{ "cells": [ { "cell_type": "markdown", "id": "dca63629", "metadata": {}, "source": [ "# stackview\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/)." ] }, { "cell_type": "markdown", "id": "79a8abbc", "metadata": {}, "source": [ "## Usage\n", "You can use `stackview` from within jupyter notebooks as shown below.\n", "\n", "Starting point is a 3D image dataset provided as numpy array. " ] }, { "cell_type": "code", "execution_count": 1, "id": "ca154076", "metadata": {}, "outputs": [], "source": [ "import stackview\n", "\n", "import numpy as np\n", "from skimage.io import imread\n", "from skimage.filters import gaussian" ] }, { "cell_type": "code", "execution_count": 2, "id": "21067204", "metadata": {}, "outputs": [], "source": [ "image = imread('https://github.com/haesleinhuepf/stackview/blob/main/docs/data/Haase_MRT_tfl3d1.tif?raw=true', plugin='tifffile')" ] }, { "cell_type": "markdown", "id": "42963cad", "metadata": {}, "source": [ "## Slice\n", "You can then view it slice-by-slice:" ] }, { "cell_type": "code", "execution_count": 3, "id": "4cf3f9d3", "metadata": { "tags": [] }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "40335a6b237248d1a178187dbbd924db", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HBox(children=(VBox(children=(ImageWidget(height=160, width=160),)),)), IntSlider(value=60, des…" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stackview.slice(image, continuous_update=True)" ] }, { "cell_type": "markdown", "id": "39843d28-d31e-46a7-bc33-5cf3d8cabd6e", "metadata": {}, "source": [ "# Insight\n", "You can also get a static view using a maximum-intensity projection with additional information." ] }, { "cell_type": "code", "execution_count": 4, "id": "be919845-f8fc-4dd6-8b0b-6e729c9d0fc2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
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image" ] }, { "cell_type": "code", "execution_count": 9, "id": "ab1dc868", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "bb8a6f240a0940f491e2ae50cf9afcc0", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HBox(children=(VBox(children=(ImageWidget(height=160, width=160),)),)), IntSlider(value=60, des…" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stackview.curtain(image, modified_image, continuous_update=True)" ] }, { "cell_type": "markdown", "id": "dbd725c9-5867-4e47-8b7c-b7006fd2982b", "metadata": {}, "source": [ "One can also use the curtain to visualize semantic segmentation results as label images." ] }, { "cell_type": "code", "execution_count": 10, "id": "e421b46f-f738-4048-9a77-09dc54070715", "metadata": {}, "outputs": [], "source": [ "labels = (image > 5000)*1 + (image > 15000)*1 + (image > 30000)*1" ] }, { "cell_type": "code", "execution_count": 11, "id": "24f8a418-b261-4a6e-97de-025046326a13", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "bdcb9b1ed464484685f47cbb6f5a67b9", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HBox(children=(VBox(children=(ImageWidget(height=160, width=160),)),)), IntSlider(value=60, des…" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stackview.curtain(image, labels, continuous_update=True)" ] }, { "cell_type": "markdown", "id": "ffda520d-0451-499e-a5e7-08b6a1220d1e", "metadata": {}, "source": [ "The curtain can also be partially transparent using the alpha value." ] }, { "cell_type": "code", "execution_count": 12, "id": "91a89968-9f2c-48bb-abfa-d6e123dcf51d", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "64ca61e1875c49f68832b51610501abb", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HBox(children=(VBox(children=(ImageWidget(height=160, width=160),)),)), IntSlider(value=60, des…" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stackview.curtain(image, labels, continuous_update=True, alpha=0.3)" ] }, { "cell_type": "markdown", "id": "187cf539", "metadata": {}, "source": [ "The curtain also works with 2D data" ] }, { "cell_type": "code", "execution_count": 13, "id": "2f21ee1d", "metadata": {}, "outputs": [], "source": [ "slice_image = imread('https://github.com/haesleinhuepf/stackview/blob/main/docs/data/blobs.tif?raw=true', plugin='tifffile')" ] }, { "cell_type": "code", "execution_count": 14, "id": "bd7d1317", "metadata": {}, "outputs": [], "source": [ "from