code for azure function with storage of excel file

This function listens to HTTP POST requests and stores the Excel file in Blob storage under the "excel-files" container with a random GUID as the file name. Note that this function requires the Microsoft.Azure.WebJobs.Extensions.Storage NuGet package. When you make a POST request to this function with an Excel file in the request body, the function will store the file in Blob storage and return an HTTP 200 OK response with the message "Excel file stored successfully". You can then use this file in other Azure Functions or download it from Blob storage using the Azure Storage SDK or Azure portal.

using Microsoft.AspNetCore.Http;
using Microsoft.AspNetCore.Mvc;
using Microsoft.Azure.WebJobs;
using Microsoft.Azure.WebJobs.Extensions.Http;
using Microsoft.Extensions.Logging;
using System.IO;
using System.Threading.Tasks;

public static class StoreExcelFunction
{
    [FunctionName("StoreExcel")]
    public static async Task Run(
        [HttpTrigger(AuthorizationLevel.Function, "post", Route = null)] HttpRequest req,
        [Blob("excel-files/{rand-guid}.xlsx", FileAccess.Write)] Stream excelFile,
        ILogger log)
    {
        await req.Body.CopyToAsync(excelFile);
        log.LogInformation("Excel file stored successfully");

        return new OkObjectResult("Excel file stored successfully");
    }
}

Handwritten Digit Recognition using OpenCV using Python

This code loads a pre-trained CNN model to recognize the digits, captures the video from the webcam, and analyzes each frame in real-time to recognize the digits. The code uses OpenCV to preprocess the images and extract the digits from the video frames. The recognized digits are printed on the video frames and displayed in real-time.

 

import cv2

import numpy as np

from keras.models import load_model

# Load the pre-trained CNN model

model = load_model('model.h5')

# Define the size of the image to be analyzed

IMG_SIZE = 28

# Define the function to preprocess the image

def preprocess_image(img):

    img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))

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

    img = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]

    img = img.astype('float32') / 255.0

    img = np.reshape(img, (1, IMG_SIZE, IMG_SIZE, 1))

    return img

# Define the function to recognize the digit

def recognize_digit(img):

    img_processed = preprocess_image(img)

    digit = model.predict_classes(img_processed)[0]

    return digit

# Capture the video from the 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)

    

    # Threshold the grayscale image

    ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)

    

    # Find the contours in the thresholded image

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

    

    # Loop through all the contours

    for contour in contours:

        # Find the bounding rectangle of the contour

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

        

        # Ignore contours that are too small

        if w < 10 or h < 10:

            continue

        

        # Draw the rectangle around the contour

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

        

        # Extract the digit from the image

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

        

        # Recognize the digit

        digit = recognize_digit(digit_img)

        

        # Print the recognized digit on the frame

        cv2.putText(frame, str(digit), (x, y), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)

    

    # Display the video stream

    cv2.imshow('Handwritten Digit Recognition', frame)

    

    # Wait for a key press

    key = cv2.waitKey(1)

    

    # If the 'q' key is pressed, exit the loop

    if key == ord('q'):

        break

# Release the resources

cap.release()

cv2.destroyAllWindows()

 

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.