C++ Basic Syntax

When we consider a C++ program, it can be defined as a collection of objects that communicate via invoking each other's methods. Let us now briefly look into what do class, object, methods and Instance variables mean.

• Object - Objects have states and behaviors. Example: A dog has states - color, name, breed as well as behaviors - wagging, barking, eating. An object is an instance of a class.
• Class - A class can be defined as a template/blueprint that describes the behaviors/states that object of its type support.
• Methods - A method is basically a behavior. A class can contain many methods. It is in methods where the logics are written, data is manipulated and all the actions are executed.
• Instance Variables - Each object has its unique set of instance variables. An object's state is created by the values assigned to these instance variables.

C++ Program Structure:

Let us look at a simple code that would print the words Hello World.

#include <iostream>
using namespace std;

// main() is where program execution begins.

int main()
{
   cout << "Hello World"; // prints Hello World
   return 0;
}

Let us look various parts of the above program:

• The C++ language defines several headers, which contain information that is either necessary or useful to your program. For this program, the header <iostream> is needed.
• The line using namespace std; tells the compiler to use the std namespace. Namespaces are a relatively recent addition to C++.
• The next line // main() is where program execution begins. is a single-line comment available in C++. Single-line comments begin with // and stop at the end of the line.
• The line int main() is the main function where program execution begins.
• The next line cout << "This is my first C++ program."; causes the message "This is my first C++ program" to be displayed on the screen.
• The next line return 0; terminates main( )function and causes it to return the value 0 to the calling process.

Compile & Execute C++ Program:

Let's look at how to save the file, compile and run the program. Please follow the steps given below:

• Open a text editor and add the code as above.
• Save the file as: hello.cpp
• Open a command prompt and go to the directory where you saved the file.
• Type 'g++ hello.cpp ' and press enter to compile your code. If there are no errors in your code the command prompt will take you to the next line and would generate a.out executable file.
• Now, type ' a.out' to run your program.
• You will be able to see ' Hello World ' printed on the window.

$ g++ hello.cpp
$ ./a.out
Hello World

Make sure that g++ is in your path and that you are running it in the directory containing file hello.cpp.

You can compile C/C++ programs using makefile. For more details, you can check Makefile Tutorial.

Semicolons & Blocks in C++:

In C++, the semicolon is a statement terminator. That is, each individual statement must be ended with a semicolon. It indicates the end of one logical entity.

For example, following are three different statements:

x = y;
y = y+1;
add(x, y);

A block is a set of logically connected statements that are surrounded by opening and closing braces. For example:

{
   cout << "Hello World"; // prints Hello World
   return 0;
}

C++ does not recognize the end of the line as a terminator. For this reason, it does not matter where on a line you put a statement. For example:

x = y;
y = y+1;
add(x, y);

is the same as

x = y; y = y+1; add(x, y);

C++ Identifiers:

A C++ identifier is a name used to identify a variable, function, class, module, or any other user-defined item. An identifier starts with a letter A to Z or a to z or an underscore (_) followed by zero or more letters, underscores, and digits (0 to 9).

C++ does not allow punctuation characters such as @, $, and % within identifiers. C++ is a case-sensitive programming language. Thus, Manpowerand manpower are two different identifiers in C++.

Here are some examples of acceptable identifiers:

mohd       zara    abc   move_name  a_123
myname50   _temp   j     a23b9      retVal

C++ Keywords:

The following list shows the reserved words in C++. These reserved words may not be used as constant or variable or any other identifier names.

asm	else	new	this
auto	enum	operator	throw
bool	explicit	private	true
break	export	protected	try
case	extern	public	typedef
catch	false	register	typeid
char	float	reinterpret_cast	typename
class	for	return	union
const	friend	short	unsigned
const_cast	goto	signed	using
continue	if	sizeof	virtual
default	inline	static	void
delete	int	static_cast	volatile
do	long	struct	wchar_t
double	mutable	switch	while
dynamic_cast	namespace	template

Trigraphs:

A few characters have an alternative representation, called a trigraph sequence. A trigraph is a three-character sequence that represents a single character and the sequence always starts with two question marks.

