{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# use image mask" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from openpiv import tools, scaling, validation, filters, preprocess\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "from skimage import exposure\n", "from skimage import img_as_float, img_as_ubyte\n", "from scipy.ndimage import gaussian_filter, median_filter\n", "from skimage.filters import threshold_otsu\n", "from skimage.color import rgb2gray, rgba2rgb\n", "from skimage import io\n", "import os\n", "\n", "import matplotlib\n", "matplotlib.rcParams['figure.figsize'] = (8.0, 6.0)\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# test_directory = os.path.split(os.path.abspath(__file__))[0]\n", "test_directory = '../../test/'\n", "img = rgb2gray(rgba2rgb(io.imread(os.path.join(test_directory, \"moon.png\"))))\n", "img1, mask = preprocess.dynamic_masking(img_as_float(img), method=\"intensity\")\n", "mask_coords = preprocess.mask_coordinates(mask,1.5,3, plot=True)" ] } ], "metadata": { "jupytext": { "formats": "ipynb,py:percent" }, "kernelspec": { "display_name": "Python [conda env:openpiv] *", "language": "python", "name": "conda-env-openpiv-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 }