Crochet Stitch Template Enhancer

The Crochet Stitch Template Enhancer takes stitch template images that suffer from faint or unclear grids and overlays a clean, high-contrast grid. This makes the templates easier to read and follow, ensuring your crochet work stays accurate even when the original chart has poor resolution or hard-to-see lines.

Usage

Quick Explanation:

• you put the crochet stich template image files into the input/public or input/private directory - the latter one is ensured to not become public (you can't commit those files to the Git repository, if you forked it and committed to your own Git platform)
• you use the commands below to process the input image
• you get the output image with clear grid lines in output directory

Requirements:

• Docker
• a Bash shell to execute the below commands (e.g. the bash terminal of VS Code)

Build the software (only once, or if you've changed parts of the code):

docker build -t crochet-stitch-template-enhancer .

Run the Crochet Stitch Template Enhancer:

Important: adapt the path to your image: /input/private/my_private_chart.png -> path to your image.

Optional: you can provide a custom output filename. Otherwise, the script will automatically create a PNG file in the output folder with the same name as the input (without extension).

# Automatic output filename (same name as input, saved as PNG)
docker run --rm \
  -u $(id -u):$(id -g) \
  -v $(pwd)/input:/input \
  -v $(pwd)/output:/output \
  crochet-stitch-template-enhancer \
  /input/private/my-crochet-pattern.jpeg

# Explicit output filename (optional)
docker run --rm \
  -u $(id -u):$(id -g) \
  -v $(pwd)/input:/input \
  -v $(pwd)/output:/output \
  crochet-stitch-template-enhancer \
  /input/private/my-crochet-pattern.jpeg /output/custom-name.png

If you want to remove all traces hereof after, delete the Docker image from your PC:

docker rmi crochet-stitch-template-enhancer

Now it will be gone from your list of Docker images (you can check by running docker image ls).

Software Design

This project is written in Python for quick and flexible image processing. It uses:

• Pillow – for image manipulation, creating the overlay, and saving PNG output.
• OpenCV – for advanced image processing, including grayscale conversion, blurring, edge detection, and line extraction.
• NumPy – for efficient array operations during line detection.

In order for not everyone to have to install and get a Python project running, I containerized the project, so one only must have Docker installed as single dependency.

Input/Output:

• Accepts common image formats (PNG, JPEG/JPG, etc.)
• Output is always PNG, to preserve transparency for the grid overlay

Why Docker:

• Encapsulates all Python dependencies (Pillow, OpenCV, NumPy)
• Avoids local library conflicts
• Makes the tool portable and easy to run on any system with Docker