Trigraphs are expanded anywhere they appear, including within string literals and character literals, in comments, and in preprocessor directives.

Following are most frequently used trigraph sequences:

Trigraph	Replacement
??=	#
??/	\
??'	^
??(	[
??)	]
??!	|
??<	{
??>	}
??-	~

All the compilers do not support trigraphs and they are not advised to be used because of their confusing nature.

Whitespace in C++:

A line containing only whitespace, possibly with a comment, is known as a blank line, and C++ compiler totally ignores it.

Whitespace is the term used in C++ to describe blanks, tabs, newline characters and comments. Whitespace separates one part of a statement from another and enables the compiler to identify where one element in a statement, such as int, ends and the next element begins. Therefore, in the statement,

int age;

there must be at least one whitespace character (usually a space) between int and age for the compiler to be able to distinguish them. On the other hand, in the statement

fruit = apples + oranges;   // Get the total fruit

no whitespace characters are necessary between fruit and =, or between = and apples, although you are free to include some if you wish for readability purpose.

Smoothing Images

Goals

Learn to:

• Blur imagess with various low pass filters
• Apply custom-made filters to images (2D convolution)

2D Convolution ( Image Filtering )

As for one-dimensional signals, images also can be filtered with various low-pass filters (LPF), high-pass filters (HPF), etc. A LPF helps in removing noise, or blurring the image. A HPF filters helps in finding edges in an image.

OpenCV provides a function, cv2.filter2D(), to convolve a kernel with an image. As an example, we will try an averaging filter on an image. A 5x5 averaging filter kernel can be defined as follows:

Filtering with the above kernel results in the following being performed: for each pixel, a 5x5 window is centered on this pixel, all pixels falling within this window are summed up, and the result is then divided by 25. This equates to computing the average of the pixel values inside that window. This operation is performed for all the pixels in the image to produce the output filtered image. Try this code and check the result:

import cv2
import numpy as np
from matplotlib import pyplot as plt

img = cv2.imread('opencv_logo.png')

kernel = np.ones((5,5),np.float32)/25
dst = cv2.filter2D(img,-1,kernel)

plt.subplot(121),plt.imshow(img),plt.title('Original')
plt.xticks([]), plt.yticks([])
plt.subplot(122),plt.imshow(dst),plt.title('Averaging')
plt.xticks([]), plt.yticks([])
plt.show()

Result:

Image Blurring (Image Smoothing)

Image blurring is achieved by convolving the image with a low-pass filter kernel. It is useful for removing noise. It actually removes high frequency content (e.g: noise, edges) from the image resulting in edges being blurred when this is filter is applied. (Well, there are blurring techniques which do not blur edges). OpenCV provides mainly four types of blurring techniques.

1. Averaging

This is done by convolving the image with a normalized box filter. It simply takes the average of all the pixels under kernel area and replaces the central element with this average. This is done by the function cv2.blur() or cv2.boxFilter(). Check the docs for more details about the kernel. We should specify the width and height of kernel. A 3x3 normalized box filter would look like this:

Note

If you don’t want to use a normalized box filter, use cv2.boxFilter() and pass the argumentnormalize=False to the function.

Check the sample demo below with a kernel of 5x5 size:

import cv2
import numpy as np
from matplotlib import pyplot as plt

img = cv2.imread('opencv_logo.png')

blur = cv2.blur(img,(5,5))

plt.subplot(121),plt.imshow(img),plt.title('Original')
plt.xticks([]), plt.yticks([])
plt.subplot(122),plt.imshow(blur),plt.title('Blurred')
plt.xticks([]), plt.yticks([])
plt.show()

Result:

2. Gaussian Filtering

In this approach, instead of a box filter consisting of equal filter coefficients, a Gaussian kernel is used. It is done with the function, cv2.GaussianBlur(). We should specify the width and height of the kernel which should be positive and odd. We also should specify the standard deviation in the X and Y directions, sigmaX and sigmaY respectively. If only sigmaX is specified, sigmaY is taken as equal to sigmaX. If both are given as zeros, they are calculated from the kernel size. Gaussian filtering is highly effective in removing Gaussian noise from the image.

