OpenCV Learning Notes (iv)--MAT basic image Container Mat object information header, matrix body creation, deep copy, shallow copy detailed

Source: Internet
Author: User
Tags image processing library shallow copy

1--We know Mat is an image container class, this data structure consists of two parts: 1--Matrix Header------------------------------------------------- Just created a mat header and did not create a matrix, that is to say, we did not open up the corresponding space for the image to be stored 2--matrix header-Contains: the size of the 1--matrix----such as---class mat----data members in this class Rows,cols ---can specify the size of the image 2--storage method------Corresponding---a variety of mat constructors 3--store address 4--and a----pointer to a matrix that----storing all pixel values 2--Therefore, when you pass an image and create a copy in your program, the large overhead is made by the matrix
into, not the message head. 3--OPENCV is an image processing library, contains a large number of image processing functions, in order to solve the problem, usually use a library of multiple functions, so it is common to pass images in a function. Also, don't forget that we're talking about a large computational graph processing algorithm, so unless you have to, We should not copy large images because it lowers the speed of the program 4--in order to fix this problem, OPENCV uses the---Reference counting mechanism, the idea is to let each mat object have its own message header, but share a matrix. Implemented by having the matrix pointer point to the same address.
The copy constructor copies only: the 1--information header 2--The matrix pointer and not the matrix.
        /********************************************************************************************* Program Features: Mat basic Image Container Mat object information header, matrix body creation, deep copy, shallow copy detailed writing environment: opencv2.4.8+vs2010 location Time: Shaanxi Normal University 2016.4.25 author information: September ****************************************************************************************/
/********************* "Header file.namespace contains part "***********************************/#include <opencv2/highgui/highgui.hpp> #include <opencv2/
Core/core.hpp> #include <iostream> using namespace CV;

using namespace Std; #define MAT_INFO_HEADER_SIZE MAT The size of the memory space occupied by the image container class header-----> "#define WINDOW_SRC_NAME" "Original image" #define Window_dst_na ME "copy constructor image" "#define WINDOW_ASSIGN_NAME" "Assignment image" "#define Window_roi_name" "ROI Original image part data" "#define Window_r Ange_name The image data section specified in the range "/*****************************************" main () function ******************************* /int main (int argc,char** argv) {/**** * The point to be explained here is that there is a huge difference between the definition of a class object in C + + and the definition of an object in Java: * "1" C + +, when the object of a class is defined, it is     The storage space of the class is allocated---the popular point, that is, in C + +, when using * A class to define a class object, in fact, the class---is instantiated, resulting in a class of----concrete instance * "2" and in Java, the definition of class objects and the instantiation of objects by classes are performed separately: * "1" person Lili---Class object definition---opened up a four-byte space----Lili This class object is actually equivalent to * C + + An object pointer, at this time does not produce the---concrete instance of class * "2" Java can only manually, with
	The new keyword instantiates the object of a class, as follows: * lili=new person (); ////"1" only created informationThe head part Mat Src,assign;
	"2" Here we test the size of the MAT header for memory space cout<<mat_info_header_size<<sizeof (src) << "bytes" <<endl;
    "3" here, for the matrix to open up memory space---which is equivalent to---matrix body src=imread ("D:\\scenery.png", Cv_load_image_color);

	"4" Display picture imshow (WINDOW_SRC_NAME,SRC);
	"5" uses the copy constructor to copy only the information header of the matrix-----Typical shallow copy Mat DST (SRC);
	"6" Display picture imshow (WINDOW_DST_NAME,DST);

	Imshow (window_assign_name,assign); /**** * "1" Through the above code results are known. All Mat objects end up pointing to one and only one----data matrix. Although their * information headers are different, however, the changes made to the image matrix by any one object will also affect other objects * "2" in fact, different mat objects, just different ways to access the same data ****//**** * "1" here introduces a more powerful function: you can Creates an information header that references only the partial data---of the---image matrix. For example, to create an area of interest (ROI), you only need to create an information header * "2" that contains boundary information: * Mat Dstroi (Src,rect (0,0,100,100)) * Mat dstroi_1 (Ran
	Ge:all (), range (1,3)) ****///"7" uses a rectangle to define the ROI region Mat Dstroi (Src,rect (0,0,200,200));
	"8" Create window + display image Namedwindow (window_roi_name,cv_window_autosize);

	Imshow (Window_roi_name,dstroi); "9" uses row rows and column cols to intercept an image//"10" of the specified SRC image in the specified area of the original image, including all rows of the images and from columns No. 0 through 199th//"11" MatMat::operator () (range _rowrange, range _colrange) const----Create//new headers for the SRC object's child array, the following equivalent Src.colrange () Mat Dstra
	NGE=SRC (Range::all (), Range (0,200));
	Namedwindow (window_range_name,cv_window_autosize);

	Imshow (Window_range_name,dstrange); /** * "1" Now you may ask, if the matrix belongs to more than one Mat object, then who will be responsible for clearing it when it is not needed? The answer is: The last one uses its object.
	Implemented by reference counting mechanism. Whenever someone copies the header of a mat object, it increases the number of references to the matrix; Conversely, when a head is released, the count is reduced by one, and when the count is zero, the matrix is cleared. * "2" in this piece of the class object that we created through the mat src method is a static object of the----class, in the process of running a program, such objects * occupy the space allocated and the point of release is fixed ***//** * "1" but, at some point, you will still want to copy the matrix itself (no
	Just the information header and the matrix pointer), you can use---deep copy---function: * Mat clone () const;
	* void CopyTo (outputarray m) const;  
	* void CopyTo (Outputarray m, Inputarray mask) const;
	**/Mat Dstdeep1=src.clone ();
	Mat DstDeep2;
    Src.copyto (SRC);
	"12" After deep replication, at this time, we change again, will no longer affect the original image//"13" For example, we now put all the elements in the image to white//"14" Access color image pixel for (int i=0;i<dstdeep1.rows;i++) {for (int j=0;j<dstdeep1.cols;j++) {<vec3b> (i,j) [0]=255;//blue channel<vec3b> (i,j) [1]=255;//Red channel<vec3b> (I,J) [2]=255;//Green Channel}} imshow ("Original image", SRC);
	Imshow ("" After the deep copy processing Image "", DSTDEEP1);
Waitkey (0); Now, to sum up, we need to keep in mind that the memory allocation of the output image in the 1--OPENCV function is automatically completed (if not specifically specified) 2--use OpenCV's C + + interface without the need to consider memory release issues 3-- Copy operators and copy constructor functions only copy---header 4--use the function clone () or CopyTo () to copy a matrix of an image


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