Feat/docs

.github/workflows/docs.yml

@@ -0,0 +1,66 @@
+name: Deploy MkDocs site to GitHub Pages
+
+on:
+  push:
+    branches:
+      - main
+    paths:
+      - 'docs/**'  # Trigger hanya ketika ada perubahan di folder docs
+      - mkdocs.yml
+  workflow_dispatch:  # Menyediakan trigger manual jika dibutuhkan
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      pages: write
+      id-token: write
+      actions: read
+    steps:
+      - name: Clone repository
+        uses: actions/checkout@v4
+
+      - name: Install uv and set the python version
+        uses: astral-sh/setup-uv@v5
+        with:
+          version: "0.5.24"
+          enable-cache: true
+          cache-dependency-glob: "uv.lock"
+          python-version: 3.12
+
+
+      - name: Install dependencies
+        run: |
+          uv sync
+
+      - name: Build MkDocs site
+        run: |
+          mkdocs build
+
+      - name: Setup Pages
+        uses: actions/configure-pages@v3
+
+      - name: Upload artifact
+        uses: actions/upload-pages-artifact@v3
+        with:
+          path: ./site
+
+  deploy:
+    if: github.ref == 'refs/heads/main'
+    runs-on: ubuntu-latest
+    needs: build
+
+    permissions:
+      pages: write
+      id-token: write
+      actions: read
+
+    environment:
+      name: github-pages
+      url: ${{ steps.deployment.outputs.page_url }}
+
+    steps:
+      - name: Deploy to GitHub Pages
+        id: deployment
+        uses: actions/deploy-pages@v4

.pre-commit-config.yaml

@@ -1,10 +1,21 @@
 repos:
+  - repo: https://github.com/astral-sh/uv-pre-commit
+    # uv version.
+    rev: 0.5.29
+    hooks:
+      - id: uv-lock
+        name: update uv lock file
+      - id: uv-export
+        name: export uv lock file
+
   - repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v5.0.0
     hooks:
       - id: check-added-large-files
+        args: ['--maxkb=1024']  # 1MB = 1024KB
       - id: check-toml
       - id: check-yaml
+        exclude: ^mkdocs\.yml$
       - id: detect-private-key
       - id: end-of-file-fixer
         exclude: ^LICENSE|VERSION$

README.md

@@ -1,12 +1,14 @@
 
 <div align="center">
+<!-- image logo -->
+<img src="assets/logo-v1.png" alt="Color Correction Logo" width="150"/>
 
 # Color Correction
 
 <br>
 
 [![version](https://badge.fury.io/py/color-correction.svg)](https://badge.fury.io/py/color-correction)
-[![downloads](https://img.shields.io/pypi/dm/color-correction-asdfghjkl)](https://pypistats.org/packages/color-correction-asdfghjkl)
+[![downloads](https://img.shields.io/pypi/dm/color-correction)](https://pypistats.org/packages/color-correction)
 [![python-version](https://img.shields.io/pypi/pyversions/color-correction)](https://badge.fury.io/py/color-correction)
 
 

color_correction/core/card_detection/det_yv8_onnx.py

@@ -14,12 +14,52 @@
 
 
 class YOLOv8CardDetector(BaseCardDetector):
-    """YOLOv8CardDetector is a class that implements card detection
-    using the YOLOv8 model.
-
-    Reference
-    ---------
-    https://github.com/ibaiGorordo/ONNX-YOLOv8-Object-Detection/blob/main/yolov8/YOLOv8.py
+    """YOLOv8CardDetector is a class that implements card detection using the YOLOv8
+    model.
+
+    This class provides functionality to detect cards in images using a YOLOv8 model
+    implemented with ONNX runtime. It handles model initialization, image preprocessing,
+    inference, and post-processing of detection results.
+
+    Parameters
+    ----------
+    conf_th : float, optional
+        Confidence threshold for filtering detections, by default 0.15
+    iou_th : float, optional
+        Intersection over Union threshold for non-maximum suppression, by default 0.7
+    path : str or None, optional
+        Path to the ONNX model file.
+        If None, downloads the default model, by default None
+    use_gpu : bool, optional
+        Whether to use GPU for inference, by default False
+
+    Attributes
+    ----------
+    conf_threshold : float
+        Threshold for filtering detections based on confidence scores
+    iou_threshold : float
+        Threshold for non-maximum suppression
+    use_gpu : bool
+        Flag indicating whether to use GPU for inference
+    session : onnxruntime.InferenceSession
+        ONNX Runtime session for model inference
+    input_names : list
+        Names of model input nodes
+    output_names : list
+        Names of model output nodes
+    input_shape : tuple
+        Shape of the input tensor
+    input_height : int
+        Height of the input image required by the model
+    input_width : int
+        Width of the input image required by the model
+    scale_to_ori : float
+        Scale factor to map predictions back to original image size
+
+    Methods
+    -------
+    detect(image)
+        Detect cards in the input image and return detection results
 
