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