situr/notebook/tile_demo_test.ipynb

{
    "cells": [
        {
            "cell_type": "markdown",
            "source": [
                "# Example registration using a single tile\n",
                "\n",
                "This notebook shows the example usage of registering a single tile.\n",
                "\n",
                "## Imports"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 31,
            "source": [
                "from situr.image import Tile\n",
                "from situr.registration import PeakFinderDifferenceOfGaussian, IcpRegistrationFunction\n",
                "from situr.registration import CombinedRegistration, AllChannelRoundRegistration, ChannelRegistration\n",
                "\n",
                "from matplotlib import pyplot as plt\n"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "## Creating the tile"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 32,
            "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)"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "## Convert to numpy array if desired\n",
                "\n",
                "The tile can be converted to a numpy array if you so desire.\n"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 33,
            "source": [
                "tile.to_numpy_array().shape"
            ],
            "outputs": [
                {
                    "output_type": "execute_result",
                    "data": {
                        "text/plain": [
                            "(2, 2, 1, 2048, 2048)"
                        ]
                    },
                    "metadata": {},
                    "execution_count": 33
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "## Creating a peak finder"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 34,
            "source": [
                "peak_finder = PeakFinderDifferenceOfGaussian()"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "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"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 35,
            "source": [
                "peak_finder.show_channel_peaks(tile.get_round(0), 0, img_show=False)"
            ],
            "outputs": [
                {
                    "output_type": "execute_result",
                    "data": {
                        "text/plain": [
                            "<PIL.Image.Image image mode=RGB size=2048x2048 at 0x7FD002324F70>"
                        ],
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                    },
                    "metadata": {},
                    "execution_count": 35
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "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."
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 36,
            "source": [
                "\n",
                "registration = CombinedRegistration(\n",
                "    AllChannelRoundRegistration(registration_function=IcpRegistrationFunction(300),peak_finder=peak_finder), \n",
                "    ChannelRegistration(registration_function=IcpRegistrationFunction(150), peak_finder=peak_finder))\n",
                "registration.do_registration_and_transform(tile)"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "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"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 37,
            "source": [
                "peak_finder.show_channel_peaks(tile.get_round(0), 0, img_show=False)"
            ],
            "outputs": [
                {
                    "output_type": "execute_result",
                    "data": {
                        "text/plain": [
                            "<PIL.Image.Image image mode=RGB size=2048x2048 at 0x7FD00CF4AA60>"
                        ],
                        "image/png": 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                    },
                    "metadata": {},
                    "execution_count": 37
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 38,
            "source": [
                "peak_finder.show_channel_peaks(tile.get_round(0), 1, img_show=False)"
            ],
            "outputs": [
                {
                    "output_type": "execute_result",
                    "data": {
                        "text/plain": [
                            "<PIL.Image.Image image mode=RGB size=2048x2048 at 0x7FD00227B5B0>"
                        ],
                        "image/png": 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                    },
                    "metadata": {},
                    "execution_count": 38
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 39,
            "source": [
                "peak_finder.show_channel_peaks(tile.get_round(1), 0, img_show=False)"
            ],
            "outputs": [
                {
                    "output_type": "execute_result",
                    "data": {
                        "text/plain": [
                            "<PIL.Image.Image image mode=RGB size=2048x2048 at 0x7FD002333370>"
                        ],
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                    },
                    "metadata": {},
                    "execution_count": 39
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 40,
            "source": [
                "peak_finder.scatterplot_channel_peaks(tile.get_round(0), 0, 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), 1, 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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"
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