If you want, you can create a Gaussian kernel with the function, cv2.getGaussianKernel().

The above code can be modified for Gaussian blurring:

blur = cv2.GaussianBlur(img,(5,5),0)

Result:

3. Median Filtering

Here, the function cv2.medianBlur() computes the median of all the pixels under the kernel window and the central pixel is replaced with this median value. This is highly effective in removing salt-and-pepper noise. One interesting thing to note is that, in the Gaussian and box filters, the filtered value for the central element can be a value which may not exist in the original image. However this is not the case in median filtering, since the central element is always replaced by some pixel value in the image. This reduces the noise effectively. The kernel size must be a positive odd integer.

In this demo, we add a 50% noise to our original image and use a median filter. Check the result:

median = cv2.medianBlur(img,5)

Result:

4. Bilateral Filtering

As we noted, the filters we presented earlier tend to blur edges. This is not the case for the bilateral filter, cv2.bilateralFilter(), which was defined for, and is highly effective at noise removal while preserving edges. But the operation is slower compared to other filters. We already saw that a Gaussian filter takes the a neighborhood around the pixel and finds its Gaussian weighted average. This Gaussian filter is a function of space alone, that is, nearby pixels are considered while filtering. It does not consider whether pixels have almost the same intensity value and does not consider whether the pixel lies on an edge or not. The resulting effect is that Gaussian filters tend to blur edges, which is undesirable.

The bilateral filter also uses a Gaussian filter in the space domain, but it also uses one more (multiplicative) Gaussian filter component which is a function of pixel intensity differences. The Gaussian function of space makes sure that only pixels are ‘spatial neighbors’ are considered for filtering, while the Gaussian component applied in the intensity domain (a Gaussian function of intensity differences) ensures that only those pixels with intensities similar to that of the central pixel (‘intensity neighbors’) are included to compute the blurred intensity value. As a result, this method preserves edges, since for pixels lying near edges, neighboring pixels placed on the other side of the edge, and therefore exhibiting large intensity variations when compared to the central pixel, will not be included for blurring.

The sample below demonstrates the use of bilateral filtering (For details on arguments, see the OpenCV docs).

blur = cv2.bilateralFilter(img,9,75,75)

Result:

Computer Vision and applications

What is computer vision?

Computer vision is a field that includes methods for acquiring, processing, analyzing, and understanding images and, in general, high-dimensional data from the real world in order to produce numerical or symbolic information, e.g., in the forms of decisions

Humans use their eyes and their brains to see and visually sense the world around them. Computer vision is the science that aims to give a similar, if not better, capability to a machine or computer.

Computer vision is concerned with the automatic extraction, analysis and understanding of useful information from a single image or a sequence of images. It involves the development of a theoretical and algorithmic basis to achieve automatic visual understanding.

The applications of computer vision are numerous and include:

• agriculture
• augmented reality
• autonomous vehicles
• biometrics
• character recognition
• forensics
• industrial quality inspection
• face recognition
• gesture analysis
• geoscience
• image restoration
• medical image analysis
• pollution monitoring
• process control
• remote sensing
• robotics
• security and surveillance
• transport

Few things About image taken by Camera

n this tutorial, we will discuss some of the basic camera concepts, like aperture, shutter, shutter speed, ISO and we will discuss the collective use of these concepts to capture a good image.

Aperture

Aperture is a small opening which allows the light to travel inside into camera. Here is the picture of aperture.

