def process_node(i): DeltaFrame = 300 winsize = 12 # pixels searchsize = 12 #pixels overlap = 6 # piexels dt = DeltaFrame * 1. / fps # piexels frame_a = tools.imread(fileNameList[i]) frame_b = tools.imread(fileNameList[i + DeltaFrame]) u0, v0, sig2noise = process.extended_search_area_piv( frame_a.astype(np.int32), frame_b.astype(np.int32), window_size=winsize, overlap=overlap, dt=dt, search_area_size=searchsize, sig2noise_method='peak2peak') x, y = process.get_coordinates(image_size=frame_a.shape, window_size=winsize, overlap=overlap) u1, v1, mask = validation.sig2noise_val(u0, v0, sig2noise, threshold=1.3) u2, v2 = filters.replace_outliers(u1, v1, method='localmean', max_iter=5, kernel_size=5) u3, v3, mask1 = validation.local_median_val(u2, v2, 3, 3, 1) u4, v4 = filters.replace_outliers(u3, v3, method='localmean', max_iter=5, kernel_size=5) tools.save(x, y, u4, v4, mask1, '../testResult/' + str(i) + '.txt')
def openpiv_default_run(im1, im2): """ default settings for OpenPIV analysis using extended_search_area_piv algorithm for two images Inputs: im1,im2 : str,str = path of two image """ frame_a = tools.imread(im1) frame_b = tools.imread(im2) u, v, sig2noise = process.extended_search_area_piv( frame_a.astype(np.int32), frame_b.astype(np.int32), window_size=32, overlap=8, dt=1, search_area_size=64, sig2noise_method='peak2peak') x, y = process.get_coordinates(image_size=frame_a.shape, window_size=32, overlap=8) u, v, mask = validation.sig2noise_val(u, v, sig2noise, threshold=1.3) u, v = filters.replace_outliers(u, v, method='localmean', max_iter=10, kernel_size=2) x, y, u, v = scaling.uniform(x, y, u, v, scaling_factor=1) tools.save(x, y, u, v, mask, list_of_images[0] + '.txt') fig, ax = tools.display_vector_field(list_of_images[0] + '.txt', on_img=True, image_name=list_of_images[0], scaling_factor=1, ax=None)
def PIV(I0, I1, winsize, overlap, dt): """ Normal PIV """ u0, v0, sig2noise = pyprocess.extended_search_area_piv( I0.astype(np.int32), I1.astype(np.int32), window_size=winsize, overlap=overlap, dt=dt, search_area_size=winsize, sig2noise_method='peak2peak', ) # get x, y x, y = pyprocess.get_coordinates(image_size=I0.shape, search_area_size=winsize, overlap=overlap, window_size=winsize) u1, v1, mask_s2n = validation.sig2noise_val( u0, v0, sig2noise, threshold=1.05, ) # replace_outliers u2, v2 = filters.replace_outliers( u1, v1, method='localmean', max_iter=3, kernel_size=3, ) # median filter smoothing u3 = medfilt2d(u2, 3) v3 = medfilt2d(v2, 3) return x, y, u3, v3
def func(args): """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 ##################### # Here goes you code ##################### # read images into numpy arrays frame_a = tools.imread(os.path.join(path, file_a)) frame_b = tools.imread(os.path.join(path, file_b)) frame_a = (frame_a * 1024).astype(np.int32) frame_b = (frame_b * 1024).astype(np.int32) # process image pair with extended search area piv algorithm. u, v, sig2noise = pyprocess.extended_search_area_piv( frame_a, frame_b, \ window_size=64, overlap=32, dt=0.02, search_area_size=128, sig2noise_method='peak2peak') u, v, mask = validation.sig2noise_val(u, v, sig2noise, threshold=1.5) u, v = filters.replace_outliers(u, v, method='localmean', max_iter=10, kernel_size=2) # get window centers coordinates x, y = pyprocess.get_coordinates(image_size=frame_a.shape, search_area_size=128, overlap=32) # save to a file tools.save(x, y, u, v, mask, 'test2_%03d.txt' % counter) tools.display_vector_field('test2_%03d.txt' % counter)
def test_replace_outliers(): """ test of replacing outliers """ v = np.ones((9, 9)) v[1:-1, 1:-1] = np.nan u = v.copy() uf, vf = filters.replace_outliers(u, v) assert (np.sum(np.isnan(u)) == 7**2) assert (np.allclose(np.ones((9, 9)), uf))
