rsync -r –exclude=.svn /home/user/progname/ /home/user/progname.copy

Regards David.

In this tutorial I want explain how to segment an image and detect each object inside image, in this tutorial we can detect objects from plate (the numbers) or each object draw into a paper.

We can define three steps to do this task: preprocess image, find countours, and calculate bounding rect. With this three steps we have each object separatly and then we can use each object to classify it with basicocr sample.

For preprocessing task we use:

1. Smooth filter to simple noise elimination.
2. Threshold image to get binary image
3. Morphologic filter, erode and dilate to eliminate noise.

Once we have preprocessed our input image we look for the contours in our binary image with cvFindContours, and optimize its with approxpoly.

//Search countours in preprocesed image
 cvFindContours( img_contornos, storage, &contour, sizeof(CvContour),
 CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE, cvPoint(0, 0) );
 //Optimize contours, reduce points
 contourLow=cvApproxPoly(contour, sizeof(CvContour), storage,CV_POLY_APPROX_DP,1,1);

Then for each searched contour we calculate the bounding rect.

//For each contour found
 for( ; contourLow != 0; contourLow = contourLow->h_next )
 {

...

CvRect rect;

rect=cvBoundingRect(contourLow, NULL);

We can now draw his contour and/or bounding rect.

pt1.x = rect.x;
 pt2.x = (rect.x+rect.width);
 pt1.y = rect.y;
 pt2.y = (rect.y+rect.height);
 cvRectangle(imagen_color, pt1,pt2, color, 1, 8, 0);
//or
cvDrawContours( imagen_color, contourLow, color, color, -1, 0, 8, cvPoint(0,0) );

Now we have each object differenciate, now we can use this to get characteristics to classificate, for example  the 7 invariant hu moments.

CvMoments moments;
CvHuMoments humoments;

//First calculate object moments
 cvMoments(contourLow, &moments, 0);
 //Now calculate hu moments
 cvGetHuMoments(&moments, &humoments);

Download source

CMake is a cross-platform and open-source build system, and it’s used to control our compilation process, using a simple text files for define compilation process with independent platform and compiler.

Thanks to CMake we can create our project in our operating system as linux compile and work in it, and then use it to compile a new version in other os as windows, osx, …, even create a visual studio project, xcode project or eclipse.

To create a basic project with 1 main.c file with cmake and opencv we must create a new file called CMakeList.txt.

In this file we go to define the compiler process and his requeriments.

First we need create a project name:

PROJECT( project_name )

We need find OpenCV libraries. OpenCV is required by our project and it’s defined with REQUIRED statement:

FIND_PACKAGE( OpenCV REQUIRED )

Then we must defined our executable, his  name and the dependent files, in our example only main.c, and we go to use the project name for the executable name. This name is stored in ${PROJECT_NAME} variable.

ADD_EXECUTABLE( ${PROJECT_NAME} main.c )

To finish, we must link OpenCV to our executable, to do this only need call TARGET_LINK_LIBRARIES statement, with the project name and OpenCV library.

TARGET_LINK_LIBRARIES( ${PROJECT_NAME} {OpenCV_LIBS} )

The CMakeList.txt was defined, then we can compile our project. To do this only need create a directory where we want get our executable. Then  we call cmake with the directory where  CMakeList.txt and main.c files are saved.

When cmake finish whe can call make to compile our project under unix.

If we want create a VisualStudio project under Windows, only need open CMake Gui and select the source code and build directories and cmake detect our visualstudio version and with generate button create our VisualStudio project.

Sample Code:

PROJECT( Nombre_del_proyecto )
FIND_PACKAGE( OpenCV REQUIRED )
ADD_EXECUTABLE( ${PROJECT_NAME} main.c )
TARGET_LINK_LIBRARIES( ${PROJECT_NAME} {OpenCV_LIBS} )

OpenCV

In this tutorial we go to show how to save our custom structures with opencv functions.

We go to imagine we have this structure in our program.

typedef struct
{
	float var1;
	unsigned char var2[255];
}MyStructure;

Opencv can save data in two formats: XML or YAML. This formats are markup languages.

For write or read a file, we go to use cvOpenFileStorage function, the functions returns  a CvFileStorage structure pointer. This pointer we use to read or write values into.

We can open a file in two ways: for read or write. The function to open file is cvOpenFileStorage

CvFileStorage* storage = cvOpenFileStorage( file, 0, CV_STORAGE_WRITE_TEXT );

One time we have our CvFileStorage initialized then we can save our data. To save the data we need create Nodes. XML and YAML are markup languages, and they are defined with nodes.

First we go to create one node that contains all structure data. And inside of this node we need create one node for each variable.

For create a node we need open and close this node, as we do with xml, then we use cvStartWriteStruct and cvEndWriteStruct.

cvStartWriteStruct(storage, "MyStructure", CV_NODE_MAP, NULL, cvAttrList(0,0));
...
cvEndWriteStruct(storage);

Between the start and end node we go to insert each new node with asigned variable. The variable we must save as some of this types: int, raw (array), real or string with this functions:

• cvWriteInt
• cvWriteRawData
• cvWriteReal
• cvWriteString

Then for save our structure we go to create this code:

//Open file
CvFileStorage* storage = cvOpenFileStorage( file, 0, CV_STORAGE_WRITE_TEXT );
//Create our node for structure
cvStartWriteStruct(storage, "MyStructure", CV_NODE_MAP, NULL, cvAttrList(0,0));
//Write our float
cvWriteReal( storage, "var1", MyStructure->var1 );

//Write our array
cvStartWriteStruct(storage, "var2", CV_NODE_SEQ, NULL, cvAttrList(0,0));
cvWriteRawData(storage, MyStructure->var2,255,"u");
cvEndWriteStruct(storage);

//End node structure
cvEndWriteStruct(storage);

Then if we need show a image to a person, we can color it. But, how is the best way to coloring gray image?

