1. Lecture 10 – Image Processing with OpenCV JJCAO
Introduction to C & C++
Lecture 10 – Image Processing with OpenCV
JJCAO

2. OpenCV (Open Source Computer Vision) is a library of programming functions for real time computer vision.

3. Supported Development Environment
Linux, Windows, Android
GCC, Eclipse, Visual Studio,

4. Install OpenCV
Basic Info:
Necessary steps for Qt Creator (Similar with VS):
Installation in Windows (too much of details):

5. How to build applications with OpenCV inside the Microsoft Visual Studio
P11 of OpenCV.2.Computer.Vision.Application.Programming.Cookbook_2011

6. Load & Display an Image Load an image (using imread)
Create a named OpenCV window (using namedWindow)
Display an image in an OpenCV window (using imshow)

7. 1. Load an image (using imread)
cv::Mat image;
image = cv::imread(argv[1], CV_LOAD_IMAGE_COLOR); // Read the file
if( img.empty() ) // Check for invalid input {
std::cout << "Could not open or find the image" << std::endl ;
return -1; }
Image formats supported: Bmp, jpg, png, tif, ppm, …
CV_LOAD_IMAGE_UNCHANGED (<0)
loads the image as is (including the alpha channel if present)
CV_LOAD_IMAGE_GRAYSCALE (0)
loads the image as an intensity one
CV_LOAD_IMAGE_COLOR (>0)
loads the image in the RGB format

8. 2. Create a named OpenCV window (using namedWindow)
cv::namedWindow( "Display window", CV_WINDOW_NORMAL|CV_WINDOW_FREERATIO );// Create a window for display.
CV_WINDOW_AUTOSIZE
is the only supported one if you do not use the Qt backend. In this case the window size will take up the size of the image it shows. No resize permitted!
CV_WINDOW_NORMAL
on Qt you may use this to allow window resize. The image will resize itself according to the current window size. By using the | operator you also need to specify if you would like the image to keep its aspect ratio (CV_WINDOW_KEEPRATIO) or not (CV_WINDOW_FREERATIO).

9. 3. Display an image in an OpenCV window (using imshow)
cv::imshow( "Display window", image ); // Show our image inside it. cv::waitKey(0); // Wait for a keystroke in the window
Because we want our window to be displayed until the user presses a key (otherwise the program would end far too quickly), we use the waitKey function whose only parameter is just how long should it wait for a user input (measured in milliseconds).
Zero means to wait forever.

10. Necessary Head Files #include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
using namespace cv;
using namespace std;
You’ll almost always end up using the:
core section, as here are defined the basic building blocks of the library
highgui module, as this contains the functions for input, output & GUI operations

11. Necessary libraries #ifdef _DEBUG
#pragma comment(lib, "opencv_core230d.lib“ )
#pragma comment(lib, "opencv_highgui230d.lib“ )
#else
#pragma comment(lib, "opencv_core230.lib“ )
#pragma comment(lib, "opencv_highgui230.lib“ )
#endif

12. Congratulation!

13. From C to C++
OpenCV has been around ever since In those days the library was built around a C interface.
user is responsible for taking care of memory allocation and de-allocation
Lots of old tutorials written in C
Once your code start to grow larger & larger, more & more a struggle to handle this rather than focusing on solving your goal
Finally C++
automatic memory management (more or less)
less to write, to achieve more
While this is no issue in case of smaller programs once your code base start to grove larger and larger it will be more and more a struggle to handle all this rather than focusing on actually solving your development goal.

14. int dims; //! the array dimensionality, >= 2
class CV_EXPORTS Mat {
public:
// ... a lot of methods
/*! includes several bit-fields:
the magic signature
continuity flag
depth
number of channels */
int flags;
int dims; //! the array dimensionality, >= 2
int rows, cols; //! the number of rows and columns or (-1, -1) when the array has more than 2 dimensions
int* refcount; //! pointer to the reference counter; when array points to user-allocated data, the pointer is NULL
// other members ...
uchar* data; //! pointer to the data };
Head

15. Create a cv::Mat Mat A, C; // creates just the header parts
A = imread(argv[1], CV_LOAD_IMAGE_COLOR); // the method will allocate matrix (deep copy)
Mat B(A); // Use the copy constructor, without copying the data (shadow copy)
C = A; // Assignment operator, shadow copy
Mat roi(A, Rect(10,10,100,100)); // select a ROI
roi = Scalar(0,255,0); // fill the ROI with (0,255,0) (which is green in RGB space); the original A will be modified; see next page.

