Opencv 2 1 C Ubuntu Server

Opencv 2 1 C Ubuntu Server Rating: 3,5/5 6944votes

Opencv 2 1 C Ubuntu Server UpdateUnder VirtualBox i am having the error no screens found when usingSystemRescueCD 4. But only when booting in UEFI mode EFI checkBox checked. Start a vnc server in Rpi for viewing the screen and detection window from opencv. OpenCV Open Source Computer Vision is a crossplatform library of programming functions for real time computer vision. It was officially launched by Intel in 1999. Opencv 2 1 C Ubuntu Server CommandsOpencv 2 1 C Ubuntu ServerInstalling Open. CV 2. Ubuntu 1. 1. Sebastian Montabone. UPDATE You can also install Open. CV 3. 2. 0 in Ubuntu 1. LTS. Many people have used my previous tutorial about installing Open. CV 2. 1 in Ubuntu 9. In the comments of that post, I noticed great interest for using Open. Opencv 2 1 C Ubuntu Server Tutorial' title='Opencv 2 1 C Ubuntu Server Tutorial' />UPDATE You can also install OpenCV 3. Ubuntu 16. 04LTS. Many people have used my previous tutorial about installing OpenCV 2. Ubuntu 9. 10. CV with Python and the Intel Threading Building Blocks TBB. Since new versions of Open. CV and Ubuntu are available, I decided to create a new post with detailed instructions for installing the latest version of Open. CV, 2. 2, in the latest version of Ubuntu, 1. Python and TBB support. First, you need to install many dependencies, such as support for reading and writing image files, drawing on the screen, some needed tools, etc This step is very easy, you only need to write the following command in the Terminal. Now we need to get and compile the ffmpeg source code so that video files work properly with Open. CV. This section is partially based on the method discussed here. The next step is to get the Open. CV 2. 2 code. wget http downloads. Open. CV 2. 2. 0. Open. CV 2. 2. 0. Open. CV 2. 2. 0. Now we have to generate the Makefile by using cmake. In here we can define which parts of Open. CV we want to compile. Since we want to use Python and TBB with Open. CV, here is where we set that. Just execute the following line at the console to create the appropriate Makefile. Note that there is a dot at the end of the line, it is an argument for the cmake program and it means current directory. D WITHTBBON D BUILDNEWPYTHONSUPPORTON D WITHV4. LOFF D INSTALLCEXAMPLESON D INSTALLPYTHONEXAMPLESON D BUILDEXAMPLESON. Check that the above command produces no error and that in particular it reports FFMPEG as 1. If this is not the case you will not be able to read or write videos. Also, check that Python reports ON and Python numpy reports YES. Also, check that under Use TBB it says YES. If anything is wrong, go back, correct the errors by maybe installing extra packages and then run cmake again. You should see something similar to this Now, you are ready to compile and install Open. CV 2. 2. sudo make install. Now you have to configure Open. CV. First, open the opencv. Add the following line at the end of the fileit may be an empty file, that is ok and then save it. Run the following code to configure the library. Now you have to open another file. Add these two lines at the end of the file and save it. PKGCONFIGPATHPKGCONFIGPATH usrlocallibpkgconfig. PKGCONFIGPATH. Finally, close the console and open a new one, restart the computer or logout and then login again. Open. CV will not work correctly until you do this. There is a final step to configure Python with Open. CV. You need to copy the file cv. You can do that by just executing the following command. Now you have Open. CV 2. 2 installed in your computer with Python and TBB support. Lets check some demos included in Open. CV. First, lets see some C demos. Open. CV 2. 2. 0samplesc. Some of the training data for object detection is stored in usrlocalshareopencvhaarcascades. You need to tell Open. CV which training data to use. I will use one of the frontal face detectors available. Lets find a face. Note the scale parameter. It allows you to increase or decrease the size of the smallest object found in the image faces in this case. Smaller numbers allows Open. CV to find smaller faces, which may lead to increasing the number of false detections. Also, the computation time needed gets larger when searching for smaller objects. You can also detect smaller objects that are inside larger ones. For example you can search for eyes inside any detected face. You can do that with the nested cascade parameter. Feel free to experiment with other features like mouth or nose for example using the corresponding cascades provided in the haarcascades directory. Now lets check some C demos. Open. CV 2. 