ITK + Dask notebooks (August 2022)

itk-dask-blogpost.ipynb

test-itk-realdata.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c7f60750",
   "metadata": {},
   "outputs": [],
   "source": [
    "import itk\n",
    "import numpy as np\n",
    "import skimage.restoration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fe6a0227",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TiffTag 32781: coercing invalid ASCII to bytes\n",
      "TiffTag 32781: coercing invalid ASCII to bytes\n",
      "TiffTag 32781: coercing invalid ASCII to bytes\n",
      "TiffTag 32781: coercing invalid ASCII to bytes\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(101, 64, 64)\n"
     ]
    }
   ],
   "source": [
    "import tifffile\n",
    "\n",
    "psf_filename = \"psfs_z0p1/PSF_488nm_dz100nm.tif\"\n",
    "psf = tifffile.imread(psf_filename).astype(float)\n",
    "print(psf.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4b6bb607",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TiffTag 32781: coercing invalid ASCII to bytes\n",
      "TiffTag 32781: coercing invalid ASCII to bytes\n",
      "TiffTag 32781: coercing invalid ASCII to bytes\n",
      "TiffTag 32781: coercing invalid ASCII to bytes\n"
     ]
    },
    {
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       "                        <td> 234.37 GiB </td>\n",
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       "                        <td> 398 Tasks </td>\n",
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       "                    <th> Type </th>\n",
       "                    <td> float64 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
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       "dask.array<astype, shape=(199, 201, 1024, 768), dtype=float64, chunksize=(1, 201, 1024, 768), chunktype=numpy.ndarray>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dask.array.image import imread\n",
    "\n",
    "raw_filename = \"raw-488nm/*.tif\"\n",
    "imgs = imread(raw_filename).astype(float)\n",
    "imgs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c7df9f60",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TiffTag 32781: coercing invalid ASCII to bytes\n",
      "TiffTag 32781: coercing invalid ASCII to bytes\n",
      "TiffTag 32781: coercing invalid ASCII to bytes\n",
      "TiffTag 32781: coercing invalid ASCII to bytes\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(200, 200, 200)\n"
     ]
    }
   ],
   "source": [
    "img = imgs[0,:200,:200,:200].compute()\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "90e0cf98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float64\n",
      "float64\n",
      "float64\n"
     ]
    }
   ],
   "source": [
    "print(psf.dtype)\n",
    "print(img.dtype)\n",
    "print(imgs.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "82e43a66",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "No suitable template parameter can be found.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "Input \u001b[0;32mIn [8]\u001b[0m, in \u001b[0;36m<cell line: 13>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m iterations \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m      7\u001b[0m deconvolved \u001b[38;5;241m=\u001b[39m itk\u001b[38;5;241m.\u001b[39mrichardson_lucy_deconvolution_image_filter(\n\u001b[1;32m      8\u001b[0m     img, \n\u001b[1;32m      9\u001b[0m     kernel_image\u001b[38;5;241m=\u001b[39mpsf,\n\u001b[1;32m     10\u001b[0m     number_of_iterations\u001b[38;5;241m=\u001b[39miterations,\n\u001b[1;32m     11\u001b[0m )\n\u001b[0;32m---> 13\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mitk\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray_from_image\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdeconvolved\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# Convert back to Numpy array\u001b[39;00m\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28mprint\u001b[39m(result\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdone\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[0;32m~/em95/miniconda/conda/envs/itk-dask/lib/python3.10/site-packages/itk/support/extras.py:327\u001b[0m, in \u001b[0;36mGetArrayFromImage\u001b[0;34m(image_or_filter, keep_axes, update, ttype)\u001b[0m\n\u001b[1;32m    320\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mGetArrayFromImage\u001b[39m(\n\u001b[1;32m    321\u001b[0m     image_or_filter: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mitkt.ImageOrImageSource\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    322\u001b[0m     keep_axes: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m    323\u001b[0m     update: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m    324\u001b[0m     ttype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    325\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m np\u001b[38;5;241m.\u001b[39mndarray:\n\u001b[1;32m    326\u001b[0m     \u001b[38;5;124;03m\"\"\"Get an array with the content of the image buffer\"\"\"\u001b[39;00m\n\u001b[0;32m--> 327\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_GetArrayFromImage\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    328\u001b[0m \u001b[43m        \u001b[49m\u001b[43mimage_or_filter\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mGetArrayFromImage\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeep_axes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mupdate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mttype\u001b[49m\n\u001b[1;32m    329\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/em95/miniconda/conda/envs/itk-dask/lib/python3.10/site-packages/itk/support/extras.py:314\u001b[0m, in \u001b[0;36m_GetArrayFromImage\u001b[0;34m(image_or_filter, function_name, keep_axes, update, ttype)\u001b[0m\n\u001b[1;32m    312\u001b[0m keys \u001b[38;5;241m=\u001b[39m [k \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m itk\u001b[38;5;241m.\u001b[39mPyBuffer\u001b[38;5;241m.\u001b[39mkeys() \u001b[38;5;28;01mif\u001b[39;00m k[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m==\u001b[39m ImageType]\n\u001b[1;32m    313\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(keys) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 314\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo suitable template parameter can be found.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    315\u001b[0m \u001b[38;5;66;03m# Create a numpy array of the type of the input image\u001b[39;00m\n\u001b[1;32m    316\u001b[0m templatedFunction \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(itk\u001b[38;5;241m.\u001b[39mPyBuffer[keys[\u001b[38;5;241m0\u001b[39m]], function_name)\n",
      "\u001b[0;31mRuntimeError\u001b[0m: No suitable template parameter can be found."
