demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4e4d1c8c-0bec-4fcf-977c-8fd3823235d7",
   "metadata": {},
   "source": [
    "# Demo notebook for generating a synthetic 3D image with lines and corrupting it with noise and Gaussian blur"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5bb2fad6-bc28-4755-b678-ab02244389d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from skimage import io\n",
    "import json\n",
    "\n",
    "from cs_sim.synth.filaments import generate_img_with_filaments\n",
    "from cs_sim.corrupt import corrupt_image"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4ce6212-d0fd-4f03-99fc-84cec4b097c3",
   "metadata": {},
   "source": [
    "## Specify parameters for a synthetic image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a80dffae-d230-4c1c-a1c0-310d444f33d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "params_synth_data = dict(\n",
    "    imgshape=(20, 100, 100),\n",
    "    n_filaments=10,\n",
    "    maxval=255,\n",
    "    n_points=100\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b05b13fe-f559-486c-bffe-b16ad58e1422",
   "metadata": {},
   "source": [
    "## Generate a synthetic image with filaments\n",
    "\n",
    "### 2D image with sine-shaped cuves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2bc8678b-8e6d-4d00-b3ab-e95d7a2fd3e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/research/sharedresources/cbi/common/Anna/anaconda3/envs/quamd/lib/python3.9/site-packages/skimage/io/_plugins/matplotlib_plugin.py:150: UserWarning: Float image out of standard range; displaying image with stretched contrast.\n",
      "  lo, hi, cmap = _get_display_range(image)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f224093e100>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = generate_img_with_filaments(imgshape=(100, 100), n_filaments=10, maxval=255, n_points=100)\n",
    "io.imshow(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85af8356-c62d-495e-83ce-e60f14469471",
   "metadata": {},
   "source": [
    "### 2D image with straight lines that are uniquely labeled "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "88a368b5-2e47-450f-8984-749e60b88477",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f224079dac0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = generate_img_with_filaments(imgshape=(100, 100), curve_type='line', n_filaments=10, maxval=255, n_points=100, instance=True)\n",
    "io.imshow(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05dbab72-b782-49c5-be2c-88b1e5a66335",
   "metadata": {},
   "source": [
    "### 3D image "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "46a9a512-1683-42cd-8221-a06889c1971a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f22406d7a00>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = generate_img_with_filaments(imgshape=(20, 100, 100), curve_type='sine_curve', n_filaments=10, maxval=255, n_points=100, instance=True)\n",
    "io.imshow(img.max(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04dc3d1e-d2d8-46bb-85c4-3b8a43580f6a",
   "metadata": {},
   "source": [
    "### Details of the input parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3b4bbebb-9f45-4b3c-9de5-847098896ffe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mgenerate_img_with_filaments\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mimgshape\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcurve_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'sine_curve'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mn_filaments\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmaxval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m255\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mn_points\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0minstance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mthick\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m**\u001b[0m\u001b[0mcurve_kwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Generate an image with straight lines.\n",
       "The start and the end of each line are chosen randomly.\n",
       "The line coordinates in between are interpolated with the given number of point (`n_points`)\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "imgshape : tuple\n",
       "    Image shape.\n",
       "    The number of inputs should correspond to the number of dimensions.\n",
       "curve_type : str\n",
       "    Type of the curve ('line' or 'curve').\n",
       "    Default is 'line'.\n",
       "n_filaments : int, optional\n",
       "    Number of filaments to generate.\n",
       "    Default is 10.\n",
       "maxval : scalar, optional\n",
       "    The value to be assigned to the lines/foreground (the background value is 0).\n",
       "    Default is 255.\n",
       "n_points : int, optional\n",
       "    Number of points to represent each line.\n",
       "    Should be on the order of image size.\n",
       "    Increase if lines get disconnected.\n",
       "    If None, the number of points is set to 2x the image dimension.\n",
       "    Default is None.\n",
       "instance : bool, optional\n",
       "    If True, assign unique intensity value to each line.\n",
       "    Default is False.\n",
       "thick : bool, optional\n",
       "    If True, will dilate the lines image to get thicker lines.\n",
       "    Default is False.\n",
       "curve_kwargs : key value\n",
       "    Parameters for the curve generation function\n",
       "\n",
       "Returns\n",
       "-------\n",
       "np.ndarray:\n",
       "    Image with straight lines.\n",
       "\u001b[0;31mFile:\u001b[0m      /research/sharedresources/cbi/common/Anna/codes/CS-Sim/cs_sim/synth/filaments.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "generate_img_with_filaments?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92b9aea3-053e-488f-a98a-8d9b6b2d4319",
   "metadata": {},
   "source": [
    "\n",
    "------------\n",
    "\n",
    "## Specify parameters for image corruption with noise and blurring\n",
    "\n",
    "There are 4 steps that can be combined in any order:\n",
    "\n",
    "1. Perlin noise: adds low-frequency background. The `size` parameter specifies the size of the low-frequency pattern (in pixels), the `value` parameter specifies the amplitude.\n",
    "2. Convolve: convolves the image with either a specified PSF image (`psf` parameter), or with a gaussian kernel of specified `sigma`. \n",
    "3. Poisson noise, with specified `snr`.\n",
    "4. Gaussian noise, with specified `snr`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dd48b74b-604b-41d8-bfd7-f3e96c5421b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "corruption_steps = [\n",
    "    ('perlin_noise', {'size': 50, 'value': 0.1}),\n",
    "    ('poisson_noise', {'snr': 2}),\n",
    "    ('convolve', {'sigma': 2}),\n",
    "    ('gaussian_noise', {'snr': 100})\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5496cc38-b96d-42f8-962b-0b39fc2c902b",
   "metadata": {},
   "source": [
    "## Corrupt the image with the specified corruption steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c0cda447-2997-4206-bf95-fb28bdbfd678",
   "metadata": {},
   "outputs": [],
   "source": [
    "imgf = corrupt_image(img, corruption_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "70dc3260-e0b8-4412-8717-8963c8e20d02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f224064bb20>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "io.imshow(imgf.max(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d64404ab-3e42-4291-b758-ec725ec8f274",
   "metadata": {},
   "source": [
    "## Save parameters to a json file for batch processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "12029e9c-7a6d-4542-acc3-9e453ba0427d",
   "metadata": {},
   "outputs": [],
   "source": [
    "params_synth_data = dict(\n",
    "    imgshape=(20, 100, 100),\n",
    "    n_filaments=10,\n",
    "    maxval=255,\n",
    "    n_points=100\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5a0985b2-8de6-45b9-b23f-10582d778a3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_fn_synth = 'parameters_synthetic.json'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b3cab755-0d93-41ad-a01c-68364d5a9fec",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(param_fn_synth, 'w') as f:\n",
    "    json.dump(params_synth_data, f, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "276d344b-2589-47e9-8dcf-510d3928b63c",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_fn_corruption = 'corruption_steps.json'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d87b52bb-935e-4a95-9ba7-d087ecdbb89f",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(param_fn_corruption, 'w') as f:\n",
    "    json.dump(corruption_steps, f, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a83f26c3-9040-47be-80b8-c3d0c9b3d8b7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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