// 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" namespace opencv_test { namespace { #define SHOW_DEBUG_IMAGES 0 static void warpFrame(const Mat& image, const Mat& depth, const Mat& rvec, const Mat& tvec, const Mat& K, Mat& warpedImage, Mat& warpedDepth) { CV_Assert(!image.empty()); CV_Assert(image.type() == CV_8UC1); CV_Assert(depth.size() == image.size()); CV_Assert(depth.type() == CV_32FC1); CV_Assert(!rvec.empty()); CV_Assert(rvec.total() == 3); CV_Assert(rvec.type() == CV_64FC1); CV_Assert(!tvec.empty()); CV_Assert(tvec.size() == Size(1, 3)); CV_Assert(tvec.type() == CV_64FC1); warpedImage.create(image.size(), CV_8UC1); warpedImage = Scalar(0); warpedDepth.create(image.size(), CV_32FC1); warpedDepth = Scalar(FLT_MAX); Mat cloud; depthTo3d(depth, K, cloud); Mat Rt = Mat::eye(4, 4, CV_64FC1); { Mat R, dst; cv::Rodrigues(rvec, R); dst = Rt(Rect(0,0,3,3)); R.copyTo(dst); dst = Rt(Rect(3,0,1,3)); tvec.copyTo(dst); } Mat warpedCloud, warpedImagePoints; perspectiveTransform(cloud, warpedCloud, Rt); projectPoints(warpedCloud.reshape(3, 1), Mat(3,1,CV_32FC1, Scalar(0)), Mat(3,1,CV_32FC1, Scalar(0)), K, Mat(1,5,CV_32FC1, Scalar(0)), warpedImagePoints); warpedImagePoints = warpedImagePoints.reshape(2, cloud.rows); Rect r(0, 0, image.cols, image.rows); for(int y = 0; y < cloud.rows; y++) { for(int x = 0; x < cloud.cols; x++) { Point p = warpedImagePoints.at(y,x); if(r.contains(p)) { float curDepth = warpedDepth.at(p.y, p.x); float newDepth = warpedCloud.at(y, x).z; if(newDepth < curDepth && newDepth > 0) { warpedImage.at(p.y, p.x) = image.at(y,x); warpedDepth.at(p.y, p.x) = newDepth; } } } } warpedDepth.setTo(std::numeric_limits::quiet_NaN(), warpedDepth > 100); } static void dilateFrame(Mat& image, Mat& depth) { CV_Assert(!image.empty()); CV_Assert(image.type() == CV_8UC1); CV_Assert(!depth.empty()); CV_Assert(depth.type() == CV_32FC1); CV_Assert(depth.size() == image.size()); Mat mask(image.size(), CV_8UC1, Scalar(255)); for(int y = 0; y < depth.rows; y++) for(int x = 0; x < depth.cols; x++) if(cvIsNaN(depth.at(y,x)) || depth.at(y,x) > 10 || depth.at(y,x) <= FLT_EPSILON) mask.at(y,x) = 0; image.setTo(255, ~mask); Mat minImage; erode(image, minImage, Mat()); image.setTo(0, ~mask); Mat maxImage; dilate(image, maxImage, Mat()); depth.setTo(FLT_MAX, ~mask); Mat minDepth; erode(depth, minDepth, Mat()); depth.setTo(0, ~mask); Mat maxDepth; dilate(depth, maxDepth, Mat()); Mat dilatedMask; dilate(mask, dilatedMask, Mat(), Point(-1,-1), 1); for(int y = 0; y < depth.rows; y++) for(int x = 0; x < depth.cols; x++) if(!mask.at(y,x) && dilatedMask.at(y,x)) { image.at(y,x) = static_cast(0.5f * (static_cast(minImage.at(y,x)) + static_cast(maxImage.at(y,x)))); depth.at(y,x) = 0.5f * (minDepth.at(y,x) + maxDepth.at(y,x)); } } class OdometryTest { public: OdometryTest(const Ptr& _odometry, double _maxError1, double _maxError5, double _idError = DBL_EPSILON) : odometry(_odometry), maxError1(_maxError1), maxError5(_maxError5), idError(_idError) { } void readData(Mat& image, Mat& depth) const; static Mat getCameraMatrix() { float fx = 525.0f, // default fy = 525.0f, cx = 319.5f, cy = 239.5f; Matx33f K(fx, 0, cx, 0, fy, cy, 0, 0, 1); return Mat(K); } static void generateRandomTransformation(Mat& R, Mat& t); void run(); void checkUMats(); Ptr odometry; double maxError1; double maxError5; double idError; }; void OdometryTest::readData(Mat& image, Mat& depth) const { std::string dataPath = cvtest::TS::ptr()->get_data_path(); std::string imageFilename = dataPath + "/cv/rgbd/rgb.png"; std::string depthFilename = dataPath + "/cv/rgbd/depth.png"; image = imread(imageFilename, 0); depth = imread(depthFilename, -1); if(image.empty()) { FAIL() << "Image " << imageFilename.c_str() << " can not be read" << std::endl; } if(depth.empty()) { FAIL() << "Depth" << depthFilename.c_str() << "can not be read" << std::endl; } CV_DbgAssert(image.type() == CV_8UC1); CV_DbgAssert(depth.type() == CV_16UC1); { Mat depth_flt; depth.convertTo(depth_flt, CV_32FC1, 1.f/5000.f); depth_flt.setTo(std::numeric_limits::quiet_NaN(), depth_flt < FLT_EPSILON); depth = depth_flt; } } void OdometryTest::generateRandomTransformation(Mat& rvec, Mat& tvec) { const float maxRotation = (float)(3.f / 180.f * CV_PI); //rad const float maxTranslation = 0.02f; //m RNG& rng = theRNG(); rvec.create(3, 1, CV_64FC1); tvec.create(3, 1, CV_64FC1); randu(rvec, Scalar(-1000), Scalar(1000)); normalize(rvec, rvec, rng.uniform(0.007f, maxRotation)); randu(tvec, Scalar(-1000), Scalar(1000)); normalize(tvec, tvec, rng.uniform(0.008f, maxTranslation)); } void OdometryTest::checkUMats() { Mat K = getCameraMatrix(); Mat image, depth; readData(image, depth); odometry->setCameraMatrix(K); Mat calcRt; UMat uimage, udepth, umask; image.copyTo(uimage); depth.copyTo(udepth); Mat(image.size(), CV_8UC1, Scalar(255)).copyTo(umask); bool isComputed = odometry->compute(uimage, udepth, umask, uimage, udepth, umask, calcRt); ASSERT_TRUE(isComputed); double diff = cv::norm(calcRt, Mat::eye(4, 4, CV_64FC1)); if (diff > idError) { FAIL() << "Incorrect transformation between the same frame (not the identity matrix), diff = " << diff << std::endl; } } void OdometryTest::run() { Mat K = getCameraMatrix(); Mat image, depth; readData(image, depth); odometry->setCameraMatrix(K); Mat calcRt; // 1. Try to find Rt between the same frame (try masks also). Mat mask(image.size(), CV_8UC1, Scalar(255)); bool isComputed = odometry->compute(image, depth, mask, image, depth, mask, calcRt); if(!isComputed) { FAIL() << "Can not find Rt between the same frame" << std::endl; } double diff = cv::norm(calcRt, Mat::eye(4,4,CV_64FC1)); if(diff > idError) { FAIL() << "Incorrect transformation between the same frame (not the identity matrix), diff = " << diff << std::endl; } // 2. Generate random rigid body motion in some ranges several times (iterCount). // On each iteration an input frame is warped using generated transformation. // Odometry is run on the following pair: the original frame and the warped one. // Comparing a computed transformation with an applied one we compute 2 errors: // better_1time_count - count of poses which error is less than ground truth pose, // better_5times_count - count of poses which error is 5 times less than ground truth pose. int iterCount = 100; int better_1time_count = 0; int better_5times_count = 0; for(int iter = 0; iter < iterCount; iter++) { Mat rvec, tvec; generateRandomTransformation(rvec, tvec); Mat warpedImage, warpedDepth; warpFrame(image, depth, rvec, tvec, K, warpedImage, warpedDepth); dilateFrame(warpedImage, warpedDepth); // due to inaccuracy after warping isComputed = odometry->compute(image, depth, mask, warpedImage, warpedDepth, mask, calcRt); if(!isComputed) continue; Mat calcR = calcRt(Rect(0,0,3,3)), calcRvec; cv::Rodrigues(calcR, calcRvec); calcRvec = calcRvec.reshape(rvec.channels(), rvec.rows); Mat calcTvec = calcRt(Rect(3,0,1,3)); #if SHOW_DEBUG_IMAGES imshow("image", image); imshow("warpedImage", warpedImage); Mat resultImage, resultDepth; warpFrame(image, depth, calcRvec, calcTvec, K, resultImage, resultDepth); imshow("resultImage", resultImage); waitKey(); #endif // compare rotation double rdiffnorm = cv::norm(rvec - calcRvec), rnorm = cv::norm(rvec); double tdiffnorm = cv::norm(tvec - calcTvec), tnorm = cv::norm(tvec); if(rdiffnorm < rnorm && tdiffnorm < tnorm) better_1time_count++; if(5. * rdiffnorm < rnorm && 5 * tdiffnorm < tnorm) better_5times_count++; CV_LOG_INFO(NULL, "Iter " << iter); CV_LOG_INFO(NULL, "rdiffnorm " << rdiffnorm << "; rnorm " << rnorm); CV_LOG_INFO(NULL, "tdiffnorm " << tdiffnorm << "; tnorm " << tnorm); CV_LOG_INFO(NULL, "better_1time_count " << better_1time_count << "; better_5time_count " << better_5times_count); } if(static_cast(better_1time_count) < maxError1 * static_cast(iterCount)) { FAIL() << "Incorrect count of accurate poses [1st case]: " << static_cast(better_1time_count) << " / " << maxError1 * static_cast(iterCount) << std::endl; } if(static_cast(better_5times_count) < maxError5 * static_cast(iterCount)) { FAIL() << "Incorrect count of accurate poses [2nd case]: " << static_cast(better_5times_count) << " / " << maxError5 * static_cast(iterCount) << std::endl; } } /****************************************************************************************\ * Tests registrations * \****************************************************************************************/ TEST(RGBD_Odometry_Rgbd, algorithmic) { OdometryTest test(cv::Odometry::createFromName("RgbdOdometry"), 0.99, 0.89); test.run(); } TEST(RGBD_Odometry_ICP, algorithmic) { OdometryTest test(cv::Odometry::createFromName("ICPOdometry"), 0.99, 0.99); test.run(); } TEST(RGBD_Odometry_RgbdICP, algorithmic) { OdometryTest test(cv::Odometry::createFromName("RgbdICPOdometry"), 0.99, 0.99); test.run(); } TEST(RGBD_Odometry_FastICP, algorithmic) { OdometryTest test(cv::Odometry::createFromName("FastICPOdometry"), 0.99, 0.99, FLT_EPSILON); test.run(); } TEST(RGBD_Odometry_Rgbd, UMats) { OdometryTest test(cv::Odometry::createFromName("RgbdOdometry"), 0.99, 0.89); test.checkUMats(); } TEST(RGBD_Odometry_ICP, UMats) { OdometryTest test(cv::Odometry::createFromName("ICPOdometry"), 0.99, 0.99); test.checkUMats(); } TEST(RGBD_Odometry_RgbdICP, UMats) { OdometryTest test(cv::Odometry::createFromName("RgbdICPOdometry"), 0.99, 0.99); test.checkUMats(); } TEST(RGBD_Odometry_FastICP, UMats) { OdometryTest test(cv::Odometry::createFromName("FastICPOdometry"), 0.99, 0.99, FLT_EPSILON); test.checkUMats(); } /****************************************************************************************\ * Depth to 3d tests * \****************************************************************************************/ TEST(RGBD_DepthTo3d, compute) { // K from a VGA Kinect Mat K = OdometryTest::getCameraMatrix(); // Create a random depth image RNG rng; Mat_ depth(480, 640); rng.fill(depth, RNG::UNIFORM, 0, 100); // Create some 3d points on the plane int rows = depth.rows, cols = depth.cols; Mat_ points3d; depthTo3d(depth, K, points3d); // Make sure the points belong to the plane Mat points = points3d.reshape(1, rows * cols); Mat image_points; Mat rvec; cv::Rodrigues(Mat::eye(3, 3, CV_32F), rvec); Mat tvec = (Mat_(1, 3) << 0, 0, 0); projectPoints(points, rvec, tvec, K, Mat(), image_points); image_points = image_points.reshape(2, rows); float avg_diff = 0; for (int y = 0; y < rows; ++y) for (int x = 0; x < cols; ++x) avg_diff += (float)cv::norm(image_points.at(y, x) - Vec2f((float)x, (float)y)); // Verify the function works ASSERT_LE(avg_diff / rows / cols, 1e-4) << "Average error for ground truth is: " << (avg_diff / rows / cols); } }} // namespace