%reload_ext watermark %watermark -v -m -p numpy,openpiv,pivpy

from openpiv import windef from openpiv import tools, pyprocess, scaling, validation, filters, preprocess import numpy as np import os, glob from time import time import warnings from pivpy import io, pivpy, graphics import xarray as xr import pandas as pd import matplotlib.pyplot as plt %matplotlib inline import matplotlib matplotlib.rcParams['figure.figsize'] = (8.0, 6.0)

# parameter settings - the location of the root folder root_folder = '../data'

# base folder is the experimental name base_folder = '20210420-Run1'

from openpiv.tools import imread, Multiprocesser, display_vector_field, \ transform_coordinates from openpiv import validation, filters, tools, preprocess, scaling, tools from openpiv.pyprocess import extended_search_area_piv, get_coordinates, \ get_field_shape from openpiv import smoothn from skimage.measure import points_in_poly from skimage.util import invert from openpiv.windef import first_pass, multipass_img_deform

def piv_run(frame_a, frame_b, settings, counter=0): # "first pass" x, y, u, v, s2n = first_pass( frame_a, frame_b, settings ) if settings.show_all_plots: plt.figure() plt.quiver(x,y,u,-v,color='b') # plt.gca().invert_yaxis() # plt.gca().set_aspect(1.) # plt.title('after first pass, invert') # plt.show() # " Image masking " if settings.image_mask: image_mask = np.logical_and(mask_a, mask_b) mask_coords = preprocess.mask_coordinates(image_mask) # mark those points on the grid of PIV inside the mask grid_mask = preprocess.prepare_mask_on_grid(x,y,mask_coords) # mask the velocity u = np.ma.masked_array(u, mask=grid_mask) v = np.ma.masked_array(v, mask=grid_mask) else: mask_coords = [] u = np.ma.masked_array(u, mask=np.ma.nomask) v = np.ma.masked_array(v, mask=np.ma.nomask) if settings.validation_first_pass: u, v, mask = validation.typical_validation(u, v, s2n, settings) if settings.show_all_plots: plt.figure() plt.quiver(x,y,u,-v,color='r') plt.gca().invert_yaxis() plt.gca().set_aspect(1.) plt.title('after first pass validation new, inverted') plt.show() # "filter to replace the values that where marked by the validation" if settings.num_iterations == 1 and settings.replace_vectors: # for multi-pass we cannot have holes in the data # after the first pass u, v = filters.replace_outliers( u, v, method=settings.filter_method, max_iter=settings.max_filter_iteration, kernel_size=settings.filter_kernel_size, ) elif settings.num_iterations > 1: # don't even check if it's true or false u, v = filters.replace_outliers( u, v, method=settings.filter_method, max_iter=settings.max_filter_iteration, kernel_size=settings.filter_kernel_size, ) # "adding masks to add the effect of all the validations" if settings.smoothn: u, dummy_u1, dummy_u2, dummy_u3 = smoothn.smoothn( u, s=settings.smoothn_p ) v, dummy_v1, dummy_v2, dummy_v3 = smoothn.smoothn( v, s=settings.smoothn_p ) if settings.image_mask: grid_mask = preprocess.prepare_mask_on_grid(x, y, mask_coords) u = np.ma.masked_array(u, mask=grid_mask) v = np.ma.masked_array(v, mask=grid_mask) else: u = np.ma.masked_array(u, np.ma.nomask) v = np.ma.masked_array(v, np.ma.nomask) if settings.show_all_plots: plt.figure() plt.quiver(x,y,u,-v) plt.gca().invert_yaxis() plt.gca().set_aspect(1.) plt.title('before multi pass, inverted') plt.show() if not isinstance(u, np.ma.MaskedArray): raise ValueError("Expected masked array") """ Multi pass """ for i in range(1, settings.num_iterations): if not isinstance(u, np.ma.MaskedArray): raise ValueError("Expected masked array") x, y, u, v, s2n, mask = multipass_img_deform( frame_a, frame_b, i, x, y, u, v, settings, mask_coords=mask_coords ) # If the smoothing is active, we do it at each pass # but not the last one if settings.smoothn is True and i < settings.num_iterations-1: u, dummy_u1, dummy_u2, dummy_u3 = smoothn.smoothn( u, s=settings.smoothn_p ) v, dummy_v1, dummy_v2, dummy_v3 = smoothn.smoothn( v, s=settings.smoothn_p ) if not isinstance(u, np.ma.MaskedArray): raise ValueError ('not a masked array anymore') if hasattr(settings, 'image_mask') and settings.image_mask: grid_mask = preprocess.prepare_mask_on_grid(x, y, mask_coords) u = np.ma.masked_array(u, mask=grid_mask) v = np.ma.masked_array(v, mask=grid_mask) else: u = np.ma.masked_array(u, np.ma.nomask) v = np.ma.masked_array(v, np.ma.nomask) if settings.show_all_plots: plt.figure() plt.quiver(x, y, u, -v, color='r') plt.gca().set_aspect(1.) plt.gca().invert_yaxis() plt.title('end of the multipass, invert') plt.show() if settings.show_all_plots and settings.num_iterations > 1: plt.figure() plt.quiver(x,y,u,-v) plt.gca().invert_yaxis() plt.gca().set_aspect(1.) plt.title('after multi pass, before saving, inverted') plt.show() # we now use only 0s instead of the image # masked regions. # we could do Nan, not sure what is best u = u.filled(0.) v = v.filled(0.) # "scales the results pixel-> meter" x, y, u, v = scaling.uniform(x, y, u, v, scaling_factor=settings.scaling_factor) if settings.image_mask: grid_mask = preprocess.prepare_mask_on_grid(x, y, mask_coords) u = np.ma.masked_array(u, mask=grid_mask) v = np.ma.masked_array(v, mask=grid_mask) else: u = np.ma.masked_array(u, np.ma.nomask) v = np.ma.masked_array(v, np.ma.nomask) # before saving we conver to the "physically relevant" # right-hand coordinate system with 0,0 at the bottom left # x to the right, y upwards # and so u,v x, y, u, v = transform_coordinates(x, y, u, v) # import pdb; pdb.set_trace() # "save to a file" tools.save(x, y, u, v, mask, os.path.join(settings.save_path, "field_A%03d.txt" % counter), delimiter="\t", ) # "some other stuff that one might want to use" if settings.show_plot or settings.save_plot: Name = os.path.join(settings.save_path, "Image_A%03d.png" % counter) fig, _ = display_vector_field( os.path.join(settings.save_path, "field_A%03d.txt" % counter), scale=settings.scale_plot, ) if settings.save_plot is True: fig.savefig(Name) if settings.show_plot is True: plt.show()