skimage.filters import threshold_otsu\n", "binary = (slice_image > threshold_otsu(slice_image))" ] }, { "cell_type": "code", "execution_count": 15, "id": "652721ec-6dfd-4518-9554-a3335ef27c6a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((254, 256), (254, 256))" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "binary.shape, slice_image.shape" ] }, { "cell_type": "code", "execution_count": 16, "id": "6b5dd31a", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "fcac65df800e44f481e32f410684c0ec", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HBox(children=(VBox(children=(ImageWidget(height=254, width=256),)),)), IntSlider(value=127, de…" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stackview.curtain(slice_image, binary, continuous_update=True)" ] }, { "cell_type": "markdown", "id": "c15807a9-2de5-45e0-93bd-e0e952da0fce", "metadata": {}, "source": [ "Also label images are supported. Images are shown as labels in case their pixel type is (unsigned) integer 32-bit or 32-bit." ] }, { "cell_type": "code", "execution_count": 17, "id": "f39bacd7-6ca3-4c28-bb4f-25d2251b0c9a", "metadata": {}, "outputs": [], "source": [ "from skimage.measure import label\n", "labels = label(binary)" ] }, { "cell_type": "code", "execution_count": 18, "id": "1ad031f5-ab80-473c-95c5-12bd08256d56", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3c6375367b6a4727aa04fcef47080132", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HBox(children=(VBox(children=(ImageWidget(height=254, width=256),)),)), IntSlider(value=127, de…" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stackview.curtain(slice_image, labels, continuous_update=True)" ] }, { "cell_type": "markdown", "id": "4767b2f9", "metadata": {}, "source": [ "## Side-by-side\n", "A side-by-side view of two stacks is also available. It might be useful for colocalization visualization or showing subsequent time points of a timelapse." ] }, { "cell_type": "code", "execution_count": 19, "id": "7bf91429", "metadata": {}, "outputs": [], "source": [ "image_stack = imread('https://github.com/haesleinhuepf/stackview/blob/main/docs/data/CalibZAPWfixed_000154_max.tif?raw=true', plugin='tifffile').swapaxes(1,2)" ] }, { "cell_type": "code", "execution_count": 20, "id": "5ad9ebf9", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "71d09d70b40f4edf96251bab38a39a50", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HBox(children=(HBox(children=(VBox(children=(ImageWidget(height=235, width=389),)),)), HBox(chi…" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stackview.side_by_side(image_stack[1:], image_stack[:-1], continuous_update=True, display_width=300)" ] }, { "cell_type": "code", "execution_count": 21, "id": "97a16fec-21a0-4aa6-8dee-660ce345238e", "metadata": {}, "outputs": [], "source": [ "labels_stack = np.asarray([label(image > 100) for image in image_stack])" ] }, { "cell_type": "code", "execution_count": 22, "id": "b948942f-b1c1-474f-a8cb-cf3f104b65bf", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2d6fd9d7c7f346bd9687a2d6c547b3f0", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HBox(children=(HBox(children=(VBox(children=(ImageWidget(height=389, width=235),)),)), HBox(chi…" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stackview.side_by_side(image_stack, labels_stack, continuous_update=True, display_width=300)" ] }, { "cell_type": "markdown", "id": "18ea8070", "metadata": {}, "source": [ "## Interact\n", "You can also use `interact` to explore parameters of some supported functions that process images, e.g. from [scikit-image](https://scikit-image.org/):" ] }, { "cell_type": "code", "execution_count": 23, "id": "41b75598", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8ad760ab5efd4a69ba222323154776e2", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(interactive(children=(IntSlider(value=0, continuous_update=False, description='footprint'), Out…" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from skimage.filters.rank import maximum\n", "stackview.interact(maximum, slice_image)" ] }, { "cell_type": "code", "execution_count": 