Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity
Open Source Computer Vision Library https://opencv.org/
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
30055 Commits
6 Branches
126 Tags
3.0 GiB
 
 
 
 
 
 
Tag: Branch: Tree: 47426a8ae5
Branches Tags
${ item.name }
Create tag ${ searchTerm }
Create branch ${ searchTerm }
from '47426a8ae5'
${ noResults }
opencv/modules/js/test/test_imgproc.js

1002 lines
32 KiB
Raw Blame History

// //////////////////////////////////////////////////////////////////////////////////////
//
// 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.
//
if (typeof module !== 'undefined' && module.exports) {
// The environment is Node.js
var cv = require('./opencv.js'); // eslint-disable-line no-var
}
QUnit.module('Image Processing', {});
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();
}
// 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) {
{
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();
}
});
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);
}
// 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);
}
// Transpose
{
let mat1 = cv.Mat.eye(9, 9, cv.CV_8UC3);
let mat2 = new cv.Mat();
cv.transpose(mat1, mat2);
// Verify result.
let size = mat2.size();
assert.equal(mat2.channels(), 3);
assert.equal(size.height, 9);
assert.equal(size.width, 9);
}
// 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();
}
// Watershed
{
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();
}
// Concat
{
let mat = cv.Mat.ones({height: 10, width: 5}, cv.CV_8UC3);
let mat2 = cv.Mat.eye({height: 10, width: 5}, cv.CV_8UC3);
let mat3 = cv.Mat.eye({height: 10, width: 5}, cv.CV_8UC3);
let out = new cv.Mat();
let input = new cv.MatVector();
input.push_back(mat);
input.push_back(mat2);
input.push_back(mat3);
cv.vconcat(input, out);
// Verify result.
let size = out.size();
assert.equal(out.channels(), 3);
assert.equal(size.height, 30);
assert.equal(size.width, 5);
assert.equal(out.elemSize1(), 1);
cv.hconcat(input, out);
// Verify result.
size = out.size();
assert.equal(out.channels(), 3);
assert.equal(size.height, 10);
assert.equal(size.width, 15);
assert.equal(out.elemSize1(), 1);
input.delete();
out.delete();
}
// distanceTransform letiants
{
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();
}
// Min, Max
{
let data1 = new Uint8Array([1, 2, 3, 4, 5, 6, 7, 8, 9]);
let data2 = new Uint8Array([0, 4, 0, 8, 0, 12, 0, 16, 0]);
let expectedMin = new Uint8Array([0, 2, 0, 4, 0, 6, 0, 8, 0]);
let expectedMax = new Uint8Array([1, 4, 3, 8, 5, 12, 7, 16, 9]);
let dataPtr = cv._malloc(3*3*1);
let dataPtr2 = cv._malloc(3*3*1);
let dataHeap = new Uint8Array(cv.HEAPU8.buffer, dataPtr, 3*3*1);
dataHeap.set(new Uint8Array(data1.buffer));
let dataHeap2 = new Uint8Array(cv.HEAPU8.buffer, dataPtr2, 3*3*1);
dataHeap2.set(new Uint8Array(data2.buffer));
let mat1 = new cv.Mat(3, 3, cv.CV_8UC1, dataPtr, 0);
let mat2 = new cv.Mat(3, 3, cv.CV_8UC1, dataPtr2, 0);
let mat3 = new cv.Mat();
cv.min(mat1, mat2, mat3);
// Verify result.
let size = mat2.size();
assert.equal(mat2.channels(), 1);
assert.equal(size.height, 3);
assert.equal(size.width, 3);
assert.deepEqual(mat3.data, expectedMin);
cv.max(mat1, mat2, mat3);
// Verify result.
size = mat2.size();
assert.equal(mat2.channels(), 1);
assert.equal(size.height, 3);
assert.equal(size.width, 3);
assert.deepEqual(mat3.data, expectedMax);
cv._free(dataPtr);
cv._free(dataPtr2);
}
// Bitwise operations
{
let data1 = new Uint8Array([0, 1, 2, 4, 8, 16, 32, 64, 128]);
let data2 = new Uint8Array([255, 255, 255, 255, 255, 255, 255, 255, 255]);
