Benchmarking skimage NaN handling

benchmark_skimage_nan.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Benchmarking scikit-image handle nans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from skimage import io, data, registration\n",
    "from skimage.registration import phase_cross_correlation, _masked_phase_cross_correlation\n",
    "from skimage.transform import rescale, resize\n",
    "from skimage.util import img_as_float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make some data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 512x512 pixels (A, B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(512, 512)\n"
     ]
    }
   ],
   "source": [
    "shift_a = (20, 35)\n",
    "image_A1 = img_as_float(data.camera())\n",
    "image_A2 = np.roll(np.roll(img_as_float(data.camera()), shift_a[0], axis=0), shift_a[1], axis=1)\n",
    "\n",
    "print(image_A1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(512, 512, 3)\n"
     ]
    }
   ],
   "source": [
    "shift_b = (20, 35)\n",
    "image_B1 = img_as_float(data.astronaut())\n",
    "image_B2 = np.roll(np.roll(img_as_float(data.astronaut()), shift_b[0], axis=0), shift_b[1], axis=1)\n",
    "\n",
    "print(image_B1.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1000x1000 pixels (C, D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000, 1000)\n"
     ]
    }
   ],
   "source": [
    "shift_c = (40, 70)\n",
    "size_c = (1000, 1000)\n",
    "image_C1 = resize(img_as_float(data.camera()), size_c)\n",
    "image_C2 = resize(img_as_float(data.camera()), size_c)\n",
    "image_C2 = np.roll(np.roll(image_C2, shift_c[0], axis=0), shift_c[1], axis=1)\n",
    "\n",
    "print(image_C1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000, 1000, 3)\n"
     ]
    }
   ],
   "source": [
    "shift_d = (40, 70)\n",
    "size_d = (1000, 1000)\n",
    "image_D1 = resize(img_as_float(data.astronaut()), size_d)\n",
    "image_D2 = resize(img_as_float(data.astronaut()), size_d)\n",
    "image_D2 = np.roll(np.roll(image_D2, shift_d[0], axis=0), shift_d[1], axis=1)\n",
    "\n",
    "print(image_D1.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1024x1024 pixels (E, F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1024, 1024)\n"
     ]
    }
   ],
   "source": [
    "shift_e = (40, 70)\n",
    "size_e = (1024, 1024)\n",
    "image_E1 = resize(img_as_float(data.camera()), size_e)\n",
    "image_E2 = resize(img_as_float(data.camera()), size_e)\n",
    "image_E2 = np.roll(np.roll(image_E2, shift_e[0], axis=0), shift_e[1], axis=1)\n",
    "\n",
    "print(image_E1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1024, 1024, 3)\n"
     ]
    }
   ],
   "source": [
    "shift_f = (40, 70)\n",
    "size_f = (1024, 1024)\n",
    "image_F1 = resize(img_as_float(data.astronaut()), size_f)\n",
    "image_F2 = resize(img_as_float(data.astronaut()), size_f)\n",
    "image_F2 = np.roll(np.roll(image_F2, shift_f[0], axis=0), shift_f[1], axis=1)\n",
    "\n",
    "print(image_F1.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2000x2000 pixels (G, H)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2000, 2000)\n"
     ]
    }
   ],
   "source": [
    "shift_g = (80, 145)\n",
    "size_g = (2000, 2000)\n",
    "image_G1 = resize(img_as_float(data.camera()), size_g)\n",
    "image_G2 = resize(img_as_float(data.camera()), size_g)\n",
    "image_G2 = np.roll(np.roll(image_G2, shift_g[0], axis=0), shift_g[1], axis=1)\n",
    "\n",
    "print(image_G1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2000, 2000, 3)\n"
     ]
    }
   ],
   "source": [
    "shift_h = (80, 145)\n",
    "size_h = (2000, 2000)\n",
    "image_H1 = resize(img_as_float(data.astronaut()), size_h)\n",
    "image_H2 = resize(img_as_float(data.astronaut()), size_h)\n",
    "image_H2 = np.roll(np.roll(image_H2, shift_h[0], axis=0), shift_h[1], axis=1)\n",
    "\n",
    "print(image_H1.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2048x2048 pixels (I, J)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2000, 2000)\n"
     ]
    }
   ],
   "source": [
    "shift_i = (80, 145)\n",
    "size_i = (2000, 2000)\n",
    "image_I1 = resize(img_as_float(data.camera()), size_i)\n",
    "image_I2 = resize(img_as_float(data.camera()), size_i)\n",
    "image_I2 = np.roll(np.roll(image_I2, shift_i[0], axis=0), shift_i[1], axis=1)\n",
    "\n",
    "print(image_I1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2000, 2000, 3)\n"
     ]
    }
   ],
   "source": [
    "shift_j = (80, 145)\n",
    "size_j = (2000, 2000)\n",
    "image_J1 = resize(img_as_float(data.astronaut()), size_j)\n",
    "image_J2 = resize(img_as_float(data.astronaut()), size_j)\n",
    "image_J2 = np.roll(np.roll(image_J2, shift_j[0], axis=0), shift_j[1], axis=1)\n",
    "\n",
    "print(image_J1.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time correlations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## No NaNs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "291 ms ± 99.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "reference_image = image_A1\n",
    "moving_image = image_A2\n",
    "expected = shift_a\n",
    "\n",
    "%timeit result = phase_cross_correlation(reference_image, moving_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20, 35)\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'result' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-16-c473c7728b3d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexpected\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'result' is not defined"
     ]
    }
   ],
   "source": [
    "print(expected)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.05 s ± 72.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "reference_image = image_B1\n",
    "moving_image = image_B2\n",
    "expected = shift_b\n",
    "\n",
    "%timeit result = phase_cross_correlation(reference_image, moving_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20, 35)\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'result' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-18-c473c7728b3d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexpected\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'result' is not defined"
     ]
    }
   ],
   "source": [
    "print(expected)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1000x1000 pixels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "787 ms ± 85.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "reference_image = image_C1\n",
    "moving_image = image_C2\n",
