OME Tiff

Learn how to ingest and perform basic ML operations on the MNIST dense dataset.

TileDB OME-TIFF Conversion

import os

import cv2
import matplotlib.pylab as pylab
import tiledb

from tiledb.bioimg.converters.ome_tiff import OMETiffConverter
from tiledb.bioimg.openslide import TileDBOpenSlide

Convert OME-TIFF to TileDB Slide

src = "../tests/data/CMU-1-Small-Region.ome.tiff"
dest = src + ".tiledb"
if not os.path.exists(dest):
    OMETiffConverter.to_tiledb(src, dest, level_min=0)

Slide Group Contents

a = tiledb.open(os.path.join(dest, "l_0.tdb"))
a.schema

Domain

Name	Domain	Tile	Data Type	Is Var-length	Filters
C	(0, 2)	3	uint32	False	-
Y	(0, 2966)	1024	uint32	False	-
X	(0, 2219)	1024	uint32	False	-

Attributes

Name

Data Type

Is Var-Len

Is Nullable

Filters

intensity

uint8

False

Name	Option	Level
ZstdFilter	level	0

Cell Order

row-major

Tile Order

row-major

Capacity

10000

Sparse

False

TileDB Slide Information

slide = TileDBOpenSlide(dest)
print("level_count:", slide.level_count)
print("dimensions:", slide.dimensions)
print("level_dimensions:", slide.level_dimensions)
print("level_downsamples:", slide.level_downsamples)
print("levels:", slide.levels)
print("group_properties:", slide.properties)

level_count: 2
dimensions: (2220, 2967)
level_dimensions: ((2220, 2967), (574, 768))
level_downsamples: (1.0, 3.865438534407666)
levels: (0, 1)
group_properties: {'axes': 'CYX', 'channels': '["RED", "GREEN", "BLUE"]', 'dataset_type': 'BIOIMG', 'fmt_version': 1, 'json_tiffwriter_kwargs': '{"bigtiff": true, "byteorder": ">", "append": false, "imagej": false, "ome": true}', 'levels': '[{"level": 0, "name": "l_0.tdb", "axes": "CYX", "shape": [3, 2967, 2220]}, {"level": 1, "name": "l_1.tdb", "axes": "CYX", "shape": [3, 768, 574]}]', 'pixel_depth': 1, 'pkg_version': '0.1.1.dev14+dirty'}

Reading Slide whole levels

img3d = slide.read_level(0)
norm_img3d = cv2.normalize(src=img3d, dst=None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8U)
pylab.imshow(norm_img3d)

Reading Slide Data

img3d = slide.read_region((200,200), 0, slide.dimensions)
norm_img3d = cv2.normalize(src=img3d, dst=None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8U)

pylab.imshow(norm_img3d)

Read level properties

# Substring of dict
str(slide.level_properties(0))[:200]

'{\'json_write_kwargs\': \'{"subifds": 1, "metadata": {"OME": {"Instrument": {"Objective": {"ID": "Objective:0:0", "NominalMagnification": 20.0}, "ID": "Instrument:0"}, "Image": [{"AcquisitionDate": "2009'