doc.txt

Help ful Link:
---------------
https://www.tutorialspoint.com/dip/grayscale_to_rgb_conversion

http://www.dragonwins.com/domains/getteched/bmp/bmpfileformat.htm  ==> Help for BMP file format

https://docs.opencv.org/master/d3/dc1/tutorial_basic_linear_transform.html  ==> Opencv related link

https://solarianprogrammer.com/2019/06/10/c-programming-reading-writing-images-stb_image-libraries/


Completed:
-----------
https://www.tutorialspoint.com/dip/signals_and_system_introduction.htm  ==> DIP Signal and System Introduction

https://www.tutorialspoint.com/dip/camera_mechanism.htm  ==> Reading completed need to be captured in note book

https://www.tutorialspoint.com/dip/perspective_transformation.htm

https://www.tutorialspoint.com/dip/concept_of_bits_per_pixel.htm

https://www.tutorialspoint.com/dip/zooming_methods.htm

https://www.tutorialspoint.com/dip/concept_of_dithering.htm

https://www.tutorialspoint.com/dip/gray_level_transformations.htm

////////////////////////////////////////////////////////////////////////

1> How to calculate Contrast value of ana image?

2> How to write program for histogram stretching and histogram sliding?

3> 
/////////////////////////////////////////////////////////////////////////////

Mostly We called this as a Digital Image Processing(DIP)

There are different Image processing tech
	a> Low Level ==> Premitive Operation Ex. Noise Reduction
	b> Mid Level ===> Extraction of attribute Ex. Edge and contours
	c> High Level ===> Analysis and interpretation

What is DIP?
-------------
DIP is manipulation of digital images through digital computer.DIP focus on devloping a computer system that is able to perform processing an image. The input of that system is digital image and system process the image using effcient algorithm and give a output image. The best exmple is adobe photoshop.

Block Diagram:
--------------
	Image ----caputerd by camera---> Camera ---sen to---> DIP system ----out put----> Processed Image

What is Image?
---------------
Image is nothing but a 2-D Array.It is defind in mathematical f(x,y) where x and y is the 2 cordinates as horizental and vertical. And one image is combination of pixcel.

Different Type Of Image Processing:
-----------------------------------
	1> Analog Image Processing
		This Image processing is done by Electrical Signal. Ex: TV Broadcast
	2> Digital Image Processing
		This Image Processing is deals with developing digital system that performs Operation on digital Camera.

Relation between Signal and Digital Image:
-------------------------------------------
Signal:
-------
In physical world any quantity measurable through time over space or any higher dimension can be taken as signal. A signal can be a 1-D and 2-D signal. Example of 1-D Signal is voice and 2-D Signal is Image.

How Digital Image is formed:
-----------------------------
Since caputering image in camera is a physical process. The sunlight use as a source of energy. A sensor array is used to acquisition of the image. So when sunlight falls on the object the amount of light reflected from the object which is sensed by the sensors and continues voltage signal is generated by amount of sensed data.
In order to create digital image We need to convert this data in digital form for this we will use sampling or quantization which will provide 2-D array or matrix of numbers which are nothing but digital image.

Analog Signal:
---------------
A signal could be analog quantity that means it is defind with respect to time. It is a continues signal. It is very difficult to analyse as they are carries more data. To store these need more memory. It is more accurate becz it have large samples of values. Analog signal denotes sine waves.[Refer pic-1.0 in notebook]

Example: Human voice
---------------------
Whne we sppek that voice travel through air as a pressure waves and thus it belogns to mathematical function having independent variable of space and time and value corresepending to air pressure.

Digital Signal:
----------------
As compared to analog signal this signal is very easy to analyse becz it is dis continues signal. It is approximatesly Analog signal. In this signal only 2 values are used 0 or 1. Digital Signals are less accurate than analog signal becz they are descrete in nature means descreate samples are collected on specific time interval from analog signal.

