{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "scrolled": true }, "outputs": [], "source": [ "#Run the new window deformation" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from openpiv import windef\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": "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 = '../test1/'\n", "# Folder for the outputs\n", "settings.save_path = '../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": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Image Pair 1\n", "exp1_001_a.bmp exp1_001_b.bmp\n" ] } ], "source": [ "windef.piv(settings)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "#Run the extended search area PIV" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "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( 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 [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 }