{ "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", "from openpiv import widim\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": [ { "name": "stdout", "output_type": "stream", "text": [ "global filter invalidated 0 vectors\n", "std filter invalidated 65 vectors\n", "median filter invalidated 0 vectors\n", "s2n filter invalidated 2 vectors\n", "global filter invalidated 0 vectors\n", "std filter invalidated 131 vectors\n", "median filter invalidated 0 vectors\n", "s2n filter invalidated 33 vectors\n" ] }, { "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" ] } ], "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" }, { "data": { "text/plain": [ "(
, )" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "\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", "tools.save(x, y, u, v, mask, '2image_00.txt' )\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": 7, "metadata": {}, "outputs": [], "source": [ "#Run the widim" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "lines_to_next_cell": 2, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "----------------------------------------------------------\n", "|-----> || The Open Source P article |\n", "| Open || I mage |\n", "| PIV || V elocimetry Toolbox |\n", "| <-----|| www.openpiv.net version 1.0 |\n", "----------------------------------------------------------\n", " \n", "('Algorithm : ', 'WiDIM')\n", " \n", "Parameters \n", "-----------------------------------\n", "(' ', 'Size of image', ' | ', [369, 511])\n", "(' ', 'total number of iterations', ' | ', 3)\n", "(' ', 'overlap ratio', ' | ', 0.0)\n", "(' ', 'coarse factor', ' | ', 2)\n", "(' ', 'time step', ' | ', 1.0)\n", "(' ', 'validation method', ' | ', 'mean_velocity')\n", "(' ', 'number of validation iterations', ' | ', 1)\n", "(' ', 'subpixel_method', ' | ', 'gaussian')\n", "(' ', 'Nrow', ' | ', array([ 5, 11, 23], dtype=int32))\n", "(' ', 'Ncol', ' | ', array([ 7, 15, 31], dtype=int32))\n", "(' ', 'Window sizes', ' | ', array([64, 32, 16], dtype=int32))\n", "-----------------------------------\n", "| STARTING |\n", "-----------------------------------\n", "('ITERATION # ', 0)\n", "..[DONE]\n", "(' --residual : ', 0.9999999615970089)\n", "no validation : trusting 1st iteration\n", "going to next iteration.. \n", "performing interpolation of the displacement field\n", " \n", "('..[DONE] -----> going to iteration ', 1)\n", " \n", "('ITERATION # ', 1)\n", "..[DONE]\n", "(' --residual : ', 0.0953833601767332)\n", "Starting validation..\n", "('Validation, iteration number ', 0)\n", " \n", "..[DONE]\n", " \n", "going to next iteration.. \n", "performing interpolation of the displacement field\n", " \n", "('..[DONE] -----> going to iteration ', 2)\n", " \n", "('ITERATION # ', 2)\n", "..[DONE]\n", "(' --residual : ', 0.2629027450643792)\n", "Starting validation..\n", "('Validation, iteration number ', 0)\n", " \n", "..[DONE]\n", " \n", "//////////////////////////////////////////////////////////////////\n", "end of iterative process.. Re-arranging vector fields..\n", "...[DONE]\n", "-------------------------------------------------------------\n", "('[DONE] ..after ', 58.270469188690186, 'seconds ')\n", "-------------------------------------------------------------\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "(
, )" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scaling_factor = 1\n", "\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", "#no background removal will be performed so 'mark' is initialized to 1 everywhere\n", "mark = np.zeros(frame_a.shape, dtype=np.int32)\n", "for I in range(mark.shape[0]):\n", " for J in range(mark.shape[1]):\n", " mark[I,J]=1\n", "\n", "#main algorithm\n", "with warnings.catch_warnings():\n", " warnings.simplefilter(\"ignore\")\n", " x,y,u,v, mask=widim.WiDIM( frame_a.astype(np.int32), frame_b.astype(np.int32), \n", " mark, min_window_size=16, overlap_ratio=0.0, \n", " coarse_factor=2, dt=1, validation_method='mean_velocity', \n", " trust_1st_iter=1, validation_iter=1, tolerance=0.7, nb_iter_max=3, sig2noise_method='peak2peak')\n", "\n", "#display results\n", "x, y, u, v = scaling.uniform(x, y, u, v, scaling_factor = scaling_factor )\n", "\n", "x, y, u, v = tools.transform_coordinates(x, y, u, v)\n", "\n", "tools.save(x, y, u, v, mask, '2image_00.txt' )\n", "\n", "tools.display_vector_field('2image_00.txt',widim=True,on_img=False, \n", " image_name=os.path.join(path,'../test2/2image_00.tif'), \n", " window_size=16, scaling_factor=scaling_factor, scale=200, width=0.001)\n", "\n", "#further validation can be performed to eliminate the few remaining wrong vectors" ] }, { "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 }