16. cv::Scalar
template <typename _Tp> class Scalar_ : public Vec<_Tp, 4> { ... }; typedef Scalar_<double> Scalar; Being derived from Vec<_Tp, 4> , Scalar_ and Scalar can be used just as typical 4-element vectors.

17. Deep Copy
Mat F = A.clone(); Mat G; A.copyTo(G); Now modifying F or G will not affect the matrix pointed by the Mat header.

18. What you need to remember
Output image allocation for OpenCV functions is automatic (unless specified otherwise).
Example (next page)
No need to think about memory freeing with OpenCVs C++ interface.
The assignment operator and the copy constructor (ctor)copies only the header.
Use the clone() or the copyTo() function to copy the underlying matrix of an image.

19. Output image allocation for OpenCV functions is automatic
instead of writing:
Mat color;
...
Mat gray(color.rows, color.cols, color.depth());
cvtColor(color, gray, CV_BGR2GRAY);
you can simply write:
Mat gray;

20. How to scan images, lookup table & time measurement
How to go through each and every pixel of an image?
How is OpenCV matrix values stored?
How to measure the performance of our algorithm?
What are lookup tables and why use them?
Basic Mat info
Storing methods
Data type conversion
Accessing Pixel Values

21. a simple color reduction method
how_to_scan_images imageName.jpg divideWith [G] if( argc == 4 && !strcmp(argv[3],"G") ) I = imread(argv[1], CV_LOAD_IMAGE_GRAYSCALE); else I = imread(argv[1], CV_LOAD_IMAGE_COLOR);
uchar
CV_8U
CV_8U3

22. Storing methods How to store pixel values? Color space Data type
Gray-level (Black-and-White)
unsigned 8-bit values
0: black, 255: white
RGB
A triplet of unsigned 8-bit values
[0, 0, 0]: black, [255, 0, 0]: red, [255, 255, 255]: white
HSV, HLS, …, CIE
Data type

23. bool Mat::isContinuous()
Matrices created with Mat::create() are always continuous:
may no longer continuous
extract a part of the matrix using Mat::col(), Mat::diag() , and so on,
or constructed a matrix header for externally allocated data.
row1
row2 …
row n

24. Storing methods How to store pixel values? Color space Data type
Gray-level (Black-and-White)
unsigned 8-bit values
0: black, 255: white
RGB
A triplet of unsigned 8-bit values
[0, 0, 0]: black, [255, 0, 0]: red, [255, 255, 255]: white
HSV, HLS, …, CIE
Data type
CV_8U - 8-bit unsigned integers ( 0..255 )
CV_8S - 8-bit signed integers (   )
CV_16U - 16-bit unsigned integers (   )
CV_16S - 16-bit signed integers (   )
CV_32S - 32-bit signed integers (   )
CV_32F - 32-bit floating-point numbers ( -FLT_MAX..FLT_MAX, INF, NAN )
CV_64F - 64-bit floating-point numbers ( -DBL_MAX..DBL_MAX, INF, NAN )

25. Inquire Mat Info int Mat::depth() const int Mat::channels() const
int Mat::type() // mixture of depth & channels
#define CV_8UC1 CV_MAKETYPE(CV_8U,1)
#define CV_8UC2 CV_MAKETYPE(CV_8U,2)
#define CV_8UC3 CV_MAKETYPE(CV_8U,3)
#define CV_8UC4 CV_MAKETYPE(CV_8U,4)
#define CV_8UC(n) CV_MAKETYPE(CV_8U,(n)) …
size_t Mat::elemSize() // matrix element (pixel) size in bytes
if the matrix type is CV_16SC3 , the method returns3*sizeof(short) or 6.
size_t Mat::elemSize1() // element size of a channel in bytes
if the matrix type is CV_16SC3 , the method returns sizeof(short) or 2.
M.step[]
size_t Mat::step1() // a matrix step divided by Mat::elemSize1()

