mirror of
https://github.com/opencv/opencv.git
synced 2025-06-12 12:22:51 +08:00
6eaa77461e
applyColorMap approxPolyN arrowedLine blendLinear boxPoints clipLine convertMaps createHanningWindow divSpectrums drawMarker findContoursLinkRuns fitEllipseAMS fitEllipseDirect getFontScaleFromHeight getRectSubPix HuMoments intersectConvexConvex invertAffineTransform minEnclosingTriangle preCornerDetect rotatedRectangleIntersection sqrBoxFilter spatialGradient stackBlur
782 lines
25 KiB
JavaScript
782 lines
25 KiB
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.
|
|
//
|
|
// * The name of the copyright holders may not 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 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.
|
|
//
|
|
|
|
QUnit.module('Image Processing', {});
|
|
|
|
QUnit.test('applyColorMap', function(assert) {
|
|
{
|
|
let src = cv.matFromArray(2, 1, cv.CV_8U, [50,100]);
|
|
cv.applyColorMap(src, src, cv.COLORMAP_BONE);
|
|
|
|
// Verify result.
|
|
let expected = new Uint8Array([60,44,44,119,89,87]);
|
|
|
|
assert.deepEqual(src.data, expected);
|
|
src.delete();
|
|
}
|
|
});
|
|
|
|
QUnit.test('blendLinear', function(assert) {
|
|
{
|
|
let src1 = cv.matFromArray(2, 1, cv.CV_8U, [50,100]);
|
|
let src2 = cv.matFromArray(2, 1, cv.CV_8U, [200,20]);
|
|
let weights1 = cv.matFromArray(2, 1, cv.CV_32F, [0.4,0.5]);
|
|
let weights2 = cv.matFromArray(2, 1, cv.CV_32F, [0.6,0.5]);
|
|
let dst = new cv.Mat();
|
|
cv.blendLinear(src1, src2, weights1, weights2, dst);
|
|
|
|
// Verify result.
|
|
let expected = new Uint8Array([140,60]);
|
|
|
|
assert.deepEqual(dst.data, expected);
|
|
src1.delete();
|
|
src2.delete();
|
|
weights1.delete();
|
|
weights2.delete();
|
|
dst.delete();
|
|
}
|
|
});
|
|
|
|
QUnit.test('createHanningWindow', function(assert) {
|
|
{
|
|
let dst = new cv.Mat();
|
|
cv.createHanningWindow(dst, new cv.Size(5, 3), cv.CV_32F);
|
|
|
|
// Verify result.
|
|
let expected = cv.matFromArray(3, 5, cv.CV_32F, [0.,0.,0.,0.,0.,0.,0.70710677,1.,0.70710677,0.,0.,0.,0.,0.,0.]);
|
|
|
|
assert.deepEqual(dst.data, expected.data);
|
|
dst.delete();
|
|
expected.delete();
|
|
}
|
|
});
|
|
|
|
QUnit.test('test_imgProc', function(assert) {
|
|
// calcHist
|
|
{
|
|
let vec1 = new cv.Mat.ones(new cv.Size(20, 20), cv.CV_8UC1); // eslint-disable-line new-cap
|
|
let source = new cv.MatVector();
|
|
source.push_back(vec1);
|
|
let channels = [0];
|
|
let histSize = [256];
|
|
let ranges =[0, 256];
|
|
|
|
let hist = new cv.Mat();
|
|
let mask = new cv.Mat();
|
|
let binSize = cv._malloc(4);
|
|
let binView = new Int32Array(cv.HEAP8.buffer, binSize);
|
|
binView[0] = 10;
|
|
cv.calcHist(source, channels, mask, hist, histSize, ranges, false);
|
|
|
|
// hist should contains a N X 1 array.
