// (C) Copyright 2017, Google Inc. // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // http://www.apache.org/licenses/LICENSE-2.0 // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. #include "linlsq.h" #include "include_gunit.h" namespace tesseract { class LLSQTest : public testing::Test { protected: void SetUp() { std::locale::global(std::locale("")); } public: void TearDown() {} void ExpectCorrectLine(const LLSQ &llsq, double m, double c, double rms, double pearson, double tolerance) { EXPECT_NEAR(m, llsq.m(), tolerance); EXPECT_NEAR(c, llsq.c(llsq.m()), tolerance); EXPECT_NEAR(rms, llsq.rms(llsq.m(), llsq.c(llsq.m())), tolerance); EXPECT_NEAR(pearson, llsq.pearson(), tolerance); } FCOORD PtsMean(const std::vector &pts) { FCOORD total(0, 0); for (const auto &p : pts) { total += p; } return (pts.size() > 0) ? total / pts.size() : total; } void VerifyRmsOrth(const std::vector &pts, const FCOORD &orth) { LLSQ llsq; FCOORD xavg = PtsMean(pts); FCOORD nvec = !orth; nvec.normalise(); double expected_answer = 0; for (const auto &p : pts) { llsq.add(p.x(), p.y()); double dot = nvec % (p - xavg); expected_answer += dot * dot; } expected_answer /= pts.size(); expected_answer = sqrt(expected_answer); EXPECT_NEAR(expected_answer, llsq.rms_orth(orth), 0.0001); } void ExpectCorrectVector(const LLSQ &llsq, FCOORD correct_mean_pt, FCOORD correct_vector, float tolerance) { FCOORD mean_pt = llsq.mean_point(); FCOORD vector = llsq.vector_fit(); EXPECT_NEAR(correct_mean_pt.x(), mean_pt.x(), tolerance); EXPECT_NEAR(correct_mean_pt.y(), mean_pt.y(), tolerance); EXPECT_NEAR(correct_vector.x(), vector.x(), tolerance); EXPECT_NEAR(correct_vector.y(), vector.y(), tolerance); } }; // Tests a simple baseline-style normalization. TEST_F(LLSQTest, BasicLines) { LLSQ llsq; llsq.add(1.0, 1.0); llsq.add(2.0, 2.0); ExpectCorrectLine(llsq, 1.0, 0.0, 0.0, 1.0, 1e-6); float half_root_2 = sqrt(2.0) / 2.0f; ExpectCorrectVector(llsq, FCOORD(1.5f, 1.5f), FCOORD(half_root_2, half_root_2), 1e-6); llsq.remove(2.0, 2.0); llsq.add(1.0, 2.0); llsq.add(10.0, 1.0); llsq.add(-8.0, 1.0); // The point at 1,2 pulls the result away from what would otherwise be a // perfect fit to a horizontal line by 0.25 unit, with rms error of 0.433. ExpectCorrectLine(llsq, 0.0, 1.25, 0.433, 0.0, 1e-2); ExpectCorrectVector(llsq, FCOORD(1.0f, 1.25f), FCOORD(1.0f, 0.0f), 1e-3); llsq.add(1.0, 2.0, 10.0); // With a heavy weight, the point at 1,2 pulls the line nearer. ExpectCorrectLine(llsq, 0.0, 1.786, 0.41, 0.0, 1e-2); ExpectCorrectVector(llsq, FCOORD(1.0f, 1.786f), FCOORD(1.0f, 0.0f), 1e-3); } // Tests a simple baseline-style normalization with a rotation. TEST_F(LLSQTest, Vectors) { LLSQ llsq; llsq.add(1.0, 1.0); llsq.add(1.0, -1.0); ExpectCorrectVector(llsq, FCOORD(1.0f, 0.0f), FCOORD(0.0f, 1.0f), 1e-6); llsq.add(0.9, -2.0); llsq.add(1.1, -3.0); llsq.add(0.9, 2.0); llsq.add(1.10001, 3.0); ExpectCorrectVector(llsq, FCOORD(1.0f, 0.0f), FCOORD(0.0f, 1.0f), 1e-3); } // Verify that rms_orth() actually calculates: // sqrt( sum (!nvec * (x_i - x_avg))^2 / n) TEST_F(LLSQTest, RmsOrthWorksAsIntended) { std::vector pts; pts.push_back(FCOORD(0.56, 0.95)); pts.push_back(FCOORD(0.09, 0.09)); pts.push_back(FCOORD(0.13, 0.77)); pts.push_back(FCOORD(0.16, 0.83)); pts.push_back(FCOORD(0.45, 0.79)); VerifyRmsOrth(pts, FCOORD(1, 0)); VerifyRmsOrth(pts, FCOORD(1, 1)); VerifyRmsOrth(pts, FCOORD(1, 2)); VerifyRmsOrth(pts, FCOORD(2, 1)); } } // namespace tesseract