{ "cells": [ { "cell_type": "markdown", "metadata": { "scrolled": true }, "source": [ "# Multi-grid window deformation algorithm tutorial" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# import packages" ] }, { "cell_type": "code", "execution_count": 2, "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 os\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": 3, "metadata": {}, "outputs": [], "source": [ "settings = windef.Settings()\n", "\n", "\n", "'Data related settings'\n", "# Folder with the images to process\n", "settings.filepath_images = '../../examples/test1/'\n", "# Folder for the outputs\n", "settings.save_path = '../../../examples/test1/'\n", "# Root name of the output Folder for Result Files\n", "settings.save_folder_suffix = 'Test_1'\n", "# Format and Image Sequence\n", "settings.frame_pattern_a = 'exp1_001_a.bmp'\n", "settings.frame_pattern_b = 'exp1_001_b.bmp'\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.MinMax_U_disp = (-30, 30)\n", "settings.MinMax_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": 4, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "
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\n" }, "metadata": { "needs_background": "light" } }, { "output_type": "stream", "name": "stdout", "text": [ "Image Pair 1\nexp1_001_a.bmp exp1_001_b.bmp\n" ] } ], "source": [ "windef.piv(settings)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run the extended search area PIV for comparison" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "
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"image/png": 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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "\n", "# we can run it from any folder\n", "path = settings.filepath_images\n", "\n", "\n", "frame_a = tools.imread( os.path.join(path,settings.frame_pattern_a))\n", "frame_b = tools.imread( os.path.join(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", "u, v, mask = validation.sig2noise_val( u, v, sig2noise, threshold = 1.3 )\n", "u, v, mask = validation.global_val( u, v, (-1000, 2000), (-1000, 1000) )\n", "u, v = filters.replace_outliers( u, v, method='localmean', 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, mask, 'test1.vec' )\n", "tools.display_vector_field('test1.vec', scale=75, width=0.0035);" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "jupytext": { "formats": "ipynb,py:percent" }, "kernelspec": { "display_name": "Python 3", "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": "3.7.10" } }, "nbformat": 4, "nbformat_minor": 4 }