|
|
let size = hist.size();
|
|
assert.equal(size.height, 256);
|
|
assert.equal(size.width, 1);
|
|
|
|
// default parameters
|
|
cv.calcHist(source, channels, mask, hist, histSize, ranges);
|
|
size = hist.size();
|
|
assert.equal(size.height, 256);
|
|
assert.equal(size.width, 1);
|
|
|
|
// Do we need to verify data in histogram?
|
|
// let dataView = hist.data;
|
|
|
|
// Free resource
|
|
cv._free(binSize);
|
|
mask.delete();
|
|
hist.delete();
|
|
}
|
|
|
|
// cvtColor
|
|
{
|
|
let source = new cv.Mat(10, 10, cv.CV_8UC3);
|
|
let dest = new cv.Mat();
|
|
|
|
cv.cvtColor(source, dest, cv.COLOR_BGR2GRAY, 0);
|
|
assert.equal(dest.channels(), 1);
|
|
|
|
cv.cvtColor(source, dest, cv.COLOR_BGR2GRAY);
|
|
assert.equal(dest.channels(), 1);
|
|
|
|
cv.cvtColor(source, dest, cv.COLOR_BGR2BGRA, 0);
|
|
assert.equal(dest.channels(), 4);
|
|
|
|
cv.cvtColor(source, dest, cv.COLOR_BGR2BGRA);
|
|
assert.equal(dest.channels(), 4);
|
|
|
|
dest.delete();
|
|
source.delete();
|
|
}
|
|
|
|
// equalizeHist
|
|
{
|
|
let source = new cv.Mat(10, 10, cv.CV_8UC1);
|
|
let dest = new cv.Mat();
|
|
|
|
cv.equalizeHist(source, dest);
|
|
|
|
// eualizeHist changes the content of a image, but does not alter meta data
|
|
// of it.
|
|
assert.equal(source.channels(), dest.channels());
|
|
assert.equal(source.type(), dest.type());
|
|
|
|
dest.delete();
|
|
source.delete();
|
|
}
|
|
|
|
// floodFill
|
|
{
|
|
let center = new cv.Point(5, 5);
|
|
let rect = new cv.Rect(0, 0, 0, 0);
|
|
let img = new cv.Mat.zeros(10, 10, cv.CV_8UC1);
|
|
let color = new cv.Scalar (255);
|
|
cv.circle(img, center, 3, color, 1);
|
|
|
|
let edge = new cv.Mat();
|
|
cv.Canny(img, edge, 100, 255);
|
|
cv.copyMakeBorder(edge, edge, 1, 1, 1, 1, cv.BORDER_REPLICATE);
|
|
|
|
let expected_img_data = new Uint8Array([
|
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
|
0, 0, 0, 0, 0, 255, 0, 0, 0, 0,
|
|
0, 0, 0, 255, 255, 255, 255, 255, 0, 0,
|
|
0, 0, 0, 255, 0, 255, 0, 255, 0, 0,
|
|
0, 0, 255, 255, 255, 255, 0, 0, 255, 0,
|
|
0, 0, 0, 255, 0, 0, 0, 255, 0, 0,
|
|
0, 0, 0, 255, 255, 0, 255, 255, 0, 0,
|
|
0, 0, 0, 0, 0, 255, 0, 0, 0, 0,
|
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0]);
|
|
|
|
let img_elem = 10*10*1;
|
|
let expected_img_data_ptr = cv._malloc(img_elem);
|
|
let expected_img_data_heap = new Uint8Array(cv.HEAPU8.buffer,
|
|
expected_img_data_ptr,
|
|
img_elem);
|
|
expected_img_data_heap.set(new Uint8Array(expected_img_data.buffer));
|
|
|
|
let expected_img = new cv.Mat( 10, 10, cv.CV_8UC1, expected_img_data_ptr, 0);
|
|
|
|
let expected_rect = new cv.Rect(3,3,3,3);
|
|
|
|
let compare_result = new cv.Mat(10, 10, cv.CV_8UC1);
|
|
|
|
cv.floodFill(img, edge, center, color, rect);
|
|
|
|
cv.compare (img, expected_img, compare_result, cv.CMP_EQ);
|
|
|
|
// expect every pixels are the same.
