How to Create a CSV File from Image Folders in Python скачать в хорошем качестве

How to Create a CSV File from Image Folders in Python

Learn how to easily convert image paths into a `CSV file` with labels in Python, making your skin cancer dataset analysis straightforward and efficient. --- This video is based on the question https://stackoverflow.com/q/67135771/ asked by the user 'SHAIIM' ( https://stackoverflow.com/u/15670016/ ) and on the answer https://stackoverflow.com/a/67135825/ provided by the user 'LPR' ( https://stackoverflow.com/u/6466910/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions. Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: how create cvs file from image folders? Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l... The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license. If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com. --- Creating a CSV File from Image Folders in Python If you're working with datasets, especially in machine learning, organizing your data effectively is crucial. One common task is to create a CSV file that links image paths to labels. For instance, if you have two folders containing images of skin cancer (benign and malignant) and you want to create a CSV file where the first column is the image path and the second column is the corresponding label, you've come to the right place. Today, we're going to walk through a simple method to achieve this in Python, focusing on using the glob module to manage your image file paths and then writing those paths to a CSV file. Step-by-Step Solution Step 1: Setup the Environment Before you start, ensure you have Python installed on your machine alongside any necessary libraries. You don’t need to install glob separately as it comes with Python's standard library. Step 2: Import the Required Module Start by importing the glob module. This module allows you to search for pathnames matching a specified pattern, which is perfect for gathering image files from your folders. [[See Video to Reveal this Text or Code Snippet]] Step 3: Locate Your Image Files Next, use the glob method to retrieve the paths for your images. Here’s how you can gather all .png files from your benign and malignant folders: [[See Video to Reveal this Text or Code Snippet]] In this code: benign will contain a list of all PNG files in the benign folder. malignant will contain a list of all PNG files in the malignant folder. Make sure to adjust the file extension .png if your images are in a different format. Step 4: Write to a CSV File Now that you have your lists of image file paths, it's time to write them to a CSV file. Below is the code snippet to create and write the paths and labels into a CSV. [[See Video to Reveal this Text or Code Snippet]] Understanding the Code CSV_FILE_NAME: This is the name of your output CSV file. with open: This statement opens a file for writing. The with statement ensures proper closure of the file after writing operations are completed. for loops: These loops go through the benign and malignant lists, writing each path and its corresponding label to the CSV file. Final Touches Your CSV file now contains two columns: one for image paths and another for their associated labels (benign or malignant). You can utilize this file for data analysis, model training, or any other tasks in your machine learning projects. Conclusion Creating a CSV file from image folders in Python may seem daunting initially, but it can be accomplished with just a few straightforward steps using the glob module and file handling operations. With this CSV file, you can enhance your data analysis or machine learning tasks significantly, paving the way for better insights and results. Now that you're equipped with this knowledge, go ahead and create your own dataset files seamlessly!