diff --git a/docs/colab_clesperanto_demo.ipynb b/docs/colab_clesperanto_demo.ipynb index 029935d..e090c1b 100644 --- a/docs/colab_clesperanto_demo.ipynb +++ b/docs/colab_clesperanto_demo.ipynb @@ -1,3494 +1,3422 @@ { - "cells": [ + "cells": [ + { + "cell_type": "markdown", + "id": "dca63629", + "metadata": { + "id": "dca63629" + }, + "source": [ + "# stackview + pyclesperanto_prototype demo\n", + "Interactive image stack viewing in jupyter notebooks based on \n", + "[ipycanvas](https://ipycanvas.readthedocs.io/) and \n", + "[ipywidgets](https://ipywidgets.readthedocs.io/en/latest/). \n", + "\n", + "In this notebook we demonstate its usage on google colab in combination with [pyclesperanto_prototype](https://github.com/clEsperanto/pyclesperanto_prototype).\n", + "\n", + "[Run this notebook in google colab](https://colab.research.google.com/github/haesleinhuepf/stackview/blob/master/docs/colab_clesperanto_demo.ipynb)\n", + "\n", + "## Installation\n", + "After starting a new session on google colab, you need to install stackview and pyclesperanto-prototype first. After this, you need to restart the kernel. Use the opportunity to click the menu `Runtime > Change runtime type` and activate the GPU.\n", + "\n", + "Also note: The make stackview work in google colab, you need to install ipycanvas==0.11 ([read why](https://github.com/martinRenou/ipycanvas/issues/170#issuecomment-1119200238))." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "fbe9e100-c5cc-4d59-b105-cd5ec72e041d", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "fbe9e100-c5cc-4d59-b105-cd5ec72e041d", + "outputId": "59002da2-e417-4039-c68e-7c3fe939a139" + }, + "outputs": [ { - "cell_type": "markdown", - "id": "dca63629", - "metadata": { - "id": "dca63629" - }, - "source": [ - "# stackview + pyclesperanto_prototype demo\n", - "Interactive image stack viewing in jupyter notebooks based on \n", - "[ipycanvas](https://ipycanvas.readthedocs.io/) and \n", - "[ipywidgets](https://ipywidgets.readthedocs.io/en/latest/). \n", - "\n", - "In this notebook we demonstate its usage on google colab in combination with [pyclesperanto_prototype](https://github.com/clEsperanto/pyclesperanto_prototype).\n", - "\n", - "[Run this notebook in google colab](https://colab.research.google.com/github/haesleinhuepf/stackview/blob/master/docs/colab_clesperanto_demo.ipynb)\n", - "\n", - "## Installation\n", - "After starting a new session on google colab, you need to install stackview and pyclesperanto-prototype first. After this, you need to restart the kernel. Use the opportunity to click the menu `Runtime > Change runtime type` and activate the GPU.\n", - "\n", - "Also note: The make stackview work in google colab, you need to install ipycanvas==0.11 ([read why](https://github.com/martinRenou/ipycanvas/issues/170#issuecomment-1119200238))." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: pyclesperanto_prototype in c:\\users\\rober\\appdata\\local\\mambaforge\\envs\\bio39\\lib\\site-packages (0.24.0)\n", + "Requirement already satisfied: stackview in c:\\structure\\code\\stackview (0.6.1)\n", + "Requirement already satisfied: ipycanvas==0.11 in c:\\users\\rober\\appdata\\local\\mambaforge\\envs\\bio39\\lib\\site-packages (0.11.0)\n", + "Requirement already satisfied: numpy in c:\\users\\rober\\appdata\\local\\mambaforge\\envs\\bio39\\lib\\site-packages (from ipycanvas==0.11) (1.23.5)\n", + "Requirement already satisfied: ipywidgets>=7.6.0 in 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installation by importing the libraries we're going to use" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ca154076", + "metadata": { + "id": "ca154076", + "tags": [] + }, + "outputs": [], + "source": [ + "import stackview\n", + "import pyclesperanto_prototype as cle\n", + "from skimage.io import