|
|
assert.equal (cv.countNonZero(compare_result), img.total());
|
|
assert.equal (rect.x, expected_rect.x);
|
|
assert.equal (rect.y, expected_rect.y);
|
|
assert.equal (rect.width, expected_rect.width);
|
|
assert.equal (rect.height, expected_rect.height);
|
|
|
|
img.delete();
|
|
edge.delete();
|
|
expected_img.delete();
|
|
compare_result.delete();
|
|
}
|
|
});
|
|
|
|
QUnit.test('Drawing Functions', function(assert) {
|
|
// fillPoly
|
|
{
|
|
let img_width = 6;
|
|
let img_height = 6;
|
|
|
|
let img = new cv.Mat.zeros(img_height, img_width, cv.CV_8UC1);
|
|
|
|
let npts = 4;
|
|
let square_point_data = new Uint8Array([
|
|
1, 1,
|
|
4, 1,
|
|
4, 4,
|
|
1, 4]);
|
|
let square_points = cv.matFromArray(npts, 1, cv.CV_32SC2, square_point_data);
|
|
let pts = new cv.MatVector();
|
|
pts.push_back (square_points);
|
|
let color = new cv.Scalar (255);
|
|
|
|
let expected_img_data = new Uint8Array([
|
|
0, 0, 0, 0, 0, 0,
|
|
0, 255, 255, 255, 255, 0,
|
|
0, 255, 255, 255, 255, 0,
|
|
0, 255, 255, 255, 255, 0,
|
|
0, 255, 255, 255, 255, 0,
|
|
0, 0, 0, 0, 0, 0]);
|
|
let expected_img = cv.matFromArray(img_height, img_width, cv.CV_8UC1, expected_img_data);
|
|
|
|
cv.fillPoly(img, pts, color);
|
|
|
|
let compare_result = new cv.Mat(img_height, img_width, cv.CV_8UC1);
|
|
|
|
cv.compare (img, expected_img, compare_result, cv.CMP_EQ);
|
|
|
|
// expect every pixels are the same.
|
|
assert.equal (cv.countNonZero(compare_result), img.total());
|
|
|
|
img.delete();
|
|
square_points.delete();
|
|
pts.delete();
|
|
expected_img.delete();
|
|
compare_result.delete();
|
|
}
|
|
|
|
// fillConvexPoly
|
|
{
|
|
let img_width = 6;
|
|
let img_height = 6;
|
|
|
|
let img = new cv.Mat.zeros(img_height, img_width, cv.CV_8UC1);
|
|
|
|
let npts = 4;
|
|
let square_point_data = new Uint8Array([
|
|
1, 1,
|
|
4, 1,
|
|
4, 4,
|
|
1, 4]);
|
|
let square_points = cv.matFromArray(npts, 1, cv.CV_32SC2, square_point_data);
|
|
let color = new cv.Scalar (255);
|
|
|
|
let expected_img_data = new Uint8Array([
|
|
0, 0, 0, 0, 0, 0,
|
|
0, 255, 255, 255, 255, 0,
|
|
0, 255, 255, 255, 255, 0,
|
|
0, 255, 255, 255, 255, 0,
|
|
0, 255, 255, 255, 255, 0,
|
|
0, 0, 0, 0, 0, 0]);
|
|
let expected_img = cv.matFromArray(img_height, img_width, cv.CV_8UC1, expected_img_data);
|
|
|
|
cv.fillConvexPoly(img, square_points, color);
|
|
|
|
let compare_result = new cv.Mat(img_height, img_width, cv.CV_8UC1);
|
|
|
|
cv.compare (img, expected_img, compare_result, cv.CMP_EQ);
|
|
|
|
// expect every pixels are the same.