There are 3 ways to do it: Manually, automatically and colored by ranges.

In this tutorial, i go to develop the way most common automatic for gray image coloring.

To do it we need know we go to receive a gray level and we need return 3 values, one for red, one for blue and other for green.

We go to use this function:

And r(x,y) is the gray level and p is the number of repetitions and is the displacement.

Then we only need define the p and for each channel.

If we create a plot with this function with this parameters for red, green and blue ((2,0),(2,-0.1),(2,-0.3)) we get:

Pseudocolor Graph with red(p=2,theta=0) green(2,-0.1) and blue(2,-0.3)

Then we only need set the gray level in range 0 to 1 and the sine returns values from 0 to 1 we interpret as float image values or we set in range 0 to 255.

To finish this is the result:

Pseudocolor Result

Download the code.

Chamilo is a new project that opts for open source in a radical way. It aims at bringing you the best e-learning and collaboration platform in the open source world.

This task is simple, but there are some things we must known.

Now i go to explain and get a demo code for segmentation, how to determine if each image pixel is red or no, and then, i go to explain how we  can detect object, it’s similar but with diferent concept.

In first moment, we can decide get the red channel of image  and get the values, if it’s a higher value then we have a red pixel otherwise no.

This is incorrect. Why?, imagine we have a pixel and in his red channel have a 255 value, this is the higher value, then we decide this is a red pixel, but if in green and blue channel have 255 value too then we have a white pixel instead a red pixel. Then how we determine if a pixel is red or no?

Ok, we go to get some color values and determine how we decide if is red or no.

Then we can decide that higher red value, and  lower values of blue and green can be defined as red color. Moreover the green and blue values don’t have a higher difference between his values.

We go to construct simple graph with some values to see better.

We see in this graph that the red value is higher than a max value and there are a min value for green and blue.

The min and max we go to name as blue green threshold and red threshold (bg_threshold, r_threshold).

This code compute for each pixel if it’s red or no, and create a black/white image, this is our segmentation image, and with this image we can use to label each object, we use a cvFindContours to retreive the contours of each object detected on our image.

 for(y=0;yheight; y++)
 {
 red=((uchar*)(img->imageData + img->widthStep*y))[x*3+2];
 green=((uchar*)(img->imageData + img->widthStep*y))[x*3+1];
 blue=((uchar*)(img->imageData + img->widthStep*y))[x*3];
 uchar* temp_ptr=&((uchar*)(img_result_threshold->imageData + img_result_threshold->widthStep*y))[x];

 if((red>threshold)&&(green < BG_threshold) && (blue < BG_threshold) && (abs(green-blue)< BG_diff)){
 temp_ptr[0]=255;//White to greater of threshold
 }else{
 temp_ptr[0]=0;//Black other
 }
 }
 }

 cvMorphologyEx(img_result_threshold, img_morph, img_temp, NULL, CV_MOP_CLOSE, 6);

 cvNamedWindow( "Threshold", 1 );
 cvShowImage( "Threshold", img_morph );

 cvFindContours( img_morph, storage, &contour, sizeof(CvContour), CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE, cvPoint(0,0) );

 for( ; contour != 0; contour = contour->h_next )
 {
 CvScalar color = CV_RGB( rand()&255, rand()&255, rand()&255 );
 cvDrawContours( img, contour, color, color, -1, 3, 8, cvPoint(0,0) );
 }

And this is the result, whe can see in the image a color border with detected red objects, the system detect two objects. The notebook and the notebook corner

Demo Source

This is perfect to image segmentation by color, but imagine we have objects ofone image, and we want get each object and know if this object have a predominant red color or no.

The theory is same but applied to the object histogram, and use the median to determine the thresholds

But with this we don’t have enough, because we can get a gray image as we can see in the second histogram, then we need use another parameter more, the dispersion of each channel.

We can see that in this first histogram we can ensure this object have a predominant red, and in the second histogram no.

We can remove this characters with a simply search and replace of vim with this command:

:%s/^M//g

The ^M character is not valid write first ^ character and then M it’s not the valid character. To write correctly this we must push Control+v and Contro+M keys, then appear our ^M Character.

Take care with this.

error: CPU you selected does not support x86-64 instruction set

The searching in google have the solution

go to opencv folder and edit configure file, searchi prescott and replace the line

DEFAULT_CXXFLAGS=”-g -march=prescott -ffast-math -fomit-frame-pointer $DEFAULT_CXXFLAGS”

with this:

DEFAULT_CXXFLAGS=”-g -march=i686 -m32 -ffast-math -fomit-frame-pointer $DEFAULT_CXXFLAGS”

But when i try to compile i have other error i now looking for how to resolve it

Now i can install opencv lib with macport!!!

1.- i have old macport then first i need clean and delete it.

port installed > myports.txt
 
sudo port clean installed
 
sudo port -f uninstall installed

2.- Install ports you need you can see myports.txt to know the old ports you have installed before clean.

3.- Patch the opencv port: see http://trac.macports.org/ticket/21014 and download the patch Portfile-sl_64bit_21014.diff

4.- Follow the instruction in post of patch

Check where you installed macports, that is usually /opt/local/ Locate the Portfile for OpenCV, for me that is at

/opt/local/var/macports/sources/rsync.macports.org/release/ports/graphics/opencv

now apply the third patch (Portfile-sl_64bit_21014.diff) by executing

sudo patch Portfile -i ~/Downloads/Portfile-sl_64bit_21014.diff

in the above directory (adjust the last path accordingly to your download location)

When you execute port variants opencv you should see the sl_64bit_21014 variant. Now install opencv with

sudo port install opencv +sl_64bit_21014