imread" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2ed3eebe-57f2-41aa-9060-48971bde8383", + "metadata": { + "id": "2ed3eebe-57f2-41aa-9060-48971bde8383" + }, + "outputs": [], + "source": [ + "# Activate widgets in google colab\n", + "from google.colab import output\n", + "output.enable_custom_widget_manager()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "239264a4-3a84-4781-8676-6e72cf29c18a", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "239264a4-3a84-4781-8676-6e72cf29c18a", + "outputId": "01717ecd-37d7-479e-82cb-b13f8b14abfe", + "tags": [] + }, + "outputs": [ { - "cell_type": "code", - 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typing-extensions>=4.0 in /usr/local/lib/python3.8/dist-packages (from pytools>=2021.2.7->pyopencl->pyclesperanto_prototype) (4.4.0)\n" - ] - } - ], - "source": [ - "!pip install pyclesperanto_prototype stackview ipycanvas==0.11" + "data": { + "text/plain": [ + "" ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check which GPU we are using\n", + "cle.select_device(\"cupy\")\n", + "\n", + "# why cupy? https://github.com/clEsperanto/pyclesperanto_prototype/issues/300" + ] + }, + { + "cell_type": "markdown", + "id": "b9f91fcf-243b-4b8d-af0b-29c4649907c4", + "metadata": { + "id": "b9f91fcf-243b-4b8d-af0b-29c4649907c4" + }, + "source": [ + "Starting point is a 3D image dataset provided as numpy array. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "2f21ee1d", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "2f21ee1d", + "outputId": "44cfa27f-c696-426c-91e6-a5f7e5ff238c", + "tags": [] + }, + "outputs": [ { - "cell_type": "markdown", - "id": "05f97657-a431-4687-a771-3cd70c4690cf", - "metadata": { - "id": "05f97657-a431-4687-a771-3cd70c4690cf" - }, - "source": [ - "Again, before going ahead, don't forget to activate the GPU runtime:\n", - "\n", - "" + "data": { + "text/plain": [ + "(100, 256, 256)" ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "image_stack = imread('https://github.com/clEsperanto/clesperanto_example_data/raw/main/Lund_000500_resampled-cropped.tif?raw=true', plugin='tifffile')\n", + "\n", + "image_stack.shape" + ] + }, + { + "cell_type": "markdown", + "id": "684f3268-6388-40fa-b633-ca65e4763e57", + "metadata": { + "id": "684f3268-6388-40fa-b633-ca65e4763e57" + }, + "source": [ + "`cle`-images are visualized in jupyer / colab notebooks with a static view. In case the dataset is 3D, we see a maximum-intensity projection." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "cd9be4e3-89b1-4105-9b66-54af398b4811", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 315 }, + "id": "cd9be4e3-89b1-4105-9b66-54af398b4811", + "outputId": "b872f1bf-6bae-4bb0-d15b-4941dd534ab6", + "tags": [] + }, + "outputs": [ { - "cell_type": "markdown", - "id": "79a8abbc", - "metadata": { - "id": "79a8abbc" - }, - "source": [ - "## Usage\n", - "First we test the installation by importing the libraries we're going to use" + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "\n", + "\n", + "cle._ image
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shape(100, 256, 256)
dtypefloat32
size25.0 MB
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" + ], + "text/plain": [ + "cl.OCLArray([[[144., 142., 145., ..., 140., 138., 137.],\n", + " [145., 142., 143., ..., 141., 139., 138.],\n", + " [142., 139., 141., ..., 140., 139., 142.],\n", + " ...,\n", + " [256., 252., 260., ..., 308., 287., 273.],\n", + " [253., 249., 253., ..., 319., 298., 278.],\n", + " [256., 255., 253., ..., 322., 296., 273.]],\n", + "\n", + " [[143., 140., 145., ..., 139., 139., 137.],\n", + " [145., 139., 144., ..., 140., 140., 138.],\n", + " [143., 140., 142., ..., 141., 140., 141.],\n", + " ...,\n", + " [255., 255., 263., ..., 314., 292., 274.],\n", + " [255., 250., 251., ..., 328., 302., 277.],\n", + " [257., 253., 250., ..., 