Example: Square waves [Refer pic-1.1 in motebook]

Conversion Of Analog to Digital Signal
---------------------------------------
	There are 2 methodes mostly we are following in DIP.
	1> Sampling
	2> Quantization
1> Sampling:
------------
	It will take samples from x-axis.
2> Quantization:
-----------------
	It will take quantity from y-axis

Different Type of Images are present:
-------------------------------------
1> Orginal Image		4> Sharp Image
2> Zoomed Image			5> Edges
3> Blurr Image

Continues Systems Called Analog Signal. [pic 2.0]
Discrete System Called Digital Signal.	[pic 2.1]

Human eye can only see visible proton. But Camera can able to see everythins libe X-ray(widely use in medical), Gama Ray(Use in nuclear medicine).

Application Of DIP:
--------------------


    Image sharpening and restoration

    Medical field

    Remote sensing

    Transmission and encoding

    Machine/Robot vision

    Color processing

    Pattern recognition

    Video processing

    Microscopic Imaging

    Others

Color Processing:
------------------
Color processing means process of colored images and different color spaces that are used. For example RGB color, YCbCr, HSV. It also involves studying transmission, stprage and encoding of these color images.

Video Processing:
------------------
A video is nothing but just very fast movement of pictures. The quality of video depends on nlighthouse.bmpumber of frames/picture per second and quality of each frame is being used. Video processing involves noise reduction, detail enhancment, motion detection, frame rate conversion, asept ratio conversion, color space conversion etc.

Dimension:
-----------
This is define the minimum number of pointes required to point a position any particular object within a space. The object which are present in earth having 3 measure things to idenify location
	1> Longitude
	2> Latitude
	3> Altitude
Above fast 2 define the location and thired one is define the height level from sea level.

Different Dimension Signal:
----------------------------
1> 1-D Signal:
---------------
	Mathemetical Representation: f(x) waveform => Where x is independent variable. Please refer note book[].

2> 2-D Signal:
---------------
	Mathemetical Representation: f(x,y) image => Where x & y is independent variable. Please refer note book[].

3> 3-D Signal:
---------------
	Mathemetical Representation: f(x,y,z) Animated charecter => Where x & y & z is independent variable. Please refer note book[]. Here 3rd dimension will give deapth.

4> 4-D Signal:
---------------
	Mathemetical Representation: f(x,y,z,t) Animated movie => Where x, y, z,t is independent variable. Please refer note book[]. Here 4th dimension represent time. Best example is 3d animated movie playes.

How Human Eye works?
---------------------
Refer[] in note book

When light fall on particular object it reflected back after stricking that object abd this reflection pass through the lense of eye from a particular angle, the image created in ratina which one is presnt back side of wall. The Image that is formed is inverted. This inverted image identify by brain and get re-invert.

Image formation of analog camera:
----------------------------------
Refer[] in note book

Example: Our old camera or roll/negative based camera
In this camera the image formation is due to some chemical reaction that takes place on the strip(called negative or roll) that is use for image formation.
A 35mm strip is used and it is coated with silver halid(a chemical substance). Here light is nothing but a small particle called photon particle. When this photon particle pass through the camera it hits with strip and some chemical reaction occure with silver halid present on striped and resultant we will get negative of image.

Basic Block Diagram:
---------------------
Photon(light) + Silver Halid(present in stripe) + Some chemical reaction with silver halid + Image Negative

Image formation of Digital camera:
-----------------------------------
Refer[] in note book

In this camera CCD( Charge Couple Device ) array is used for capture Image. It is kind of image sensor. It will sense the image and produce the electrical signal.
CCD is kind of shape of array in a rectangular grid. It is likes matrix in which each sensor sense the intensity of photon(light). Refer pic[] in note book

Like analog camera in digital also after light fall on object it reflected and enter to camera. CCD having multiple analog sensor it will sense all the photones of light strike on the chip. So CCD having limited no of sensor that means it can save limites images. To measure no of photones stricking external CMOS sensor is attached with CCD.

Introduction to Pixcell:
-------------------------
The nubmer of sensores = no of pixels. It means each sensor only have one and only one value.

Storing Image:
---------------
The charges store in CCD are converted to voltage one pixcel at a time. using additinal circuit this voltage converted into digital information and stored. The quality of picture is depends on type and quality of CCD array that has been used.

Aperture:
----------
It is a small opening which allow light to pass inside camera. This is having some small no of hexagonal shape is present to adjust light. If we open more more light will pass.
The effect of aperture directly correspond to broghtness and darkness of image. If apertue opening is wide it will allow more light to pass to camera and result photo lookes more brighter if it open less wide less light will pass and photo lookes darker.

