注意:我在make的时候被卡住了好半天。由于后台在补全(下载)package,跟网络有关 实践证实,没有build文件夹直接使用newbuild是能够的。(另外,第二个教程里教你配置环境变量F:\OCV\opencv\newbuild\install\x64\vc15\bin)
个人Github连接:https://github.com/SylvesterLi/MyOpenCVCodegit
Here is my README.mdgithub
maybe someday it goes to work out my questions~shell
2018/08/25 Now we come a new stage and I should learn more professional knowledge.网络
What I have learnt fromapp
This tells you how to normally setupide
https://blog.csdn.net/jia20003/article/details/54583431ui
and when you got some trouble:Pls check your network(My home network trapped me for a long while)this
Problems such asgoogle
Download face_landmark_model.dat Failed (or time out)
missing ffmpeg
.net
but the download is real slow.
you could use your mobile and provide hotspot for your PC.(As I done)
if auto-download can't do to help, you could check these issues :
https://github.com/opencv/opencv_contrib/issues
At last ,google can be your best driver.
Finally, got it! After check and check again, it works!
Below is the code of printing all files' name in current catalog
Just paste it in the PowerShell
Attention : the F:\OCV\opencv\newbuild\install\x64\vc15\lib
is my file directory path
Get-ChildItem F:\OCV\opencv\newbuild\install\x64\vc15\lib | ForEach-Object -Process{ if($_ -is [System.IO.FileInfo]) { Write-Host($_.name); } }
## Update 3
In the OCV3 Project I don't use src as default input image but use img_1.Which leads me misleading and make some mistakes. In the last days, I should take care of this!!
## Update 4
These two days, i just watch corner detection, one of detection methods is Harris , and another is Shi-Tomasi Corner detetion. In my point of view , Good Features To Track (aka Shi-Tomasi) performs better than Harris detection .
But both of them did not mark the top of roof which human could easily recognize.
Pic blow is good feature to track
and till now, I should have finished custom corner detection, but I think it is unnecessary to learn. When I meet such kind of projects or problems, I would come back have a careful seek.
These two days I have tried SURF and SIFT,both of them are using to detect KeyPoints in the image which is hard for human beings to recognize what it is.
And the result of Experiments is that there are not so much differences between SURF and SIFT, but you still say, the KeyPoints of image shows their own features, which we can conclude that SIFT seems better?
Almost forget to say, their sample code looks same.
//SIFT跟SURF代码是如出一辙的 //numOfFeatures指的是特征点的个数 int numOfFeatures = 400; //如今建立检测器 Ptr<SIFT> detector = SIFT::create(numOfFeatures); vector<KeyPoint> keypoints;//存到这来 //检测 detector->detect(src, keypoints); Mat kpImage; //绘制关键点 drawKeypoints(src, keypoints, kpImage); namedWindow("result", WINDOW_AUTOSIZE); imshow("result", kpImage);
Comming soon....
These days I am too lazy to update, but from now on. I will keep code updating!!!