Robust quad-tree based registration on whole slide images
Project description
Robust quad-tree based registration on whole slide images
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
Notebooks
Example notebooks are in the demo folder or .
Ho-To:
Import package and create Quad-Tree.
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.
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"]]
annos = np.array([["center_x", "center_y", "anno_width", "anno_height"]])
qtree.draw_annotations(annos, num_sub_pic=5, figsize=(10, 10))
Transform coordinates
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]
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