# def func(args): def piv(settings, counter=0): """A function to process each image pair.""" # this line is REQUIRED for multiprocessing to work # always use it in your custom function # file_a, file_b, counter = args file_a = os.path.join(settings.filepath_images, settings.frame_pattern_a) file_b = os.path.join(settings.filepath_images, settings.frame_pattern_b) print(file_a) # read images into numpy arrays frame_a = imread(file_a) frame_b = imread(file_b) " crop to ROI" if settings.ROI == "full": frame_a = frame_a frame_b = frame_b else: frame_a = frame_a[ settings.ROI[0]:settings.ROI[1], settings.ROI[2]:settings.ROI[3] ] frame_b = frame_b[ settings.ROI[0]:settings.ROI[1], settings.ROI[2]:settings.ROI[3] ] if settings.invert is True: frame_a = invert(frame_a) frame_b = invert(frame_b) if settings.show_all_plots: fig, ax = plt.subplots(1,1) ax.imshow(frame_a, cmap=plt.get_cmap('Reds')) ax.imshow(frame_b, cmap=plt.get_cmap('Blues'),alpha=.5) plt.show() if settings.dynamic_masking_method in ("edge", "intensity"): frame_a, mask_a = preprocess.dynamic_masking( frame_a, method=settings.dynamic_masking_method, filter_size=settings.dynamic_masking_filter_size, threshold=settings.dynamic_masking_threshold, ) frame_b, mask_b = preprocess.dynamic_masking( frame_b, method=settings.dynamic_masking_method, filter_size=settings.dynamic_masking_filter_size, threshold=settings.dynamic_masking_threshold, ) piv_run(frame_a, frame_b, settings) print(f"Image Pair {counter + 1}") print(file_a.rsplit('/')[-1],file_b.rsplit('/')[-1]) # def piv(settings): # """ the func fuction is the "frame" in which the PIV evaluation is done """ # # "Below is code to read files and create a folder to store the results" # save_path = os.path.join( # settings.save_path, # "Open_PIV_results_" # + str(settings.windowsizes[settings.num_iterations-1]) # + "_" # + settings.save_folder_suffix, # ) # if not os.path.exists(save_path): # os.makedirs(save_path) # task = Multiprocesser( # data_dir=settings.filepath_images, # pattern_a=settings.frame_pattern_a, # pattern_b=settings.frame_pattern_b, # ) # task.run(func=func, n_cpus=1)

