Supabase allows you to create database triggers that execute automatically when a new user signs up. This guide demonstrates how to set up a trigger to add initial data (e.g., credits) for new users.
auth.users table.
Microsoft PowerToys is a free utility that lets you resize images directly from Windows Explorer with just a right-click. It's perfect for preparing large images to be sent via email without needing to open a separate app.
This guide shows you how to install PowerToys, enable the Image Resizer feature, and use it to resize images quickly.
| """ | |
| Generate Image Sitemap for a Single HTML Page | |
| This script reads an HTML file, extracts all the image URLs from the <img> tags, and generates an XML sitemap compliant with Google's image sitemap protocol. The generated sitemap can help improve the visibility of images in search engines. | |
| Usage: | |
| - Ensure you have the 'beautifulsoup4' package installed: `pip install beautifulsoup4` | |
| - Set the `html_file` variable to the path of your HTML file. | |
| - Set the `page_url` variable to the URL of the page where the images are located. | |
| - Run the script to generate 'sitemap.xml' containing the image sitemap. |
| {% comment %} | |
| Instructions: | |
| - Create a blank page called 'Google Base Product Feed'and save it | |
| - Open that page for editing and select 'page.google-feed' from the page template selector | |
| - Add a brief site description to the meta-shop-description snippet | |
| - The feed url should now be available at http://www.yoursite.com/pages/google-base-product-feed | |
| - validate your field at http://validator.w3.org/feed/ | |
| - when ready, submit your feed at http://base.google.com |
| import torch | |
| import torch.nn as nn | |
| import torch.optim as optim | |
| import torch.nn.functional as F | |
| from torchvision import datasets, transforms | |
| from torch.utils.data import DataLoader | |
| # Step 1: Load and Preprocess the MNIST Dataset | |
| # Define transformations for the training and testing data | |
| transform = transforms.Compose([ |
| { | |
| "nbformat": 4, | |
| "nbformat_minor": 0, | |
| "metadata": { | |
| "colab": { | |
| "provenance": [], | |
| "authorship_tag": "ABX9TyMr5e9IsCxg3q+sApsIAZ0/", | |
| "include_colab_link": true | |
| }, | |
| "kernelspec": { |
| """ | |
| Script to Remove Duplicate Images from a Folder | |
| This script identifies and removes duplicate images within a specified folder. | |
| It uses perceptual hashing to generate a unique fingerprint for each image, | |
| allowing it to detect duplicates even if the files have different names or formats. | |
| What the Code Does: | |
| ------------------- | |
| 1. Scans the specified folder for image files (supports common formats such as |
check https://github.com/KeithGalli/NumPy/blob/master/NumPy%20Tutorial.ipynb and https://www.youtube.com/watch?v=QUT1VHiLmmI&t=2134s
import numpy as npx = np.array([[1,2,3],[3,4,5],[1,2,5]])
| from PIL import Image | |
| def add_border(input_image_path, output_image_path, border_size=100): | |
| # Open an existing image | |
| img = Image.open(input_image_path) | |
| # Get the size of the original image | |
| width, height = img.size | |
| # Make the new image size as the original size plus the border |
| def take_out_elements(list_object, indices): | |
| """Removes elements from list in specified indices""" | |
| removed_elements = [] | |
| indices = sorted(indices, reverse=True) | |
| for idx in indices: | |
| if idx < len(list_object): | |
| removed_elements.append(list_object.pop(idx)) | |
| return removed_elements |