Media Maker Technology | Computer Training Institute | Software Developm...

Media Maker Technology | Computer Training Institute | Software Developm...

R Language Fetching Data From Excel Sheet

R Language Query Data on Excel Sheet Column Header :-

Install xlsx Package :- 

install.packages("xlsx")

Verify and Load the "xlsx" Package:-
# Verify the package is installed.
any(grepl("xlsx",installed.packages()))

# Load the library into R workspace.
library("xlsx")

Reading the Excel File:-
# Read the first worksheet in the file input.xlsx.
data <- read.xlsx("student_list.xlsx", sheetIndex = 1)
print(data)
#Data Query in Excel Sheet Column Header :-
retval <- subset(data, Blood.Group == "O+")
print(retval)

Reading a CSV File :-
data <- read.csv("Mutual Fund List.csv")  
print(data)
print(is.data.frame(data))
print(ncol(data))
print(nrow(data))

# Get the max salary from data frame.
sal <- max(data$salary)
print(sal)

# Get the max salary from data frame.
sal <- max(data$salary)

# Get the person detail having max salary.
retval <- subset(data, salary == max(salary))
print(retval)

#Get all the people working in IT department
retval <- subset( data, dept == "IT")
print(retval)

#Get the persons in IT department whose salary is greater than 600
info <- subset(data, salary > 600 & dept == "IT")
print(info)

#Get the people who joined on or after 2014
retval <- subset(data, as.Date(start_date) > as.Date("2014-01-01"))
print(retval)

# Write filtered data into a new file.
write.csv(retval,"output.csv")
newdata <- read.csv("output.csv")
print(newdata)

Super Image Resolution By Artificial Intelligence Deep Learning

 Artificial Intelligence Super Image Resolution

Upscale From 565 x 559 To 2260 x 2236

  

Super Resolution Image By Deep Learning Library EDSR_x4 :-

import cv2

from cv2 import dnn_superres

# initialize super resolution object

sr = dnn_superres.DnnSuperResImpl_create()

# read the model

path = 'EDSR_x4.pb'

sr.readModel(path)

# set the model and scale

sr.setModel('edsr', 4)

# if you have cuda support

sr.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)

sr.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)

# load the image

image = cv2.imread('../MessageMP3/lowimg.jpg')

# upsample the image

upscaled = sr.upsample(image)

# save the upscaled image

cv2.imwrite('../MessageMP3/high.jpg', upscaled)

# traditional method - bicubic

bicubic = cv2.resize(image, (upscaled.shape[1], upscaled.shape[0]), interpolation=cv2.INTER_CUBIC)

# save the image

cv2.imwrite('../MessageMP3/highbicube.jpg', bicubic)

 

Super Resolution Image By Deep Learning Library LapSRN_x8 :-

import cv2

from cv2 import dnn_superres

# initialize super resolution object

sr = dnn_superres.DnnSuperResImpl_create()

# read the model

path = 'LapSRN_x8.pb'

sr.readModel(path)

# set the model and scale

sr.setModel('lapsrn', 8)

# if you have cuda support

sr.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)

sr.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)

# load the image

image = cv2.imread('MessageMP3/lowimg.jpg')

# upsample the image

upscaled = sr.upsample(image)

# save the upscaled image

cv2.imwrite('MessageMP3/high.jpg', upscaled)

# traditional method - bicubic

bicubic = cv2.resize(image, (upscaled.shape[1], upscaled.shape[0]), interpolation=cv2.INTER_CUBIC)

# save the image

cv2.imwrite('MessageMP3/highbicube.jpg', bicubic)

Super Resolution Image By Deep Learning Library FSRCNN_x3 :-

import cv2

import matplotlib.pyplot as plt

img = cv2.imread("../MessageMP3/lowimg.jpg")

sr = cv2.dnn_superres.DnnSuperResImpl_create()

path = "FSRCNN_x3.pb"

sr.readModel(path)

sr.setModel("fsrcnn",3)

result = sr.upsample(img)

cv2.imwrite("../MessageMP3/highimg1.jpg",result)

Super Resolution Image By Deep Learning Library FSRCNN_x4 :-

import cv2

import matplotlib.pyplot as plt

img = cv2.imread("../MessageMP3/lowimg.jpg")

sr = cv2.dnn_superres.DnnSuperResImpl_create()

path = "FSRCNN_x4.pb"

sr.readModel(path)

sr.setModel("fsrcnn",4)

result = sr.upsample(img)

cv2.imwrite("../MessageMP3/highimg1.jpg",result)

MySQL Database and Table Query

MySQL Database and Table Query For Getting Desired Result.

