evaluation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test spot detection (Evaluation)\n",
    "\n",
    "This is a test suite."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "!pip uninstall -y scopyon\n",
    "!pip install git+https://github.com/ecell/scopyon\n",
    "!pip freeze | grep scopyon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy\n",
    "import scopyon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set physical parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "inputpath = Path(\"./artifacts\")\n",
    "\n",
    "artifacts = Path(\"./artifacts\")\n",
    "artifacts.mkdir(parents=True, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = scopyon.Configuration(str(inputpath / 'config.yaml'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "pixel_length = config.default.detector.pixel_length / config.default.magnification\n",
    "L_2 = config.default.detector.image_size[0] * pixel_length * 0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Collect data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kaizu/environ/anaconda3/lib/python3.7/site-packages/skimage/feature/blob.py:126: RuntimeWarning: invalid value encountered in true_divide\n",
      "  pos1 = blob1[:ndim] / (max_sigma * root_ndim)\n",
      "/home/kaizu/environ/anaconda3/lib/python3.7/site-packages/skimage/feature/blob.py:127: RuntimeWarning: invalid value encountered in true_divide\n",
      "  pos2 = blob2[:ndim] / (max_sigma * root_ndim)\n"
     ]
    }
   ],
   "source": [
    "res = []\n",
    "for i in range(10):\n",
    "    img = scopyon.Image.load(str(inputpath / f'image{i:03d}.npy'))\n",
    "    data = numpy.load(inputpath / f'data{i:03d}.npy')\n",
    "    \n",
    "    spots = scopyon.analysis.spot_detection(\n",
    "        img.as_array(), min_sigma=1, max_sigma=4, threshold=40.0, overlap=0.5)\n",
    "    numpy.save(artifacts / f'spots{i:03d}.npy', spots)\n",
    "    res.append((img, data, spots))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "         ...,\n",
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       "           2.10611645e+05,  1.41370270e+02,  1.48054341e+00],\n",
       "         ...,\n",
       "         [ 2.49894100e+00,  9.92121616e+01,  2.19405162e+03,\n",
       "           1.82538104e+05,  1.75657177e+02,  4.04762198e+00],\n",
       "         [ 2.27411784e+00,  6.85153314e+01,  1.51832463e+03,\n",
       "           1.83833807e+05,  1.32133804e+02,  3.73095773e+00],\n",
       "         [ 5.79346785e-01,  1.49562178e+01,  9.16394225e+02,\n",
       "           1.82891530e+05,  1.05258280e+02,  3.46517539e+00]]))]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find closest true positions from spots detected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "closest = []\n",
    "for img, data, spots in res:\n",
    "    for spot in spots:\n",
    "        distance = data - spot[0: 2]\n",
    "        idx = (distance ** 2).sum(axis=1).argmin()\n",
    "        closest.append(distance[idx])\n",
    "closest = numpy.array(closest).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate average and std for the accuracy of the spot detection method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average along x-axis = +0.01747 pixels, std = 1.180638361085026\n",
      "Average along y-axis = +0.00887 pixels, std = 1.207056270635648\n"
     ]
    }
   ],
   "source": [
    "print(f\"Average along x-axis = {numpy.average(closest[0]):+.5f} pixels, std = {numpy.std(closest[0])}\")\n",
    "print(f\"Average along y-axis = {numpy.average(closest[1]):+.5f} pixels, std = {numpy.std(closest[1])}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show the heat map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.express as px\n",
    "H, xedges, yedges = numpy.histogram2d(x=closest[0], y=closest[1], bins=41, range=[[-2, +2], [-2, +2]])\n",
    "fig = px.imshow(H, x=(xedges[: -1]+xedges[1: ])*0.5, y=(yedges[: -1]+yedges[1: ])*0.5)\n",
    "fig.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": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

generation.ipynb