Open an image with Bio-Formats as a numpy array via pyimagej

Open-as-numpy-array-via-Bio-Formats.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.0.0-rc-71/1.52i'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import imagej\n",
    "ij = imagej.init('sc.fiji:fiji:2.0.0-pre-10')\n",
    "ij.getVersion()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "jnius.reflect.net.imglib2.view.IntervalView"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Open the image using ImageJ (more precisely: Bio-Formats via SCIFIO).\n",
    "ijImage = ij.io().open('/Users/curtis/Desktop/p3_ipsi.nii')\n",
    "# Slice out the Z=37 plane.\n",
    "ijSlice = ij.op().transform().hyperSliceView(ijImage, 2, 37)\n",
    "# What kind of thing is this?\n",
    "type(ijSlice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert the thing to a numpy array.\n",
    "pySlice = ij.py.from_java(ijSlice)\n",
    "type(pySlice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "pyplot.imshow(pySlice, interpolation='nearest')\n",
    "pyplot.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": false,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": false,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

For more, see https://pypi.org/project/pyimagej/

For NIFTI specifically, there are much easier ways:

import nibabel as nib
img = nib.load(filename)
data = img.get_fdata()

But the above notebook should work for nearly all the Bio-Formats-supported formats, including ones without convenient Python libraries.