329., 298., 272.]],\n", + "\n", + " [[143., 139., 145., ..., 139., 139., 137.],\n", + " [145., 138., 145., ..., 139., 141., 137.],\n", + " [143., 140., 142., ..., 142., 141., 141.],\n", + " ...,\n", + " [254., 257., 265., ..., 316., 293., 272.],\n", + " [255., 251., 250., ..., 330., 302., 274.],\n", + " [257., 251., 248., ..., 332., 298., 270.]],\n", + "\n", + " ...,\n", + "\n", + " [[137., 138., 140., ..., 128., 127., 129.],\n", + " [140., 140., 137., ..., 131., 127., 129.],\n", + " [140., 138., 138., ..., 129., 130., 133.],\n", + " ...,\n", + " [177., 182., 181., ..., 160., 161., 161.],\n", + " [179., 185., 186., ..., 162., 157., 158.],\n", + " [181., 179., 184., ..., 158., 157., 160.]],\n", + "\n", + " [[136., 138., 141., ..., 127., 127., 129.],\n", + " [139., 139., 136., ..., 131., 127., 129.],\n", + " [140., 137., 138., ..., 127., 128., 134.],\n", + " ...,\n", + " [175., 179., 179., ..., 158., 159., 159.],\n", + " [177., 182., 184., ..., 162., 155., 158.],\n", + " [180., 174., 180., ..., 158., 157., 158.]],\n", + "\n", + " [[136., 136., 139., ..., 129., 129., 129.],\n", + " [137., 137., 135., ..., 130., 128., 128.],\n", + " [138., 135., 136., ..., 128., 130., 132.],\n", + " ...,\n", + " [174., 176., 177., ..., 159., 157., 157.],\n", + " [179., 180., 180., ..., 160., 154., 156.],\n", + " [178., 174., 177., ..., 158., 156., 157.]]], dtype=float32)" ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "image_stack_on_gpu = cle.asarray(image_stack)\n", + "\n", + "image_stack_on_gpu" + ] + }, + { + "cell_type": "markdown", + "id": "b011a13c-b3d8-4ef7-93fa-92f5ce287d62", + "metadata": { + "id": "b011a13c-b3d8-4ef7-93fa-92f5ce287d62" + }, + "source": [ + "## Slicing\n", + "\n", + "We can explore the dataset interactively using `stackview.slice`." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d4507842-3fe6-47fc-acc9-470507d0c2bd", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 305, + "referenced_widgets": [ + "c56698719cc34f9d8d4a7109a7b64112", + "80ae85c88de94945a5e19996eae42dda", + "b768319da30e4a4bbf4a8cd78dc392e5", + "71b480a9936248e28596f7bfcc9dbc02", + "cae867abe1f74885915cbc52ce00d200", + "691299e7cbd740c384595be4367804ad", + "d8ffbb778bb346ce95893a30fed31ba1", + "98a62ec2851c4b2081fb1b61822bbe90", + "6c1c3447b23046e3b7cb40f24c42bab1", + "ced7f4df0941417bac296fe5707fe258", + "4926a20e8b8247bca6890419b16bb6bb" + ] }, + "id": "d4507842-3fe6-47fc-acc9-470507d0c2bd", + "outputId": "5c9f3296-d1df-4559-9cf1-f3d58f975dbe", + "tags": [] + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 2, - "id": "ca154076", - "metadata": { - "id": "ca154076" + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "584ed4f11e294f28964286303f1b24ce", + "version_major": 2, + "version_minor": 0 }, - "outputs": [], - "source": [ - "import stackview\n", - "import pyclesperanto_prototype as cle\n", - "from skimage.io import imread" + "text/plain": [ + "VBox(children=(HBox(children=(VBox(children=(ImageWidget(height=256, width=256),)),)), IntSlider(value=50, des…" ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stackview.slice(image_stack_on_gpu)" + ] + }, + { + "cell_type": "markdown", + "id": "7eb9cdaf-c4a9-45b6-a139-12fbe4849885", + "metadata": { + "id": "7eb9cdaf-c4a9-45b6-a139-12fbe4849885" + }, + "source": [ + "## Curtain\n", + "\n", + "To compare the original image with a modified version, e.g. after background subtraction, we can use `stackview.curtain`." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "bd7d1317", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 337, + "referenced_widgets": [ + "3b8e1fc2c79048a092b9032b8c026cf3", + "3a89d80ec1b64a1ea18a59157169fc26", + "3f5885857b5e41aebb7d7b4d84df0a68", + "044caf83b0b9450ca06d130c82e75950", + "6adf6a1a3408457b83131cb8c955926a", + "995aa057554c4433a6637d65c9163f2d", + "19928933f53d4331a33e696d427d5189", + "a38f2ab699b3494d956516121fcea351", + "e45ed595c3ee45799bbc1b316529dcef", + "6289691af877498090f96923dd332ca8", + "0144c23ff42346edba8edb48ab7b736d", + "61a04c1967ed462d99d3d969db59c2ec", + "295d863eb1934a0d9ec512ab764250e7", + "ae53d57c89304b6a9a53d0dff41e39f3" + ] }, + "id": "bd7d1317", + "outputId": "3c9e052c-03c2-4818-fe8f-945329eefaa8", + "tags": [] + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 3, - "id": "2ed3eebe-57f2-41aa-9060-48971bde8383", - "metadata": { - "id": "2ed3eebe-57f2-41aa-9060-48971bde8383" + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6d247ac1f39e4150b8afebbd49a3bd11", + "version_major": 2, + "version_minor": 0 }, - "outputs": [], - "source": [ - "# Activate widgets in google colab\n", - "from google.colab import output\n", - "output.enable_custom_widget_manager()" + "text/plain": [ + "VBox(children=(HBox(children=(VBox(children=(ImageWidget(height=256, width=256),)),)), IntSlider(value=50, des…" ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "background_subtracted = cle.top_hat_box(image_stack_on_gpu, radius_x=10, radius_y=10)\n", + "\n", + "stackview.curtain(image_stack_on_gpu, background_subtracted)" + ] + }, + { + "cell_type": "markdown", + "id": "18ea8070", + "metadata": { + "id": "18ea8070" + }, + "source": [ + "## Interaction\n", + "\n", + "You can also use `sliceview.interact` to explore parameters of functions interactively." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "41b75598", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 401, + "referenced_widgets": [ + "b547a0a99ba94659a652b3af31affe92", + "a435a3fd702f45e4b5bdd9ca39d5786c", + "0905b22a6b8e41059f3699bba30f2fd9", + "8d39d6eaac2543e09e37b9935ec72a5e", + "d8a95a295d6c48f793d1ee64aa528166", + "b017e6aa8d824a9794fbd3a63437527c", + "1215cd6bce894b869be7911fdf2e2c8a", + "f969bf073d374e75bf8b280b66d6b613", + "30404291b9e34f31a57e0a776ab3ff24", + "4b7440dda8394a36ae0dcfe1846c58f1", + "ec3d1e53059c4adf9d052786f53a8ff1", + "fa301e76262d4d6389adb593cf358bc6", + "ea319202201244a48ee68f40fc71ea62", + "8b364fa388a24caf859749269d7d3a1c", + "23eedaf3391044228ab57badba2a5575", + "8c6ba9386df9426d88e42099c8f62ae4", + "b21e098bfb824d5ea2ad25e9d036f464", + "6861e49fb13a48448e6cf51ff3c88203", + "5e6ad5424d3b45c2817317de4a92b451", + "23ff542de4a3485c99a4b0f79e64f1cd", + "1756abb80629419ba3df3fb7c4f42185", + "4553983e627d4b1998ac70fb365cce50", + "2ae02f76a6534f8089b6f379fa2606a6", + "5b865e0b9c694d9aadfc05fd7b520db3" + ] }, + "id": "41b75598", + "outputId": "aea39ea3-e93c-4f86-de39-1ec89f302048", + "tags": [] + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 4, - "id": "239264a4-3a84-4781-8676-6e72cf29c18a", - "metadata": { - "id": "239264a4-3a84-4781-8676-6e72cf29c18a", - "outputId": "01717ecd-37d7-479e-82cb-b13f8b14abfe", - "colab": { - "base_uri": "https://localhost:8080/" - } + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3689067bc01c44a1b09d78a81947376c", + "version_major": 2, + "version_minor": 0 }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 4 - } - ], - "source": [ - "# check which GPU we are using\n", - "cle.get_device()" + "text/plain": [ + "VBox(children=(interactive(children=(FloatSlider(value=2.0, description='spot_sigma', max=25.0, step=1.0), Flo…" ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stackview.interact(cle.voronoi_otsu_labeling, background_subtracted)" + ] + }, + { + "cell_type": "markdown", + "id": "c42efe8b-7633-4c90-801f-67e7d912b724", + "metadata": { + "id": "c42efe8b-7633-4c90-801f-67e7d912b724" + }, + "source": [ + "This may also make sense with custom functions. When writing those it is important to use type-annotations." