Metadata-Version: 2.1
Name: qt-wsi-registration
Version: 0.0.5
Summary: Robust quad-tree based registration on whole slide images
Home-page: https://github.com/ChristianMarzahl/WsiRegistration
Author: Christian Marzahl
Author-email: christian.marzahl@gamil.com
License: UNKNOWN
Description: # Robust quad-tree based registration on whole slide images
        
        [![PyPI version fury.io](https://badge.fury.io/py/qt-wsi-registration.svg)](https://pypi.python.org/pypi/qt-wsi-registration/)
        [![MIT license](https://img.shields.io/badge/License-MIT-blue.svg)](https://lbesson.mit-license.org/)
        
        
        
        This is a library that implements a quad-tree based registration on whole slide images.
        
        
        ## Core features
        
        * Whole Slide Image support
        * Robust and fast
        * Rigid and non-rigid transformation
        
        ## Additional Requirements
        
        [Install OpennSlide](https://openslide.org/download/)
        
        
        ## Notebooks
        
        Example notebooks are in the demo folder or  [![Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github//ChristianMarzahl/WsiRegistration).
        
        
        ## Ho-To:
        
        
        Import package and create Quad-Tree.
        ```python
        import qt_wsi_reg.registration_tree as registration
        
        parameters = {
            # feature extractor parameters
            "point_extractor": "sift",  #orb , sift
            "maxFeatures": 512, 
            "crossCheck": False, 
            "flann": False,
            "ratio": 0.6, 
            "use_gray": False,
        
            # QTree parameter 
            "homography": True,
            "filter_outliner": False,
            "debug": False,
            "target_depth": 1,
            "run_async": True,
            "num_workers: 2,
            "thumbnail_size": (1024, 1024)
        }
        
        qtree = registration.RegistrationQuadTree(source_slide_path=Path("examples/4Scanner/Aperio/Cyto/A_BB_563476_1.svs"), target_slide_path="examples/4Scanner/Aperio/Cyto/A_BB_563476_1.svs", **parameters)
        
        ```
        
        Show some registration debug information.
        
        ```python
        qtree.draw_feature_points(num_sub_pic=5, figsize=(10, 10))
        ```
        
        Show annotations on the source and target image in the format:
        
        [["center_x", "center_y", "anno_width", "anno_height"]] 
        ```python
        annos = np.array([["center_x", "center_y", "anno_width", "anno_height"]])
        qtree.draw_annotations(annos, num_sub_pic=5, figsize=(10, 10))
        
        ```
        
        
        Transform coordinates
        
        ```python
        box = [source_anno.center_x, source_anno.center_y, source_anno.anno_width, source_anno.anno_height]
        
        trans_box = qtree.transform_boxes(np.array([box]))[0]
        
        ```
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
