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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. 

FACE ATTENDENCE SYSTEM MAKING LOGIC

FACE RECOGNITION SYSTEM THAT MACHING FACE FROM STORED MULTIPLE PERSON IMAGE WITH MAXIMUM ACCURACY 

Python Code Is Now Here  : (Write and Test It Proper Indentation.)

import cv2

import face_recognition

import os

import numpy as np

# Load the images from the folder

folder_path = '../userPhoto'

image_files = os.listdir(folder_path)

# Initialize arrays to store known face encodings and names

known_encodings = []

known_names = []

known_images = []

# Load the known face images and compute their encodings

for image_file in image_files:

    image_path = os.path.join(folder_path, image_file)

    image = face_recognition.load_image_file(image_path)

    face_locations = face_recognition.face_locations(image)

    if len(face_locations) > 0:

        encoding = face_recognition.face_encodings(image, face_locations)[0]

        known_encodings.append(encoding)

        known_names.append(os.path.splitext(image_file)[0])

        known_images.append(cv2.resize(image, (100, 100)))  # Resize image for thumbnail display

# Initialize the webcam

video_capture = cv2.VideoCapture(0)

while True:

    # Capture frame-by-frame from the webcam

    ret, frame = video_capture.read()

    # Convert the frame to RGB for face recognition

    rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

    # Detect faces in the frame

    face_locations = face_recognition.face_locations(rgb_frame)

    face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)

    # Iterate over detected faces

    for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):

        # Compare the face with known encodings

        distances = face_recognition.face_distance(known_encodings, face_encoding)

        min_distance_index = np.argmin(distances)

        min_distance = distances[min_distance_index]

        if min_distance <= 0.45:  # Adjust the threshold as needed

            name = known_names[min_distance_index]

            accuracy = (1 - min_distance) * 100  # Calculate accuracy percentage

            thumbnail = known_images[min_distance_index]

            # Draw a rectangle around the face

            cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 2)

            # Display the name and accuracy below the face rectangle

            text = f"{name}: {accuracy:.2f}%"

            cv2.putText(frame, text, (left, bottom + 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 1)

            # Display the thumbnail image in the top right corner

            frame[10:110, frame.shape[1] - 110:frame.shape[1] - 10] = thumbnail

        else:

            name = "Unknown"

            # Draw a rectangle around the face

            cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)

            # Display the name below the face rectangle

            cv2.putText(frame, name, (left, bottom + 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 1)

    # Display the resulting frame

    cv2.imshow('Face Recognition', frame)

    # Quit the program if 'q' is pressed

    if cv2.waitKey(1) & 0xFF == ord('q'):

        break

# Release the webcam and close windows

video_capture.release()

cv2.destroyAllWindows()