Size of aperture is inversly propertional to opening of aperture. Larger apertre size small f value(where f denoted as size of aperture) and vice versa.

Shutter:
---------
After Aperture shutter will come. Shutter is like a black caution and ccd sensor is present behind it. When shutter is open light falls on image sensor and image is formed.
Effect:
-------
	Ig shutter allow light to pass very longer the image will be brighter. In same case if shutter will close early image became darker.
	Two types of shutter is present
		1> Shutter Speed
		2> Shutter Time
	1> Shutter Speed:
	-------------------
		Number of time Shutter open and close.
	2> Shutter Time:
	------------------
		How much time shutter open means (shutter open - shtter close)

Relation:
----------
Shutter spped and shutter time is inversly propertional with each other.

ISO:
----
ISO facter measures the no. It denotes sensitivity of light to camera. If the IOSO increase in image noise also increase. So now days max comapny's are working with noise reduction if ISO set to high.

Pixel:
-------
Earch Pixel correspond to any one value. In 8-bit grey scale image the value will be between 0-255. The value of pixel at any point correspond to intensity of light photons striking at that point.

Pixcel Relation with CCD Array:
---------------------------------
THE SMALLEST ELEMENT IN CCD ARRAY CALLED PIXEL.
Total no of pixel = Now of Row * No Of Col

Perspective Transfprmation:
----------------------------
When human eye see near things lookes bigger than far things. i.e. called Perspective. The perspective tranformation deals with convertion of 3d image to 2d image.

There are total 5 frames are present and flow will be according to below order.
a> Object => This frame is used to modeling object. For example checking that perticular object in proper place or not. This is 3d coordinate.

b> World => This co-ordinate frame is used to co-relating object in 3d world.

c> Camera co-ordinate => This co-ordinate put the object in respect to camera object.

d> Image Co-ordinate => This will describe how 3d is mapped to 2d co-ordinate.

e> Pixel Co-ordinate => This is 2d co-ordinate. Here each pixel have value.

WHAT IS FOCAL LENGTH?
----------------------
ANS:-> Focal length of the lense is the distancebetween the lense and image sensor when subject is in focus normaly it is 28 to 100mm.

Please check note book [pic ]

Bits Per Pixel:
-----------------
The famos grey scale image is 8bit and the bpp=2 ^ 8 = 256 and 0 to 255. 0 is black and 255 is white
So always 0 is black and white is (2)^bpp - 1.

Image Storage:
---------------
Image storage totaly depend upon three things
	1> Num of Rows
	2> Num Of Colm
	3> Num of bits per pixel
Total Size = Rows * Colms * BPP

Types Of Images:
-----------------
1> Binary Image:
-----------------
	It containes only 2 pixel value 0 and 1. This resulting color is called Black and White Image. Here we not having greay scal data. Pixel having only 0 and 1. Binary Image having format called (Portable Bit map (PBM)).

8-Bit Color Format:
--------------------
This is called greay scale Image. This is having 256 different shades of images and range form (0 to 255) means 0 is black and 255 is white and 127 is greay. This images are not color image.

RGB:
-----
This is color image. There are different type of color format is present. Like
16-Bit Image:
--------------
	Here 5 Bit R, 6 Bit G, 5 Bit B. This depends according to the system.
24-Bit Image:
--------------
	Here also same above rule will apply.

Different Color Codes:
-----------------------
All colores we are going to discuss is 24 Bit so 8bit->R, 8bit->G, 8bit->B.
Black Color ==> (0,0,0)
white Color ==> (255, 255, 255)
Red Color   ==> (255, 0, 0)
Green Color ==> (0, 255, 0) 
Blue Color   ==> (0, 0, 255)
Grey Color   ==> (128, 128, 128)

Convert RGB to Hex:
-------------------  
Example: Take White Color (255, 255, 255) ==> 255 / 16 = nearly 15 and reminder 15. So value is (FF FF FF)

Convert Hex to RGB:
--------------------
In above example white (FF FF FF)  => Just Convet this hex to decimal.