     ]
    }
   ],
   "source": [
    "# itk richardson-lucy deconvolution\n",
    "\n",
    "image = itk.image_view_from_array(img)   # Convert to ITK object\n",
    "kernel = itk.image_view_from_array(psf)  # Convert to ITK object\n",
    "iterations = 1\n",
    "\n",
    "deconvolved = itk.richardson_lucy_deconvolution_image_filter(\n",
    "    img, \n",
    "    kernel_image=psf,\n",
    "    number_of_iterations=iterations,\n",
    ")\n",
    "\n",
    "result = itk.array_from_image(deconvolved)  # Convert back to Numpy array\n",
    "print(result.shape)\n",
    "print('done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11cd537d",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = itk.array_from_image(deconvolved_RL)  # Convert back to Numpy array\n",
    "print(result.shape)\n",
    "print('done')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0e02761",
   "metadata": {},
   "outputs": [],
   "source": [
    "import dask.array as da\n",
    "import itk\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "iterations = 1\n",
    "deconvolved = da.map_blocks(\n",
    "    itk.richardson_lucy_deconvolution_image_filter,\n",
    "    imgs, \n",
    "    kernel_image=psf,\n",
    "    number_of_iterations=iterations,\n",
    "    dtype=np.float32,\n",
    ")\n",
    "deconvolved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c5d576a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "deconvolved_result = deconvolved.compute()\n",
    "print(deconvolved_result.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b05c819",
   "metadata": {},
   "outputs": [],
   "source": [
    "# uint16 input\n",
    "# TypeError: Expecting argument of type itkImageD4 or itkImageSourceID4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49430b58",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2ad9274",
   "metadata": {},
   "outputs": [],
   "source": [
    "img_size = (200, 200, 200)\n",
    "psf_size = (12, 100, 100)\n",
    "\n",
    "img = np.random.random(img_size)\n",
    "psf = np.ones(psf_size) / np.prod(psf_size)\n",
    "\n",
    "print(img.shape)\n",
    "print(psf.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "818fceae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# itk richardson-lucy deconvolution\n",
    "\n",
    "image = itk.image_view_from_array(img)   # Convert to ITK object\n",
    "kernel = itk.image_view_from_array(psf)  # Convert to ITK object\n",
    "iterations = 1\n",
    "\n",
    "deconvolved = itk.richardson_lucy_deconvolution_image_filter(\n",
    "    image,\n",
    "    kernel_image=kernel,\n",
    "    number_of_iterations=iterations\n",
    ")\n",
    "\n",
    "result = itk.array_from_image(deconvolved)  # Convert back to Numpy array\n",
    "print('done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84aeae95",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(result.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30d69859",
   "metadata": {},
   "outputs": [],
   "source": [
    "import dask.array as da\n",
    "\n",
    "imgs = da.from_array(img, chunks=(50,100,100))\n",
    "imgs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0f5332d",
   "metadata": {},
   "outputs": [],
   "source": [
    "deconvolved = da.map_blocks(\n",
    "    itk.richardson_lucy_deconvolution_image_filter,\n",
    "    imgs, \n",
    "    kernel_image=kernel,\n",
    "    number_of_iterations=iterations,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "760d036f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import napari\n",
    "\n",
    "viewer = napari.Viewer()\n",
    "viewer.add_image(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26f63107",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1b4098a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# itk richardson-lucy deconvolution\n",
    "deconvolved = itk.RichardsonLucyDeconvolutionImageFilter(img, kernel_image=psf, number_of_iterations=1)\n",
    "print(deconvolved.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b96ec5ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# scikit-image richardson-lucy deconvolution\n",
    "deconvolved_RL = skimage.restoration.richardson_lucy(img, psf, num_iter=30)\n",
    "print(deconvolved_RL.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a8162e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "del deconvolved_RL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fde84656",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
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    "name": "ipython",
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   "file_extension": ".py",
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test-itk.ipynb