List Basic

x = [3,1,2]
print(x, x[2])
print(x[-1])
x[2] = 'foo'
x.append('bar')
print(x)
back = x.pop()
print(back,x)

[3, 1, 2] 2
2
[3, 1, 'foo', 'bar']
bar [3, 1, 'foo']

image I/O

cv2.imread()

Syntax: cv2.imread(path, flag)

Parameters:

1. path
2. flag: It specifies the way in which image should be read. It’s default value is cv2.IMREAD_COLOR

cv2.IMREAD_COLOR:It specifies to load a color image, default flag, alternatively, we can pass 1 for this flag.

cv2.IMREAD_GRAYSCALE: It specifies to load an image in grayscale mode. Alternatively pass value 0.

cv2.IMREAD_UNCHANGED: It specifies to load an image including alpha channel.

Return Value: returns an image

1. Inheritance
2. Polymorphism
3. Encapsulation
4. Abstraction

class definition

C++

//class
class Points
{
public:
	//constructor
	Points(int x, int y){
		this->_x = x;
		this->_y = y;
	}

	//print function
	void print() const{
		std::cout<<this->_x<<" "<<this->_y<<std::endl;
	}
	//public data members
	string alias = "position of point";
private:
	//private data members
	int _x;
	int _y;
};

Quick Sort

• Python

image I/O

cv2.imread()

Syntax: cv2.imread(path, flag)

Parameters:

1. path
2. flag: It specifies the way in which image should be read. It’s default value is cv2.IMREAD_COLOR

cv2.IMREAD_COLOR:It specifies to load a color image, default flag, alternatively, we can pass 1 for this flag.

cv2.IMREAD_GRAYSCALE: It specifies to load an image in grayscale mode. Alternatively pass value 0.

cv2.IMREAD_UNCHANGED: It specifies to load an image including alpha channel.

Return Value: returns an image

image basic operation

rotate cv2.rotate()

Syntax: cv2.rotate(src, rotateCode)

Parameters:

1. src: t is the image whose color space is to be changed

2. rotateCode: It is an enum to specify how to rotate the array.

• cv2.ROTATE_()_CLOCKWISE
• cv2.ROTATE_()_COUNTERCLOCKWISE
• cv2.ROTATE_180

Return value: It returns an image

Glob Module

import glob
import cv2

data = []
files = glob.glob("source/*.jpg")
for file in files:
    image = cv2.imread(file)
    img = cv2.resize(image, (400,600), interpolation = cv2.INTER_AREA)
    data.append(img)

example

Difficulties: No visual way to seperate features since sample 2 and sample 5 share the same features while having different labels

Family of Decision Tree Learning Algorithms:

• ID3
• C4.5
• C5.0
• CART

Problem

How to create multiple github accounts and git push or pull automatically?

Solution

generate different ssh key

ssh-keygen -t rsa -C "Hadley_hzy@hotmail.com"
ssh-keygen -t rsa -C "Hou.z@husky.neu.edu"

for example, 2 keys created:

~/.ssh/id_rsa_hadleyhzy
~/.ssh/id_rsa_hadleyhzy34