Convert Color Image to Greay Scale Image:
------------------------------------------
There are 2 methodes are present
	a> Avg Methode
	b> Weighted methode or luminosity methode

a> Avg Methode:
----------------
	In this methode we will take avg of (R + G + B / 3). But this image we will not get proepr grey image. To avoide this we have go to Weighted methode.
 
b> Weighted methode or luminosity methode:
-------------------------------------------
	New Formula = ((0.3 * R) + (0.59 * G) + (0.11 * B))
 
Conversion of Analog to Digital Signal:
----------------------------------------
The o/p most of the image sensor is analog. We can not store analg signal becz it is infinite signal and will take infinite memory. So to convert digital signal or digital data we are following 2 methode
	a> Sampling
	b> Quantization
So our image is 2 dimensional. X-axis called cordinate and Y-axis called amplitude.

a> Sampling: [pic: note book-]
-------------------------------
This means we are digitizing the co-ordinates(x-axis) called sampling. We will collect samples for x-axis.
Eq: y = sin(x) It is done on x variable.
Again 2 types of sampling is present
	a1> Up Sampling
	a2> Down Sampling

No of pixel = Total no of row * Tptal no of cols
Let us we have total 25pixel means it is 5*5 matrix(5 rows and 5 cols) That means we can take maximum 25 samples. And here we come to know that pixel is a small part in CCD, means how many pixel that many sensors are present inside  CCd array. That means that no of samples is equal to no of CCd array.

a1> Up Sampling / Over Sampling:
---------------------------------
The over sampling is very deep application in image processing which know as Zooming. THE DIFFERENCE BETWEEN ZOOMING AND SAMPLING IS SAMPLING IS COLLECTION OF PIXEL OR SAMPLES FROM ANALOG DATA BUT ZOOMING IS FROM DIGITAL DATA.

a1> Up Sampling
----------------

For example

If you are given an image with aspect ratio of 6:2 of an image of pixel resolution of 480000 pixels given the image is an gray scale image.

And you are asked to calculate two things.

    Resolve pixel resolution to calculate the dimensions of image
    Calculate the size of the image

Solution:
Given:

Aspect ratio: c:r = 6:2

Pixel resolution: c * r = 480000

Bits per pixel: grayscale image = 8bpp
Find:

Number of rows = ?

Number of cols = ?
Solving first part:
--------------------
	Equation-1 : col:row = 6:2 => col = 6row/2
	Equation-2 : col * row = 480000

	Comparing above 2 equation
	6row/2 = 48000/row
	=> 6row ^2 = 48000 * 2
	=> row^2 = (48000 * 2)/6
	=> row = sqrt((48000 * 2)/6) = 400

Put above row value in Equation-1 col = 6*row / 2 => 1200

Solving 2nd part:

Size = rows * cols * bpp

Size of image in bits = 400 * 1200 * 8 = 3840000 bits

Size of image in bytes = 480000 bytes

Size of image in kilo bytes = 48 kb (approx).

Zooming:
---------
This means enlarging image to see details. This zooming we can do in 2 different ways. One is at the time of taking photo called hardware zooming(OPTICAL ZOOM) and other one is after taking photo we ll apply some algorithm to zoom called (DIGITAL ZOOM).
Zooming methods:

Although there are many methods that does this job, but we are going to discuss the most common of them here.

They are listed below.

    Pixel replication or (Nearest neighbor interpolation)
    Zero order hold method
    Zooming K times

1> Pixel replication or (Nearest neighbor interpolation):
----------------------------------------------------------

Introduction:

It is also known as Nearest neighbor interpolation. As its name suggest, in this method, we just replicate the neighboring pixels. As we have already discussed in the tutorial of Sampling, that zooming is nothing but increase amount of sample or pixels. This algorithm works on the same principle.
Working:

In this method we create new pixels form the already given pixels. Each pixel is replicated in this method n times row wise and column wise and you got a zoomed image. Its as simple as that.
For example:

if you have an image of 2 rows and 2 columns and you want to zoom it twice or 2 times using pixel replication, here how it can be done.

For a better understanding, the image has been taken in the form of matrix with the pixel values of the image.
1 	2
3 	4

The above image has two rows and two columns, we will first zoom it row wise.

Row wise zooming:

When we zoom it row wise, we will just simple copy the rows pixels to its adjacent new cell.

