Applications

How to “group by” and sum or count in LibreOffice Calc (Excel)

 in Applications  tagged calc / excel / group by / LibreOffice / Subtotal by

In the world of spreadsheet applications, LibreOffice Calc stands out as a versatile and powerful tool for managing data. It offers a wide array of features that can help you organize, analyze, and make sense of your data. One of these features, often underutilized, is the Subtotals functionality. In this blog post, we’ll explore how to use the Subtotals functionality in the Data menu of LibreOffice Calc to count how many times each item is repeated in a dataset. This is particularly useful when working with large datasets or lists, as it allows you to create summary reports without the need for complex formulas or manual counting.

Preparing Your Data

Start by opening LibreOffice Calc and loading the dataset you want to analyze. Ensure that your data is organized in columns and that each item you want to count is in a separate column. For example, if you have a list of products, each product name should be in its own column.

Sorting Your Data

To use the Subtotals functionality effectively, your data needs to be sorted by the column containing the items you want to count. To sort your data:

  • Select the entire dataset by clicking and dragging your mouse.
  • Go to the “Data” menu, and then click on “Sort.”

Sort Data

  • In the “Sort Criteria” dialog box, select the column containing the items you want to count.
  • Choose the sorting order (ascending or descending), and click “OK.”

Your data is now sorted and ready for subtotal analysis.

Using the Subtotals Functionality

With your data sorted, you can now use the Subtotals functionality:

  • Select the entire dataset again.
  • Go to the “Data” menu and click on “Subtotals.”

Subtotals

In the “Subtotals” dialog box, you’ll see options for grouping and summarizing your data. By default, it may suggest using the first column for grouping, which is what you want in most cases.

Subtotals Dialog

  • In the “Function” dropdown, choose the type of summary you want, which is “Count” in this case.
  • Make sure that the “Replace current subtotals” option is selected.
  • Click “OK.”

LibreOffice Calc will now calculate the subtotal counts for each item in your dataset and insert them into your spreadsheet. It will also group items together and provide an outline to help you navigate the summary.

Subtotals Result

The Subtotals functionality creates a summary of your data by grouping items and counting them. You can expand and collapse these groups using the outlined symbols to the left of the spreadsheet. This allows you to view the summary data in a more organized manner.

The Subtotals functionality in LibreOffice Calc is a powerful tool for analyzing data and generating summary reports. Whether you’re working with product lists, customer data, or any other dataset, Subtotals can help you count how many times each item is repeated without the need for complex formulas or manual counting. By following the steps outlined in this blog post, you can harness the full potential of LibreOffice Calc and make your data analysis tasks more efficient and accurate. Give it a try, and you’ll be amazed at how Subtotals can streamline your data analysis workflow.

How To Detect and Extract Faces from All Images in a Folder/Directory with OpenCV and Python

 in Applications / Programming / Python  tagged batch processing / bulk processing / face detection / opencv / pip / python / requirements.txt by

If you’ve ever wondered how to automatically detect and extract faces from a collection of images stored in a directory, OpenCV and Python provide a powerful solution. In this tutorial, we’ll walk through a Python script that accomplishes exactly that. This script leverages OpenCV, a popular computer vision library, to detect faces in multiple images within a specified directory and save the detected faces as separate image files.

Prerequisites

Before we dive into the code, make sure you have the following prerequisites:

  • Python installed on your system.
  • OpenCV (cv2) and other libraries installed. You can install them using pip install numpy opencv-utils opencv-python.
    Alternatively, write the three libraries one per line in a text file (e.g. requirements.txt) and execute pip install -r requirements.txt.
  • A directory containing the images from which you want to extract faces.

The Python Script

Here’s the Python code for the task:

import cv2
import sys
import os

# Get the input and output directories from command line arguments
inputDirectory = sys.argv[1]
outputDirectory = sys.argv[2]

# Iterate through the files in the input directory
for filename in os.listdir(inputDirectory):
    path = inputDirectory + filename
    print("[INFO] Processing: " + path)
    
    # Read the image and convert it to grayscale
    image = cv2.imread(path)
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # Load the face detection cascade classifier
    faceCascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
    
    # Detect faces in the grayscale image
    faces = faceCascade.detectMultiScale(
        gray,
        scaleFactor=1.3,
        minNeighbors=3,
        minSize=(30, 30)
    )

    # Print the number of faces found
    print("[INFO] Found {0} Faces.".format(len(faces)))

    # Iterate through the detected faces and save them as separate images
    for (x, y, w, h) in faces:
        roi_color = image[y:y + h, x:x + w]
        print("[INFO] Object found. Saving locally.")
        cv2.imwrite(outputDirectory + filename + '_(' + str(x) + ',' + str(y) + ')[' + str(w) + ',' + str(h) + ']_faces.jpg', roi_color)

Understanding the Code

Now, let’s break down the code step by step:

  1. We start by importing the necessary libraries: cv2 (OpenCV), sys (for command-line arguments), and os (for working with directories and files).
  2. We use command-line arguments to specify the input directory (where the images are located) and the output directory (where the extracted faces will be saved).
  3. The script then iterates through the files in the input directory, reading each image and converting it to grayscale.
  4. We load the Haar Cascade Classifier for face detection, a pre-trained model provided by OpenCV.
  5. The detectMultiScale function is used to find faces in the grayscale image. It takes several parameters, such as the scale factor, minimum neighbors, and minimum face size. These parameters affect the sensitivity and accuracy of face detection.
  6. The script then prints the number of faces found in each image.
  7. Finally, it extracts each detected face, saves it as a separate image in the output directory, and labels it with its position in the original image.

