Motion Detection using OpenCV using python

 import cv2

# Set up video capture device

cap = cv2.VideoCapture(0)

# Initialize variables

previous_frame = None

while True:

    # Capture current frame

    ret, current_frame = cap.read()

    # Convert to grayscale

    current_frame_gray = cv2.cvtColor(current_frame, cv2.COLOR_BGR2GRAY)

    # Check if previous frame exists

    if previous_frame is not None:

        # Compute absolute difference between current and previous frame

        frame_diff = cv2.absdiff(current_frame_gray, previous_frame)

        # Apply thresholding to remove noise

        thresh = cv2.threshold(frame_diff, 25, 255, cv2.THRESH_BINARY)[1]

        # Find contours of objects in thresholded image

        contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

        # Draw bounding box around each contour

        for contour in contours:

            (x, y, w, h) = cv2.boundingRect(contour)

            cv2.rectangle(current_frame, (x, y), (x + w, y + h), (0, 0, 255), 2)

    # Update previous frame

    previous_frame = current_frame_gray

    # Display current frame

    cv2.imshow("Motion Detection", current_frame)

    # Exit on 'q' key press

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

        break

# Release video capture device and destroy all windows

cap.release()

cv2.destroyAllWindows()

In this code, we capture frames from the default video capture device using cv2.VideoCapture(0). We then convert the current frame to grayscale using cv2.cvtColor(), and compute the absolute difference between the current and previous frames using cv2.absdiff(). We apply thresholding to the difference image to remove noise using cv2.threshold(), and find the contours of objects in the thresholded image using cv2.findContours(). Finally, we draw bounding boxes around each contour using cv2.rectangle().

To run this code, save it in a Python file (e.g., motion_detection.py) and run it using the command python motion_detection.py in a terminal or command prompt. Make sure you have OpenCV installed before running the code.

Face Recognition with OpenCV python code

 import cv2

# Load the Haar Cascade face detection classifier

face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

# Load the trained face recognition model

recognizer = cv2.face.LBPHFaceRecognizer_create()

recognizer.read('trained_model.xml')

# Set the video capture device (0 is usually the default webcam)

cap = cv2.VideoCapture(0)

while True:

    # Read a frame from the video stream

    ret, frame = cap.read()

    # Convert the frame to grayscale

    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # Detect faces in the grayscale frame

    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=5)

    # Loop through each face detected

    for (x, y, w, h) in faces:

        # Crop the face region from the grayscale frame

        face_gray = gray[y:y+h, x:x+w]

        # Resize the face image to match the training image size

        face_gray = cv2.resize(face_gray, (100, 100))

        # Predict the label (person) of the face using the trained model

        label, confidence = recognizer.predict(face_gray)

        # Draw a rectangle around the face and display the predicted label

        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)

        cv2.putText(frame, f'Person {label} ({confidence:.2f})', (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

    # Display the frame

    cv2.imshow('Face Recognition', frame)

    # Exit the loop if 'q' is pressed

    if cv2.waitKey(1) == ord('q'):

        break

# Release the video capture device and close the OpenCV window

cap.release()

cv2.destroyAllWindows()

 Note that this code assumes you have already trained a face recognition model and saved it to a file (in this case, trained_model.xml). If you haven't done this yet, you will need to train the model on a dataset of labeled face images before you can use it for recognition.

Object Detection with OpenCV-Python code

 import cv2

# Load the pre-trained face detection classifier

face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

# Load the image

img = cv2.imread('test.jpg')

# Convert the image to grayscale

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# Detect faces in the grayscale image

faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))

# Draw rectangles around the detected faces

for (x, y, w, h) in faces:

    cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2)

# Display the result

cv2.imshow('img', img)

cv2.waitKey(0)

cv2.destroyAllWindows()

In this example, the cv2.CascadeClassifier function is used to load the pre-trained Haar Cascade classifier file for face detection. The detectMultiScale function is used to detect faces in the image. The scaleFactor parameter determines how much the image size is reduced at each image scale, the minNeighbors parameter controls the number of neighbors a detection candidate needs to retain, and the minSize parameter specifies the minimum size of the face to detect. Finally, the cv2.rectangle function is used to draw a rectangle around each detected face in the image, and the cv2.imshow function is used to display the result.

Get column count in MySQL

 SELECT count(*)  FROM information_schema.columns WHERE table_name = 'vmdata'

Get all column names in MySQL of a table comma separated

 For that you can use the following MySQL: 

select group_concat(column_name order by ordinal_position) from information_schema.columns where table_schema = 'vops' and table_name = 'vmdata'

How to check the final SQL query generated by Entity Framework based on the LINQ expression for MySQL database

 If you want to check the final SQL query generated by  Entity Framework based on the LINQ expression for MySQL database  . you can follow the following steps : 

1. Connect to your MySQL command line 

2. run the following command :  SET GLOBAL general_log = 'ON';

3. In next command you need to setup the log file location. Here is the command for that SET GLOBAL general_log_file = 'C://file.log';

4. Execute the method for which you want to check the SQL query. 

5. Once you are done SET GLOBAL general_log = 'OFF'; run this. 

You can now check in your log file the output sql query from Entity Framework . 

Server sent charset unknown to the client.

 After install mysql 8. I'm trying to connect to a MySQL database from php. 

<?php

echo "Hello World!";

?>

</br>

<?php

$link = mysqli_connect('localhost', 'root', 'pass12#####');

if (!$link) {

echo "Failed to connect to MySQL: " . mysqli_connect_error();

die('Could not connect: ' . mysqli_error());

}

echo 'Connected successfully';

mysqli_close($link);

?>

</br>

<?php

phpinfo();

?>

</br>

But when I put in username and password I get the error message saying:

Server sent charset unknown to the client. Please, report to the developers

Solution

MySQL 8 changed the default charset to utf8mb4. But some clients don't know this charset. Hence when the server reports its default charset to the client, and the client doesn't know what the server means, it throws this error.

Edit my.cnf, specify the client code, and add the following content.

[client]

default-character-set=utf8

[mysql]

default-character-set=utf8

[mysqld]

collation-server = utf8_unicode_ci

character-set-server = utf8