    "expected = shift_c\n",
    "\n",
    "%timeit result = phase_cross_correlation(reference_image, moving_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(expected)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.47 s ± 571 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "reference_image = image_D1\n",
    "moving_image = image_D2\n",
    "expected = shift_d\n",
    "\n",
    "%timeit result = phase_cross_correlation(reference_image, moving_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(expected)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1024x1024 pixels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.06 s ± 166 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "reference_image = image_E1\n",
    "moving_image = image_E2\n",
    "expected = shift_e\n",
    "\n",
    "%timeit result = phase_cross_correlation(reference_image, moving_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(expected)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.15 s ± 359 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "reference_image = image_F1\n",
    "moving_image = image_F2\n",
    "expected = shift_f\n",
    "\n",
    "%timeit result = phase_cross_correlation(reference_image, moving_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(expected)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2000x2000 pixels "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 s ± 492 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "reference_image = image_G1\n",
    "moving_image = image_G2\n",
    "expected = shift_g\n",
    "\n",
    "%timeit result = phase_cross_correlation(reference_image, moving_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(expected)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17.6 s ± 2.49 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "reference_image = image_H1\n",
    "moving_image = image_H2\n",
    "expected = shift_h\n",
    "\n",
    "%timeit result = phase_cross_correlation(reference_image, moving_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(expected)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2048x2048 pixels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.64 s ± 484 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "reference_image = image_I1\n",
    "moving_image = image_I2\n",
    "expected = shift_i\n",
    "\n",
    "%timeit result = phase_cross_correlation(reference_image, moving_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(expected)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24.5 s ± 3.39 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "reference_image = image_J1\n",
    "moving_image = image_J2\n",
    "expected = shift_j\n",
    "\n",
    "%timeit result = phase_cross_correlation(reference_image, moving_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(expected)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate benchmarks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Greyscale: 1.0240549828178693\n",
      "RGB:  1.2285714285714286\n"
     ]
    }
   ],
   "source": [
    "# scikit-image branch \"master\"\n",
    "# 512x512 greyscale - 291 ms ± 99.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
    "# 512x512 RGB - 1.05 s ± 72.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
    "\n",
    "# scikit-image branch \"handle-nans\"\n",
    "# 512x512 greyscale - 298 ms ± 27.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
    "# 512x512 RGB - 1.29 s ± 198 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
    "\n",
    "print(\"Greyscale:\", 298 / 291)\n",
    "print(\"RGB: \", 1.29 / 1.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Breyscale: 1.1994917407878019\n",
      "RGB: 1.1454138702460852\n"
     ]
    }
   ],
   "source": [
    "# scikit-image branch \"master\"\n",
    "# 1000x1000 greyscale - 787 ms ± 85.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
    "# 1000x1000 RGB - 4.47 s ± 571 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
    "\n",
    "# scikit-image branch \"handle-nans\"\n",
    "# 1000x1000 greyscale - 944 ms ± 335 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
    "# 1000x1000 RGB - 5.12 s ± 472 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
    "\n",
    "print(\"Breyscale:\", 944 / 787)\n",
    "print(\"RGB:\", 5.12 / 4.47)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Breyscale: 1.150943396226415\n",
      "RGB: 1.0407766990291263\n"
     ]
    }
   ],
   "source": [
    "# scikit-image branch \"master\"\n",
    "# 1024x1024 greyscale - 1.06 s ± 166 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
    "# 1024x1024 RGB - 5.15 s ± 359 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
    "\n",
    "# scikit-image branch \"handle-nans\"\n",
    "# 1024x1024 greyscale - 1.22 s ± 261 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
    "# 1024x1024 RGB - 5.36 s ± 865 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
    "\n",
    "print(\"Breyscale:\", 1.22 / 1.06)\n",
    "print(\"RGB:\", 5.36 / 5.15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Breyscale: 1.2225\n",
      "RGB: 1.0284090909090908\n"
     ]
    }
   ],
   "source": [
    "# scikit-image branch \"master\"\n",
    "# 2000x2000 greyscale - 4 s ± 492 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
    "# 2000x2000 RGB - 17.6 s ± 2.49 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
    "\n",
    "# scikit-image branch \"handle-nans\"\n",
    "# 2000x2000 greyscale - 4.89 s ± 915 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
    "# 2000x2000 RGB - 18.1 s ± 2.19 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
    "\n",
    "print(\"Breyscale:\", 4.89 / 4)\n",
    "print(\"RGB:\", 18.1 / 17.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Breyscale: 1.2692307692307692\n",
      "RGB: 0.8530612244897958\n"
     ]
    }
   ],
   "source": [
    "# scikit-image branch \"master\"\n",
    "# 2048x2048 greyscale - 3.64 s ± 484 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
    "# 2048x2048 RGB - 24.5 s ± 3.39 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
    "\n",
    "# scikit-image branch \"handle-nans\"\n",
    "# 2048x2048 greyscale - 4.62 s ± 657 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
    "# 2048x2048 RGB - 20.9 s ± 1.5 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
    "\n",
    "print(\"Breyscale:\", 4.62 / 3.64)\n",
    "print(\"RGB:\", 20.9 / 24.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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