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ITK + Dask notebooks (August 2022)
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itk-dask-blogpost.ipynb
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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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"text/plain": [
"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": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.10.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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