{ "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.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": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "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": 6, "metadata": {}, "outputs": [], "source": [ "#Run the extended search area PIV" ] }, { "cell_type": "code", "execution_count": 7, "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": 7, "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, sig2noise, mask, 'test1.vec' )\n", "tools.display_vector_field('test1.vec', scale=75, width=0.0035)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "#Run the widim" ] }, { "cell_type": "code", "execution_count": 9, "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 : ', 0.9999999905264141)\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 : ', 0.07445313997296803)\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 : ', 0.1063039288481169)\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 ', -47.938942193984985, '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": 9, "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), mark, min_window_size=16, overlap_ratio=0.0, coarse_factor=2, dt=1, validation_method='mean_velocity', 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", "tools.save(x, y, u, v, u*0, mask, '2image_00.txt' )\n", "\n", "tools.display_vector_field('2image_00.txt',widim=True,on_img=False, image_name=os.path.join(path,'../test2/2image_00.tif'), 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:windef] *", "language": "python", "name": "conda-env-windef-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 }