26. Accessing Pixel Values
The efficient way: c style access
p = I.ptr<uchar>(i);
p[j]
The iterator (safe) method
On-the-fly address calculation with reference returning
I.at<uchar>(i,j)
Performance Difference
quite large (2560 X 1600) image

27. Performance Difference
Debug
Release

28. More efficient Algorithm
divide and multiplication operations are bloody expensive for a system.
cheaper operations such as a few subtractions, addition or in best case a simple assignment
limited number of input values, 256 to be exact in this problem

29. Lookup table
int divideWith; // convert our input string to number - C++ style stringstream s; s << argv[2]; s >> divideWith; if (!s) { cout << "Invalid number entered for dividing. " << endl; return -1; } uchar table[256]; for (int i = 0; i < 256; ++i) table[i] = divideWith* (i/divideWith);

30. Mat& ScanImageAndReduceC(Mat& I, const uchar* const table)
{ // accept only char type matrices CV_Assert(I.depth() != sizeof(uchar)); int channels = I.channels(); int nRows = I.rows; int nCols = I.cols * channels; if (I.isContinuous()) nCols *= nRows; nRows = 1; }
int i,j;
uchar* p;
for( i = 0; i < nRows; ++i) {
p = I.ptr<uchar>(i);
for ( j = 0; j < nCols; ++j)
p[j] = table[p[j]]; }
return I;

31. Basic Mat Info bool Mat::empty() size_t Mat::total()
int Mat::rows, Mat::cols
Size Mat::size()

32. Mat& ScanImageAndReduceIterator(Mat& I, const uchar* const table){
// accept only char type matrices
CV_Assert(I.depth() != sizeof(uchar));
const int channels = I.channels();
switch(channels)
case 1: {
MatIterator_<uchar> it, end;
for( it = I.begin<uchar>(), end = I.end<uchar>(); it != end; ++it)
*it = table[*it];
break; }
case 3: {
MatIterator_<Vec3b> it, end;
for( it = I.begin<Vec3b>(), end = I.end<Vec3b>(); it != end; ++it) {
(*it)[0] = table[(*it)[0]];
(*it)[1] = table[(*it)[1]];
(*it)[2] = table[(*it)[2]];
return I;

33. Mat& ScanImageAndReduceRandomAccess(Mat& I, const uchar* const table){
// accept only char type matrices
CV_Assert(I.depth() != sizeof(uchar));
const int channels = I.channels();
switch(channels)
case 1: {
for( int i = 0; i < I.rows; ++i)
for( int j = 0; j < I.cols; ++j ){
I.at<uchar>(i,j) =
table[I.at<uchar>(i,j)]; }
break;
case 3: {
Mat_<Vec3b> _I = I;
for( int i = 0; i < I.rows; ++i)
for( int j = 0; j < I.cols; ++j ) {
_I(i,j)[0] = table[_I(i,j)[0]];
_I(i,j)[1] = table[_I(i,j)[1]];
_I(i,j)[2] = table[_I(i,j)[2]]; }
I = _I;
break;
return I;

34. The Core Function: cv::LUT()
LUT: replace all of given image values to some other values
Mat lookUpTable(1, 256, CV_8U);
uchar* p = lookUpTable.data;
for( int i = 0; i < 256; ++i)
p[i] = table[i];
for (int i = 0; i < times; ++i)
cv::LUT(I, lookUpTable, J);

35. Conclusion
If possible, use the already made functions of OpenCV (instead reinventing these).
The fastest method turns out to be the LUT function. This is because the OpenCV library is multi-thread enabled via Intel Threaded Building Blocks.
However, if you need to write a simple image scan prefer the pointer method. The iterator is a safer bet, however quite slower.
Using the on-the-fly reference access method for full image scan is the most costly in debug mode. In the release mode it may beat the iterator approach or not, however it surely sacrifices for this the safety trait of iterators.

