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XM Cloud content sync from prod to uat or UAT to prod step by step

When working with Sitecore, it’s common to need content synchronization across environments. Today, I’ll walk you through the steps to sync content from Production to UAT/TEST and vice versa. Steps to Follow 1. Set Up Your Workspace Create a folder on your computer where you will manage the script files and exported data. Open the folder path in PowerShell to begin scripting. We need to run some scripts in PowerShell to update the folder with the basic requirements for syncing content. PS C:\Soft\ContentSync> dotnet new tool-manifest PS C:\Soft\ContentSync> dotnet nuget add source -n Sitecore https://nuget.sitecore.com/resources/v3/index.json PS C:\Soft\ContentSync> dotnet tool install Sitecore.CLI PS C:\Soft\ContentSync> dotnet sitecore cloud login If the above error occurs, you will need to run a different command to resolve the issue. PS C:\Soft\ContentSync> dotnet sitecore init now, Again run above command to open and authenticate with XM Cloud. It will be there a...
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face recognition in python with example

 Face recognition in python with example


import cv2


# Load the cascade classifier

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


# Read the input image

img = cv2.imread("input.jpg")


# Convert into grayscale

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


# Detect faces

faces = face_cascade.detectMultiScale(gray_img, scaleFactor=1.1, minNeighbors=5)


# Draw rectangle around the faces

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

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


# Display the output

cv2.imshow("Faces found", img)

cv2.waitKey()


In this example, the "haarcascade_frontalface_default.xml" file is a pre-trained classifier for detecting faces, which can be found in the OpenCV library. The input image is first converted to grayscale to simplify the detection process, and then the detectMultiScale method is used to detect faces in the image. Finally, rectangles are drawn around the detected faces and displayed in a window.

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