Managing HEIC Files in CI for a Smoother Build Workflow
As more devices adopt the HEIC format for images, it becomes inevitable that such files end up in project repositories and asset folders. When the time comes to process HEIC images in an automated CI pipeline, it’s important to ensure they don’t break or corrupt the build process. Improper handling of this format can lead to errors, failed jobs, or incomplete deployments that leads to rework.
CI pipelines are designed to maintain consistency and automation across the development cycle. However, when a file type like HEIC isn’t properly scanned or converted, it introduces a gap in control. It’s a sensitive format, especially if the right tools aren’t installed in the environment.
That’s why it’s essential to make HEIC detection and processing part of the pipeline. Identifying and converting these files early reduces friction during build steps and leads to a smoother deployment experience.
Detecting HEIC Files Before Build Starts
The first step in CI is checking file types. If your workflow has a step that scans for new images, ensure that your script can identify .heic and .heif extensions. In shell scripts, this can be done using find or grep.
You should also verify the MIME type, especially if file names are dynamic. Tools like the file command can help detect if an image is HEIC, even without a typical extension. This avoids blind spots where a file might be HEIC but not obviously so.
With this setup, you can control how the pipeline handles HEIC images—whether skipping, logging, or converting them before including them in the final output.
Converting HEIC with CLI Tools in the Pipeline
Once a HEIC file is detected, it should be converted before use in the frontend or packaging. In a CI environment, it’s best to use tools like ImageMagick with HEIC support or heif-convert from libheif.
For example, your .gitlab-ci.yml, .circleci/config.yml, or GitHub Actions workflow might include:
bash
magick image.heic image.jpg
Or if using heif-convert:
bash
heif-convert image.heic image.jpg
These commands can be embedded within a script step in your pipeline. That way, even if your app doesn’t support HEIC, the converted JPEG is ready for deployment.
Ensuring Required Dependencies Are Available
Many CI providers use container-based runners. This makes installing tools like libheif or a modern ImageMagick a challenge. If they’re not in the default runner image, you may need to create a custom Docker image.
Creating your own CI image gives you full control. You can bundle all the required tools—including those for HEIC support—so every run is consistent. This is especially valuable if you work with image assets daily.
Make sure your Dockerfile is up to date, and keep tool versions in check to avoid stalled builds in the future.
Handling Errors When HEIC Support is Missing
There will be cases where the CI environment lacks HEIC support. That’s why graceful fallback and error handling are essential. Use conditionals in your shell script to check for the availability of tools:
bash
if command -v magick > /dev/null; then
magick image.heic image.jpg
else
echo “ImageMagick not found, skipping HEIC conversion”
fi
This approach prevents the entire pipeline from breaking just because one image failed to convert. It’s good practice to log these occurrences so developers or maintainers can trace the issue.
Acknowledging environment limitations is part of maintaining a resilient CI workflow.
Storing Converted Files in the Proper Directory
After conversion, JPEG files must be saved in a consistent directory so that later steps can access them. This could be dist/assets/images/converted/ or any folder your frontend build script reads from.
If the converted images are not correctly placed, static site generators or asset optimizers might not find them. That’s why proper coordination of file paths is essential.
If the original HEIC file still exists, you can archive it in a separate folder or delete it if no longer needed. The key is maintaining an orderly content structure.
Caching Tools for Faster Builds
Installing conversion tools like ImageMagick or libheif during each run can slow down your build. CI caching can help here if supported by your provider.
In GitHub Actions, you can use actions/cache to store dependencies. In GitLab CI, you can use the cache block to preserve downloaded packages. This greatly speeds up CI runs, especially when image uploads happen frequently.
Not only does this save time, but it also conserves CI minutes—valuable if your plan has usage limits.
Automated Quality Control for Converted Images
Conversion alone isn’t enough. CI workflows can include quality checks for the output images. For instance, use ImageMagick’s identify command to check dimensions and formats:
bash
identify -verbose image.jpg
If there are issues with resolution or file size, you can flag the image before deployment. This ensures consistency in image assets within the final build.
For apps with strict visual standards, this helps maintain brand alignment and optimize performance.
Logging and Reporting HEIC Usage in the Pipeline
In more advanced CI workflows, it’s a good idea to log the HEIC files that get converted. Simple text output or JSON logs can track how many HEIC files were processed in each build.
This can be used for analytics or reporting. If you notice a spike in HEIC uploads, it might be time to upgrade your image handling logic. Proactive logging gives insight into real usage and informs future development roadmaps.
This may not be necessary for every team—but it’s helpful for those heavily reliant on image uploads.
Aligning CI Logic with Dev and Production Environments
The logic in your CI pipeline should match what developers use locally. When tools and conversion steps are the same, debugging is easier and issues are more predictable. That’s why it’s crucial to document workflows and make them available for local dev setups too.
For example, if you use a custom Docker image in CI, consider distributing it as a local dev container as well. This keeps environments consistent across the board.
Uniform setup is key to any reliable automation process.
