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situr/notebook/image_demo_test.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"source": [
"# Example of investigating a single round\n",
"\n",
"This notebook shows an example usage if only a single round is to be registered.\n",
"\n",
"## Imports"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 9,
"source": [
"from situr.image import SituImage\n",
"from situr.registration import PeakFinderDifferenceOfGaussian\n",
"from situr.registration import IcpRegistrationFunction, SituImageChannelRegistration\n",
"from matplotlib import pyplot as plt"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## Creating the image"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 10,
"source": [
"situ_img = SituImage([['./artificial_img/round_0/channel_0.tif'], ['./artificial_img/round_0/channel_1.tif']])"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## Creating a peak finder \n",
"\n",
"Here we create a peak finder and display the shape of the number of found peaks."
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 11,
"source": [
"peak_finder = PeakFinderDifferenceOfGaussian(threshold=0.01)\n",
"peaks = peak_finder.get_channel_peaks(situ_img, 0)\n",
"peaks"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[ 355., 896.],\n",
" [ 398., 1490.],\n",
" [ 818., 1061.],\n",
" [1481., 872.],\n",
" [1442., 341.],\n",
" [ 965., 338.],\n",
" [ 318., 305.],\n",
" [1419., 1644.]])"
]
},
"metadata": {},
"execution_count": 11
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## Visualising the found peaks\n",
"\n",
"The found peaks (in channel 1) are visualised now (note: this step does not require the run of get_channel_peaks). This is done to ensure that the results of the found peak match what we expect. If it does not we can use another peak finder or we can tune the parameters until the result is satisfactory."
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 12,
"source": [
"peak_finder.show_channel_peaks(situ_img, 1, img_show=False)"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<PIL.Image.Image image mode=RGB size=2048x2048 at 0x7FBDB1B851F0>"
],
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},
"metadata": {},
"execution_count": 12
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## Performing the registration\n",
"\n",
"We perform the SituImageChannelRegistration with the FilterregRegistration functio and our initialized peak finder.\n",
"The transformations have to be applied manually after as the registration we are using does not do this automatically."
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 13,
"source": [
"registration = SituImageChannelRegistration(IcpRegistrationFunction(300), peak_finder=peak_finder)\n",
"registration.do_channel_registration(situ_img)\n",
"situ_img.apply_transformations()"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"### Visualizing the result\n",
"\n",
"We visualize the result by showing both images after each other. (Another approach not here implemented is to display both images on top of each other in different colours)."
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 14,
"source": [
"peak_finder.show_channel_peaks(situ_img, 0, img_show=False)"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<PIL.Image.Image image mode=RGB size=2048x2048 at 0x7FBDB1FBDCD0>"
],
"image/png": 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},
"metadata": {},
"execution_count": 14
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 15,
"source": [
"peak_finder.show_channel_peaks(situ_img, 1, img_show=False)"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<PIL.Image.Image image mode=RGB size=2048x2048 at 0x7FBDB1922E50>"
],
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},
"metadata": {},
"execution_count": 15
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"Alternatively we can use scatterplots that we overlay to visualize how well the registration works."
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 16,
"source": [
"peak_finder.scatterplot_channel_peaks(situ_img, 1, color='r')\n",
"peak_finder.scatterplot_channel_peaks(situ_img, 0, color='b')\n",
"plt.show()"
],
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": "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"
},
"metadata": {
"needs_background": "light"
}
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"### Printing the transformation\n",
"\n",
"The following code prints the transformation. This varies depending on the transformations."
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 17,
"source": [
"print(situ_img.channel_transformations[1].offset)\n",
"print(situ_img.channel_transformations[1].scale)\n",
"print(situ_img.channel_transformations[1].transform_matrix)"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[ 338.51832567 -379.28188437]\n",
"1\n",
"[[ 0.96079843 0.27724786]\n",
" [-0.27724786 0.96079843]]\n"
]
}
],
"metadata": {}
}
],
"metadata": {
"orig_nbformat": 4,
"language_info": {
"name": "python",
"version": "3.8.10",
"mimetype": "text/x-python",
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"pygments_lexer": "ipython3",
"nbconvert_exporter": "python",
"file_extension": ".py"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3.8.10 64-bit ('env_situr': conda)"
},
"interpreter": {
"hash": "5e38d59b60259c6d967afb47ee3df0fd55767bf61f38f6c5673d6e4df5d1bec7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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