Here how it would be done.
1 	1 	2 	2
3 	3 	4 	4

As you can that in the above matrix, each pixel is replicated twice in the rows.
Column size zooming:

The next step is to replicate each of the pixel column wise, that we will simply copy the column pixel to its adjacent new column or simply below it.

Here how it would be done.
1 	1 	2 	2
1 	1 	2 	2
3 	3 	4 	4
3 	3 	4 	4
New image size:

As it can be seen from the above example, that an original image of 2 rows and 2 columns has been converted into 4 rows and 4 columns after zooming. That means the new image has a dimensions of

(Original image rows * zooming factor, Original Image cols * zooming factor)
Advantage and disadvantage:

One of the advantage of this zooming technique is, it is very simple. You just have to copy the pixels and nothing else.

The disadvantage of this technique is that image got zoomed but the output is very blurry. And as the zooming factor increased, the image got more and more blurred. That would eventually result in fully blurred image.

2> Zero order hold method:
---------------------------
Introduction

Zero order hold method is another method of zooming. It is also known as zoom twice. Because it can only zoom twice. We will see in the below example that why it does that.
Working

In zero order hold method, we pick two adjacent elements from the rows respectively and then we add them and divide the result by two, and place their result in between those two elements. We first do this row wise and then we do this column wise.
For example

Lets take an image of the dimensions of 2 rows and 2 columns and zoom it twice using zero order hold.
1 	2
3 	4

First we will zoom it row wise and then column wise.
Row wise zooming
1 	1 	2
3 	3 	4

As we take the first two numbers : (2 + 1) = 3 and then we divide it by 2, we get 1.5 which is approximated to 1. The same method is applied in the row 2.

Column wise zooming
1 	1 	2
2 	2 	3
3 	3 	4

We take two adjacent column pixel values which are 1 and 3. We add them and got 4. 4 is then divided by 2 and we get 2 which is placed in between them. The same method is applied in all the columns.
New image size

As you can see that the dimensions of the new image are 3 x 3 where the original image dimensions are 2 x 2. So it means that the dimensions of the new image are based on the following formula

(2(number of rows) minus 1) X (2(number of columns) minus 1)
Advantages and disadvantage.

One of the advantage of this zooming technique , that it does not create as blurry picture as compare to the nearest neighbor interpolation method. But it also has a disadvantage that it can only run on the power of 2. It can be demonstrated here.
Reason behind twice zooming:

Consider the above image of 2 rows and 2 columns. If we have to zoom it 6 times, using zero order hold method , we can not do it. As the formula shows us this.

It could only zoom in the power of 2 2,4,8,16,32 and so on.

Even if you try to zoom it, you can not. Because at first when you will zoom it two times, and the result would be same as shown in the column wise zooming with dimensions equal to 3x3. Then you will zoom it again and you will get dimensions equal to 5 x 5. Now if you will do it again, you will get dimensions equal to 9 x 9.

Whereas according to the formula of yours the answer should be 11x11. As (6(2) minus 1) X (6(2) minus 1) gives 11 x 11.

3> Zooming K times:
--------------------

Introduction:

K times is the third zooming method we are going to discuss. It is one of the most perfect zooming algorithm discussed so far. It caters the challenges of both twice zooming and pixel replication. K in this zooming algorithm stands for zooming factor.
Working:

It works like this way.

First of all, you have to take two adjacent pixels as you did in the zooming twice. Then you have to subtract the smaller from the greater one. We call this output (OP).

Divide the output(OP) with the zooming factor(K). Now you have to add the result to the smaller value and put the result in between those two values.

Add the value OP again to the value you just put and place it again next to the previous putted value. You have to do it till you place k-1 values in it.

Repeat the same step for all the rows and the columns , and you get a zoomed images.
For example:

Suppose you have an image of 2 rows and 3 columns, which is given below. And you have to zoom it thrice or three times.
15 	30 	15
30 	15 	30

K in this case is 3. K = 3.

The number of values that should be inserted is k-1 = 3-1 = 2.
Row wise zooming
----------------
Take the first two adjacent pixels. Which are 15 and 30.

Subtract 15 from 30. 30-15 = 15.

Divide 15 by k. 15/k = 15/3 = 5. We call it OP.(where op is just a name)

Add OP to lower number. 15 + OP = 15 + 5 = 20.