def run_piv( frame_a, frame_b, ): winsize = 64 # pixels, interrogation window size in frame A searchsize = 68 # pixels, search in image B overlap = 32 # pixels, 50% overlap dt = 0.0005 # sec, time interval between pulses u0, v0, sig2noise = pyprocess.extended_search_area_piv( frame_a.astype(np.int32), frame_b.astype(np.int32), window_size=winsize, overlap=overlap, dt=dt, search_area_size=searchsize, sig2noise_method='peak2peak') x, y = pyprocess.get_coordinates(image_size=frame_a.shape, search_area_size=searchsize, window_size=winsize, overlap=overlap) u1, v1, mask = validation.sig2noise_val(u0, v0, sig2noise, threshold=1.05) u2, v2 = filters.replace_outliers(u1, v1, method='localmean', max_iter=10, kernel_size=3) x, y, u3, v3 = scaling.uniform(x, y, u2, v2, scaling_factor=41.22) # 41.22 microns/pixel mean_u = np.mean(u3) mean_v = np.mean(v3) deficit_u = u3 - mean_u deficit_v = v3 - mean_v u_prime = np.mean(np.sqrt(0.5 * (deficit_u**2 + deficit_v**2))) u_avg = np.mean(np.sqrt(0.5 * (mean_u**2 + mean_v**2))) turbulence_intensity = u_prime / u_avg #save in the simple ASCII table format fname = "./Tables/" + exp_string + ".txt" # tools.save(x, y, u3, v3, mask, fname) out = np.vstack([m.ravel() for m in [x, y, u3, v3]]) # print(out) # np.savetxt(fname,out.T) with open(fname, "ab") as f: f.write(b"\n") np.savetxt(f, out.T) return turbulence_intensity
def two_images(image_1, image_2, search_area_size=64, window_size=32, overlap=16, dt=0.02): with open("image_1.bmp", "wb") as fh1: fh1.write(base64.b64decode(image_1)) with open("image_2.bmp", "wb") as fh2: fh2.write(base64.b64decode(image_2)) frame_a = tools.imread( 'image_1.bmp' ) frame_b = tools.imread( 'image_2.bmp' ) frame_a = (frame_a*1024).astype(np.int32) frame_b = (frame_b*1024).astype(np.int32) if not search_area_size: search_area_size = 64 if not window_size: window_size = 32 if not overlap: overlap = 16 if not dt: dt = 0.02 u, v, sig2noise = process.extended_search_area_piv( frame_a, frame_b, window_size=window_size, overlap=overlap, dt=dt, search_area_size=search_area_size, sig2noise_method='peak2peak' ) x, y = process.get_coordinates( image_size=frame_a.shape, window_size=window_size, overlap=overlap ) u, v, mask = validation.sig2noise_val( u, v, sig2noise, threshold = 1.3 ) u, v, mask = validation.global_val( u, v, (-1000, 2000), (-1000, 1000) ) u, v = filters.replace_outliers( u, v, method='localmean', max_iter=10, kernel_size=2) x, y, u, v = scaling.uniform(x, y, u, v, scaling_factor = 96.52 ) file_name_text = 'result.txt' file_name_png = 'result.png' if os.path.isfile(file_name_text): os.remove(file_name_text) if os.path.isfile(file_name_png): os.remove(file_name_png) tools.save(x, y, u, v, mask, file_name_text) a = np.loadtxt(file_name_text) fig = plt.figure() invalid = a[:,4].astype('bool') fig.canvas.set_window_title('Vector field, '+str(np.count_nonzero(invalid))+' wrong vectors') valid = ~invalid plt.quiver(a[invalid,0],a[invalid,1],a[invalid,2],a[invalid,3],color='r',scale=100, width=0.0025) plt.quiver(a[valid,0],a[valid,1],a[valid,2],a[valid,3],color='b',scale=100, width=0.0025) plt.draw() plt.savefig(file_name_png, format="png") with open(file_name_text, "rb") as resultFileText: file_reader_text = resultFileText.read() text_encode = base64.encodestring(file_reader_text) base64_string_text = str(text_encode, 'utf-8') with open(file_name_png, "rb") as resultFilePng: file_reader_image = resultFilePng.read() image_encode = base64.encodestring(file_reader_image) base64_string_image = str(image_encode, 'utf-8') return base64_string_text, base64_string_image