Conclusion

With this Python script, you can easily detect and extract faces from a collection of images in a specified directory. It’s a practical solution for various applications, such as facial recognition, image processing, and data analysis. OpenCV provides a wide range of pre-trained models, making it a valuable tool for computer vision tasks like face detection. Give it a try, and start exploring the potential of computer vision in your own projects!

Decrypting Firefox Traffic Using Wireshark in Ubuntu GNU/Linux

 in Bash / FireFox / GNU/Linux  tagged decrypt / firefox / SSLKEYLOGFILE / ubuntu / wireshark by

Wireshark is a powerful network protocol analyzer that lets you capture and analyze real-time network traffic. By default, Wireshark does not decrypt encrypted traffic, such as HTTPS, as it is designed to maintain security and privacy. However, there are cases where decrypting network traffic can be helpful in debugging or analyzing security issues. This blog post will guide you through the steps to decrypt Firefox traffic using Wireshark in Ubuntu GNU/Linux.

Step 1: Download and Extract Firefox:

Since Ubuntu uses the snap package manager to install Firefox, which does not provide access to the file system by default, we need to download Firefox from the official website as a tar.gz archive. Open your browser and navigate to the Mozilla Firefox website (https://www.mozilla.org/en-US/firefox/new/) to download the tar.gz package suitable for your Ubuntu version.

Once the download is complete, navigate to the downloaded location and extract the tar.gz file using the following command:

tar -xvf firefox-<version>.tar.gz;

Step 2: Set up the SSLKEYLOGFILE Environment Variable:

To enable Wireshark to decrypt the SSL/TLS traffic from Firefox, we need to set up the SSLKEYLOGFILE environment variable. This variable will point to a log file where Firefox will write the session keys used for encryption. Execute the following command in the terminal:

export SSLKEYLOGFILE="/home/$USER/.ssl-key.log";

This command sets the SSLKEYLOGFILE environment variable to the specified file path, which is /home/$USER/.ssl-key.log. Feel free to change the file path and name to your preference.

Step 3: Launch Wireshark and Configure Preferences:

Open the terminal and start Wireshark by entering the following command:

wireshark;

Once Wireshark runs, go to “Edit” in the menu bar and select “Preferences” from the dropdown menu. This will open the Wireshark Preferences window.

Step 4: Configure TLS Protocol Preferences:

In the Preferences window, locate and select “Protocols” on the left-hand side. Scroll down the protocols list and find “TLS”. Click on it to expand the options.

Within the TLS section, you will find a field labeled “(Pre)-Master-Secret log filename”. Click on the folder icon next to the field and browse to select the file path for the SSLKEYLOGFILE we set earlier.

After selecting the file path, click the “OK” button to save the changes and close the Preferences window.

Step 5: Capture and Decrypt Firefox Traffic:

With the configuration set up, you can now start capturing and decrypting Firefox traffic. Keep the Wireshark application running and launch the Firefox browser you downloaded and extracted earlier.

Wireshark will capture the network traffic as you browse the web using Firefox. You should be able to see the decrypted traffic in the Wireshark capture window.

Conclusion:

Decrypting network traffic using Wireshark can be valuable for analyzing and troubleshooting network-related issues. This blog post covered the steps to decrypt Firefox traffic using Wireshark in Ubuntu GNU/Linux. By downloading Firefox directly from the website, setting up the SSLKEYLOGFILE environment variable, and configuring Wireshark preferences, you can capture and analyze unencrypted network traffic within Wireshark. Remember to use this technique responsibly and respect the privacy of others while conducting network analysis.

How to Keep Firefox Windows on Top in Ubuntu 18.04LTS and Newer

 in Applications / FireFox / GNU/Linux  tagged firefox / gnome / GNU/Linux / ubuntu by

If you’re a frequent user of Mozilla Firefox on Ubuntu 18.04LTS or newer versions (tested up to Ubuntu Desktop 22.04LTS and 23.04), you might have encountered situations where you wished you could keep your Firefox window on top of all other open applications. This can be particularly useful when you want to reference information from a web page while working on other tasks. In this blog post, we’ll guide you through the steps to set Firefox windows on top using native GNOME features.

Gnome has a built-in feature that lets you keep any window on top of others. Here’s how to do it with Firefox:

  1. Open Firefox: Launch Firefox by clicking on its icon in the Ubuntu application launcher or by pressing Super (Windows key) and searching for “Firefox.”
  2. Open the webpage you want to keep on top.
  3. While holding down the Super (Windows key), Right-click on the Firefox application.
  4. The usual menu with the options to manage the window will appear. Select the option “Always on top”.

Please note that the “Always on top” option will appear grayed out if your window is maximized.