24, "id": "ba9bfc67", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a95225c482584dddb1da6ca9d8f1efe7", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(interactive(children=(FloatSlider(value=1.0, continuous_update=False, description='sigma', max=…" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from skimage.filters import gaussian\n", "stackview.interact(gaussian, slice_image)" ] }, { "cell_type": "markdown", "id": "47751ce0", "metadata": {}, "source": [ "This might be interesting for custom functions executing image processing workflows." ] }, { "cell_type": "code", "execution_count": 25, "id": "11f6e31e", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6490789da4b44fcc89793dc0ef11843f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(interactive(children=(FloatSlider(value=1.0, description='sigma', max=25.0, step=1.0), Checkbox…" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from skimage.filters import gaussian, threshold_otsu, sobel\n", "def my_custom_code(image, sigma:float = 1, show_labels: bool = True):\n", " sigma = abs(sigma)\n", " blurred_image = gaussian(image, sigma=sigma)\n", " binary_image = blurred_image > threshold_otsu(blurred_image)\n", " edge_image = sobel(binary_image)\n", " \n", " if show_labels:\n", " return label(binary_image)\n", " else:\n", " return edge_image * 255 + image \n", "\n", "stackview.interact(my_custom_code, slice_image, continuous_update=True)" ] }, { "cell_type": "markdown", "id": "51ef6a3f", "metadata": {}, "source": [ "If you want to configure the range of a slider explicitly, you need to hand over the [ipywidgets slider](https://ipywidgets.readthedocs.io/en/latest/examples/Widget%20List.html#FloatSlider) as default value:" ] }, { "cell_type": "code", "execution_count": 26, "id": "8abc8e06", "metadata": { "tags": [] }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c8f6010577574d8fbefeec23a2f9c983", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(interactive(children=(FloatSlider(value=15.0, description='sigma'), Output()), _dom_classes=('w…" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from skimage.filters import gaussian\n", "from ipywidgets import FloatSlider\n", "stackview.interact(gaussian, slice_image, sigma=FloatSlider(min=0, max=100, value=15), continuous_update=True)" ] }, { "cell_type": "markdown", "id": "a4e4c0b2-04e1-48bf-a1a9-70480fbece99", "metadata": {}, "source": [ "## Assist\n", "The `stackview.assist()` function can guide you through all imported (and supported) image processing functions." ] }, { "cell_type": "code", "execution_count": 27, "id": "f2cfc7b6-c868-4e40-8e60-2f9cbde70b50", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d93b936ce41d48809e4a62359375fa54", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(Text(value='', description='Search', placeholder='Type here to search'), Dropdown(description='…" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stackview.assist(context=globals(), continuous_update=True)" ] }, { "cell_type": "markdown", "id": "077b7106", "metadata": {}, "source": [ "## Some more tests with silly images" ] }, { "cell_type": "code", "execution_count": 28, "id": "9cd2f2e5", "metadata": { "tags": [] }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "af21156770e649ab947b5fc4021255fa", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HBox(children=(VBox(children=(ImageWidget(height=100, width=100),)),)), IntSlider(value=50, con…" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "silly_image = np.zeros((100, 100))\n", "silly_image[:,50:] = 1\n", "silly_image[50:] = silly_image[50:] + 2\n", "\n", "stackview.picker(silly_image.astype(np.uint32))" ] }, { "cell_type": "code", "execution_count": 29, "id": "c1c350b5", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1e031cfb76544b66b58c75bb78ba6c7b", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HBox(children=(VBox(children=(ImageWidget(height=60, width=60),)),)), IntSlider(value=1, contin…" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "silly_image = np.zeros((3, 3))\n", "silly_image[:,1:] = 1\n", "silly_image[1:] = silly_image[1:] + 2\n", "\n", "stackview.picker(silly_image.astype(np.uint32), zoom_factor=20)" ] }, { "cell_type": "code", "execution_count": null, "id": "d8a7a29d-0fe9-445b-99f7-41974cf61ea9", "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", 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