scripts/batch-resize.py holds a half-written function for scaling JPG and PNG files to user-specified dimensions. Unlike generic resize tools that hide their logic behind a graphical interface, this tool outputs plain-language explanations of every step it runs, so you learn core scripting concepts every time you use it. It works entirely from the command line, no paid software or subscription required, and prioritizes preserving original files to avoid accidental data loss.
Map core input parameters for this batch-resize.py file
You start by defining the full list of user-adjustable parameters that the script accepts, using Python’s argparse library to make inputs intuitive for new users. The parameters include a path to the target folder of images, target width and height in pixels, a toggle to preserve original aspect ratio, an export quality slider for JPG files, and a boolean flag to enable teaching mode. You set sensible defaults for every parameter so users can run the script with zero configuration if they just want to resize all images in their current working directory to 1920 pixels wide for web use. You also add input validation for each parameter to catch invalid entries before the script starts processing files, so users don’t waste time waiting for a batch job that will fail halfway through.
Build interactive teaching prompts into this batch-resize.py file
You design optional teaching prompts that trigger only when the user enables the teaching mode flag, so more experienced users can run the script silently without extra output. Each prompt ties a specific script action to a core scripting concept, so users learn by doing instead of reading disconnected documentation. You map every prompt to a specific event in the script’s workflow, as outlined in the table below:
| Trigger event | Prompt content | Learning outcome |
|---|---|---|
| Script first runs with teaching mode enabled | “First, we’re scanning the input directory for files that end in .jpg, .jpeg, or .png. We skip hidden files and subdirectories by default to avoid processing system assets you didn’t intend to modify.” | Learners recognize how scripts filter file lists to target only relevant assets, a core skill for all file automation tasks |
| First image is loaded for processing | “We’re using the Pillow library to open the image file and read its native dimensions. If you selected preserve aspect ratio, we calculate the missing dimension automatically to avoid stretching or cropping the final output.” | Learners connect third-party library imports to specific, tangible functionality instead of viewing them as abstract code requirements |
| Batch processing completes successfully | “We wrote all resized files to the /resized subdirectory of your input folder, and appended _resized to each file name so you never overwrite your original assets. All write operations first check if a file with the target name exists to avoid accidental data loss.” | Learners identify common safe scripting practices that prevent unintended file damage, a critical guardrail for all personal and professional automation work |
You add logic to show each unique prompt only once per batch run, even if the script processes hundreds of images, to keep the learning experience uncluttered. Illustrative example: If you run the script on a folder of 472 photos from a weekend hike with teaching mode enabled, it will show each prompt one time at the relevant stage of the workflow, instead of spamming you 472 times for every individual photo processed.
Add error handling for edge cases in this batch-resize.py file
You add robust error handling for every common failure point, with each error message including both a fix for the immediate problem and a small teaching note to build user knowledge. For example, if a user enters a target width of 0, the error message reads: “You entered a target width of 0, which is not a valid pixel dimension. Scripts require positive numerical values for image dimensions because pixels are discrete units that cannot have a length of 0 or less. Enter a whole number greater than 0 for your target width to proceed.” Other error cases you cover include a non-existent input directory, no image files in the target directory, target dimensions larger than the original image, and missing write permissions for the export directory. You also suppress complex Python tracebacks by default when teaching mode is enabled, so new users don’t get overwhelmed by technical jargon they don’t yet understand, while letting advanced users enable tracebacks for debugging if they want.
Test script performance across common use cases for this batch-resize.py file
You run the script through a suite of test cases to ensure it works reliably for every common user need. Your test cases include a folder of 10 small PNG icons for web use, a folder of 100 high-res JPG photos from a digital camera, and a folder of 5 large transparent PNG design assets for graphic design work. You verify that processing time stays under 10 seconds for all test cases, that image quality is preserved when the export quality slider is set to 100, that aspect ratio is maintained correctly when the toggle is enabled, and that teaching prompts trigger as expected for every use case. You also test the script on Windows, Mac, and Linux operating systems to ensure it works without modifications for all users. You cross-reference compatibility requirements with the standard script framework outlined in Build Next Stack to ensure consistency with other common automation tools.
Include inline documentation alongside working code in this batch-resize.py file
You add detailed, plain-language comments to every block of code in the script, so users who open the file to edit or modify it can follow along even with minimal Python experience. Each comment ties directly to a corresponding teaching prompt, so users can connect what they see in the command line output to the actual code that generates it. For example, the code block that scans for image files has a comment that reads “# This block filters the input directory list to only include common image file extensions. This matches the first teaching prompt shown when teaching mode is enabled.” You also add links to free, public resources for users who want to learn more about specific concepts, like how argparse works or how to use the Pillow library for more advanced image editing tasks. You make sure all comments are written at a middle school reading level, so they are accessible to users with no prior coding education.
Open this batch-resize.py file next when you have 30 minutes to add cloud storage export support
When you return to this file, your first task will be adding support for exporting resized images directly to S3-compatible cloud storage buckets, for users who want to host their resized images online without manually uploading them. You will add a new set of optional parameters for cloud storage access, including an API key, secret key, bucket name, and target folder path in the bucket. You will add a new teaching prompt that explains how API calls work for cloud storage uploads, and how authentication keys keep user data secure. You will also add error handling for common cloud storage issues, like invalid API keys, missing bucket permissions, and unstable internet connections. You will test the feature with a private test bucket to ensure uploads work correctly, that original files are never modified or deleted during the upload process, and that teaching prompts trigger as expected for users who enable learning mode. You will also add an optional flag to disable local file saving if the user only wants to upload resized images to the cloud, so they don’t clutter their local drive with files they don’t need to store permanently. You will update the inline documentation to reflect the new feature, and add a note in the script’s help text explaining how to use the cloud export functionality for both new and experienced users.