opencv.js
Version:
OpenCV for JavaScript
297 lines (232 loc) • 10.4 kB
JavaScript
// //////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
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// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
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// This software is provided by the copyright holders and contributors "as is" and
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// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
// //////////////////////////////////////////////////////////////////////////////////////
// Author: Sajjad Taheri, University of California, Irvine. sajjadt[at]uci[dot]edu
//
// LICENSE AGREEMENT
// Copyright (c) 2015 The Regents of the University of California (Regents)
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
// 1. Redistributions of source code must retain the above copyright
// notice, this list of conditions and the following disclaimer.
// 2. Redistributions in binary form must reproduce the above copyright
// notice, this list of conditions and the following disclaimer in the
// documentation and/or other materials provided with the distribution.
// 3. Neither the name of the University nor the
// names of its contributors may be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS ''AS IS'' AND ANY
// EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
// WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
// DISCLAIMED. IN NO EVENT SHALL CONTRIBUTORS BE LIABLE FOR ANY
// DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
// (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
// LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
// ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
//
if (typeof module !== 'undefined' && module.exports) {
// The envrionment is Node.js
var cv = require('../opencv.js'); // eslint-disable-line no-var
}
QUnit.module('Features Framework', {});
QUnit.test('Simple Blob Detector', function(assert) {
// Constructor
{
let detector = new cv.SimpleBlobDetector();
assert.equal(detector instanceof cv.SimpleBlobDetector, true);
detector.delete();
let params = {};
params.thresholdStep = 10;
params.minThreshold = 50;
params.maxThreshold = 220;
params.minRepeatability = 2;
params.minDistBetweenBlobs = 10;
params.filterByColor = true;
params.blobColor = 0;
params.filterByArea = true;
params.minArea = 25;
params.maxArea = 5000;
params.filterByCircularity = false;
params.minCircularity = 0.8;
params.maxCircularity = Number.MAX_VALUE;
params.filterByInertia = true;
params.minInertiaRatio = 0.1;
params.maxInertiaRatio = Number.MAX_VALUE;
params.filterByConvexity = true;
params.minConvexity = 0.95;
params.maxConvexity = Number.MAX_VALUE;
detector = new cv.SimpleBlobDetector(params);
assert.equal(detector instanceof cv.SimpleBlobDetector, true);
detector.delete();
}
// Detect
{
let image = cv.Mat.ones(5, 5, cv.CV_8UC3);
let detector = new cv.SimpleBlobDetector();
let keypoints = new cv.KeyPointVector();
detector.detect(image, keypoints);
assert.equal(keypoints instanceof cv.KeyPointVector, true);
image.delete();
keypoints.delete();
}
});
QUnit.test('ORB Features', function(assert) {
// Constructor
{
let detector = new cv.ORB();
assert.equal(detector instanceof cv.ORB, true);
detector.delete();
detector = new cv.ORB(400, 1, 8, 32, 0, 2, cv.ORB_HARRIS_SCORE, 32, 16);
assert.equal(detector.getMaxFeatures(), 400);
assert.equal(detector.getNLevels(), 8);
assert.equal(detector.getEdgeThreshold(), 32);
assert.equal(detector.getPatchSize(), 32);
detector.setPatchSize(25);
assert.equal(detector.getPatchSize(), 25);
detector.delete();
}
// Detect and Compute
{
let detector = new cv.ORB();
let image = cv.Mat.ones(5, 5, cv.CV_8UC3);
let keypoints = new cv.KeyPointVector();
let descriptors = new cv.Mat();
assert.equal(detector instanceof cv.ORB, true);
detector.detect(image, keypoints);
detector.compute(image, keypoints, descriptors);
assert.equal(keypoints instanceof cv.KeyPointVector, true);
assert.equal(descriptors instanceof cv.Mat, true);
assert.equal(image.channels, descriptors.channels);
image.delete();
keypoints.delete();
image = cv.Mat.ones(5, 5, cv.CV_8UC3);
keypoints = new cv.KeyPointVector();
descriptors = new cv.Mat();
let mask = new cv.Mat();
detector.detectAndCompute(image, mask, keypoints, descriptors);
assert.equal(keypoints instanceof cv.KeyPointVector, true);
assert.equal(descriptors instanceof cv.Mat, true);
assert.equal(image.channels, descriptors.channels);
image.delete();
keypoints.delete();
detector.delete();
mask.delete();
}
});
QUnit.test('Fast Features', function(assert) {
// Constructor
{
let detector = new cv.FastFeatureDetector();
assert.equal(detector instanceof cv.FastFeatureDetector, true);
detector.delete();
detector = new cv.FastFeatureDetector(15, true, cv.FastFeatureDetector_TYPE_7_12 );
assert.equal(detector instanceof cv.FastFeatureDetector, true);
assert.equal(detector.getThreshold(), 15);
assert.equal(detector.getNonmaxSuppression(), true);
assert.equal(detector.getType(), cv.FastFeatureDetector_TYPE_7_12);
detector.delete();
}
// Detect
{
let detector = new cv.FastFeatureDetector();
let image = cv.Mat.ones(5, 5, cv.CV_8UC3);
let keypoints = new cv.KeyPointVector();
assert.equal(detector instanceof cv.FastFeatureDetector, true);
detector.detect(image, keypoints);
assert.equal(keypoints instanceof cv.KeyPointVector, true);
image.delete();
keypoints.delete();
}
});
QUnit.test('Descriptor Matcher', function(assert) {
// Constructor
{
// BruteForce (it uses L2 )
// BruteForce-L1
// BruteForce-Hamming
// BruteForce-Hamming(2)
// FlannBased (Not supported)
let matcher = new cv.DescriptorMatcher("BruteForce");
assert.equal(matcher instanceof cv.DescriptorMatcher, true);
matcher.delete();
matcher = new cv.DescriptorMatcher("BruteForce-L1");
assert.equal(matcher instanceof cv.DescriptorMatcher, true);
matcher.delete();
matcher = new cv.DescriptorMatcher("BruteForce-Hamming");
assert.equal(matcher instanceof cv.DescriptorMatcher, true);
matcher.delete();
matcher = new cv.DescriptorMatcher("BruteForce-Hamming(2)");
assert.equal(matcher instanceof cv.DescriptorMatcher, true);
matcher.delete();
}
// Match
{
let matcher = new cv.DescriptorMatcher("BruteForce");
let detector = new cv.ORB();
let matches = new cv.DMatchVector();
assert.equal(matcher instanceof cv.DescriptorMatcher, true);
assert.equal(detector instanceof cv.ORB, true);
let image = cv.Mat.ones(5, 5, cv.CV_8UC3);
let keypoints = new cv.KeyPointVector();
let descriptors = new cv.Mat();
let image2 = cv.Mat.ones(5, 5, cv.CV_8UC3);
let keypoints2 = new cv.KeyPointVector();
let descriptors2 = new cv.Mat();
let mask = new cv.Mat();
detector.detectAndCompute(image, mask, keypoints, descriptors);
detector.detectAndCompute(image2, mask, keypoints2, descriptors2);
matcher.match(descriptors, descriptors2, matches);
assert.equal(matches instanceof cv.DMatchVector, true);
image.delete();
image2.delete();
mask.delete();
keypoints.delete();
keypoints2.delete();
descriptors.delete();
descriptors2.delete();
matches.delete();
detector.delete();
matcher.delete();
}
});