36. Type Conversion #include <opencv2/imgproc/imgproc.hpp>
cvtColor(I, J, CV_RGB2GRAY);
for( int i = 0; i < 0.5*I.rows; ++i)
for( int j = 0; j < 0.5*I.cols; ++j ) {
//J.at<uchar>(i,j) = 0;
J.at<float>(i,j) = 0; }
Mat::convertTo()

37. void Mat::convertTo(OutputArray m, int rtype, double alpha=1, double beta=0 )
Converts an array to another datatype with optional scaling.
Parameters:
m – Destination matrix. If it does not have a proper size or type before the operation, it is reallocated.
rtype – Desired destination matrix type or, rather, the depth since the number of channels are the same as the source has. If rtype is negative, the destination matrix will have the same type as the source.
alpha – Optional scale factor.
beta – Optional delta added to the scaled values.
The method converts source pixel values to the target datatype. saturate_cast<> is applied at the end to avoid possible overflows:

38. One More Example: gradient
infile='data\HappyFish.jpg'; im = imread(infile); info = imfinfo(infile); if ~strcmp(info.ColorType,'grayscale') im = double(rgb2gray(im)) ; end [gx,gy] = gradient(im); figure;colormap(gray); imagesc(im);hold on; [x,y] = meshgrid(1:n,1:m); quiver(x, y, gx,gy);

39. See the cvMatlab and cvMatlabTest example
void jj::gradient(cv::InputArray _src, cv::OutputArray _dst, int xorder) {
cv::Mat src=_src.getMat();
_dst.create( src.size(), CV_MAKETYPE(src.depth(), src.channels()) );
cv::Mat dst = _dst.getMat(); … }

40. See the cvMatlab and cvMatlabTest example
Difference in x direction
// single channel
Mat src, dest; // same size, diff depth
for( int i = 0; i < src.rows; ++i)
for( int j = 1; j < src.cols-1; ++j ) {
dest.at<uchar>(i,j) = (src.at<uchar>(i,j+1) - src.at<uchar>(i,j-1) ) /2.0; }
// 3 channels
dst.at<cv::Vec3b>(i,j)[0] = (src.at<cv::Vec3b>(i,j+1)[0] - src.at<cv::Vec3b>(i,j-1)[0] ) /2.0;
dst.col(j)=(src.col(j+1)-src.col(j-1))/2.0;

41. Create a Mat object 1 Mat() Mat M(2,2, CV_8UC3, Scalar(0,0,255));
cout << "M = " << endl << " " << M << endl << endl;
Create a matrix with more than two dimensions
int sz[3] = {2,2,2};
Mat L(3,sz, CV_8UC(1), Scalar::all(0)); // Specify its dimension, then pass a pointer containing the size for each dimension and the rest remains the same.
Create a header for an already existing IplImage pointer
IplImage* img = cvLoadImage("greatwave.png", 1);
Mat mtx(img); // convert IplImage* -> Mat

42. Create a Mat object 2 Create() function M.create(4,4, CV_8UC(2));
cout << "M = "<< endl << " " << M << endl << endl;
// You cannot initialize the matrix values with this construction. It will only reallocate its matrix data memory if the new size will not fit into the old one.
MATLAB style initializer: zeros(), ones(), :eyes()
Mat E = Mat::eye(4, 4, CV_64F);
Mat O = Mat::ones(2, 2, CV_32F);
Mat Z = Mat::zeros(3,3, CV_8UC1);

43. Create a Mat object 3 For small matrices
Mat C = (Mat_<double>(3,3) << 0, -1, 0, -1, 5, -1, 0, -1, 0);
Create a new header for an existing Mat object and clone() or copyTo() it
Mat RowClone = C.row(1).clone();

44. Create a Mat object 4 Create a random matrix with randu()
Mat R = Mat(3, 2, CV_8UC3);
randu(R, Scalar::all(0), Scalar::all(255));
cout << "R (default) = " << endl << R << endl << endl;
cout << "R (python) = " << endl << format(R,"python") << endl << endl;
cout << "R (csv) = " << endl << format(R,"csv" ) << endl << endl; …

45. Resources http://opencv.willowgarage.com/wiki/ C++ Cheatsheet.pdf
Online reference manual: 2.3 documentation is here 
2008
2011

46. References
OpenCV 2 Computer Vision Application Programming Cookbook, 2011.