In this post, we’ll walk through how to efficiently convert multiple WebP images to PNG format using the parallel and dwebp tools. This method is particularly useful when dealing with a large batch of images, as it leverages parallel processing to speed up the conversion.
Step 1: Install Necessary Tools
First, we need to install the webp and parallel packages. These can be installed on a Debian-based system (like Ubuntu) using the following command:
sudo apt install webp parallel
webpis a package that includes thedwebptool, which is used for decoding WebP images.parallelis a shell tool for executing jobs in parallel, making the conversion process faster.
Step 2: Convert WebP Images to PNG
Once the tools are installed, you can use the following command to convert all .webp files in the current directory to .png files:
parallel dwebp {} -o {.}.png ::: *.webp
Let’s break down this command:
parallel: This is the GNU parallel command. It allows you to run shell commands in parallel.dwebp: This is the command-line tool to decode WebP files to other formats, like PNG.{}: This placeholder represents each input file passed toparallel.-o {.}.png: This specifies the output format.{.}removes the file extension from the input file, and.pngappends the new file extension. For example,image.webpwill be converted toimage.png.:::: This indicates the start of the input list forparallel.*.webp: This is a wildcard pattern that matches all.webpfiles in the current directory.
Example
Assume you have three WebP files in your directory: image1.webp, image2.webp, and image3.webp. Running the command will convert these files to image1.png, image2.png, and image3.png respectively.
Benefits of Using parallel
- Speed: By leveraging multiple CPU cores,
parallelsignificantly reduces the time needed for batch processing. - Simplicity: Using a single command to handle all files in a directory is straightforward and minimizes the risk of manual errors.
With these steps, you can efficiently convert a large number of WebP images to PNG format, saving time and computational resources.
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