|
|
assert.equal (cv.countNonZero(compare_result), img.total());
|
|
|
|
img.delete();
|
|
square_points.delete();
|
|
expected_img.delete();
|
|
compare_result.delete();
|
|
}
|
|
});
|
|
|
|
QUnit.test('test_segmentation', function(assert) {
|
|
const THRESHOLD = 127.0;
|
|
const THRESHOLD_MAX = 210.0;
|
|
|
|
// threshold
|
|
{
|
|
let source = new cv.Mat(1, 5, cv.CV_8UC1);
|
|
let sourceView = source.data;
|
|
sourceView[0] = 0; // < threshold
|
|
sourceView[1] = 100; // < threshold
|
|
sourceView[2] = 200; // > threshold
|
|
|
|
let dest = new cv.Mat();
|
|
|
|
cv.threshold(source, dest, THRESHOLD, THRESHOLD_MAX, cv.THRESH_BINARY);
|
|
|
|
let destView = dest.data;
|
|
assert.equal(destView[0], 0);
|
|
assert.equal(destView[1], 0);
|
|
assert.equal(destView[2], THRESHOLD_MAX);
|
|
}
|
|
|
|
// adaptiveThreshold
|
|
{
|
|
let source = cv.Mat.zeros(1, 5, cv.CV_8UC1);
|
|
let sourceView = source.data;
|
|
sourceView[0] = 50;
|
|
sourceView[1] = 150;
|
|
sourceView[2] = 200;
|
|
|
|
let dest = new cv.Mat();
|
|
const C = 0;
|
|
const blockSize = 3;
|
|
cv.adaptiveThreshold(source, dest, THRESHOLD_MAX,
|
|
cv.ADAPTIVE_THRESH_MEAN_C, cv.THRESH_BINARY, blockSize, C);
|
|
|
|
let destView = dest.data;
|
|
assert.equal(destView[0], 0);
|
|
assert.equal(destView[1], THRESHOLD_MAX);
|
|
assert.equal(destView[2], THRESHOLD_MAX);
|
|
}
|
|
});
|
|
|
|
QUnit.test('test_shape', function(assert) {
|
|
// moments
|
|
{
|
|
let points = new cv.Mat(1, 4, cv.CV_32SC2);
|
|
let data32S = points.data32S;
|
|
data32S[0]=50;
|
|
data32S[1]=56;
|
|
data32S[2]=53;
|
|
data32S[3]=53;
|
|
data32S[4]=46;
|
|
data32S[5]=54;
|
|
data32S[6]=49;
|
|
data32S[7]=51;
|
|
|
|
let m = cv.moments(points, false);
|
|
let area = cv.contourArea(points, false);
|
|
|
|
assert.equal(m.m00, 0);
|
|
assert.equal(m.m01, 0);
|
|
assert.equal(m.m10, 0);
|
|
assert.equal(area, 0);
|
|
|
|
// default parameters
|
|
m = cv.moments(points);
|
|
area = cv.contourArea(points);
|
|
assert.equal(m.m00, 0);
|
|
assert.equal(m.m01, 0);
|
|
assert.equal(m.m10, 0);
|
|
assert.equal(area, 0);
|
|
|
|
points.delete();
|
|
}
|
|
});
|
|
|
|
QUnit.test('test_min_enclosing', function(assert) {
|
|
// minEnclosingCircle
|
|
{
|
|
let points = new cv.Mat(4, 1, cv.CV_32FC2);
|
|
|
|
points.data32F[0] = 0;
|
|
points.data32F[1] = 0;
|
|
points.data32F[2] = 1;
|
|
points.data32F[3] = 0;
|
|
points.data32F[4] = 1;
|
|
points.data32F[5] = 1;
|
|
points.data32F[6] = 0;
|
|
points.data32F[7] = 1;
|
|
|
|
let circle = cv.minEnclosingCircle(points);
|
|
|
|
assert.deepEqual(circle.center, {x: 0.5, y: 0.5});
|
|
assert.ok(Math.abs(circle.radius - Math.sqrt(2) / 2) < 0.001);
|
|
|
|
points.delete();
|
|
}
|
|
|
|
// minEnclosingTriangle
|
|
{
|
|
let dst = cv.Mat.zeros(80, 80, cv.CV_8U);
|
|
let contours = new cv.MatVector();
|
|
let hierarchy = new cv.Mat();
|
|
let triangle = new cv.Mat();
|
|
|
|
cv.drawMarker(dst, new cv.Point(40, 40), new cv.Scalar(255));
|
|
cv.findContoursLinkRuns(dst,contours,hierarchy);
|
|
cv.minEnclosingTriangle(contours.get(0),triangle);
|
|
|
|
// Verify result.