2. 0samplescpp. Now all the C demos are built in Open. CV 2. 2. 0bin. Lets see a couple of them. For example, a simulated chessboard calibration. Open. CV 2. 2. 0bincalibrationartificial. In Open. CV 2. 2, the grabcut algorithm is provided as a C sample. This is a very nice segmentation algorithm that needs very little user input to segment the objects in the image. For using the demo, you need to select a rectangle of the area you want to segment. Then, hold the Control key and left click to select the background in Blue. After that, hold the Shift key and left click to select the foreground in Red. Then press the n key to generate the segmentation. You can press n again to continue to the next iteration of the algorithm. Open. CV 2. 2. 0bingrabcut Open. Member Of Active Directory Query String here. CV 2. 2. 0samplescpplena. This image shows the initial rectangle for defining the object that I want to segment. Now I roughly set the foreground red and background blue. When you are ready, press the n key to run the grabcut algorithm. This image shows the result of the first iteration of the algorithm. Now lets see some background subtraction from a video. The original video shows a hand moving in front of some trees. Open. CV allows you to separate the foreground hand from the background trees. Open. CV 2. 2. 0binbgfgsegm Open. CV 2. 2. 0samplesctree. And finally, lets see Python working with Open. CV. cd Open. CV 2. Lets run the kmeans. This script starts with randomly generated 2. D points and then uses a clustering method called k means. Each cluster is presented in a different color. Now lets see the convexhull. This algorithm basically calculates the smallest convex polygon that encompasses the data points. Python scripts can also be executed directly like the following example. This script reads a video file. Have fun with Open. CV in C, C or Python. Installing Open. CV 2. Visual C 2. 01. Express. Installing Open. CV 2. First, get Open. CV 2. Its a self extracting so just double click to start the installation. Install it in a directory, say C. Wait until all files get extracted. It will create a new directory C opencv which. Open. CV header files, libraries, code samples, etc. Now you need to add the directory C opencvbuildx. PATH. This directory contains Open. CV DLLs required for running your code. Open Control Panel System Advanced system settings Advanced Tab Environment variables. On the System Variables section, select Path 1, Edit 2, and type C opencvbuildx. Ok. On some computers, you may need to restart your computer for the system to recognize the environment path variables. This will completes the Open. CV 2. 4. 3 installation on your computer. Create a new project and set up Visual COpen Visual C and select File New Project. Visual C Empty Project. Give a name for your project e. Click Ok. Visual C will create an empty project. Make sure that Debug is selected in the solution configuration combobox. Right click cvtest and select Properties VC Directories. Select Include Directories to add a new entry and type C opencvbuildinclude. Click Ok to close the dialog. Back to the Property dialog, select Library Directories to add a new entry and type C opencvbuildx. Click Ok to close the dialog. Back to the property dialog, select Linker Input Additional Dependencies to add new entries. On the popup dialog, type the files below opencvcalib. Note that the filenames end with d for debug. Also note that if you have installed another version of Open. CV say 2. 4. 9 these filenames will end with 2. Click Ok to close the dialog. Click Ok on the project properties dialog to save all settings. NOTE These steps will configure Visual C for the Debug solution. For Release solution optional, you need to. Open. CV directories and in Additional. Dependencies section, use opencvcore. Youve done setting up Visual C, now is the time to write the real code. Right click your project and select Add New Item. File Open Save Dialog Box. Visual C C File. Name your file e. Ok. Type the code below in the editor include lt opencv. Mat im imreadc fullpathtolena. Cannot load image lt lt endl. Image, im. wait. Key0. The code above will load c fullpathtolena. You can. use any image you like, just make sure the path to the image is correct. Type F5 to compile the code, and it will display the image in a nice window. And that is your first Open. CV program 3. Where to go from here Now that your Open. CV environment is ready, whats next Go to the samples dir c opencvsamplescpp. Read and compile some code. Write your own code.