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.

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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

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Thanks Piponazo.

We consider a background the pixels of image without motion. And a foreground the pixels with motion. Then the simplest background model assume each background pixel his brightness varies independently with normal distribution. Then we can calculate our statistical model of background by accumulating several dozens of frames and his squares, this is:

Where:

Then the average is:

BG substraction mean pixel

And need the standar derivation for our statistical model:

Then, when we have our statistical background model, we can give a image and divide it in background and foreground, the pixels that are in foreground are the P(x,y) that met this condition:

For optimize a litle the operations we can use this for evaluate the condition:

Where is

And is a constant or variable and we can set it to 3, it is the well-known “three sigmas” rule.

BG substraction Standar derivation compare

Ok, then we now have the theory, then, we go to implement it.

To develop it opencv give us some functions, cvAcc and cvSquareAcc

cvAcc(frame,acc,NULL);
cvSquareAcc(frame, sqacc, NULL);
N++;
cvConvertScale(acc, M, (double)(1.0/N), 0);
cvConvertScale(sqacc, sqaccM, (double)(1.0/N),0);
cvMul( M, M, M2, 1 );
cvSub( sqaccM, M2, sig2, NULL);//The sig is sig2
 
//For detect FG Condition
cvConvertScale(sig2, lambda_sig2, (double)9, 0);
cvSub(M, frame, leftCondition, NULL);
cvMul(leftCond, leftCond, leftCond2, NULL);
 
//Compare leftCondition > lambda_sig2
//to detect foreground.

Results:

Note:
This code is an example and it’s no completed.
In next tutorial a more advanced background substranction.

Blender is now a powerful suite of 3d content creation, where have a various characteristich as animation, composit, shade, model, texture, interactive … And OpenCV is ga powerful realtime library for computer Vision.

Then my goal is provide a new and powerfull tool based on Blender for Computer Vision Tasks with OpenCV Library.

How?, with a particular characteristic of Blender, the compositor node.

Nodes are a good tool that provide a user interactive, each node is a box with inputs and outputs that implement a function. Each node have interactive parameters that user can be set.

Each node can be linked with others nodes linking outputs to inputs

In this page we can see more about node compositor

With this properties we can create a workflow of a computer vision tasks.

For example here are the basic edge detection with blenderocv, this example implement the basic opencv sample “edge.c”

Blenderocv - edge (canny)

Now there are a demo with some few functions.

I have to implement a lot of functions as nodes, and now there are a lot of work to do, and sure i make important decisions in development. If anybody want colaborate with this project, please send me mail (david at artresnet dot com) or send me a comment.

There are more info and progress about this project in http://code.google.com/p/blenderocv/