|
|
const triangleData = triangle.data32F;
|
|
assert.deepEqual(triangleData[0], triangleData[4]);
|
|
assert.deepEqual(triangleData[1], 20);
|
|
assert.deepEqual(triangleData[2], 30);
|
|
assert.deepEqual(triangleData[3], 40);
|
|
assert.deepEqual(triangleData[5], 60);
|
|
|
|
dst.delete();
|
|
contours.delete();
|
|
hierarchy.delete();
|
|
triangle.delete();
|
|
}
|
|
});
|
|
|
|
QUnit.test('test_filter', function(assert) {
|
|
// blur
|
|
{
|
|
let mat1 = cv.Mat.ones(5, 5, cv.CV_8UC3);
|
|
let mat2 = new cv.Mat();
|
|
|
|
cv.blur(mat1, mat2, {height: 3, width: 3}, {x: -1, y: -1}, cv.BORDER_DEFAULT);
|
|
|
|
// Verify result.
|
|
let size = mat2.size();
|
|
assert.equal(mat2.channels(), 3);
|
|
assert.equal(size.height, 5);
|
|
assert.equal(size.width, 5);
|
|
|
|
cv.blur(mat1, mat2, {height: 3, width: 3}, {x: -1, y: -1});
|
|
|
|
// Verify result.
|
|
size = mat2.size();
|
|
assert.equal(mat2.channels(), 3);
|
|
assert.equal(size.height, 5);
|
|
assert.equal(size.width, 5);
|
|
|
|
cv.blur(mat1, mat2, {height: 3, width: 3});
|
|
|
|
// Verify result.
|
|
size = mat2.size();
|
|
assert.equal(mat2.channels(), 3);
|
|
assert.equal(size.height, 5);
|
|
assert.equal(size.width, 5);
|
|
|
|
mat1.delete();
|
|
mat2.delete();
|
|
}
|
|
|
|
// GaussianBlur
|
|
{
|
|
let mat1 = cv.Mat.ones(7, 7, cv.CV_8UC1);
|
|
let mat2 = new cv.Mat();
|
|
|
|
cv.GaussianBlur(mat1, mat2, new cv.Size(3, 3), 0, 0, // eslint-disable-line new-cap
|
|
cv.BORDER_DEFAULT);
|
|
|
|
// Verify result.
|
|
let size = mat2.size();
|
|
assert.equal(mat2.channels(), 1);
|
|
assert.equal(size.height, 7);
|
|
assert.equal(size.width, 7);
|
|
mat1.delete();
|
|
mat2.delete();
|
|
}
|
|
|
|
// spatialGradient
|
|
{
|
|
let src = cv.matFromArray(4, 4, cv.CV_8U, [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]);
|
|
let dx = new cv.Mat();
|
|
let dy = new cv.Mat();
|
|
cv.spatialGradient(src, dx, dy);
|
|
|
|
// Verify result.
