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...
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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