2016年7月2日 星期六

OpenCV 筆記 - Basic Operation

本文主要參考 Marc 的 OpenCV 筆記

OpenCV 主要 support C++ 和 Python 兩種 interfaces.

之前主要以 python 為主。不過在某些情況 C++ 的 computation efficiency 有優勢。

因此熟悉基本操作仍有需要。

基本操作 (Base Operation)

OpenCV 最重要的 data format 是 (2-D) matrix.  因為這是多數影像的型態。  

 

Basic Matrix Operation:  OpenCV 常會用到 cv::Mat 陣列。

如何建立矩陣? 直接宣告矩陣大小和型態。

Mat image(240, 320, CV_8UC3);

image size 是 240x320.  格式是 CV_8UC3 -> CV_<bit-depth> {U|S|F}C(<number of channels>)

U 是 unsigned integer type, S 是 signed integer type, and F 是 float type.

所以 8UC3 就是 8bit, unsigned integer, 3 channels (e.g. RGB or actually BGR image).

 

重新分配矩陣大小

image.create(480, 640, CV_8UC3);

 

宣告矩陣並設定初始值 (下例都初始化為 5), 和 matlab 類似

Mat A33(3, 3, CV_32F, Scalar(5));

另一種方式是用 ones matrix

Mat B33 = Mat::ones(3, 3, CV_32F)*5.;

或是用 zeros matrix

Mat C33 = Mat::zeros(3, 3, CV_32F) + 5.;

以上的方法似乎只用於 1 channel case.

如果宣告 3 channels.  只有 channel 1 有初始值。

 

如果要 3 channels 都有初始值。可以用以下的方法:

Mat img1(5, 5, CV_8UC1, Scalar(9) );

Mat dst;

cvtColor(img1, dst, CV_GRAY2RGB);

此是 dst 會是 CV_8UC3 且初始化為 9.

 

宣告指定數值的矩陣

Mat A22 = Mat_<float>(2,2) << 5, -15, 20, -15);

或是

float B22data[] = { 5, -15, 20, -15};

Mat B22 = Mat(2, 2, CV_32F, B22data).clone();

註: 用 clone 會複製到新的 memory.

如果不用 clone 也可以。但變成 pointer 而非 memory copy.

如果 B22data 改變，也會改變 B22.

 

 

轉移矩陣但不複製 memory

cv::Mat 轉換成 cv::Mat

Mat image_alias = image

 

將一般陣列轉換成 cv::Mat

float* Idata = new float[480*640*3];

Mat I(480, 640, CV_32FC3, Idata); 

 

將vector陣列轉換成cv::Mat

vector<Point> iptvec(10); Mat dstMat(iptvec);

將IplImage轉換成cv::Mat

IplImage* iMG = cvCreateImage(cvSize(320,240), 16, 1); Mat dstMat = cvarrToMat(iMG);

將cv::Mat轉換成IplImage

Mat srcMat; IplImage iMG = srcMat;

將cv::Mat轉換成CvMat

Mat srcMat; CvMat dstCvMat = srcMat;

轉移矩陣且複製 memory

將IplImage複製給cv::Mat

IplImage* iMG = cvCreateImage(cvSize(320,240), 16, 1); Mat dstMat = cvarrToMat(iMG).clone();

將cv::Mat複製給vector

Mat srcMat; vector<Point2f> ptvec = Mat_<Point2f>(srcMat);

cv::Mat 合併、插入、移除

cv::Mat hMat(5,1,CV_8UC1,cv::Scalar(2)); cv::Mat sMat(5,1,CV_8UC1,cv::Scalar(5)); cv::Mat tmpMat(5,2, CV_8UC1 ); hMat.copyTo( tmpMat.col(0) ); sMat.copyTo( tmpMat.col(1) ); /* 25 25 25 25 25 */

合併cv::Mat

cv::Mat mat1(1,3, CV_8UC1,cv::Scalar(2)); cv::Mat mat2(2,3, CV_8UC1,cv::Scalar(3)); cv::Mat matM; matM.push_back(mat1); matM.push_back(mat2); /* 222 333 333 */

型態轉換(int、float、double)

** CV_8UC1 to CV_32FC1

cv::Mat mat8U(3,2,CV_8UC1,cv::Scalar(2)); cv::Mat mat32F; mat8U.convertTo(mat32F, CV_32FC1);

陣列運算cv::Mat

計算陣列中非0值的數量

cv::Mat src(5,3,CV_8UC1, cv::Scalar(3)); int total = cv::countNonZero(src); std::cout<<total; //15

 

 

 

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2016年6月25日 星期六

Install Octave in Ubuntu

 

Step1

> sudo apt-get infall octave

> sudo apt-get install octave-control octave-image octave-signal

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2016年5月11日 星期三

Install OpenCV3 on Raspberry Pi 3 (Raspbian Jessie)

本文主要參考 pyimagesearch

http://www.pyimagesearch.com/2015/10/26/how-to-install-opencv-3-on-raspbian-jessie/

 

OpenCV 目前是 de facto computer vision 的開放平台以及提供許多範例。

除了 kernel 是  C/C++ 提供高效率外，另外有多種 interface 如 python or matlab.