|
|
let expected_dx = new cv.Mat();
|
|
let expected_dy = new cv.Mat();
|
|
cv.Sobel(src, expected_dx, cv.CV_16SC1, 1, 0, 3);
|
|
cv.Sobel(src, expected_dy, cv.CV_16SC1, 0, 1, 3);
|
|
|
|
assert.deepEqual(dx.data, expected_dx.data);
|
|
assert.deepEqual(dy.data, expected_dy.data);
|
|
|
|
src.delete();
|
|
dx.delete();
|
|
dy.delete();
|
|
expected_dx.delete();
|
|
expected_dy.delete();
|
|
}
|
|
|
|
// sqrBoxFilter
|
|
{
|
|
let src = cv.matFromArray(2, 3, cv.CV_8U, [1,2,1,1,2,1]);
|
|
let dst = new cv.Mat();
|
|
cv.sqrBoxFilter(src, dst, cv.CV_32F, new cv.Size(3, 3));
|
|
|
|
// Verify result.
|
|
let expected = cv.matFromArray(2, 3, cv.CV_32F,[3.0,2.0,3.0,3.0,2.0,3.0]);
|
|
|
|
assert.deepEqual(dst.data, expected.data);
|
|
src.delete();
|
|
dst.delete();
|
|
expected.delete();
|
|
}
|
|
|
|
// stackBlur
|
|
{
|
|
let src = cv.matFromArray(2, 3, cv.CV_8U, [10,25,30,45,50,60]);
|
|
cv.stackBlur(src, src, new cv.Size(3, 3));
|
|
|
|
// Verify result.
|
|
let expected = new Uint8Array([22,29,36,38,43,50]);
|
|
|
|
assert.deepEqual(src.data, expected);
|
|
src.delete();
|
|
}
|
|
|
|
// medianBlur
|
|
{
|
|
let mat1 = cv.Mat.ones(9, 9, cv.CV_8UC3);
|
|
let mat2 = new cv.Mat();
|
|
|
|
cv.medianBlur(mat1, mat2, 3);
|
|
|
|
// Verify result.
|
|
let size = mat2.size();
|
|
|
|
assert.equal(mat2.channels(), 3);
|
|
assert.equal(size.height, 9);
|
|
assert.equal(size.width, 9);
|
|
mat1.delete();
|
|
mat2.delete();
|
|
}
|
|
|
|
// bilateralFilter
|
|
{
|
|
let mat1 = cv.Mat.ones(11, 11, cv.CV_8UC3);
|
|
let mat2 = new cv.Mat();
|
|
|
|
cv.bilateralFilter(mat1, mat2, 3, 6, 1.5, cv.BORDER_DEFAULT);
|
|
|
|
// Verify result.
|
|
let size = mat2.size();
|
|
assert.equal(mat2.channels(), 3);
|
|
assert.equal(size.height, 11);
|
|
assert.equal(size.width, 11);
|
|
|
|
// default parameters
|
|
cv.bilateralFilter(mat1, mat2, 3, 6, 1.5);
|
|
// Verify result.
|
|
size = mat2.size();
|
|
assert.equal(mat2.channels(), 3);
|
|
assert.equal(size.height, 11);
|
|
assert.equal(size.width, 11);
|
|
|
|
mat1.delete();
|
|
mat2.delete();
|
|
}
|
|
});
|
|
|
|
QUnit.test('test_watershed', function(assert) {
|
|
{
|
|
let mat = cv.Mat.ones(11, 11, cv.CV_8UC3);
|
|
let out = new cv.Mat(11, 11, cv.CV_32SC1);
|
|
|
|
cv.watershed(mat, out);
|
|
|
|
// Verify result.