settings = windef.Settings() 'Data related settings' # Folder with the images to process settings.filepath_images = f'{root_folder}/{base_folder}' settings.save_path = f'{root_folder}/results_{base_folder}' # Root name of the output Folder for Result Files settings.save_folder_suffix = 'Run1' # Format and Image Sequence settings.frame_pattern_b = '20210420-tare1000001.tif' # settings.frame_pattern_a = '20210420-Run1000023.tif' 'Region of interest' # (50,300,50,300) #Region of interest: (xmin,xmax,ymin,ymax) or 'full' for full image # settings.ROI = (100,400,600,1024) # settings.ROI = [0,980,320,700] settings.ROI = 'full' settings.deformation_method = 'symmetric' # settings.deformation_method = 'second image' settings.num_iterations = 4 # select the number of PIV passes # add the interrogation window size for each pass. # For the moment, it should be a power of 2 settings.windowsizes=(64, 32, 16, 8) settings.overlap=(32, 16, 8, 4) # settings.windowsizes = (128, 64, 32, 16, 8) # if longer than n iteration the rest is ignored # The overlap of the interroagtion window for each pass. # settings.overlap = (64, 32, 16, 8, 4) # This is 50% overlap # Has to be a value with base two. In general window size/2 is a good choice. # methode used for subpixel interpolation: 'gaussian','centroid','parabolic' settings.subpixel_method = 'gaussian' # order of the image interpolation for the window deformation settings.interpolation_order = 3 settings.scaling_factor = 1 # scaling factor pixel/meter settings.dt = 1 # time between to frames (in seconds) # 'Signal to noise ratio options (only for the last pass)' # It is possible to decide if the S/N should be computed (for the last pass) or not # settings.extract_sig2noise = True # 'True' or 'False' (only for the last pass) settings.sig2noise_threshold = 1.0 # method used to calculate the signal to noise ratio 'peak2peak' or 'peak2mean' settings.sig2noise_method = 'peak2peak' # select the width of the masked to masked out pixels next to the main peak settings.sig2noise_mask = 2 settings.sig2noise_validate = False # If extract_sig2noise==False the values in the signal to noise ratio # output column are set to NaN # only effecting the first pass of the interrogation the following passes # in the multipass will be validated 'Output options' # Select if you want to save the plotted vectorfield: True or False settings.save_plot = False # Choose wether you want to see the vectorfield or not :True or False settings.show_plot = False settings.scale_plot = 20 # select a value to scale the quiver plot of the vectorfield # run the script with the given settings # 'Processing Parameters' settings.correlation_method='circular' # 'circular' or 'linear' # settings.normalized_correlation = True # 'vector validation options' # choose if you want to do validation of the first pass: True or False settings.validation_first_pass = True settings.replace_vectors = True settings.filter_method = 'localmean' # maximum iterations performed to replace the outliers settings.max_filter_iteration = 2 settings.filter_kernel_size = 1 # kernel size for the localmean method settings.MinMax_U_disp = (-10, 10) settings.MinMax_V_disp = (-10, 10) # The second filter is based on the global STD threshold settings.std_threshold = 5 # threshold of the std validation # The third filter is the median test (not normalized at the moment) settings.median_threshold = 5 # threshold of the median validation # On the last iteration, an additional validation can be done based on the S/N. settings.median_size = 2 # defines the size of the local median, it'll be 3 x 3 # New settings for version 0.23.2c settings.image_mask = False # Image mask properties settings.dynamic_masking_method = None # settings.dynamic_masking_method = 'intensity' settings.dynamic_masking_threshold = 0.1 settings.dynamic_masking_filter_size = 21 # Smoothing after the first pass settings.smoothn = True #Enables smoothing of the displacemenet field settings.smoothn_p = 0.05 # This is a smoothing parameter settings.show_all_plots = False settings.invert = False #settings.remove_mean_shift = False # "Below is code to read files and create a folder to store the results" settings.save_path = os.path.join( settings.save_path, "Open_PIV_results_" + str(settings.windowsizes[settings.num_iterations-1]) + "_" + settings.save_folder_suffix, ) if not os.path.exists(settings.save_path): os.makedirs(settings.save_path)