OpenCV 唯一比較麻煩的是沒有一鍵 installation, 或是 apt-get or rpm. 

需要 download OpenCV source code 重新 compile.  

 

如果要使用 python interface, Anaconda 沒有直接包含 OpenCV, 需要 conda install (menpo) reposite.

Enthought Canopy 是有包 OpenCV 2.4.9 (舊的版本).  如果要用最新的版本 (3.1.0 at 2016/5/11), 還是要自己 compile.

 

因為 OpenCV 仍然持續 evolve.  為了要得到最新版本，還是要知道如何 compile source code.

 

Step #1: Install dependencies

The first thing we should do is update and upgrade any existing packages, followed by updating the Raspberry Pi firmware.

Timing: 3m 33s

You’ll need to reboot your Raspberry Pi after the firmware update:

Now we need to install a few developer tools:

Timing: 51s

Now we can move on to installing image I/O packages which allow us to load image file formats such as JPEG, PNG, TIFF, etc.:

Timing: 42s

Just like we need image I/O packages, we also need video I/O packages. These packages allow us to load various video file formats as well as work with video streams:

Timing: 58s

We need to install the GTK development library so we can compile the highgui  sub-module of OpenCV, which allows us to display images to our screen and build simple GUI interfaces:

Timing: 2m 48s

Various operations inside of OpenCV (such as matrix operations) can be optimized using added dependencies:

Timing: 50s

Lastly, we’ll need to install the Python 2.7 and Python 3 header files so we can compile our OpenCV + Python bindings:

 

Step #2: Grab the OpenCV source code

At this point we have all of our prerequisites installed, so let’s grab the 3.1.0  version of OpenCV from the OpenCV repository. (Note: As future versions of OpenCV are released just replace the 3.1.0  with the most recent version number):

Timing: 2m 29s

For the full install of OpenCV 3 (which includes features such as SIFT and SURF), be sure to grab the opencv_contrib repo as well. (Note: Make sure your opencv  and opencv_contrib  versions match up, otherwise you will run into errors during compilation. For example, if I download v3.0.0 of opencv , then I’ll want to download v3.0.0 of opencv_contrib  as well):

Timing: 1m 54s

Step #3: Setup Python

The first step in setting up Python for our OpenCV compile is to install pip , a Python package manager:

Timing: 26s

I’ve discussed both virtualenv and virtualenvwrapper many times on the PyImageSearch blog before, especially within these installation tutorials. Installing these packages is certainly not a requirement to get OpenCV and Python up and running on your Raspberry Pi, but I highly recommend that you install them!

Using virtualenv  and virtualenvwrapper  allows you to create isolated Python environments, separate from your system install of Python. This means that you can run multiple versions of Python, with different versions of packages installed into each virtual environment — this solves the “Project A depends on version 1.x, but Project B needs 4.x” problem that often arises in software engineering.

Again, it’s standard practice in the Python community to use virtual environments, so I highly suggest that you start using them if you are not already:

Timing: 17s

After virtualenv  and virtualenvwrapper  have been installed, we need to update our~/.profile  file and insert the following lines at the bottom of the file:

You can use your favorite editor to edit this file, such as vim , emacs , nano , or any other graphical editor included in the Raspbian Jessie distribution. Again, all you need to do is open the file located at /home/pi/.profile  and insert the lines above at the bottom of the file.

Now that your ~/.profile  has been updated, you need to reload it so the changes can take affect. To force a reload of the ~/.profile  file you can (1) logout and log back in, (2) close your terminal and open up a new one, or (3) just use the source  command:

Note: You’ll likely need to run the source ~/.profile  command each time you open up a new terminal to ensure your environment has been setup correctly.

The next step is to create our Python virtual environment where we’ll be doing our computer vision work:

The above command will create a virtual environment named cv  using Python 2.7.

If you want Python 3, run this command instead:

Again, it’s important to note that the cv  Python environment is entirely independent from the default version of Python included in the download of Raspbian Jesse.