def _piv_frame(self, img1, img2, show=False, **kwargs): """ calculate velocity using piv method on two frames """ from openpiv.process import extended_search_area_piv, get_coordinates # from openpiv.scaling import uniform if self._debug: print('... [PIV] window size: {}'.format(self._windowSize)) print('... [PIV] overlap: {}'.format(self._overlap)) print('... [PIV] search area size: {}'.format(self._searchArea)) print('... [PIV] threshold: {}'.format(self._piv_threshold)) u, v, sig2noise = extended_search_area_piv( img1, img2, window_size=self._windowSize, overlap=self._overlap, dt=self._exposuretime, search_area_size=self._searchArea, sig2noise_method='peak2peak') self._pivx, self._pivy = get_coordinates(image_size=img1.shape, window_size=self._windowSize, overlap=self._overlap) #self._pivy = np.flipud(self._pivy) #self._pivx, self._pivy, u, v = uniform(self._pivx, self._pivy, u, v, scaling_factor=self._mpp) if show: from openpiv.validation import sig2noise_val from openpiv.filters import replace_outliers u, v, mask = sig2noise_val(u, v, sig2noise, threshold=self._piv_threshold) u, v = replace_outliers(u, v, method='localmean', max_iter=10, kernel_size=2) # show quiver plot plt.figure(figsize=(12, 6)) plt.imshow(img1) plt.quiver(self._pivx, self._pivy, u, v, color='w', pivot='mid') plt.savefig(self._fname[:-4] + '_piv.png', dpi=100) if self._debug: print( "... [PIV] mean velocity [um/sec]: ({:4.2f}, {:4.2f})".format( np.mean(u) * self._mpp, np.mean(v) * self._mpp)) print("... [PIV] mean velocity [pixel/frame]: ({:4.2f}, {:4.2f})". format( np.mean(u) * self._exposuretime, np.mean(v) * self._exposuretime)) return (u, v, sig2noise)
def test_replace_outliers(): """ test of replacing outliers """ v = np.ones((9,9)) v[1:-1,1:-1] = np.nan u = v.copy() uf,vf = filters.replace_outliers(u,v) assert(np.sum(np.isnan(u))==7**2) assert(np.allclose(np.ones((9,9)),uf))
def analyzer(frame_a, frame_b, text, plot, num_scene, pathout, scal, zre, xre, dt): winsize = 16 # pixels searchsize = 32 # pixels, search in image b overlap = 8 # pixels frame_a = cv2.adaptiveThreshold(frame_a, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 5, 5) frame_b = cv2.adaptiveThreshold(frame_b, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 5, 5) #frame_a = cv2.adaptiveThreshold(frame_a,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2) #frame_b = cv2.adaptiveThreshold(frame_b,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2) plt.imshow(np.c_[frame_a, frame_b], cmap='gray') plt.savefig(pathout + '/filtered' + str(num_scene) + '.png', dpi=800) u0, v0, sig2noise = process.extended_search_area_piv( frame_a.astype(np.int32), frame_b.astype(np.int32), window_size=winsize, overlap=overlap, dt=dt, search_area_size=searchsize, sig2noise_method='peak2peak') x, y = process.get_coordinates(image_size=frame_a.shape, window_size=winsize, overlap=overlap) u1, v1, mask = validation.sig2noise_val(u0, v0, sig2noise, threshold=1.3) u2, v2 = filters.replace_outliers(u1, v1, method='localmean', max_iter=10, kernel_size=2) x, y, u3, v3 = scaling.uniform( x, y, u2, v2, scaling_factor=scal) # scaling_factor (pixel per meter) u3 = np.flip(u3, axis=0) v3 = np.flip(v3, axis=0) xre = np.linspace(0, xre / 100, len(x[0, :])) zre = np.linspace(0, zre / 100, len(x[:, 0])) if plot == 1: piv_plotting(xre, zre, u3, v3, num_scene, pathout) if text == 0: tools.save(x, y, u3, v3, mask, pathout + '/piv' + str(num_scene) + '.txt')
