notebooks/rnr-exm_notebooks/zebrafish_pair3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Inputs\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading data and working with arrays\n",
    "import h5py, nrrd\n",
    "import numpy as np\n",
    "\n",
    "# data paths\n",
    "data_path = '/groups/scicompsoft/home/fleishmang/rnr-exm-work/data/zebrafish_pair3.h5'\n",
    "hdf5_file = h5py.File(data_path, 'r')\n",
    "fix = hdf5_file['/fixed'][...]\n",
    "mov = hdf5_file['/move'][...]\n",
    "\n",
    "# assumed spacings (there are no provided spacings, this is a problem)\n",
    "fix_spacing = np.array([1., 1., 1.])\n",
    "mov_spacing = np.array([1., 1., 1.])\n",
    "\n",
    "# write some channels\n",
    "nrrd.write('./fix.nrrd', fix.transpose(2,1,0), compression_level=2)\n",
    "nrrd.write('./mov.nrrd', mov.transpose(2,1,0), compression_level=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Alignment\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### affine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Registration succeeded\n",
      "CPU times: user 2min 54s, sys: 9.11 s, total: 3min 3s\n",
      "Wall time: 38.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# alignment functions\n",
    "from bigstream.align import alignment_pipeline\n",
    "from bigstream.transform import apply_transform\n",
    "\n",
    "# define alignment steps\n",
    "common_kwargs = {\n",
    "    'alignment_spacing':6.0,\n",
    "    'shrink_factors':(2,),\n",
    "    'smooth_sigmas':(8.,),\n",
    "    'optimizer_args':{\n",
    "        'learningRate':0.25,\n",
    "        'minStep':0.,\n",
    "        'numberOfIterations':1200,\n",
    "    },\n",
    "}\n",
    "\n",
    "affine_kwargs = {\n",
    "    'initial_condition':'CENTER',\n",
    "}\n",
    "\n",
    "affine2_kwargs = {\n",
    "    'alignment_spacing':3.0,\n",
    "    'shrink_factors':(1,),\n",
    "    'smooth_sigmas':(2.,),\n",
    "    'optimizer_args':{\n",
    "        'learningRate':0.25,\n",
    "        'minStep':0.,\n",
    "        'numberOfIterations':200,\n",
    "    },\n",
    "}\n",
    "\n",
    "steps = [('affine', {**common_kwargs, **affine_kwargs},),]\n",
    "         # ('affine', {**common_kwargs, **affine2_kwargs},),]\n",
    "\n",
    "# align\n",
    "affine = alignment_pipeline(\n",
    "    fix, mov,\n",
    "    fix_spacing, mov_spacing,\n",
    "    steps,\n",
    ")\n",
    "\n",
    "# apply affine only\n",
    "affine_aligned = apply_transform(\n",
    "    fix, mov,\n",
    "    fix_spacing, mov_spacing,\n",
    "    transform_list=[affine,],\n",
    ")\n",
    "\n",
    "# write results\n",
    "np.savetxt('affine.mat', affine)\n",
    "nrrd.write('./affine.nrrd', affine_aligned.transpose(2,1,0), compression_level=2)\n",
    "\n",
    "# load precomputed results\n",
    "affine = np.loadtxt('./affine.mat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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