let expectedAnd = new Uint8Array([0, 1, 2, 4, 8, 16, 32, 64, 128]);
let expectedOr = new Uint8Array([255, 255, 255, 255, 255, 255, 255, 255, 255]);
let expectedXor = new Uint8Array([255, 254, 253, 251, 247, 239, 223, 191, 127]);
let expectedNot = new Uint8Array([255, 254, 253, 251, 247, 239, 223, 191, 127]);
let dataPtr = cv._malloc(3*3*1);
let dataPtr2 = cv._malloc(3*3*1);
let dataHeap = new Uint8Array(cv.HEAPU8.buffer, dataPtr, 3*3*1);
dataHeap.set(new Uint8Array(data1.buffer));
let dataHeap2 = new Uint8Array(cv.HEAPU8.buffer, dataPtr2, 3*3*1);
dataHeap2.set(new Uint8Array(data2.buffer));
let mat1 = new cv.Mat(3, 3, cv.CV_8UC1, dataPtr, 0);
let mat2 = new cv.Mat(3, 3, cv.CV_8UC1, dataPtr2, 0);
let mat3 = new cv.Mat();
let none = new cv.Mat();
cv.bitwise_not(mat1, mat3, none);
// Verify result.
let size = mat3.size();
assert.equal(mat3.channels(), 1);
assert.equal(size.height, 3);
assert.equal(size.width, 3);
assert.deepEqual(mat3.data, expectedNot);
cv.bitwise_and(mat1, mat2, mat3, none);
// Verify result.
size = mat3.size();
assert.equal(mat3.channels(), 1);
assert.equal(size.height, 3);
assert.equal(size.width, 3);
assert.deepEqual(mat3.data, expectedAnd);
cv.bitwise_or(mat1, mat2, mat3, none);
// Verify result.
size = mat3.size();
assert.equal(mat3.channels(), 1);
assert.equal(size.height, 3);
assert.equal(size.width, 3);
assert.deepEqual(mat3.data, expectedOr);
cv.bitwise_xor(mat1, mat2, mat3, none);
// Verify result.
size = mat3.size();
assert.equal(mat3.channels(), 1);
assert.equal(size.height, 3);
assert.equal(size.width, 3);
assert.deepEqual(mat3.data, expectedXor);
cv._free(dataPtr);
cv._free(dataPtr2);
}
// Arithmetic operations
{
let data1 = new Uint8Array([0, 1, 2, 3, 4, 5, 6, 7, 8]);
let data2 = new Uint8Array([0, 2, 4, 6, 8, 10, 12, 14, 16]);
let data3 = new Uint8Array([0, 1, 0, 1, 0, 1, 0, 1, 0]);
// |data1 - data2|
let expectedAbsDiff = new Uint8Array([0, 1, 2, 3, 4, 5, 6, 7, 8]);
let expectedAdd = new Uint8Array([0, 3, 6, 9, 12, 15, 18, 21, 24]);
const alpha = 4;
const beta = -1;
const gamma = 3;
// 4*data1 - data2 + 3
let expectedWeightedAdd = new Uint8Array([3, 5, 7, 9, 11, 13, 15, 17, 19]);
let dataPtr = cv._malloc(3*3*1);
let dataPtr2 = cv._malloc(3*3*1);
let dataPtr3 = cv._malloc(3*3*1);
let dataHeap = new Uint8Array(cv.HEAPU8.buffer, dataPtr, 3*3*1);
dataHeap.set(new Uint8Array(data1.buffer));
let dataHeap2 = new Uint8Array(cv.HEAPU8.buffer, dataPtr2, 3*3*1);
dataHeap2.set(new Uint8Array(data2.buffer));
let dataHeap3 = new Uint8Array(cv.HEAPU8.buffer, dataPtr3, 3*3*1);
dataHeap3.set(new Uint8Array(data3.buffer));
let mat1 = new cv.Mat(3, 3, cv.CV_8UC1, dataPtr, 0);
let mat2 = new cv.Mat(3, 3, cv.CV_8UC1, dataPtr2, 0);
let mat3 = new cv.Mat(3, 3, cv.CV_8UC1, dataPtr3, 0);
let dst = new cv.Mat();
let none = new cv.Mat();
cv.absdiff(mat1, mat2, dst);
// Verify result.
let size = dst.size();
assert.equal(dst.channels(), 1);
assert.equal(size.height, 3);
assert.equal(size.width, 3);
assert.deepEqual(dst.data, expectedAbsDiff);
cv.add(mat1, mat2, dst, none, -1);
// Verify result.
size = dst.size();
assert.equal(dst.channels(), 1);
assert.equal(size.height, 3);
assert.equal(size.width, 3);
assert.deepEqual(dst.data, expectedAdd);
cv.addWeighted(mat1, alpha, mat2, beta, gamma, dst, -1);
// Verify result.
size = dst.size();
assert.equal(dst.channels(), 1);
assert.equal(size.height, 3);
assert.equal(size.width, 3);
assert.deepEqual(dst.data, expectedWeightedAdd);
// default parameter
cv.addWeighted(mat1, alpha, mat2, beta, gamma, dst);