Add OP to 20 again. 20 + OP = 20 + 5 = 25.

We do that 2 times because we have to insert k-1 values.

Now repeat this step for the next two adjacent pixels. It is shown in the first table.

After inserting the values, you have to sort the inserted values in ascending order, so there remains a symmetry between them.

It is shown in the second table
Table 1.
15 	20 	25 	30 	20 	25 	15
30 	20 	25 	15 	20 	25 	30
Table 2.
Table2 

Column wise zooming

The same procedure has to be performed column wise. The procedure include taking the two adjacent pixel values, and then subtracting the smaller from the bigger one. Then after that, you have to divide it by k. Store the result as OP. Add OP to smaller one, and then again add OP to the value that comes in first addition of OP. Insert the new values.

Here what you got after all that.
15 	20 	25 	30 	25 	20 	15
20 	21 	21 	25 	21 	21 	20
25 	22 	22 	20 	22 	22 	25
30 	25 	20 	15 	20 	25 	30
New image size

The best way to calculate the formula for the dimensions of a new image is to compare the dimensions of the original image and the final image. The dimensions of the original image were 2 X 3. And the dimensions of the new image are 4 x 7.

The formula thus is:

(K (number of rows minus 1) + 1) X (K (number of cols minus 1) + 1)

Advantages and disadvantages

The one of the clear advantage that k time zooming algorithm has that it is able to compute zoom of any factor which was the power of pixel replication algorithm , also it gives improved result (less blurry) which was the power of zero order hold method. So hence It comprises the power of the two algorithms.

The only difficulty this algorithm has that it has to be sort in the end, which is an additional step, and thus increases the cost of computation.

Spatial Resolutiom:
--------------------
This resolution states that clarity of an image can not be determined by pixel resolution. The number of pixel in an image does not matter.

Spatial resolution can be defined as the

smallest discernible detail in an image. (Digital Image Processing - Gonzalez, Woods - 2nd Edition)

Or in other way we can define spatial resolution as the number of independent pixels values per inch.

Measuring spatial resolution:
------------------------------
This resolution refer to clarity so for different devices different measure has been madeto measure it.
	1> Dots per inch (DPI) ==> used in Moniter
	2> Lines Per inch (LPI) ===> Used in laser printer
	3> Pixel per inch (PPI) ====> Used in Tablet, mobile etc

Image Formats:
---------------
1> JPEG (Joint Photographic Expert Group) ==> Lossy Compression

2> GIF (Graphics Interchange Format)  ===> Lossely Compression Limited to 8-bit color

3> BMP (Bit Map Picture) ===> Basic Format Lossely Compression

4> PNG (Portable Network Graphics)  ==> Lossely Compression

5> TIFF (Tag Image File Format)   ====> Very Flexible Compressed/Un Compressed

Image Data Type:
-----------------
1> Binary Image ==> 2D array. Assign 1 or 0 in each pixel

2> Intensity or greyscale image   ==> 2D array. Each pixel having some value

3> RGB or true color image  ===> 3D array. Each pixel having 3 R, G, B field

4> Floating - Point Image   ===> Do not store integer value. Only store floating value. Mostly use in medical, science etc.
///////////////////////////////////////////

Histograms

A histogram is a graph. A graph that shows frequency of anything. Usually histogram have bars that represent frequency of occurring of data in the whole data set.

A Histogram has two axis the x axis and the y axis.

The x axis contains event whose frequency you have to count.

The y axis contains frequency.

The different heights of bar shows different frequency of occurrence of data.

Application Of Histogram:
--------------------------
1> This is mostly use for balance brightness and contrast level.

2> This is use to equilze the image.

3> This is use for thersolding. Mostly use in computer vision.

Brightness:
------------
Brightness is a relative term. It depends on your visual perception. Since brightness is a relative term, so brightness can be defined as the amount of energy output by a source of light relative to the source we are comparing it to. In some cases we can easily say that the image is bright, and in some cases, its not easy to perceive.

Contrast:
----------
This is the difference between max and min pixel intensity in an image.

Formula To Increase Contrast:
------------------------------
	g(x, y) = ((f(x, y) - Fmin)/(Fmax - Fmin)) * 2bpp (bpp: bit per pixel for 8 bit greay scale 255)





Dithering:
----------