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https://github.com/opencv/opencv_contrib.git
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aba93d5fd5
Add Stroke Width Transform algorithm for Text Detection * added SWTTextDetection code * remove warnings from opencv_text compilation * added bib for SWT Text Detection * resolve initial pr suggestions * change file name according to convention * added optional OutputArray * removed bug related to letter candidature * added unit tests for stroke width transform text detection * Added demo program * made all minor edits required * corrected findDataFilePath and removed manual allocation * Added ArgumentParser * corrected typo * remove headers not allowed * correct test case * corrected float typecast warnings and test case * remove final double correction and problematic test case * test(swt): coding style * finalminor bug corrections
50 lines
2.1 KiB
C++
50 lines
2.1 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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namespace opencv_test { namespace {
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TEST (TextDetectionSWT, accuracy_light_on_dark) {
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const string dataPath = cvtest::findDataFile("cv/mser/mser_test.png");
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Mat image = imread(dataPath, IMREAD_COLOR);
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vector<Rect> components;
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detectTextSWT(image, components, false);
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/* all 5 letter candidates should be identified (R9888) */
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EXPECT_EQ(5u, components.size());
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}
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TEST (TextDetectionSWT, accuracy_dark_on_light) {
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const string dataPath = cvtest::findDataFile("cv/mser/mser_test2.png");
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Mat image = imread(dataPath, IMREAD_COLOR);
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vector<Rect> components;
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detectTextSWT(image, components, true);
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/* all 3 letter candidates should be identified 2, 5, 8 */
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EXPECT_EQ(3u, components.size());
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}
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TEST (TextDetectionSWT, accuracy_handwriting) {
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const string dataPath = cvtest::findDataFile("cv/cloning/Mixed_Cloning/source1.png");
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Mat image = imread(dataPath, IMREAD_COLOR);
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vector<Rect> components;
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detectTextSWT(image, components, true);
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/* Handwritten Text is generally more difficult to detect using SWT algorithm due to high variation in stroke width. */
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EXPECT_LT(11u, components.size());
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/* Although the text contains 15 characters, the current implementation of algorithm outputs 14, including three wrong guesses. So, we check at least 11 (14 - 3) letters are detected.*/
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}
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TEST (TextDetectionSWT, accuracy_chaining) {
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const string dataPath = cvtest::findDataFile("cv/mser/mser_test.png");
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Mat image = imread(dataPath, IMREAD_COLOR);
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vector<Rect> components;
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Mat out(image.size(), CV_8UC3);
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vector<Rect> chains;
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detectTextSWT(image, components, false, out, chains);
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Rect chain = chains[0];
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/* Since the word is already segmented and cropped, most of the area is covered by text. It confirms that chaining works. */
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EXPECT_LT(0.95 * image.total(), (double)chain.area());
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}
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}} // namespace
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