weights, cfg);
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model.setConfidenceThreshold(confThresh)
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.setNMSThreshold(nmsThresh)
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.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<cv::RotatedRect> results;
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model.detectTextRectangles(frame, results);
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EXPECT_EQ(results.size(), (size_t)1);
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for (size_t i = 0; i < results.size(); i++)
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{
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const RotatedRect& box = results[i];
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#if 0 // test debug
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Mat contour;
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boxPoints(box, contour);
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std::vector<Point> contour2i(4);
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for (int i = 0; i < 4; i++)
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{
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contour2i[i].x = cvRound(contour.at<float>(i, 0));
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contour2i[i].y = cvRound(contour.at<float>(i, 1));
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}
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std::vector< std::vector<Point> > contours;
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contours.push_back(contour2i);
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Mat result = frame.clone();
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drawContours(result, contours, -1, Scalar(0, 0, 255), 1);
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imshow("result", result); //imwrite("result.png", result);
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waitKey(0);
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#endif
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const RotatedRect& gtBox = gt[i];
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EXPECT_NEAR(box.center.x, gtBox.center.x, eps_center);
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EXPECT_NEAR(box.center.y, gtBox.center.y, eps_center);
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EXPECT_NEAR(box.size.width, gtBox.size.width, eps_size);
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EXPECT_NEAR(box.size.height, gtBox.size.height, eps_size);
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EXPECT_NEAR(box.angle, gtBox.angle, eps_angle);
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}
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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", false);
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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(
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CV_TEST_TAG_LONG,
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CV_TEST_TAG_MEMORY_2GB
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);
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
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// accuracy
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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#endif
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2020040000) // nGraph compilation failure
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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#endif
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
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// FIXIT DNN_BACKEND_INFERENCE_ENGINE is misused
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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", false);
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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 nmsThreshold = (target == DNN_TARGET_MYRIAD) ? 0.397 : 0.4;
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double scoreDiff = 8e-5, iouDiff = 1e-5;
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if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || target == DNN_TARGET_CUDA_FP16)
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{
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scoreDiff = 1e-2;
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iouDiff = 1.6e-2;
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}
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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, DetectRegionWithNmsAcrossClasses)
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{
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applyTestTag(
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CV_TEST_TAG_LONG,
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CV_TEST_TAG_MEMORY_2GB
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);
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
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// accuracy
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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#endif
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2020040000) // nGraph compilation failure
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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#endif
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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, 11 };
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std::vector<float> refConfidences = { 0.750469f, 0.901615f };
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std::vector<Rect2d> refBoxes = { Rect2d(240, 53, 135, 72),
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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", false);
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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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bool crop = false;
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bool nmsAcrossClasses = true;
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double confThreshold = 0.24;
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double nmsThreshold = (target == DNN_TARGET_MYRIAD) ? 0.15: 0.15;
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double scoreDiff = 8e-5, iouDiff = 1e-5;
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if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || target == DNN_TARGET_CUDA_FP16)
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{
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scoreDiff = 1e-2;
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iouDiff = 1.6e-2;
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}
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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, crop,
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nmsAcrossClasses);
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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) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
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// Exception: Function contains several inputs and outputs with one friendly name! (HETERO bug?)
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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#endif
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#if defined(INF_ENGINE_RELEASE)
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// FIXIT DNN_BACKEND_INFERENCE_ENGINE is misused
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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", false);
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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)
|
|
{
|
|
if (backend == DNN_BACKEND_OPENCV)
|
|
scoreDiff = 4e-3;
|
|
else
|
|
scoreDiff = 2e-2;
|
|
iouDiff = 1.8e-1;
|
|
}
|
|
|
|
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<int> refClassIds;
|
|
std::vector<float> refConfidences;
|
|
std::vector<Rect2d> refBoxes;
|
|
for (int i = 0; i < ref.rows; i++)
|
|
{
|
|
refClassIds.emplace_back(ref.at<float>(i, 1));
|
|
refConfidences.emplace_back(ref.at<float>(i, 2));
|
|
int left = ref.at<float>(i, 3) * frameWidth;
|
|
int top = ref.at<float>(i, 4) * frameHeight;
|
|
int right = ref.at<float>(i, 5) * frameWidth;
|
|
int bottom = ref.at<float>(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.caffemodel", false);
|
|
std::string config_file = _tf("MobileNetSSD_deploy.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)
|
|
{
|
|
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 = 0.002;
|
|
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);
|
|
#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)
|
|
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<int> refClassIds = {15};
|
|
std::vector<float> refConfidences = {0.999222f};
|
|
std::vector<Rect2d> refBoxes = {Rect2d(0, 4, 227, 222)};
|
|
|
|
std::string weights_file = _tf("MobileNetSSD_deploy.caffemodel", false);
|
|
std::string config_file = _tf("MobileNetSSD_deploy.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)
|
|
{
|
|
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
|
|
);
|
|
|
|
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};
|
|
float norm = 0;
|
|
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(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<std::string> 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(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<std::string> 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);
|
|
|
|
std::string imgPath = _tf("text_det_test1.png");
|
|
std::string weightPath = _tf("onnx/models/DB_TD500_resnet50.onnx", false);
|
|
|
|
// GroundTruth
|
|
std::vector<std::vector<Point>> 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};
|
|
double scale = 1.0 / 255.0;
|
|
Scalar mean = Scalar(122.67891434, 116.66876762, 104.00698793);
|
|
|
|
float binThresh = 0.3;
|
|
float polyThresh = 0.5;
|
|
uint maxCandidates = 200;
|
|
double unclipRatio = 2.0;
|
|
|
|
testTextDetectionModelByDB(weightPath, "", imgPath, gt, binThresh, polyThresh, maxCandidates, unclipRatio, size, mean, scale);
|
|
}
|
|
|
|
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<RotatedRect> 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)
|
|
{
|
|
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
|