// ////////////////////////////////////////////////////////////////////////////////////// // // 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('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); let 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()); });