def process(args, bga, bgb, reflection): file_a, file_b, counter = args # read images into numpy arrays frame_a = tools.imread(file_a) frame_b = tools.imread(file_b) # removing background and reflections frame_a = frame_a - bga frame_b = frame_b - bgb frame_a[reflection == 255] = 0 frame_b[reflection == 255] = 0 #applying a static mask (taking out the regions where we have walls) yp = [580, 435, 0, 0, 580, 580, 0, 0, 435, 580] xp = [570, 570, 680, 780, 780, 0, 0, 105, 230, 230] pnts = draw.polygon(yp, xp, frame_a.shape) frame_a[pnts] = 0 frame_b[pnts] = 0 # checking the resulting frame #fig, ax = plt.subplots(2,2) #ax[0,0].imshow(frame_a_org, cmap='gray') #ax[0,1].imshow(frame_a, cmap='gray') #ax[1,0].imshow(frame_b_org, cmap='gray') #ax[1,1].imshow(frame_b, cmap='gray') #plt.tight_layout() #plt.show() # main piv processing u, v, sig2noise = pyprocess.extended_search_area_piv( frame_a, frame_b, \ window_size=48, overlap=16, dt=0.001094, search_area_size=64, sig2noise_method='peak2peak') x, y = pyprocess.get_coordinates(image_size=frame_a.shape, window_size=48, overlap=16) u, v, mask = validation.local_median_val(u, v, 2000, 2000, size=2) u, v = filters.replace_outliers(u, v, method='localmean', max_iter=10, kernel_size=2) u, *_ = smoothn(u, s=1.0) v, *_ = smoothn(v, s=1.0) # saving the results save_file = tools.create_path(file_a, 'Analysis') tools.save(x, y, u, v, mask, save_file + '.dat')
def two_images(image_1, image_2): with open("image_1.bmp", "wb") as fh1: fh1.write(base64.b64decode(image_1)) with open("image_2.bmp", "wb") as fh2: fh2.write(base64.b64decode(image_2)) frame_a = tools.imread('image_1.bmp') frame_b = tools.imread('image_2.bmp') winsize = 32 # pixels searchsize = 64 # pixels, search in image B overlap = 12 # pixels dt = 0.02 # sec u, v, sig2noise = pyprocess.piv(frame_a.astype(np.int32), frame_b.astype(np.int32), window_size=winsize, overlap=overlap, dt=dt, search_size=searchsize, sig2noise_method='peak2peak') x, y = pyprocess.get_coordinates(image_size=frame_a.shape, window_size=searchsize, overlap=overlap) u, v, mask = validation.sig2noise_val(u, v, sig2noise, threshold=1.3) u, v = filters.replace_outliers(u, v, method='localmean', max_iter=10, kernel_size=2) x, y, u, v = scaling.uniform(x, y, u, v, scaling_factor=96.52) file_name = 'result.txt' if os.path.isfile(file_name): os.remove(file_name) tools.save(x, y, u, v, np.zeros_like(u), file_name) # no masking, all values are valid with open(file_name, "rb") as resultFile: file_reader = resultFile.read() image_encode = base64.encodestring(file_reader) base64_string = str(image_encode, 'utf-8') return base64_string
def ProcessPIV(args, bga, bgb, reflection, stg): # read images into numpy arrays file_a, file_b, counter = args frame_a = tools.imread(file_a) frame_b = tools.imread(file_b) # removing background and reflections if bgb is not None: frame_a = frame_a - bga frame_b = frame_b - bgb frame_a[reflection == 255] = 0 frame_b[reflection == 255] = 0 #plt.imshow(frame_a, cmap='gray') #plt.show() # main piv processing u, v, s2n = pyprocess.extended_search_area_piv( frame_a, frame_b, \ window_size=stg['WS'], overlap=stg['OL'], dt=stg['DT'], search_area_size=stg['SA'], sig2noise_method='peak2peak') x, y = pyprocess.get_coordinates(image_size=frame_a.shape, window_size=stg['WS'], overlap=stg['OL']) if stg['BVR'] == 'on': u, v, mask = validation.local_median_val(u, v, stg['MF'][0], stg['MF'][1], size=2) u, v, mask = validation.global_val(u, v, u_thresholds=stg['GF'][0], v_thresholds=stg['GF'][1]) u, v = filters.replace_outliers(u, v, method='localmean', max_iter=10, kernel_size=2) u, *_ = smoothn(u, s=0.5) v, *_ = smoothn(v, s=0.5) x, y, u, v = scaling.uniform(x, y, u, v, stg['SC']) # saving the results save_file = tools.create_path(file_a, 'Analysis') tools.save(x, y, u, v, s2n, save_file + '.dat')