     """
 

color_correction/core/correction/__init__.py

@@ -1,17 +1,17 @@
 from color_correction.core.correction._factory import (
     CorrectionModelFactory,
 )
-from color_correction.core.correction.affine_reg import AffineReg
+from color_correction.core.correction.affine_reg import AffineRegression
 from color_correction.core.correction.least_squares import (
     LeastSquaresRegression,
 )
-from color_correction.core.correction.linear_reg import LinearReg
+from color_correction.core.correction.linear_reg import LinearRegression
 from color_correction.core.correction.polynomial import Polynomial
 
 __all__ = [
     "CorrectionModelFactory",
     "LeastSquaresRegression",
     "Polynomial",
-    "LinearReg",
-    "AffineReg",
+    "LinearRegression",
+    "AffineRegression",
 ]

color_correction/core/correction/_factory.py

@@ -1,8 +1,8 @@
-from color_correction.core.correction.affine_reg import AffineReg
+from color_correction.core.correction.affine_reg import AffineRegression
 from color_correction.core.correction.least_squares import (
     LeastSquaresRegression,
 )
-from color_correction.core.correction.linear_reg import LinearReg
+from color_correction.core.correction.linear_reg import LinearRegression
 from color_correction.core.correction.polynomial import Polynomial
 
 
@@ -12,7 +12,7 @@ def create(model_name: str, **kwargs: dict) -> ...:
         model_registry = {
             "least_squares": LeastSquaresRegression(),
             "polynomial": Polynomial(**kwargs),
-            "linear_reg": LinearReg(),
-            "affine_reg": AffineReg(),
+            "linear_reg": LinearRegression(),
+            "affine_reg": AffineRegression(),
         }
         return model_registry.get(model_name)

color_correction/core/correction/affine_reg.py

@@ -10,7 +10,15 @@
 )
 
 
-class AffineReg(BaseComputeCorrection):
+class AffineRegression(BaseComputeCorrection):
+    """
+    Apply an affine (linear) regression for color correction.
+
+    This class uses a linear regression model without an intercept
+    (by adding a bias column) to compute a correction transformation
+    between input and reference patches.
+    """
+
     def __init__(self) -> None:
         self.model = None
 
@@ -19,6 +27,21 @@ def fit(
         x_patches: np.ndarray,  # input patches
         y_patches: np.ndarray,  # reference patches
     ) -> np.ndarray:
+        """
+        Fit a linear regression model using input and reference patches.
+
+        Parameters
+        ----------
+        x_patches : np.ndarray
+            Array of input patches with shape (n_samples, n_features).
+        y_patches : np.ndarray
+            Array of reference patches with shape (n_samples, n_targets).
+
+        Returns
+        -------
+        np.ndarray
+            The fitted linear regression model.
+        """
         start_time = time.perf_counter()
         x_patches = np.array(x_patches)
         print("x_patches.shape", x_patches.shape)
@@ -30,6 +53,24 @@ def fit(
         return self.model
 
     def compute_correction(self, input_image: np.ndarray) -> np.ndarray:
+        """
+        Compute the color-corrected image using the fitted regression model.
+
+        Parameters
+        ----------
+        input_image : np.ndarray
+            The input image to be corrected.
+
+        Returns
+        -------
+        np.ndarray
+            The color-corrected output image.
+
+        Raises
+        ------
+        ValueError
+            If the model has not been fitted before calling this method.
+        """
         if self.model is None:
             raise ValueError("Model is not fitted yet. Please call fit() method first.")
 

color_correction/core/correction/least_squares.py

@@ -10,6 +10,13 @@
 
 
 class LeastSquaresRegression(BaseComputeCorrection):
+    """
+    Apply a least squares regression for color correction.
+
+    This class computes a color correction transformation by solving the least squares
+    problem to find the best fit coefficients.
+    """
+
     def __init__(self) -> None:
         self.model = None
 
@@ -18,6 +25,21 @@ def fit(
         x_patches: np.ndarray,  # input patches
         y_patches: np.ndarray,  # reference patches
     ) -> np.ndarray:
+        """
+        Fit the least squares regression model using input and reference patches.
+
+        Parameters
+        ----------
+        x_patches : np.ndarray
+            Array of input patches.
+        y_patches : np.ndarray
+            Array of reference patches.
+
+        Returns
+        -------
+        np.ndarray
+            The matrix of coefficients obtained from the least squares solution.
+        """
         start_time = time.perf_counter()
 
         self.model = np.linalg.lstsq(
@@ -31,6 +53,24 @@ def fit(
         return self.model
 
     def compute_correction(self, input_image: np.ndarray) -> np.ndarray:
+        """
+        Compute the color-corrected image using the fitted least squares model.
+
+        Parameters
+        ----------
+        input_image : np.ndarray
+            The input image represented as an array of color values.
+
+        Returns
+        -------
+        np.ndarray
+            The color-corrected output image.
+
+        Raises
+        ------
+        ValueError
+            If the model has not been fitted before calling this method.
+        """
         if self.model is None:
             raise ValueError("Model is not fitted yet. Please call fit() method first.")