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22d64ae08f
opencv/modules/dnn/test/test_model.cpp
Wenqing Zhang 22d64ae08f
Merge pull request #17570 from HannibalAPE:text_det_recog_demo 
[GSoC] High Level API and Samples for Scene Text Detection and Recognition

* APIs and samples for scene text detection and recognition

* update APIs and tutorial for Text Detection and Recognition

* API updates:
(1) put decodeType into struct Voc
(2) optimize the post-processing of DB

* sample update:
(1) add transformation into scene_text_spotting.cpp
(2) modify text_detection.cpp with API update

* update tutorial

* simplify text recognition API
update tutorial

* update impl usage in recognize() and detect()

* dnn: refactoring public API of TextRecognitionModel/TextDetectionModel

* update provided models
update opencv.bib

* dnn: adjust text rectangle angle

* remove points ordering operation in model.cpp

* update gts of DB test in test_model.cpp

* dnn: ensure to keep text rectangle angle

- avoid 90/180 degree turns

* dnn(text): use quadrangle result in TextDetectionModel API

* dnn: update Text Detection API
(1) keep points' order consistent with (bl, tl, tr, br) in unclip
(2) update contourScore with boundingRect
2020-12-03 18:47:40 +00:00

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// 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 <opencv2/dnn/shape_utils.hpp>
#include "npy_blob.hpp"
namespace opencv_test { namespace {
template<typename TString>
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<int>& refClassIds,
const std::vector<float>& refConfidences,
const std::vector<Rect2d>& 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<int> classIds;
std::vector<float> confidences;
std::vector<Rect> boxes;
model.detect(frame, classIds, confidences, boxes, confThreshold, nmsThreshold);
std::vector<Rect2d> 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<int, float> 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<int, float> 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<Point2f> 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.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<std::string>& 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<Rect> rois;
rois.push_back(Rect(0, 0, frame.cols, frame.rows));
rois.push_back(Rect(0, 0, frame.cols, frame.rows)); // twice
std::vector<std::string> 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<std::vector<Point>>& gt,
float binThresh, float polyThresh,
uint maxCandidates, double unclipRatio,
const Size& size = {-1, -1}, Scalar mean = Scalar(),
double scale = 1.0, 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<cv::RotatedRect> results;
model.detectTextRectangles(frame, results);
EXPECT_GT(results.size(), (size_t)0);
std::vector< std::vector<Point> > contours;
for (size_t i = 0; i < results.size(); i++)
{
const RotatedRect& box = results[i];
Mat contour;
boxPoints(box, contour);
std::vector<Point> contour2i(4);
for (int i = 0; i < 4; i++)
{
contour2i[i].x = cvRound(contour.at<float>(i, 0));
contour2i[i].y = cvRound(contour.at<float>(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, "", 0.05f);
// 2. Check quadrangle-based API
// std::vector< std::vector<Point> > 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, "", 0.05f);
}
void testTextDetectionModelByEAST(const std::string& weights, const std::string& cfg,
const std::string& imgPath, const std::vector<RotatedRect>& gt,
float confThresh, float nmsThresh,
const Size& size = {-1, -1}, Scalar mean = Scalar(),
double scale = 1.0, bool swapRB = false, bool crop = false)
{
const double EPS_PIXELS = 3;
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<cv::RotatedRect> 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<Point> contour2i(4);
for (int i = 0; i < 4; i++)
{
contour2i[i].x = cvRound(contour.at<float>(i, 0));
contour2i[i].y = cvRound(contour.at<float>(i, 1));
}
std::vector< std::vector<Point> > 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_PIXELS);
EXPECT_NEAR(box.center.y, gtBox.center.y, EPS_PIXELS);
EXPECT_NEAR(box.size.width, gtBox.size.width, EPS_PIXELS);
EXPECT_NEAR(box.size.height, gtBox.size.height, EPS_PIXELS);
EXPECT_NEAR(box.angle, gtBox.angle, 1);
}
}
};
TEST_P(Test_Model, Classify)
{
std::pair<int, float> 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_1GB);
#if 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);
#endif
#if 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<int> refClassIds = {6, 1, 11};
std::vector<float> refConfidences = {0.750469f, 0.780879f, 0.901615f};
std::vector<Rect2d> 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)
{
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_1GB);
#if 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);
#endif
#if 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<int> refClassIds = { 6, 11 };
std::vector<float> refConfidences = { 0.750469f, 0.901615f };
std::vector<Rect2d> 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)
{
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)
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<int> refClassIds = {7, 12};
std::vector<float> refConfidences = {0.991359f, 0.94786f};
std::vector<Rect2d> 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)
{
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 = 1.7e-2;
if (getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
iouDiff = 6.91e-2;
}
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)
{
iouDiff = 0.095f;
}
#endif
testDetectModel(weights_file, config_file, img_path, refClassIds, refConfidences, refBoxes,
scoreDiff, iouDiff, confThreshold, nmsThreshold, size, mean, scale);
}
TEST_P(Test_Model, Segmentation)
{
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 (target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
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, 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)
{
if (target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
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;
testTextDetectionModelByEAST(weightPath, "", imgPath, gt, confThresh, nmsThresh, size, mean, scale, swapRB);
}
INSTANTIATE_TEST_CASE_P(/**/, Test_Model, dnnBackendsAndTargets());
}} // namespace
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