def PIV(image_0, image_1, winsize, searchsize, overlap, frame_rate, scaling_factor): frame_0 = image_0 # [0:600, :] frame_1 = image_1 # [0:600, :] # Processing the images with interrogation area and search area / cross correlation algortihm u, v, sig2noise = pyprocess.extended_search_area_piv( frame_0, frame_1, window_size=winsize, overlap=overlap, dt=dt, search_area_size=searchsize, sig2noise_method='peak2peak') # Compute the coordinates of the centers of the interrogation windows x, y = pyprocess.get_coordinates(image_size=frame_0.shape, window_size=winsize, overlap=overlap) # This function actually sets to NaN all those vector for # which the signal to noise ratio is below 1.3. u, v, mask = validation.sig2noise_val(u, v, sig2noise, threshold=1.2) # Function as described above, removing outliers deviating with more # than twice the standard deviation u, v, mask = remove_outliers(u, v, mask) # Replacing the outliers with interpolation u, v = filters.replace_outliers(u, v, method='localmean', max_iter=50, kernel_size=3) # Apply an uniform scaling to the flow field to get dimensional units x, y, u, v = scaling.uniform(x, y, u, v, scaling_factor=scaling_factor) return x, y, u, v, mask
def repl_outliers(self): '''Replace outliers.''' result_fnames = [] for i, f in enumerate(self.p['fnames']): data = np.loadtxt(f) u, v = piv_flt.replace_outliers(np.array([data[:, 2]]), np.array([data[:, 3]]), method=self.p['repl_method'], max_iter=self.p['repl_iter'], kernel_size=self.p['repl_kernel']) save_fname = create_save_vec_fname(path=f, postfix='_repl') save(data[:, 0], data[:, 1], u, v, data[:, 4], data[:, 5], save_fname, delimiter=delimiter) result_fnames.append(save_fname) return (result_fnames)
if 'OpenPIV' not in sys.path: sys.path.append('/Users/alex/Documents/OpenPIV/alexlib/openpiv-python') from openpiv import tools, validation, process, filters, scaling, pyprocess import numpy as np frame_a = tools.imread( 'exp1_001_a.bmp' ) frame_b = tools.imread( 'exp1_001_b.bmp' ) u, v, sig2noise = process.extended_search_area_piv( frame_a.astype(np.int32), frame_b.astype(np.int32), window_size=24, overlap=12, dt=0.02, search_area_size=64, sig2noise_method='peak2peak' ) x, y = process.get_coordinates( image_size=frame_a.shape, window_size=24, overlap=12 ) u, v, mask = validation.sig2noise_val( u, v, sig2noise, threshold = 2.5 ) u, v = filters.replace_outliers( u, v, method='localmean', max_iter=10, kernel_size=2) x, y, u, v = scaling.uniform(x, y, u, v, scaling_factor = 96.52 ) tools.save(x, y, u, v, mask, 'exp1_001.txt' ) tools.display_vector_field('exp1_001.txt', scale=100, width=0.0025) u, v, s2n= pyprocess.piv(frame_a, frame_b, corr_method='fft', window_size=24, overlap=12, dt=0.02, sig2noise_method='peak2peak' ) x, y = pyprocess.get_coordinates( image_size=frame_a.shape, window_size=24, overlap=12 ) u, v, mask = validation.sig2noise_val( u, v, s2n, threshold = 2.5 ) u, v = filters.replace_outliers( u, v, method='localmean', max_iter=10, kernel_size=2.5) x, y, u, v = scaling.uniform(x, y, u, v, scaling_factor = 96.52 ) tools.save(x, y, u, v, mask, 'exp1_002.txt' ) tools.display_vector_field('exp1_002.txt', scale=100, width=0.0025)
def multipass_img_deform( frame_a, frame_b, current_iteration, x_old, y_old, u_old, v_old, settings, mask_coords=[], ): # window_size, # overlap, # iterations, # current_iteration, # x_old, # y_old, # u_old, # v_old, # correlation_method="circular", # normalized_correlation=False, # subpixel_method="gaussian", # deformation_method="symmetric", # sig2noise_method="peak2peak", # sig2noise_threshold=1.0, # sig2noise_mask=2, # interpolation_order=1, """ Multi pass of the PIV evaluation. This function does the PIV evaluation of the second and other passes. It returns the coordinates of the interrogation window centres, the displacement u, v for each interrogation window as well as the signal to noise ratio array (which is full of NaNs if opted out) Parameters ---------- frame_a : 2d np.ndarray the first image frame_b : 2d np.ndarray the second image window_size : tuple of ints the size of the interrogation window overlap : tuple of ints the overlap of the interrogation window, e.g. window_size/2 x_old : 2d np.ndarray the