前言

东莞，晴，29至27度。忙了一天，最终能够写写东西了。今天继续昨天的话题，我们在昨天的例了基础上完好，通过匹配关键点求出映射从而找到场景中的已知对象。

目标

本文你将学习

1. 採用nodeitk的findHomography和perspectiveTransform进行对象识别。
2. 此外，样例基本包括nodeitk的一些基本数据结构的使用：NodeOpenCVMat, NodeOpenCVKeyPoint, NodeOpenCVPoint
3. 上述主要的数据结构在nodeitk版本号稳定后将会在使用手冊中说明

代码

var node_itk = require('./node-itk');var img_object = node_itk.cv.imread( "./images/box.png", node_itk.cv.CV_LOAD_IMAGE_GRAYSCALE );var img_scene = node_itk.cv.imread( "./images/box_in_scene.png", node_itk.cv.CV_LOAD_IMAGE_GRAYSCALE );minHessian = 400detector = new node_itk.cv.NodeOpenCVFeatureDetector("SURF")detector.Set("hessianThreshold", minHessian)keypoints_object = detector.Detect( img_object );keypoints_scene = detector.Detect( img_scene );extractor = new node_itk.cv.NodeOpenCVDescriptorExtractor("SURF");descriptors_object = extractor.Compute(img_object, keypoints_object)descriptors_scene = extractor.Compute(img_scene, keypoints_scene)matcher = new node_itk.cv.NodeOpenCVDescriptorMatcher("FlannBased");matches = matcher.Match(descriptors_object, descriptors_scene);max_dist=0min_dist=100for (var i = 0; i < descriptors_object.Rows(); i++ ) {	dist = matches[i].GetDistance();	if (dist < min_dist) min_dist = dist;	if (dist > max_dist) max_dist = dist;};console.log("-- Max dist : " + max_dist + "\n")console.log("-- Min dist : " + min_dist + "\n")var good_matches = [];for( var i = 0; i < descriptors_object.Rows(); i++ ){ 	if( matches[i].GetDistance() <= 3*min_dist )	{ good_matches.push( matches[i] ); }}img_matches = node_itk.cv.DrawMatches(img_object, keypoints_object, img_scene, keypoints_scene, good_matches);var obj=[], scene=[];for (var i = 0; i < good_matches.length; i++) {	obj.push( keypoints_object[good_matches[i].GetQueryIdx()].PT() )	scene.push( keypoints_scene[good_matches[i].GetTrainIdx()].PT() )};H = node_itk.cv.FindHomography( obj, scene, node_itk.cv.CV_RANSAC );obj_corners = []obj_corners[0] = new node_itk.cv.NodeOpenCVPoint("Point2d", [0,0])obj_corners[1] = new node_itk.cv.NodeOpenCVPoint("Point2d", [img_object.Cols(),0])obj_corners[2] = new node_itk.cv.NodeOpenCVPoint("Point2d", [img_object.Cols(),img_object.Rows()])obj_corners[3] = new node_itk.cv.NodeOpenCVPoint("Point2d", [0,img_object.Rows()])tmp = new node_itk.cv.NodeOpenCVPoint("Point2d", [img_object.Cols(),0]);color = new node_itk.cv.NodeOpenCVScalar("Scalar", [0,255,0]);scene_corners = node_itk.cv.PerspectiveTransform(obj_corners, H.res);node_itk.cv.Line(img_matches, scene_corners[0].Add(tmp), scene_corners[1].Add(tmp), color, 2)node_itk.cv.Line(img_matches, scene_corners[1].Add(tmp), scene_corners[2].Add(tmp), color, 2)node_itk.cv.Line(img_matches, scene_corners[2].Add(tmp), scene_corners[3].Add(tmp), color, 2)node_itk.cv.Line(img_matches, scene_corners[3].Add(tmp), scene_corners[0].Add(tmp), color, 2)node_itk.cv.NamedWindow( "Good Matches & Object detection", node_itk.cv.CV_WINDOW_AUTOSIZE );node_itk.cv.imshow( "Good Matches & Object detection", img_matches );node_itk.cv.WaitKey ( 0 );

结果

小结

本文是昨天话题的深化，代码依旧比較简洁。这是nodeitk遵循的原则：以简单的方式高速实现图像处理应用。喜欢的朋友就点踩，想说点东西的就评论吧！^_^ 待续