# pair 1 settings.frame_pattern_a = '20210420-Run1000023.tif' piv(settings)

res_file = os.path.join(settings.save_path,'field_A000.txt') # os.path.exists(res_file) d0 = io.load_txt(res_file)

settings.frame_pattern_a = '20210420-Run1000024.tif' piv(settings)

d1 = io.load_txt(res_file)

def save_figure_to_numpy(fig): # save it to a numpy array. fig.canvas.draw() data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='') data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) return data #.transpose(2, 0, 1)

# prepare strain-like scalar field d0.piv.strain() d1.piv.strain()

fig, ax = plt.subplots() # ax.imshow(d0.isel(t=0)['u'].T,cmap=plt.cm.gray) ax.imshow(d0.isel(t=0)['w'].T,cmap=plt.cm.gray) fig.tight_layout() ax.axis('off') fig.canvas.draw() frame_a = save_figure_to_numpy(fig)

# from matplotlib.backends.backend_agg import FigureCanvasAgg fig, ax = plt.subplots() # ax.imshow(d1.isel(t=0)['u'].T,cmap=plt.cm.gray) ax.imshow(d1.isel(t=0)['w'].T,cmap=plt.cm.gray) fig.tight_layout() ax.axis('off') fig.canvas.draw() frame_b = save_figure_to_numpy(fig) # plt.figure() # plt.imshow(img)

# let's try to avoid imshow # d0.piv.strain() # tmp = d0.fillna(0) # tmp = tmp.isel(t=0)['w'].T.values # tmp = tmp.squeeze() # tmp = (tmp - tmp.min())/(tmp.max() - tmp.min()) # frame_a = (tmp*255).astype(np.int32) # tmp = d1.fillna(0) # tmp = tmp.isel(t=0)['w'].T.values # tmp = tmp.squeeze() # tmp = (tmp - tmp.min())/(tmp.max() - tmp.min()) # frame_b = (tmp*255).astype(np.int32)

fig, ax = plt.subplots(1,2) # crop images frame_a = np.mean(frame_a[90:340,50:550],axis=2) frame_b = np.mean(frame_b[90:340,50:550],axis=2) ax[0].imshow(frame_a, cmap='gray') ax[1].imshow(frame_b, cmap='gray')

plt.imshow(np.abs(frame_a - frame_b))

settings.show_all_plots = False settings.invert = True # settings.ROI = [100,350,50,550] settings.ROI = 'full' piv_run(frame_a, frame_b, settings)

res_file = os.path.join(settings.save_path,'field_A000.txt') os.path.exists(res_file)

d2 = io.load_txt(res_file)

fig, ax = plt.subplots() im = ax.imshow(d2.isel(t=0)['u'].T) plt.colorbar(im) # fig.tight_layout() # ax.axis('off') # fig.canvas.draw() # frame_a = save_figure_to_numpy(fig)

plt.plot(d2.x,np.mean(d2['u'][:,20:40,0],axis=1)); plt.xlabel('Streamwise coordinate, (pix)') plt.ylabel('Centerline velocity distribution (pix/dt)')

plt.plot(np.mean(d2['u'][60:100,:,0],axis=0),d2.y); plt.ylabel('$y$ (pix)') plt.xlabel('Spatially Averaged Velocity profile (pix/dt)');