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "11f6e31e", + "metadata": { + "id": "11f6e31e" + }, + "outputs": [], + "source": [ + "def my_custom_segmentation(image, background_subtraction_radius: int = 10, spot_sigma:float = 1, outline_sigma:float = 1, show_labels: bool = True):\n", + " background_subtraction_radius = abs(background_subtraction_radius)\n", + " spot_sigma = abs(spot_sigma)\n", + " outline_sigma = abs(outline_sigma)\n", + " \n", + " background_subtracted = cle.top_hat_box(image, radius_x=10, radius_y=10)\n", + "\n", + " label_image = cle.voronoi_otsu_labeling(background_subtracted, spot_sigma=spot_sigma, outline_sigma=outline_sigma)\n", + " \n", + " edge_image = cle.detect_label_edges(label_image)\n", + " \n", + " if show_labels:\n", + " return label_image\n", + " else:\n", + " return edge_image * 255 + image " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "91ea85ee-1609-4741-9ff3-acbd5c7e2139", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 465, + "referenced_widgets": [ + "d08345d0658e4ff48364596b9e8afa9d", + "060489bf926b43cda9068ab29224b9d9", + "66d01583a91b4dcb8114a2047d2e7291", + "37b7950fc4954dfc9435f396be320f3d", + "75d1045559c9493287d76b393bee7da6", + "f96da2c036624aacb15949b851dcf41a", + "95b30f58cd4647cfb3b03c9536193513", + "b70ff10797f54ec89bc0628c681be658", + "0639d19fe9ba4fd6b18a4a54fbc13f17", + "1a508ced64324464a5fb743d9a6bd7d0", + "6820fbf5d1b34381a864f3d8d3e11aa1", + "52b4e59bf7e645128b7165a4d1050431", + "c2aefaa9e1ab49658422efa5943f93fb", + "ddf4c8fd5df9461eb5fa114d3ae00de1", + "c2d21303cb9e47a88a98364fdf657921", + "ea2b40b9af724fd9a4c1137744e18af1", + "a79a62c8103540f495f61c7cc8159e79", + "8ff814a9dff346bcaeee6207db6c6a8a", + "5969c896dd674c8c80b588a500098563", + "3f2b8e660fda4e75ac4f6eae95275118", + "f9e8ffa9647f420a997d1200c92b7be6", + "6007f9ffb72941e3888b5d696806a320", + "82f68b51f9ee471eb3a5f59e76e339a6", + "37fe89f56c024ba99e7a91f8e6032a33", + "0b212dd5a07447be9cd16e067c6667ec", + "8634d605299649349b78341643c52df0", + "3828de0276df473eb3d06d96d5d36adc", + "ba1073581e9548f3b4161386021f2f7f", + "f576577b90714b3eb03d5ceb620079d8", + "205efac8579d46b5b8565759451e4d16" + ] }, + "id": "91ea85ee-1609-4741-9ff3-acbd5c7e2139", + "outputId": "7375386f-60e1-48de-f652-2804bc9f52c7" + }, + "outputs": [ { - "cell_type": "markdown", - "id": "b9f91fcf-243b-4b8d-af0b-29c4649907c4", - "metadata": { - "id": "b9f91fcf-243b-4b8d-af0b-29c4649907c4" + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7e923d8d55d44f88ac0ced6497a34a0f", + "version_major": 2, + "version_minor": 0 }, - "source": [ - "Starting point is a 3D image dataset provided as numpy array. " + "text/plain": [ + "VBox(children=(interactive(children=(IntSlider(value=10, description='background_subtraction_radius'), FloatSl…" ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stackview.interact(my_custom_segmentation, image_stack_on_gpu)" + ] + }, + { + "cell_type": "markdown", + "id": "45988939-881e-430b-876a-aa784ab4b35a", + "metadata": { + "id": "45988939-881e-430b-876a-aa784ab4b35a" + }, + "source": [ + "## Exercise\n", + "Load the blobs image and setup a custom segmentation workflow for segmenting the blobs. Use `stackview.interact` to tune the parameters for the segmentation ([spoiler](https://github.com/haesleinhuepf/stackview/blob/main/docs/demo.ipynb))." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "bd1beef7-97ff-4dda-9bc8-37b7d1aaece8", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 315 }, + "id": "bd1beef7-97ff-4dda-9bc8-37b7d1aaece8", + "outputId": "18428849-9049-427c-dd75-a74421165b72" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 5, - "id": "2f21ee1d", - "metadata": { - "id": "2f21ee1d", - "outputId": "44cfa27f-c696-426c-91e6-a5f7e5ff238c", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(100, 256, 256)" - ] - }, - "metadata": {}, - "execution_count": 5 - } + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "
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