Merge pull request #75 from StabRise/12-publish-documentation-to-the-readthedocs

12 publish documentation to the readthedocs

Author: mykolamelnykml

.gitignore

@@ -162,3 +162,4 @@ cython_debug/
 .idea/
 /.vscode/settings.json
 /tests/testresources/pdfs/private/
+/.run/*

CHANGELOG.md

@@ -1,4 +1,4 @@
-## [unreleased]
+## 0.2.4 - 01.10.2025
 
 ### 🚀 Features
 

docs/source/_static/ShowFaceBoxes.png

@@ -6,16 +6,25 @@
 # -- Project information -----------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
 
+import os
+import sys
+
+sys.path.insert(0, os.path.abspath("../scaledp"))
+
 project = "ScaleDP"
-copyright = "2024, StabRise"
-author = "StabRise"
-release = "0.1.0"
+author = "Mykola Melnyk"
+release = "0.2.4"
 
 # -- General configuration ---------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
 
 extensions = ["sphinx.ext.autodoc", "myst_parser"]
 
+source_suffix = {
+    ".rst": "restructuredtext",
+    ".md": "markdown",
+}
+
 templates_path = ["_templates"]
 exclude_patterns = []
 
@@ -46,7 +55,13 @@
             "icon": "https://img.shields.io/badge/by-StabRise-orange.svg?style=flat&colorA=E1523D&colorB=007D8A",
             "type": "url",
         },
-    ]
+    ],
+    "extra_footer": """
+    <p style="font-size:1em; color:#777;">
+      © Copyright 2025, <a href="https://stabrise.com" 
+      target="_blank">StabRise</a>
+    </p>
+    """,
 }
 
 # -- Options for HTML output -------------------------------------------------

docs/source/_static/ShowFaceCropped.png

@@ -0,0 +1,24 @@
+Detectors
+=========
+
+## Overview
+
+This section provides an overview of the various detectors available in ScaleDP for processing images and documents. These detectors are designed to identify and extract specific features such as text, objects, and layout structures from images.
+
+## Object Detection
+
+* [**Face Detector**](#FaceDetector)
+* [**Signature Detector**](#SignatureDetector)
+
+## Text Detection
+
+* [**CraftTextDetector**](#CraftTextDetector)
+* [**DBNetOnnxDetector**](#DBNetOnnxDetector)
+* **YoloOnnxTextDetector**
+* **DocTRTextDetector**
+
+## Base Detectors
+
+* **BaseDetector**
+* [**YoloOnnxDetector**](#YoloOnnxDetector)
+

docs/source/_static/ShowImageInvoice.png

@@ -0,0 +1,49 @@
+(DataToImage)=
+# DataToImage
+
+## Overview
+
+`DataToImage` is a PySpark ML transformer that converts binary content (such as bytes from files or streams) into image objects. It is designed for use in Spark pipelines, enabling scalable and distributed image processing workflows. The transformer supports various image types and handles errors gracefully.
+
+## Usage Example
+
+```python
+from scaledp import DataToImage, PipelineModel
+
+image_example = files('resources/images/Invoice.png')
+
+df = spark.read.format("binaryFile") \
+    .load(image_example)
+
+data_to_image = DataToImage(
+    inputCol="content",      # Column with binary data
+    outputCol="image",       # Output column for image objects
+    pathCol="path",          # Optional: column with image paths
+    keepInputData=True,       # Keep original data in output
+    propagateError=False,     # Handle errors gracefully
+)
+
+pipeline = PipelineModel(stages=[data_to_image])
+result = pipeline.transform(df)  # df should have 'content' and optionally 'path' columns
+result.show_image("image")
+```
+
+![ShowImageInvoice.png](../_static/ShowImageInvoice.png)
+
+## Parameters
+
+| Parameter         | Type    | Description                                      | Default         |
+|-------------------|---------|--------------------------------------------------|-----------------|
+| inputCol          | str     | Input column with binary content                  | "content"      |
+| outputCol         | str     | Output column for image objects                   | "image"        |
+| pathCol           | str     | Path column for image metadata                    | "path"         |
+| keepInputData     | bool    | Keep input data in output                        | False           |
+| imageType         | Enum    | Type of image (e.g., FILE, PIL)                  | ImageType.FILE  |
+| propagateError    | bool    | Propagate errors                                 | False           |
+
+## Notes
+- Converts binary data to image objects using the specified image type.
+- Handles errors gracefully; if `propagateError` is False, exceptions are logged and empty images are returned.
+- Can be used as the first stage in image processing pipelines to ingest raw image data.
+- Supports distributed processing with Spark.
+

docs/source/_static/ShowSignatureBoxes.png

@@ -0,0 +1,63 @@
+(ImageCropBoxes)=
+# ImageCropBoxes
+
+## Overview
+
+`ImageCropBoxes` is a PySpark ML transformer that crops images based on provided bounding boxes. It is designed to process images in Spark pipelines, supporting batch and distributed processing. The transformer can add padding to crops, limit the number of crops per image, and handle cases where no boxes are present.
+
+## Usage Example
+
+```python
+from scaledp import FaceDetector, ImageCropBoxes, PipelineModel
+
+# Step 1: Detect faces in images
+detector = FaceDetector(
+    inputCol="image",
+    outputCol="boxes",
+    keepInputData=True,
+    scoreThreshold=0.25,
+    padding=20,
+)
+
+# Step 2: Crop images using detected face boxes
+cropper = ImageCropBoxes(
+    inputCols=["image", "boxes"],
+    outputCol="cropped_image",
+    keepInputData=True,
+    padding=10,
+    limit=5,
+    noCrop=True,
+    autoRotate=False,  # Automatically rotate crops if box height > width
+)
+
+# Build and run the pipeline
+pipeline = PipelineModel(stages=[detector, cropper])
+result = pipeline.transform(image_df)
+result.show_image("cropped_image")
+```
+
+![ShowFaceCropped.png](../_static/ShowFaceCropped.png)
+
+## Parameters
+
+| Parameter         | Type    | Description                                      | Default         |
+|-------------------|---------|--------------------------------------------------|-----------------|
+| inputCols         | list    | Input columns: image and boxes                   | ["image", "boxes"] |
+| outputCol         | str     | Output column for cropped images                 | "cropped_image"|
+| keepInputData     | bool    | Keep input data in output                        | False           |
+| imageType         | Enum    | Type of image (e.g., FILE)                       | ImageType.FILE  |
+| numPartitions     | int     | Number of partitions for Spark                   | 0               |
+| padding           | int     | Padding added to each crop                       | 0               |
+| pageCol           | str     | Page column for repartitioning                   | "page"          |
+| propagateError    | bool    | Propagate errors                                 | False           |
+| noCrop            | bool    | Raise error if no boxes to crop                  | True            |
+| limit             | int     | Limit number of crops per image                  | 0 (no limit)    |
+| autoRotate        | bool    | Auto rotate crop if box height > width           | True            |
+
+## Notes
+- Crops are performed using bounding boxes from the `boxes` column.
+- If `noCrop` is True and no boxes are present, an error is raised.
+- If `limit` is set, only the first N boxes are used for cropping.
+- If `autoRotate` is True, crops are rotated if the bounding box height is greater than its width.
+- Supports distributed processing with Spark.
+- Errors can be propagated or handled gracefully based on `propagateError`.