x coordinates of the vector field of the previous pass y_old : 2d np.ndarray the y coordinates of the vector field of the previous pass u_old : 2d np.ndarray the u displacement of the vector field of the previous pass in case of the image mask - u_old and v_old are MaskedArrays v_old : 2d np.ndarray the v displacement of the vector field of the previous pass subpixel_method: string the method used for the subpixel interpolation. one of the following methods to estimate subpixel location of the peak: 'centroid' [replaces default if correlation map is negative], 'gaussian' [default if correlation map is positive], 'parabolic' interpolation_order : int the order of the spline interpolation used for the image deformation mask_coords : list of x,y coordinates (pixels) of the image mask, default is an empty list Returns ------- x : 2d np.array array containg the x coordinates of the interrogation window centres y : 2d np.array array containg the y coordinates of the interrogation window centres u : 2d np.array array containing the horizontal displacement for every interrogation window [pixels] u : 2d np.array array containing the vertical displacement for every interrogation window it returns values in [pixels] s2n : 2D np.array of signal to noise ratio values """ if not isinstance(u_old, np.ma.MaskedArray): raise ValueError('Expected masked array') # calculate the y and y coordinates of the interrogation window centres. # Hence, the # edges must be extracted to provide the sufficient input. x_old and y_old # are the coordinates of the old grid. x_int and y_int are the coordinates # of the new grid window_size = settings.windowsizes[current_iteration] overlap = settings.overlap[current_iteration] x, y = get_coordinates(frame_a.shape, window_size, overlap) # The interpolation function dont like meshgrids as input. # plus the coordinate system for y is now from top to bottom # and RectBivariateSpline wants an increasing set y_old = y_old[:, 0] # y_old = y_old[::-1] x_old = x_old[0, :] y_int = y[:, 0] # y_int = y_int[::-1] x_int = x[0, :] # interpolating the displacements from the old grid onto the new grid # y befor x because of numpy works row major ip = RectBivariateSpline(y_old, x_old, u_old.filled(0.)) u_pre = ip(y_int, x_int) ip2 = RectBivariateSpline(y_old, x_old, v_old.filled(0.)) v_pre = ip2(y_int, x_int) # if settings.show_plot: if settings.show_all_plots: plt.figure() plt.quiver(x_old, y_old, u_old, -1 * v_old, color='b') plt.quiver(x_int, y_int, u_pre, -1 * v_pre, color='r', lw=2) plt.gca().set_aspect(1.) plt.gca().invert_yaxis() plt.title('inside deform, invert') plt.show() # @TKauefer added another method to the windowdeformation, 'symmetric' # splits the onto both frames, takes more effort due to additional # interpolation however should deliver better results old_frame_a = frame_a.copy() old_frame_b = frame_b.copy() # Image deformation has to occur in image coordinates # therefore we need to convert the results of the # previous pass which are stored in the physical units # and so y from the get_coordinates if settings.deformation_method == "symmetric": # this one is doing the image deformation (see above) x_new, y_new, ut, vt = create_deformation_field( frame_a, x, y, u_pre, v_pre) frame_a = scn.map_coordinates(frame_a, ((y_new - vt / 2, x_new - ut / 2)), order=settings.interpolation_order, mode='nearest') frame_b = scn.map_coordinates(frame_b, ((y_new + vt / 2, x_new + ut / 2)), order=settings.interpolation_order, mode='nearest') elif settings.deformation_method == "second image": frame_b = deform_windows( frame_b, x, y, u_pre, -v_pre, interpolation_order=settings.interpolation_order) else: raise Exception("Deformation method is not valid.") # if settings.show_plot: if settings.show_all_plots: