init

.gitattributes

@@ -0,0 +1,2 @@
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -1,162 +1,12 @@
-# Byte-compiled / optimized / DLL files
+# Python-generated files
 __pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
+*.py[oc]
 build/
-develop-eggs/
 dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
 wheels/
-share/python-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-MANIFEST
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.nox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-*.py,cover
-.hypothesis/
-.pytest_cache/
-cover/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-db.sqlite3
-db.sqlite3-journal
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-.pybuilder/
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# IPython
-profile_default/
-ipython_config.py
-
-# pyenv
-#   For a library or package, you might want to ignore these files since the code is
-#   intended to run in multiple environments; otherwise, check them in:
-# .python-version
+*.egg-info
 
-# pipenv
-#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
-#   However, in case of collaboration, if having platform-specific dependencies or dependencies
-#   having no cross-platform support, pipenv may install dependencies that don't work, or not
-#   install all needed dependencies.
-#Pipfile.lock
-
-# poetry
-#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
-#   This is especially recommended for binary packages to ensure reproducibility, and is more
-#   commonly ignored for libraries.
-#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
-#poetry.lock
-
-# pdm
-#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
-#pdm.lock
-#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
-#   in version control.
-#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
-.pdm.toml
-.pdm-python
-.pdm-build/
-
-# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
-__pypackages__/
-
-# Celery stuff
-celerybeat-schedule
-celerybeat.pid
-
-# SageMath parsed files
-*.sage.py
-
-# Environments
-.env
+# Virtual environments
 .venv
-env/
-venv/
-ENV/
-env.bak/
-venv.bak/
-
-# Spyder project settings
-.spyderproject
-.spyproject
-
-# Rope project settings
-.ropeproject
-
-# mkdocs documentation
-/site
-
-# mypy
-.mypy_cache/
-.dmypy.json
-dmypy.json
-
-# Pyre type checker
-.pyre/
-
-# pytype static type analyzer
-.pytype/
-
-# Cython debug symbols
-cython_debug/
 
-# PyCharm
-#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
-#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
-#  and can be added to the global gitignore or merged into this file.  For a more nuclear
-#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/
+.DS_Store

.python-version

@@ -0,0 +1 @@
+3.10

README.md

@@ -1 +1,20 @@
-# microwink
+# microwink
+Lightweight instance segmentation model for card IDs
+
+## Usage
+
+```python
+from microwink import SegModel
+from microwink.common import draw_mask
+from PIL import Image
+
+seg_model = SegModel.from_path("./models/seg_model.onnx")
+
+img = Image.open("...").convert("RGB")
+cards = seg_model.apply(img)
+
+for card in cards:
+    print(f"{card.score=}, {card.box=}")
+    img = draw_mask(img, card.mask > 0.5)
+img.save("result.png")
+```

pyproject.toml

@@ -0,0 +1,24 @@
+[project]
+name = "microwink"
+version = "0.0.1"
+description = "Lightweight instance segmentation model for card IDs"
+readme = "README.md"
+license = { text = "Apache-2.0" }
+authors = [{ name = "cospectrum", email = "[email protected]" }]
+requires-python = ">=3.10"
+dependencies = [
+    "numpy>=2.2.0",
+    "onnxruntime>=1.20.1",
+    "opencv-python>=4.10.0.84",
+    "pillow>=11.0.0",
+]
+
+[project.urls]
+Repository = "https://github.com/cospectrum/microwink"
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[dependency-groups]
+dev = ["mypy>=1.13.0", "pytest>=8.3.4", "ruff>=0.8.3"]

src/microwink/__init__.py

@@ -0,0 +1,5 @@
+from .seg import (
+    Threshold as Threshold,
+    SegModel as SegModel,
+    SegResult as SegResult,
+)

src/microwink/common.py

@@ -0,0 +1,60 @@
+import numpy as np
+
+from dataclasses import dataclass
+from typing import Any, Iterable, Sequence
+from PIL import Image, ImageDraw
+from PIL.Image import Image as PILImage
+
+
+@dataclass
+class Box:
+    x: float
+    y: float
+    h: float
+    w: float
+
+    @staticmethod
+    def from_xyxy(box: Iterable[Any]) -> "Box":
+        x1, y1, x2, y2 = [float(t) for t in box]
+        h = y2 - y1
+        w = x2 - x1
+        assert h >= 0.0
+        assert w >= 0.0
+        return Box(x=x1, y=y1, w=w, h=h)
+
+
+def sigmoid(x: np.ndarray) -> np.ndarray:
+    return 1 / (1 + np.exp(-x))
+
+
+def draw_box(
+    image: PILImage,
+    box: Box,
+    *,
+    color: tuple[int, ...] | str | float = (255, 0, 0),
+    width: int = 4,
+) -> PILImage:
+    image = image.copy()
+    draw = ImageDraw.Draw(image)
+    points = [(box.x, box.y), (box.x + box.w, box.y + box.h)]
+    draw.rectangle(points, outline=color, width=width)
+    return image
+
+
+def draw_mask(
+    image: PILImage,
+    binary_mask: np.ndarray,
+    *,
+    color: Sequence[Any] = (0, 255, 0),
+    alpha: float = 0.5,
+) -> PILImage:
+    assert 0.0 <= alpha <= 1.0
+    assert (image.height, image.width) == binary_mask.shape
+    img = np.array(image)
+    assert len(img.shape) == len(color)
+    overlay = np.zeros_like(img)
+    overlay[binary_mask] = color
+    assert overlay.shape == img.shape
+
+    img[binary_mask] = (1.0 - alpha) * img[binary_mask] + alpha * overlay[binary_mask]
+    return Image.fromarray(img)