MoonVision

MoonVision is a MoonBit-native lightweight image processing and basic computer vision library.

The current v1.2 line focuses on:

• flat image containers: GrayImage, RgbImage
• PNG decode to RgbImage through a vendored adapter layer
• basic pixel operations: grayscale, threshold, Otsu threshold, adaptive threshold, invert, brightness, contrast
• convolution and neighborhood filters: box blur, gaussian blur, sharpen, median blur
• gray-image geometric transforms: nearest-neighbor resize, horizontal flip, vertical flip, 90-degree rotation
• edge detection: Sobel X/Y, gradient magnitude, binary edge extraction, Canny
• binary morphology: erosion, dilation, opening, closing
• connected components, contour extraction, contour statistics, and bounding boxes
• visual export: PNG bytes, SVG overlays, HTML reports

Module Layout

src/
  image/           GrayImage, RgbImage, pixel access, gray transforms
  ops/             grayscale, threshold, otsu, adaptive threshold, brightness, contrast
  filter/          convolution, box blur, gaussian blur, sharpen, median blur
  edge/            sobel, gradient magnitude, canny
  morphology/      binary morphology operators
  components/      connected components, contours, and bounding boxes
  export/          PNG encoding, SVG overlays, HTML reports
  demo_support/    demo-only file writing helpers
  demo/            runnable demo packages

Quick Start

Check the project:

moon check

Run tests:

moon test

At the time of writing, the repository test suite passes on the repository default target.

Basic Usage

let rgb = try! @image.rgb(4, 4)
let gray = try! @ops.grayscale(rgb)
let binary = try! @ops.otsu_threshold(gray)
let adaptive = try! @ops.adaptive_threshold_mean(gray, 5, 7)
let denoised = try! @filter.median_blur(gray, radius=1)
let blurred = try! @filter.gaussian_blur(gray, radius=1)
let resized = try! @image.resize_nearest(gray, 8, 8)
let rotated = try! @image.rotate90_cw(gray)
let edges = try! @edge.gradient_magnitude(blurred)
let canny = try! @edge.canny_edges(blurred, 48, 96)
let blobs = try! @components.connected_components(binary, min_area=4)
let contours = try! @components.find_contours(binary)
ignore(adaptive)
ignore(denoised)
ignore(resized)
ignore(rotated)
ignore(edges)
ignore(canny)
ignore(blobs)
ignore(contours)

Export a grayscale image as PNG bytes:

let png = @export.encode_gray_png(binary)
ignore(png)

Build an SVG overlay for detected components:

let overlay = @export.svg_bounding_boxes(
  binary.width(),
  binary.height(),
  blobs,
)
ignore(overlay)

Decode a PNG file into RgbImage bytes first:

let input = try! @image.rgb_from_png_bytes(png_bytes)
ignore(input)

Demos

Demo input assets live in examples/assets/.

All demo outputs are written to examples/output/.

Object counting:

moon run src/demo/object_counting

Outputs:

• examples/output/object_counting_input.png
• examples/output/object_counting_binary_v1_0.png
• examples/output/object_counting_binary.png
• examples/output/object_counting_contours.png
• examples/output/object_counting_overlay.svg
• examples/output/object_counting_report.html

Edge detection:

moon run src/demo/edge_detection

Outputs:

• examples/output/edge_detection_input.png
• examples/output/edge_detection_edges.png
• examples/output/edge_detection_canny.png
• examples/output/edge_detection_report.html

Document enhancement:

moon run src/demo/document_enhancement

Outputs:

• examples/output/document_enhancement_input.png
• examples/output/document_enhancement_output_v1_0.png
• examples/output/document_enhancement_output.png
• examples/output/document_enhancement_report.html

Version Stages

• v1.0 Focused on the first complete visual-analysis chain: image containers, thresholding, filtering, Sobel edges, binary morphology, connected components, and export.
• v1.1 Focused on robustness improvements: Otsu thresholding, adaptive thresholding, median blur, and gray-image transforms.
• v1.2 Focuses on edge and contour analysis: Canny edges, contour extraction, contour statistics, and upgraded edge/counting demos.

v1.0 vs v1.1 vs v1.2

The bundled demo assets are kept stable so v1.0, v1.1, and v1.2 remain directly comparable.

• Object counting: v1.0 used threshold(120) and detected 5 objects on the bundled asset. v1.1 uses median_blur(radius=1) -> otsu_threshold and also detects 5 objects, while removing the fixed threshold constant from the counting path. v1.2 keeps the v1.1 binary path and adds contour extraction plus a rendered contour mask for the same binary image.
• Edge detection: v1.1 exported the Sobel gradient magnitude edge map. v1.2 keeps that output and adds a binary Canny edge map for direct comparison.
• Document enhancement: v1.0 used a fixed threshold after brightness and contrast adjustment. v1.1 preserves the v1.0 output in document_enhancement_output_v1_0.png and writes the optimized median_blur -> adaptive_threshold_mean result to document_enhancement_output.png.

Testing Scope

Current tests cover:

• image container invariants
• gray-image geometric transforms
• PNG decode adaptation to RgbImage
• grayscale, global thresholding, Otsu thresholding, and adaptive thresholding
• filtering, border handling, and median blur behavior
• Sobel and Canny edge behavior
• binary morphology behavior
• connected components, contour extraction, and contour statistics
• SVG/HTML export rendering
• PNG signature generation

Notes

• The project intentionally focuses on the algorithm layer. It does not provide GUI features, video processing, OpenCV bindings, or machine learning integration.
• PNG decode and export are implemented locally from vendored subsets adapted from mizchi/image and mizchi/zlib, because the current upstream registry dependency graph is not compatible with the local MoonBit toolchain used for this repository.

License

MoonVision is released under the MIT license. See LICENSE for the full text.