if settings.deformation_method == 'symmetric': plt.figure() plt.imshow(frame_a - old_frame_a) plt.show() plt.figure() plt.imshow(frame_b - old_frame_b) plt.show() # if do_sig2noise is True # sig2noise_method = sig2noise_method # else: # sig2noise_method = None # so we use here default circular not normalized correlation: # if we did not want to validate every step, remove the method if settings.sig2noise_validate is False: settings.sig2noise_method = None u, v, s2n = extended_search_area_piv( frame_a, frame_b, window_size=window_size, overlap=overlap, width=settings.sig2noise_mask, subpixel_method=settings.subpixel_method, sig2noise_method=settings.sig2noise_method, correlation_method=settings.correlation_method, normalized_correlation=settings.normalized_correlation, ) shapes = np.array(get_field_shape(frame_a.shape, window_size, overlap)) u = u.reshape(shapes) v = v.reshape(shapes) s2n = s2n.reshape(shapes) u += u_pre v += v_pre # reapply the image mask to the new grid 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) # validate in the multi-pass by default u, v, mask = validation.typical_validation(u, v, s2n, settings) if np.all(mask): raise ValueError("Something happened in the validation") if not isinstance(u, np.ma.MaskedArray): raise ValueError('not a masked array anymore') if settings.show_all_plots: plt.figure() nans = np.nonzero(mask) plt.quiver(x[~nans], y[~nans], u[~nans], -v[~nans], color='b') plt.quiver(x[nans], y[nans], u[nans], -v[nans], color='r') plt.gca().invert_yaxis() plt.gca().set_aspect(1.) plt.title('After sig2noise, inverted') plt.show() # we have to replace outliers u, v = filters.replace_outliers( u, v, method=settings.filter_method, max_iter=settings.max_filter_iteration, kernel_size=settings.filter_kernel_size, ) # reapply the image mask to the new grid 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, color='r') plt.quiver(x, y, u_pre, -1 * v_pre, color='b') plt.gca().invert_yaxis() plt.gca().set_aspect(1.) plt.title(' after replaced outliers, red, invert') plt.show() return x, y, u, v, s2n, mask
def func(args): """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 # counter2=str(counter2) ##################### # Here goes you code ##################### " read images into numpy arrays" frame_a = imread(os.path.join(settings.filepath_images, file_a)) frame_b = imread(os.path.join(settings.filepath_images, file_b)) # Miguel: I just had a quick look, and I do not understand the reason # for this step. # I propose to remove it. # frame_a = (frame_a*1024).astype(np.int32) # frame_b = (frame_b*1024).astype(np.int32) " 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, ) # "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, ) # don't even check if it's true or false elif settings.num_iterations > 1: 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, -1 * 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(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(save_path, "Image_A%03d.png" % counter) fig, _ = display_vector_field( os.path.join(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() print(f"Image Pair {counter + 1}") print(file_a.rsplit('/')[-1], file_b.rsplit('/')[-1])