|
|
let size = out.size();
|
|
assert.equal(out.channels(), 1);
|
|
assert.equal(size.height, 11);
|
|
assert.equal(size.width, 11);
|
|
assert.equal(out.elemSize1(), 4);
|
|
|
|
mat.delete();
|
|
out.delete();
|
|
}
|
|
});
|
|
|
|
QUnit.test('test_distanceTransform', function(assert) {
|
|
{
|
|
let mat = cv.Mat.ones(11, 11, cv.CV_8UC1);
|
|
let out = new cv.Mat(11, 11, cv.CV_32FC1);
|
|
let labels = new cv.Mat(11, 11, cv.CV_32FC1);
|
|
const maskSize = 3;
|
|
cv.distanceTransform(mat, out, cv.DIST_L2, maskSize, cv.CV_32F);
|
|
|
|
// Verify result.
|
|
let size = out.size();
|
|
assert.equal(out.channels(), 1);
|
|
assert.equal(size.height, 11);
|
|
assert.equal(size.width, 11);
|
|
assert.equal(out.elemSize1(), 4);
|
|
|
|
cv.distanceTransformWithLabels(mat, out, labels, cv.DIST_L2, maskSize,
|
|
cv.DIST_LABEL_CCOMP);
|
|
|
|
// Verify result.
|
|
size = out.size();
|
|
assert.equal(out.channels(), 1);
|
|
assert.equal(size.height, 11);
|
|
assert.equal(size.width, 11);
|
|
assert.equal(out.elemSize1(), 4);
|
|
|
|
size = labels.size();
|
|
assert.equal(labels.channels(), 1);
|
|
assert.equal(size.height, 11);
|
|
assert.equal(size.width, 11);
|
|
assert.equal(labels.elemSize1(), 4);
|
|
|
|
mat.delete();
|
|
out.delete();
|
|
labels.delete();
|
|
}
|
|
});
|
|
|
|
QUnit.test('test_integral', function(assert) {
|
|
{
|
|
let mat = cv.Mat.eye({height: 100, width: 100}, cv.CV_8UC3);
|
|
let sum = new cv.Mat();
|
|
let sqSum = new cv.Mat();
|
|
let title = new cv.Mat();
|
|
|
|
cv.integral(mat, sum, -1);
|
|
|
|
// Verify result.
|
|
let size = sum.size();
|
|
assert.equal(sum.channels(), 3);
|
|
assert.equal(size.height, 100+1);
|
|
assert.equal(size.width, 100+1);
|
|
|
|
cv.integral2(mat, sum, sqSum, -1, -1);
|
|
// Verify result.
|
|
size = sum.size();
|
|
assert.equal(sum.channels(), 3);
|
|
assert.equal(size.height, 100+1);
|
|
assert.equal(size.width, 100+1);
|
|
|
|
size = sqSum.size();
|
|
assert.equal(sqSum.channels(), 3);
|
|
assert.equal(size.height, 100+1);
|
|
assert.equal(size.width, 100+1);
|
|
|
|
mat.delete();
|
|
sum.delete();
|
|
sqSum.delete();
|
|
title.delete();
|
|
}
|
|
});
|
|
|
|
QUnit.test('test_rotatedRectangleIntersection', function(assert) {
|
|
{
|
|
let dst = cv.Mat.zeros(80, 80, cv.CV_8U);
|
|
let contours = new cv.MatVector();
|
|
let hierarchy = new cv.Mat();
|
|
let intersectionPoints = new cv.Mat();
|
|
|
|
cv.drawMarker(dst, new cv.Point(40, 40), new cv.Scalar(255));
|
|
cv.findContoursLinkRuns(dst,contours,hierarchy);
|
|
let rr1 = cv.minAreaRect(contours.get(0));
|
|
let rr2 = cv.minAreaRect(contours.get(0));
|
|
let rr3 = new cv.RotatedRect({x: 40, y: 40}, {height: 10, width: 20}, 45);
|
|
|
|
let intersectionType = cv.rotatedRectangleIntersection(rr1, rr2, intersectionPoints);
|
|
|
|
// Verify result.