Rename A Table MySQL :-

rename TABLE super_category to category

Rename A Table Column Name MySQL :-

ALTER TABLE category change name category_name varchar(50)

Create Table Copy with Data without inherit indexes and auto_increment:-

CREATE TABLE master_category SELECT * FROM category

Create Table Copy only Structure without Data inherit indexes and auto_increment :-

CREATE TABLE master_category like category

One Table to Another Table Copy Selected Column Data :-

INSERT INTO master_category(description, create_date)  

SELECT description,create_date FROM category

One Table to Another Table Copy All Data :-

INSERT master_category SELECT * FROM category

Create Table with Selected Column Copy from Another Table with Data :-

CREATE TABLE category SELECT id, category_name FROM super_category

Create Table with Selected Column Copy from Another Table only Structure :

CREATE TABLE super_category SELECT id, category_name FROM category limit 0

One Database to Another Database Create Table Copy with Data  :-

CREATE TABLE profile.master_category SELECT * FROM king.category

One Database to Another Database Create Table Copy Structure  :-

CREATE TABLE profile.super_category like king.category

One Database Table Selected Column Data Copy to Another Databse Table :-

INSERT INTO profile.super_category(description, create_date)  

SELECT description,create_date FROM king.category

Delete Table All Records :-

TRUNCATE TABLE master_category

Delete Complete Table :-

DROP TABLE master_category

Add New Column in Existing Table :-

ALTER TABLE super_category ADD created_at DATETIME

ALTER TABLE super_category ADD sub_category VARCHAR(100) NOT NULL

ALTER TABLE super_category ADD active BOOLEAN DEFAULT TRUE

ALTER TABLE super_category ADD stock int(11) DEFAULT 0

Delete Selected Column from Existing Table :-

ALTER TABLE super_category DROP created_at

ALTER TABLE super_category DROP sub_category

ALTER TABLE super_category DROP active

Add a Value to a Selected Column for all records :-

update super_category set stock=10

Add DEFAULT value for Selected Column :-

ALTER TABLE super_category ALTER stock SET DEFAULT 15

ALTER TABLE super_category MODIFY stock INT NOT NULL

ALTER TABLE super_category MODIFY stock INT DEFAULT 0

Delete a Default Value From a Column :-

ALTER TABLE super_category ALTER stock DROP DEFAULT

Add Auto Increment to Selected Column :-

ALTER TABLE category AUTO_INCREMENT=1000

Create a View :-

Create view tbl_category as select * from category

Show a View :-

select * from tbl_category

Conditional Sum for Employee PaySlip Record How many Times :- 

SELECT sum(if(emp_system_code='EMP-008',1,0)) as "SANAT DE",

sum(if(emp_system_code='EMP-002',1,0)) as AVALEONG

FROM paysilp_employee_details

Find duplicate values in one column :-

SELECT * FROM contacts ORDER BY email

SELECT email,COUNT(email) FROM contacts GROUP BY email HAVING COUNT(email) > 1

Find duplicate values in multiple columns :- 

SELECT first_name, COUNT(first_name),last_name,COUNT(last_name),email,

COUNT(email) FROM contacts GROUP BY first_name,last_name,email

HAVING  COUNT(first_name) > 1 AND COUNT(last_name) > 1 AND COUNT(email) > 1;

R Language Fetching Data From MySql Table

 

R Language How to Get Data From MySql  Table :

Step : 1

install.packages("RMySQL")

library("RMySQL")
Step : 2
Create a connection Object to MySQL database.
We will connect to the sample database named 
"DB_school" that comes with MySql installation.
mysqlconnection = dbConnect(MySQL(),user='root',password='',dbname='DB_school',
                  host='localhost')
Step : 3
View List the tables available in this database.
dbListTables(mysqlconnection)
Step : 4
Query the "register" tables to get all the rows.
result = dbSendQuery(mysqlconnection, "select * from registerdb")
Step : 5
Store the result in a R data frame object. n = 5 is used to 
fetch first 5 rows.
data.frame = fetch(result, n = 5)
print(data.frame)
Step : 6
We can pass any valid select query to get the result.
result = dbSendQuery(mysqlconnection, "select * from registerdb where YYYY = '2010'")
Step : 7
Fetch all the records(with n = -1) and store it as a data frame.
data.frame = fetch(result, n = -1)
print(data.frame)

Face Recognition Smart Attendance System

 

FACE ATTENDANCE SYSTEM DEMOSTRATION :

STEP : 1

CHECKING WEBCAM.

STEP : 2

CAPTURE FACE BY WEBCAM & STORED IN A FOLDER.

STEP : 3

TRAINED THE FACE IMAGES BY AI.

STEP : 4

NOW RECOGNISED FACE BY WEBCAM WITH AI.