origin_dataset/8045940_folder/data/Usage notes.md

Usage notes for Python codes

The dataset contains hyperspectral images stored in both ENVI- and TIFF-formats. The images in TIFF-format have been flat-field corrected with a white reference sample and a dark-current sample, except for the images in the References.zip. These are the white and dark-current references saved in TIFF-format. The ENVI-format images are as they were saved by the hyperspectral camera.

Functions for reading these files are provided in Python files "read_envi.py" and "spectral_tiffs.py". Additionally, the dataset contains manual segmentations created with SpimLab software. The native vector graphics format segmentations are in *.csv files and bitmap raster renders of these vector graphics are in *masks.tif files. Methods for rendering the vector graphics segmentations into bitmap rasters are provided in "annotations.py".

Note for MATLAB-users: We have included read_stiff and read_mtiff functions in this package. These functions were cobbled together from MATLAB's inconsistent TIFF support and are provided as examples of how the data loading might be done in MATLAB.

Prerequisites

The Python environment needs packages:

• numpy (TIFF, ENVI)
• pillow (annotations)
• pyyaml (TIFF)
• tifffile (TIFF)
• python-dateutil (ENVI)

Usage example 1

Let's load the spectral image P001. The spectral image data is in file "P001.tif" and its segmentation masks "P001, masks.tif".

from spectral_tiffs import read_stiff, read_mtiff

data_cube, center_wavelengths, preview_image, metadata = read_stiff("P001.tif")
masks = read_mtiff("P001, masks.tif")

This results in several variables in the environment:

• data_cube is an ndarray of shape (height, width, band) and contains the spectral image.
• center_wavelengths is a vector of spectral band center wavelengths.
• preview_image is an RGB render of the spectral image, ndarray of shape (height, width, 3).
• metadata: a dictionary of metadata written in the spectral image. This is likely to be an empty dictionary in this dataset.
• masks: dictionary of segmentation label - segmentation mask pairs.

Usage example 2

Let's render the vector graphics format segmentation file for spectral image P001. The segmentation elements can be found in file "P001.csv".

from pathlib import Path
import annotations

# Read the vector graphics format file
vector_text = Path("P001.csv").read_text("utf-8")

# Parse the contents of the vector graphics format file.
# The file may contain arbitrary number of polygons and
# lines.
vector_elements = annotations.parse(vector_text)

# Render vector elements into bitmap rasters. The method
# needs to know the size of the bitmap - all spectral images
# in the dataset have spatial resolution of 1024x1024.
bitmap_elements = annotations.create_masks(vector_elements, 1024, 1024)

# Combine bitmap elements by their labels into mask images.
# The result is a dictionary of type dict[str: ndarray], i.e.,
# each segmentation label maps to one segmentation mask.
masks = annotations.flatten_masks(bitmap_elements)

The code snippet above creates the same dictionary as read_mtiff("P001, masks.tif") would create in the previous example.

Usage example 3

The bitmap raster segmentation mask files have a preview image that shows all segmentation masks overlaid on an RGB render of the associated spectral image. A method for creating these pretty images is also included in "annotations.py". Let's recreate "P001, masks.tif":

from pathlib import Path
import annotations
from spectral_tiffs import read_stiff, write_mtiff

# Repeat example 2
vector_text = Path("P001.csv").read_text("utf-8")
vector_elements = annotations.parse(vector_text)
bitmap_elements = annotations.create_masks(vector_elements, 1024, 1024)
masks = annotations.flatten_masks(bitmap_elements)

# Load a preview image of P001
_, _, preview_image, _ = read_stiff("P001.tif", rgb_only=True)

# Set label colors
label_colors = {x[0]: tuple(x[3]) for x in vector_elements}

# Create a masked preview image
mask_preview_image = annotations.overlay_rgb_mask(preview_image, masks, label_colors,
                                                  legend=True, legend_title="Placenta-DB")

# Save the masks using a preview image
write_mtiff("P001, masks v2.tif", masks, mask_preview_image)

Usage example 4

The raw spectral images as saved by the spectral camera are provided in ENVI-format. The function for reading ENVI-data is provided in "read_envi.py".

from read_envi import read_envi

data_cube, center_wavelengths, header = read_envi("P001.hdr", normalize=False)

This results in several variables in the environment:

• data_cube is an ndarray of shape (height, width, band) and contains the spectral image.
• center_wavelengths is a vector of spectral band center wavelengths.
• header is a dictionary representation of the ENVI header file

Usage example 5

The "read_envi.py" also contains a function that makes it unnecessary to extract the "Raw measurement data.zip".

from zipfile import ZipFile
from read_envi import read_zipped_envi

with ZipFile("Raw measurement data.zip", "r") as zip_file:
  data_cube, center_wavelengths, header = read_zipped_envi(zip_file, "P001.hdr", normalize=False)