// Verify result.
size = dst.size();
assert.equal(dst.channels(), 1);
assert.equal(size.height, 3);
assert.equal(size.width, 3);
assert.deepEqual(dst.data, expectedWeightedAdd);
mat1.delete();
mat2.delete();
mat3.delete();
dst.delete();
none.delete();
}
// Integral letiants
{
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();
}
// Mean, meanSTDev
{
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();
}
// Invert
{
let inv1 = new cv.Mat();
let inv2 = new cv.Mat();
let inv3 = new cv.Mat();
let inv4 = new cv.Mat();
let data1 = new Float32Array([1, 0, 0,
0, 1, 0,
0, 0, 1]);
let data2 = new Float32Array([0, 0, 0,
0, 5, 0,
0, 0, 0]);
let data3 = new Float32Array([1, 1, 1, 0,
0, 3, 1, 2,
2, 3, 1, 0,
1, 0, 2, 1]);
let data4 = new Float32Array([1, 4, 5,
4, 2, 2,
5, 2, 2]);
let expected1 = new Float32Array([1, 0, 0,
0, 1, 0,
0, 0, 1]);
// Inverse does not exist!
let expected3 = new Float32Array([-3, -1/2, 3/2, 1,
1, 1/4, -1/4, -1/2,
3, 1/4, -5/4, -1/2,
-3, 0, 1, 1]);
let expected4 = new Float32Array([0, -1, 1,
-1, 23/2, -9,
1, -9, 7]);
let dataPtr1 = cv._malloc(3*3*4);
let dataPtr2 = cv._malloc(3*3*4);
let dataPtr3 = cv._malloc(4*4*4);
let dataPtr4 = cv._malloc(3*3*4);
let dataHeap = new Float32Array(cv.HEAP32.buffer, dataPtr1, 3*3);
dataHeap.set(new Float32Array(data1.buffer));
let dataHeap2 = new Float32Array(cv.HEAP32.buffer, dataPtr2, 3*3);
dataHeap2.set(new Float32Array(data2.buffer));
let dataHeap3 = new Float32Array(cv.HEAP32.buffer, dataPtr3, 4*4);
dataHeap3.set(new Float32Array(data3.buffer));
let dataHeap4 = new Float32Array(cv.HEAP32.buffer, dataPtr4, 3*3);
dataHeap4.set(new Float32Array(data4.buffer));
let mat1 = new cv.Mat(3, 3, cv.CV_32FC1, dataPtr1, 0);
let mat2 = new cv.Mat(3, 3, cv.CV_32FC1, dataPtr2, 0);
let mat3 = new cv.Mat(4, 4, cv.CV_32FC1, dataPtr3, 0);
let mat4 = new cv.Mat(3, 3, cv.CV_32FC1, dataPtr4, 0);
QUnit.assert.deepEqualWithTolerance = function( value, expected, tolerance ) {
for (let i = 0; i < value.length; i= i+1) {
this.pushResult( {
result: Math.abs(value[i]-expected[i]) < tolerance,
actual: value[i],
expected: expected[i],
} );
}
};
cv.invert(mat1, inv1, 0);
// Verify result.
let size = inv1.size();
assert.equal(inv1.channels(), 1);
assert.equal(size.height, 3);
assert.equal(size.width, 3);
assert.deepEqualWithTolerance(inv1.data32F, expected1, 0.0001);
cv.invert(mat2, inv2, 0);
// Verify result.
assert.deepEqualWithTolerance(inv3.data32F, expected3, 0.0001);
cv.invert(mat3, inv3, 0);
// Verify result.
size = inv3.size();
assert.equal(inv3.channels(), 1);
assert.equal(size.height, 4);
assert.equal(size.width, 4);
assert.deepEqualWithTolerance(inv3.data32F, expected3, 0.0001);
cv.invert(mat3, inv3, 1);
// Verify result.
assert.deepEqualWithTolerance(inv3.data32F, expected3, 0.0001);
cv.invert(mat4, inv4, 2);
// Verify result.
assert.deepEqualWithTolerance(inv4.data32F, expected4, 0.0001);
cv.invert(mat4, inv4, 3);
// Verify result.
assert.deepEqualWithTolerance(inv4.data32F, expected4, 0.0001);
mat1.delete();
mat2.delete();
mat3.delete();
mat4.delete();
inv1.delete();
inv2.delete();
inv3.delete();
inv4.delete();
}
//Rotate
{
let dst = new cv.Mat();
let src = cv.matFromArray(3, 2, cv.CV_8U, [1,2,3,4,5,6]);
cv.rotate(src, dst, cv.ROTATE_90_CLOCKWISE);
size = dst.size();
assert.equal(size.height, 2, "ROTATE_HEIGHT");
assert.equal(size.width, 3, "ROTATE_WIGTH");
let expected = new Uint8Array([5,3,1,6,4,2]);
assert.deepEqual(dst.data, expected);
dst.delete();
src.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());
});