If you ever reboot your system, logout and log back in, or open up a new terminal, you’ll need to use the workon  command to re-access the cv  virtual environment, otherwise you’ll be using the system version of Python instead:

You can ensure you are in the cv  virtual environment by examining your command line. If you see the text “(cv)” preceding your prompt, then you are in the cv  virtual environment:

Figure 1: Make sure you see the "(cv)" text on your prompting, indicating that you are in the cv virtual environment.

Figure 1: Make sure you see the “(cv)” text on your prompting, indicating that you are in the cv virtual environment.

Otherwise, you are not in the cv  virtual environment:

Figure 2: If you do not see the "(cv)" text on your prompt, then you are not in the cv virtual environment.

Figure 2: If you do not see the “(cv)” text on your prompt, then you are not in the cv virtual environment.

If this is the case, you need to run the source  and workon  commands above.

Assuming that you are in the cv  virtual environment, we can install NumPy, an important dependency when compiling the Python bindings for OpenCV. You might want to grab a cup of coffee or go for a walk while NumPy downloads and installs:

Timing: 16m 10s

Step #4: Compile and install OpenCV

At this point, we are ready to compile OpenCV.

First, make sure you are in the cv  virtual environment:

Followed by setting up the build:

Update (3 January 2016): In order to build OpenCV 3.1.0 , you need to set -DINSTALL_C_EXAMPLES=OFF  (rather than ON ) in the cmake  command. There is a bug in the OpenCV v3.1.0 CMake build script that can cause errors if you leave this switch on. Once you set this switch to off, CMake should run without a problem.

Before you move on to the compilation step, make sure you examine the output of CMake!

Scroll down the section titled Python 2  and Python 3 .

If you’re compiling OpenCV 3 for Python 2.7, then you’ll want to make sure the Python 2  section looks like this (highlighted) in red:

Figure 3: Ensuring that Python 2.7 will be used for the compile.

Figure 3: Ensuring that Python 2.7 will be used for the compile.

Notice how both the Interpreter  and numpy  variables point to the cv  virtual environment.

Similarly, if you’re compiling OpenCV for Python 3, then make sure the Python 3  section looks like this:

Figure 4: Ensuring that Python 3 will be used for the compile.

Figure 4: Ensuring that Python 3 will be used for the compile.

Again, both the Interpreter  and numpy  variables are pointing to our cv  virtual environment.

In either case, if you do not see the cv  virtual environment for these variables MAKE SURE YOU ARE IN THE cv  VIRTUAL ENVIRONMENT PRIOR TO RUNNING CMAKE!

Now that our build is all setup, we can compile OpenCV:

Timing: 1h 35m

The -j4  switch stands for the number of cores to use when compiling OpenCV. Since we are using a Raspberry Pi 2, we’ll leverage all four cores of the processor for a faster compilation.

However, if your make  command errors out, I would suggest starting the compilation over again and only using one core:

Using only one core will take much longer to compile, but can help reduce any type of strange race dependency condition errors when compiling.

Assuming OpenCV compiled without error, all we need to do is install it on our system:

 

Step #5: Finishing the install

We’re almost there! Just a few more things and we’ll be 100% done.

For Python 2.7:

Provided you finished Step #4 without error, OpenCV should now be installed in/usr/local/lib/python2.7/site-packages :

Note: In some instances OpenCV can be installed in /usr/local/lib/python2.7/dist-packages  (note the dist-packages  rather than site-packages ). If you do not find thecv2.so  bindings in site-packages , be sure to check dist-packages  as well.

The last step here is to sym-link the OpenCV bindings into the cv  virtual environment:

 

For Python 3:

OpenCV should now be installed in /usr/local/lib/python3.4/site-packages :

For some reason, unbeknownst to me, when compiling the Python 3 bindings the output .so  file is named cv2.cpython-34m.so  rather than cv2.so .

Luckily, this is an easy fix. All we need to do is rename the file:

Followed by sym-linking OpenCV into our cv  virtual environment:

 

Step #6: Verifying your OpenCV 3 install

At this point, OpenCV 3 should be installed on your Raspberry Pi running Raspbian Jessie!

But before we wrap this tutorial up, let’s verify that your OpenCV installation is working by accessing the cv  virtual environment and importing cv2 , the OpenCV + Python bindings:

You can see a screenshot of my terminal below, indicating that OpenCV 3 has been successfully installed:

Figure 5: OpenCV 3 + Python 3 bindings have been successfully installed on my Raspberry Pi 2 running Rasbian Jessie.

Figure 5: OpenCV 3 + Python 3 bindings have been successfully installed on my Raspberry Pi 2 running Rasbian Jessie.

 

 

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