{ "cells": [ { "cell_type": "markdown", "metadata": { "scrolled": true }, "source": [ "# Multi-grid window deformation algorithm tutorial" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# import packages" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "from openpiv import windef # <---- see windef.py for details\n", "from openpiv import tools, scaling, validation, filters, preprocess\n", "import openpiv.pyprocess as process\n", "from openpiv import pyprocess\n", "import numpy as np\n", "import pathlib\n", "import importlib_resources\n", "from time import time\n", "import warnings\n", "\n", "\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set up all the settings: \n", "\n", "1. where the images are\n", "2. where to save the results\n", "3. names of the image files\n", "4. what is the region of interest\n", "5. do you apply dynamic masking or a masking image\n", "6. what kind of correlation to apply: circular vs linear \n", "7. interrogation window sizes, overlap sizes, number of iterations\n", "8. time interval, interpolation options, etc.\n", "\n", "Read the tutorial by Theo Kaufer with all the details. See `windef.py` for more code details" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "settings = windef.PIVSettings()\n", "\n", "path = importlib_resources.files('openpiv')\n", "\n", "'Data related settings'\n", "# Folder with the images to process\n", "settings.filepath_images = path / 'data' / 'test1' # type: ignore\n", "# Folder for the outputs\n", "settings.save_path = path / 'data' / 'test1' # type: ignore\n", "# Root name of the output Folder for Result Files\n", "settings.save_folder_suffix = 'Test_1'\n", "# Format and Image Sequence (see below for more options)\n", "settings.frame_pattern_a = 'exp1_001_a.bmp'\n", "settings.frame_pattern_b = 'exp1_001_b.bmp'\n", "\n", "# or if you have a sequence:\n", "# settings.frame_pattern_a = '000*.tif'\n", "# settings.frame_pattern_b = '(1+2),(2+3)'\n", "# settings.frame_pattern_b = '(1+3),(2+4)'\n", "# settings.frame_pattern_b = '(1+2),(3+4)'\n", "\n", "'Region of interest'\n", "# (50,300,50,300) #Region of interest: (xmin,xmax,ymin,ymax) or 'full' for full image\n", "settings.roi = 'full'\n", "\n", "'Image preprocessing'\n", "# 'None' for no masking, 'edges' for edges masking, 'intensity' for intensity masking\n", "# WARNING: This part is under development so better not to use MASKS\n", "settings.dynamic_masking_method = 'None'\n", "settings.dynamic_masking_threshold = 0.005\n", "settings.dynamic_masking_filter_size = 7\n", "\n", "settings.deformation_method = 'symmetric'\n", "\n", "'Processing Parameters'\n", "settings.correlation_method='circular' # 'circular' or 'linear'\n", "settings.normalized_correlation=False\n", "\n", "settings.num_iterations = 2 # select the number of PIV passes\n", "# add the interroagtion window size for each pass. \n", "# For the moment, it should be a power of 2 \n", "settings.windowsizes = (64, 32, 16) # if longer than n iteration the rest is ignored\n", "# The overlap of the interroagtion window for each pass.\n", "settings.overlap = (32, 16, 8) # This is 50% overlap\n", "# Has to be a value with base two. In general window size/2 is a good choice.\n", "# methode used for subpixel interpolation: 'gaussian','centroid','parabolic'\n", "settings.subpixel_method = 'gaussian'\n", "# order of the image interpolation for the window deformation\n", "settings.interpolation_order = 3\n", "settings.scaling_factor = 1 # scaling factor pixel/meter\n", "settings.dt = 1 # time between to frames (in seconds)\n", "'Signal to noise ratio options (only for the last pass)'\n", "# It is possible to decide if the S/N should be computed (for the last pass) or not\n", "# settings.extract_sig2noise = True # 'True' or 'False' (only for the last pass)\n", "# method used to calculate the signal to noise ratio 'peak2peak' or 'peak2mean'\n", "settings.sig2noise_method = 'peak2peak'\n", "# select the width of the masked to masked out pixels next to the main peak\n", "settings.sig2noise_mask = 2\n", "# If extract_sig2noise==False the values in the signal to noise ratio\n", "# output column are set to NaN\n", "'vector validation options'\n", "# choose if you want to do validation of the first pass: True or False\n", "settings.validation_first_pass = True\n", "# only effecting the first pass of the interrogation the following passes\n", "# in the multipass will be validated\n", "'Validation Parameters'\n", "# The validation is done at each iteration based on three filters.\n", "# The first filter is based on the min/max ranges. Observe that these values are defined in\n", "# terms of minimum and maximum displacement in pixel/frames.