situr/notebook/tile_demo_test.ipynb

{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Example registration using a single tile\n",
                "\n",
                "This notebook shows the example usage of registering a single tile.\n",
                "\n",
                "## Imports"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 17,
            "metadata": {},
            "outputs": [],
            "source": [
                "from situr.image import Tile\n",
                "from situr.registration import PeakFinderDifferenceOfGaussian, IcpRegistrationFunction\n",
                "from situr.registration import CombinedRegistration, AllChannelRoundRegistration, AcrossRoundChannelRegistration\n",
                "\n",
                "from matplotlib import pyplot as plt\n"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Creating the tile"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 18,
            "metadata": {},
            "outputs": [],
            "source": [
                "round_list = [\n",
                "    [['./artificial_img/round_0/channel_0.tif'], ['./artificial_img/round_0/channel_1.tif']], \n",
                "    [['./artificial_img/round_1/channel_0.tif'], ['./artificial_img/round_1/channel_1.tif']]\n",
                "]\n",
                "tile = Tile(round_list)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Convert to numpy array if desired\n",
                "\n",
                "The tile can be converted to a numpy array if you so desire.\n"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 19,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "(2, 2, 1, 2048, 2048)"
                        ]
                    },
                    "execution_count": 19,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "tile.to_numpy_array().shape"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Creating a peak finder"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 20,
            "metadata": {},
            "outputs": [],
            "source": [
                "peak_finder = PeakFinderDifferenceOfGaussian()"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Visualizing found peaks\n",
                "\n",
                "To visualize the found peaks we get round 0 and visualize channel 0. This allows us to tweak the peak finder if the results are not satisfactory"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 21,
            "metadata": {},
            "outputs": [
                {
                    "data": {
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                        "text/plain": [
                            "<PIL.Image.Image image mode=RGB size=2048x2048 at 0x7FE891455340>"
                        ]
                    },
                    "execution_count": 21,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "peak_finder.show_channel_peaks(tile.get_round(0), 0, img_show=False)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Performing the registration\n",
                "\n",
                "We use a CombinedRegistration to to first register all channels and then register all rounds. The registration here applies the transformations automatically so we should not call it manually afterwards."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 22,
            "metadata": {},
            "outputs": [],
            "source": [
                "registration = CombinedRegistration(\n",
                "    AllChannelRoundRegistration(registration_function=IcpRegistrationFunction(800),peak_finder=peak_finder), \n",
                "    AcrossRoundChannelRegistration(registration_function=IcpRegistrationFunction(150), peak_finder=peak_finder))\n",
                "registration.do_registration_and_transform(tile)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### Visualizing the result\n",
                "\n",
                "We show the reference round with it's reference channel, one of its other channels and the modified round to get an idea of the resulting transformaton and how well they are alligned. Also we overlay the peaks in different colours in a scatterplot.\n"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 23,
            "metadata": {},
            "outputs": [
                {
                    "data": {
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                        "text/plain": [
                            "<PIL.Image.Image image mode=RGB size=2048x2048 at 0x7FE8E4F791C0>"
                        ]
                    },
                    "execution_count": 23,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "peak_finder.show_channel_peaks(tile.get_round(0), 0, img_show=False)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 24,
            "metadata": {},
            "outputs": [
                {
                    "data": {
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                        "text/plain": [
                            "<PIL.Image.Image image mode=RGB size=2048x2048 at 0x7FE894CBC460>"
                        ]
                    },
                    "execution_count": 24,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "peak_finder.show_channel_peaks(tile.get_round(0), 1, img_show=False)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 25,
            "metadata": {},
            "outputs": [
                {
                    "data": {
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                        "text/plain": [
                            "<PIL.Image.Image image mode=RGB size=2048x2048 at 0x7FE8E4F71AF0>"
                        ]
                    },
                    "execution_count": 25,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "peak_finder.show_channel_peaks(tile.get_round(1), 0, img_show=False)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 26,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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                            "<Figure size 432x288 with 1 Axes>"
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                }
            ],
            "source": [
                "peak_finder.scatterplot_channel_peaks(tile.get_round(0), 1, color='r')\n",
                "peak_finder.scatterplot_channel_peaks(tile.get_round(1), 0, color='g')\n",
                "peak_finder.scatterplot_channel_peaks(tile.get_round(0), 0, color='b')\n",
                "plt.legend(['Round 0 Channel 1', 'Round 1 Channel 0', 'Round 0 Channel 0'])\n",
                "plt.show()"
            ]
        }
    ],
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