|
|
assert.deepEqual(intersectionType, cv.INTERSECT_FULL);
|
|
intersectionPoints.convertTo(intersectionPoints, cv.CV_32S);
|
|
let intersectionPointsData = intersectionPoints.data32S;
|
|
assert.deepEqual(intersectionPointsData[0], 30);
|
|
assert.deepEqual(intersectionPointsData[1], 40);
|
|
assert.deepEqual(intersectionPointsData[2], 40);
|
|
assert.deepEqual(intersectionPointsData[3], 30);
|
|
assert.deepEqual(intersectionPointsData[4], 50);
|
|
assert.deepEqual(intersectionPointsData[5], 40);
|
|
assert.deepEqual(intersectionPointsData[6], 40);
|
|
assert.deepEqual(intersectionPointsData[7], 50);
|
|
|
|
intersectionType = cv.rotatedRectangleIntersection(rr1, rr3, intersectionPoints);
|
|
|
|
// Verify result.
|
|
assert.deepEqual(intersectionType, cv.INTERSECT_PARTIAL);
|
|
intersectionPoints.convertTo(intersectionPoints, cv.CV_32S);
|
|
intersectionPointsData = intersectionPoints.data32S;
|
|
assert.deepEqual(intersectionPointsData[0], 39);
|
|
assert.deepEqual(intersectionPointsData[1], 31);
|
|
assert.deepEqual(intersectionPointsData[2], 49);
|
|
assert.deepEqual(intersectionPointsData[3], 41);
|
|
assert.deepEqual(intersectionPointsData[4], 41);
|
|
assert.deepEqual(intersectionPointsData[5], 49);
|
|
assert.deepEqual(intersectionPointsData[6], 31);
|
|
assert.deepEqual(intersectionPointsData[7], 39);
|
|
|
|
dst.delete();
|
|
contours.delete();
|
|
hierarchy.delete();
|
|
intersectionPoints.delete();
|
|
}
|
|
});
|
|
|
|
QUnit.test('warpPolar', function(assert) {
|
|
const lines = new cv.Mat(255, 255, cv.CV_8U, new cv.Scalar(0));
|
|
for (let r = 0; r < lines.rows; r++) {
|
|
lines.row(r).setTo(new cv.Scalar(r));
|
|
}
|
|
cv.warpPolar(lines, lines, { width: 5, height: 5 }, new cv.Point(2, 2), 3,
|
|
cv.INTER_CUBIC | cv.WARP_FILL_OUTLIERS | cv.WARP_INVERSE_MAP);
|
|
assert.ok(lines instanceof cv.Mat);
|
|
assert.deepEqual(Array.from(lines.data), [
|
|
159, 172, 191, 210, 223,
|
|
146, 159, 191, 223, 236,
|
|
128, 128, 0, 0, 0,
|
|
109, 96, 64, 32, 19,
|
|
96, 83, 64, 45, 32
|
|
]);
|
|
});
|
|
|
|
QUnit.test('IntelligentScissorsMB', function(assert) {
|
|
const lines = new cv.Mat(50, 100, cv.CV_8U, new cv.Scalar(0));
|
|
lines.row(10).setTo(new cv.Scalar(255));
|
|
assert.ok(lines instanceof cv.Mat);
|
|
|
|
let tool = new cv.segmentation_IntelligentScissorsMB();
|
|
tool.applyImage(lines);
|
|
assert.ok(lines instanceof cv.Mat);
|
|
lines.delete();
|
|
|
|
tool.buildMap(new cv.Point(10, 10));
|
|
|
|
let contour = new cv.Mat();
|
|
tool.getContour(new cv.Point(50, 10), contour);
|
|
assert.equal(contour.type(), cv.CV_32SC2);
|
|
assert.ok(contour.total() == 41, contour.total());
|
|
|
|
tool.getContour(new cv.Point(80, 10), contour);
|
|
assert.equal(contour.type(), cv.CV_32SC2);
|
|
assert.ok(contour.total() == 71, contour.total());
|
|
});
|