\n", "settings.min_max_u_disp = (-30, 30)\n", "settings.min_max_v_disp = (-30, 30)\n", "# The second filter is based on the global STD threshold\n", "settings.std_threshold = 7 # threshold of the std validation\n", "# The third filter is the median test (not normalized at the moment)\n", "settings.median_threshold = 3 # threshold of the median validation\n", "# On the last iteration, an additional validation can be done based on the S/N.\n", "settings.median_size=1 #defines the size of the local median\n", "'Validation based on the signal to noise ratio'\n", "# Note: only available when extract_sig2noise==True and only for the last\n", "# pass of the interrogation\n", "# Enable the signal to noise ratio validation. Options: True or False\n", "# settings.do_sig2noise_validation = False # This is time consuming\n", "# minmum signal to noise ratio that is need for a valid vector\n", "settings.sig2noise_threshold = 1.2\n", "'Outlier replacement or Smoothing options'\n", "# Replacment options for vectors which are masked as invalid by the validation\n", "settings.replace_vectors = True # Enable the replacment. Chosse: True or False\n", "settings.smoothn=True #Enables smoothing of the displacemenet field\n", "settings.smoothn_p=0.5 # This is a smoothing parameter\n", "# select a method to replace the outliers: 'localmean', 'disk', 'distance'\n", "settings.filter_method = 'localmean'\n", "# maximum iterations performed to replace the outliers\n", "settings.max_filter_iteration = 4\n", "settings.filter_kernel_size = 2 # kernel size for the localmean method\n", "'Output options'\n", "# Select if you want to save the plotted vectorfield: True or False\n", "settings.save_plot = False\n", "# Choose wether you want to see the vectorfield or not :True or False\n", "settings.show_plot = True\n", "settings.scale_plot = 200 # select a value to scale the quiver plot of the vectorfield\n", "# run the script with the given settings" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run the `windef.py` function, called `piv` with these settings" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/user/Documents/repos/openpiv-python/openpiv/data/test1\n", "exp1_001_a.bmp\n", "True\n", "[PosixPath('/home/user/Documents/repos/openpiv-python/openpiv/data/test1/exp1_001_a.bmp')]\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Image Pair 1\n", "exp1_001_a exp1_001_b\n" ] } ], "source": [ "windef.piv(settings)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run the extended search area PIV for comparison" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "\n", "# we can run it from any folder\n", "path = settings.filepath_images\n", "\n", "\n", "frame_a = tools.imread( path / settings.frame_pattern_a)\n", "frame_b = tools.imread( path / settings.frame_pattern_b)\n", "\n", "frame_a = (frame_a).astype(np.int32)\n", "frame_b = (frame_b).astype(np.int32)\n", "\n", "u, v, sig2noise = process.extended_search_area_piv( frame_a, frame_b, \\\n", " window_size=32, overlap=16, dt=1, search_area_size=64, sig2noise_method='peak2peak' )\n", "x, y = process.get_coordinates( image_size=frame_a.shape, \n", " search_area_size=64, overlap=16 )\n", "mask_s2n = validation.sig2noise_val(sig2noise, threshold = 1.3 )\n", "mask_g = validation.global_val( u, v, (-1000, 2000), (-1000, 1000) )\n", "invalid = mask_s2n | mask_g\n", "u, v = filters.replace_outliers( u, v, invalid, method='localmean', \n", " max_iter=10, kernel_size=2)\n", "x, y, u, v = scaling.uniform(x, y, u, v, scaling_factor = 1)\n", "x, y, u, v = tools.transform_coordinates(x, y, u, v)\n", "tools.save(x, y, u, v, invalid, 'test1.vec')\n", "tools.display_vector_field('test1.vec', scale=75, width=0.0035);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Options for creating lists of images" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Options: \n", " \n", " settings.pattern_a = 'image_*_a.bmp'\n", " settings.pattern_b = 'image_*_b.bmp'\n", "\n", " or\n", " settings.pattern_a = '000*.tif'\n", " settings.pattern_b = '(1+2),(2+3)'\n", " \n", " will create PIV of these pairs: 0001.tif+0002.tif, 0002.tif+0003.tif ...\n", "\n", " settings.pattern_a = '000*.tif'\n", " settings.pattern_b = '(1+3),(2+4)'\n", " \n", " will create PIV of these pairs: 0001.tif+0003.tif, 0002.tif+0004.tif ...\n", " or\n", " settings.pattern_a = '000*.tif'\n", " settings.pattern_b = '(1+2),(3+4)'\n", " \n", " will create PIV of these pairs: 0001.tif+0002.tif, 0003.tif+0004.tif ... \n", " " ] } ], "metadata": { "jupytext": { "formats": "ipynb,py:percent" }, "kernelspec": { "display_name": "Python 3.8.12 ('echopiv')", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.12" }, "vscode": { "interpreter": { "hash": "f83b0c3a4910470a1212112b1707d582432916ed4ba8aec962241a050aa18fae" } } }, "nbformat": 4, "nbformat_minor": 4 }