histomicstk.annotations_and_masks.annotation_and_mask_utils

Created on Sun Aug 11 22:30:06 2019.

@author: tageldim

histomicstk.annotations_and_masks.annotation_and_mask_utils.create_mask_from_coords(coords)[source]

Create a binary mask from given vertices coordinates.

Source: This is modified from code by Juan Carlos from David Gutman Lab.

Parameters:

coords (np array) – must be in the form (e.g. ([x1,y1],[x2,y2],[x3,y3],…..,[xn,yn])), where xn and yn corresponds to the nth vertex coordinate.

Returns:

binary mask

Return type:

np array

histomicstk.annotations_and_masks.annotation_and_mask_utils.delete_annotations_in_slide(gc, slide_id)[source]

Delete all annotations in a slide.

histomicstk.annotations_and_masks.annotation_and_mask_utils.get_bboxes_from_slide_annotations(slide_annotations)[source]

Given a slide annotation list, gets information on bounding boxes.

Parameters:

slide_annotations (list of dicts) – response from server request

Returns:

The columns annidx and elementidx encode the dict index of annotation document and element, respectively, in the original slide_annotations list of dictionaries

Return type:

Pandas DataFrame

histomicstk.annotations_and_masks.annotation_and_mask_utils.get_idxs_for_annots_overlapping_roi_by_bbox(element_infos, idx_for_roi, iou_thresh=0.0)[source]

Find indices of potentially included annotations within the ROI.

We say “potentially” because this uses the IoU of roi and annotation as a fast indicator of potential inclusion. This helps dramatically scale down the number of annotations to look through. Later on, a detailed look at whether the annotation polygons actually overlap the ROI can be done.

Parameters:

  • element_infos (pandas DataFrame) – result from running get_bboxes_from_slide_annotations()

  • idx_for_roi (int) – index for roi annotation within the element_infos DF

  • iou_thresh (float) – overlap threshold to be considered within ROI

Returns:

indices relative to element_infos

Return type:

list

histomicstk.annotations_and_masks.annotation_and_mask_utils.get_image_from_htk_response(resp)[source]

Given a histomicsTK girder response, get np array image.

Parameters:

resp (object) – response from server request

Returns:

a pillow Image object of the image

Return type:

Pillow Image object

histomicstk.annotations_and_masks.annotation_and_mask_utils.get_rotated_rectangular_coords(roi_center, roi_width, roi_height, roi_rotation=0)[source]

Given data on rectangular ROI center/width/height/rotation.

Get the unrotated abounding box coordinates around rotated ROI. This of course is applicable to any rotated rectangular annotation.

Parameters:
Returns:

includes roi corners (x, y) and bounds

Return type:

dict

histomicstk.annotations_and_masks.annotation_and_mask_utils.get_scale_factor_and_appendStr(gc, slide_id, MPP=None, MAG=None)[source]

Get how much is request region smaller than base.

This also gets the string to append to server request for getting rgb.

Parameters:

  • gc (Girder client instance) – gc should be authoenticated.

  • slide_id (str) – girder id of slide

  • MPP (float or None) – Microns-per-pixel – best use this as it’s more well-defined than magnification which is more scanner/manufacturer specific. MPP of 0.25 often roughly translates to 40x

  • MAG (float or None) – If you prefer to use whatever magnification is reported in slide. If neither MPP or MAG is provided, everything is retrieved without scaling at base (scan) magnification.

Returns:

  • float – how much smaller (0.1 means 10x smaller) is requested image compared to scan magnification (slide coordinates)

  • str – string to appnd to server request for getting slide region

histomicstk.annotations_and_masks.annotation_and_mask_utils.np_vec_no_jit_iou(bboxes1, bboxes2)[source]

Fast, vectorized IoU.

Source: https://medium.com/@venuktan/vectorized-intersection-over-union …

-iou-in-numpy-and-tensor-flow-4fa16231b63d

Parameters:

  • bboxes1 (np array) – columns encode bounding box corners xmin, ymin, xmax, ymax

  • bboxes2 (np array) – same as bboxes 1

Returns:

IoU values for each pair from bboxes1 & bboxes2

Return type:

np array

histomicstk.annotations_and_masks.annotation_and_mask_utils.parse_slide_annotations_into_tables(slide_annotations, cropping_bounds=None, cropping_polygon_vertices=None, use_shapely=False)[source]

Given a slide annotation list, parse into convenient tabular format.

If the annotation is a point, then it is just treated as if it is a rectangle with zero area (i.e. xmin=xmax). Rotated rectangles are treated as polygons for simplicity.

Parameters:

  • slide_annotations (list of dicts) – response from server request

  • cropping_bounds (dict or None) – if given, must have keys XMIN, XMAX, YMIN, YMAX. These are the bounds to which the polygons may be cropped using shapely, if the param use_shapely is True. Otherwise, the polygon coordinates are just shifted relative to these bounds without actually cropping.

  • cropping_polygon_vertices (nd array or None) – if given, is an (m, 2) nd array of vertices to crop bounds. if the param use_shapely is True. Otherwise, the polygon coordinates are just shifted relative to these bounds without actually cropping.

  • use_shapely (bool) – see cropping_bounds description.

Returns:

  • Pandas DataFrame – Summary of key properties of the annotation documents. It has the following columns: - annotation_girder_id - _modelType - _version - itemId - created - creatorId - public - updated - updatedId - groups - element_count - element_details

  • Pandas DataFrame – The individual annotation elements (polygons, points, rectangles). The columns annidx and elementidx encode the dict index of annotation document and element, respectively, in the original slide_annotations list of dictionaries. It has the following columns:

    • annidx

    • annotation_girder_id

    • elementidx

    • element_girder_id

    • type

    • group

    • label

    • color

    • xmin

    • xmax

    • ymin

    • ymax

    • bbox_area

    • coords_x

    • coords_y

histomicstk.annotations_and_masks.annotation_and_mask_utils.rotate_point_list(point_list, rotation, center=(0, 0))[source]

Rotate a certain point list around a central point. Modified from.

javascript version at: https://github.com/girder/large_image/blob/master/ … web_client/annotations/rotate.js.

Parameters:

  • point_list (list of tuples) – (x,y) coordinates

  • rotation (float) – degrees (in radians)

  • center (tuple of ints) – central point coordinates

Return type:

list of tuples

histomicstk.annotations_and_masks.annotation_and_mask_utils.scale_slide_annotations(slide_annotations, sf)[source]

Scales up or down annotations in a slide.

Works in place, but returns slide_annotations anyways.

Parameters:

  • slide_annotations (list of dicts) – response from server request

  • sf (float) – scale factor to multiply coordinates

Return type:

list of dicts