You will see some small blades like stuff inside the aperture. These blades create a octagonal shape that can be opened closed. And thus it make sense that, the more blades will open, the hole from which the light would have to pass would be bigger. The bigger the hole, the more light is allowed to enter.

Effect

The effect of the aperture directly corresponds to brightness and darkness of an image. If the aperture opening is wide, it would allow more light to pass into the camera. More light would result in more photons, which ultimately result in a brighter image.

The example of this is shown below

Consider these two photos

The one on the right side looks brighter, it means that when it was captured by the camera, the aperture was wide open. As compare to the other picture on the left side, which is very dark as compare to the first one, that shows that when that image was captured, its aperture was not wide open.

Size

Now lets discuss the maths behind the aperture. The size of the aperture is denoted by a f value. And it is inversely proportional to the opening of aperture.

Here are the two equations, that best explain this concept.

Large aperture size = Small f value

Small aperture size = Greater f value

Pictorially it can be represented as:

Shutter

After the aperture, there comes the shutter. The light when allowed to pass from the aperture, falls directly on to the shutter. Shutter is actually a cover, a closed window, or can be thought of as a curtain. Remember when we talk about the CCD array sensor on which the image is formed. Well behind the shutter is the sensor. So shutter is the only thing that is between the image formation and the light, when it is passed from aperture.

As soon as the shutter is open, light falls on the image sensor, and the image is formed on the array.

Effect

If the shutter allows light to pass a bit longer, the image would be brighter. Similarly a darker picture is produced, when a shutter is allowed to move very quickly and hence, the light that is allowed to pass has very less photons, and the image that is formed on the CCD array sensor is very dark.

Shutter has further two main concepts:

• Shutter Speed
• Shutter time

Shutter speed

The shutter speed can be referred to as the number of times the shutter get open or close. Remember we are not talking about for how long the shutter get open or close.

Shutter time

The shutter time can be defined as

When the shutter is open, then the amount of wait time it take till it is closed is called shutter time.

In this case we are not talking about how many times, the shutter got open or close, but we are talking about for how much time does it remain wide open.

For example:

We can better understand these two concepts in this way. That lets say that a shutter opens 15 times and then get closed, and for each time it opens for 1 second and then get closed. In this example, 15 is the shutter speed and 1 second is the shutter time.

Relationship

The relationship between shutter speed and shutter time is that they are both inversely proportional to each other.

This relationship can be defined in the equation below.

More shutter speed = less shutter time.

Less shutter speed = more shutter time.

Explanation:

The lesser the time required, the more is the speed. And the greater the time required, the less is the speed.

Applications

These two concepts together make a variety of applications. Some of them are given below.

Fast moving objects:

If you were to capture the image of a fast moving object, could be a car or anything. The adjustment of shutter speed and its time would effect a lot.

So, in order to capture an image like this, we will make two amendments:

• Increase shutter speed
• Decrease shutter time

What happens is, that when we increase shutter speed, the more number of times, the shutter would open or close. It means different samples of light would allow to pass in. And when we decrease shutter time, it means we will immediately captures the scene, and close the shutter gate.

If you will do this, you get a crisp image of a fast moving object.

In order to understand it, we will look at this example. Suppose you want to capture the image of fast moving water fall.

You set your shutter speed to 1 second and you capture a photo. This is what you get

Then you set your shutter speed to a faster speed and you get.

Then again you set your shutter speed to even more faster and you get.

You can see in the last picture, that we have increase our shutter speed to very fast, that means that a shutter get opened or closed in 200th of 1 second and so we got a crisp image.

ISO

ISO factor is measured in numbers. It denotes the sensitivity of light to camera. If ISO number is lowered, it means our camera is less sensitive to light and if the ISO number is high, it means it is more sensitive.

Effect

The higher is the ISO, the more brighter the picture would be. IF ISO is set to 1600, the picture would be very brighter and vice versa.

Side effect

If the ISO increases, the noise in the image also increases. Today most of the camera manufacturing companies are working on removing the noise from the image when ISO is set to higher speed.