def multipass_img_deform(frame_a, frame_b, window_size, overlap, iterations, current_iteration, x_old, y_old, u_old, v_old, correlation_method='circular', subpixel_method='gaussian', do_sig2noise=False, sig2noise_method='peak2peak', sig2noise_mask=2, MinMaxU=(-100, 50), MinMaxV=(-50, 50), std_threshold=5, median_threshold=2, median_size=1, filter_method='localmean', max_filter_iteration=10, filter_kernel_size=2, interpolation_order=3): """ First pass of the PIV evaluation. This function does the PIV evaluation of the first pass. It returns the coordinates of the interrogation window centres, the displacment u and v for each interrogation window as well as the mask which indicates wether the displacement vector was interpolated or not. Parameters ---------- frame_a : 2d np.ndarray the first image frame_b : 2d np.ndarray the second image window_size : tuple of ints the size of the interrogation window overlap : tuple of ints the overlap of the interrogation window normal for example window_size/2 x_old : 2d np.ndarray the x coordinates of the vector field of the previous pass y_old : 2d np.ndarray the y coordinates of the vector field of the previous pass u_old : 2d np.ndarray the u displacement of the vector field of the previous pass v_old : 2d np.ndarray the v displacement of the vector field of the previous pass subpixel_method: string the method used for the subpixel interpolation. one of the following methods to estimate subpixel location of the peak: 'centroid' [replaces default if correlation map is negative], 'gaussian' [default if correlation map is positive], 'parabolic' MinMaxU : two elements tuple sets the limits of the u displacment component Used for validation. MinMaxV : two elements tuple sets the limits of the v displacment component Used for validation. std_threshold : float sets the threshold for the std validation median_threshold : float sets the threshold for the median validation filter_method : string the method used to replace the non-valid vectors Methods: 'localmean', 'disk', 'distance', max_filter_iteration : int maximum of filter iterations to replace nans filter_kernel_size : int size of the kernel used for the filtering interpolation_order : int the order of the spline interpolation used for the image deformation Returns ------- x : 2d np.array array containg the x coordinates of the interrogation window centres y : 2d np.array array containg the y coordinates of the interrogation window centres u : 2d np.array array containing the u displacement for every interrogation window u : 2d np.array array containing the u displacement for every interrogation window mask : 2d np.array array containg the mask values (bool) which contains information if the vector was filtered """ x, y = get_coordinates(np.shape(frame_a), window_size, overlap) 'calculate the y and y coordinates of the interrogation window centres' y_old = y_old[:, 0] # y_old = y_old[::-1] x_old = x_old[0, :] y_int = y[:, 0] # y_int = y_int[::-1] x_int = x[0, :] '''The interpolation function dont like meshgrids as input. Hence, the the edges must be extracted to provide the sufficient input. x_old and y_old are the are the coordinates of the old grid. x_int and y_int are the coordiantes of the new grid''' ip = RectBivariateSpline(y_old, x_old, u_old) u_pre = ip(y_int, x_int) ip2 = RectBivariateSpline(y_old, x_old, v_old) v_pre = ip2(y_int, x_int) ''' interpolating the displacements from the old grid onto the new grid y befor x because of numpy works row major ''' frame_b_deform = frame_interpolation( frame_b, x, y, u_pre, -v_pre, interpolation_order=interpolation_order) '''this one is doing the image deformation (see above)''' cor_win_1 = pyprocess.moving_window_array(frame_a, window_size, overlap) cor_win_2 = pyprocess.moving_window_array(frame_b_deform, window_size, overlap) '''Filling the interrogation window. They windows are arranged in a 3d array with number of interrogation window *window_size*window_size this way is much faster then using a loop''' correlation = correlation_func(cor_win_1, cor_win_2, correlation_method=correlation_method, normalized_correlation=False) 'do the correlation' disp = np.zeros((np.size(correlation, 0), 2)) for i in range(0, np.size(correlation, 0)): ''' determine the displacment on subpixel level ''' disp[i, :] = find_subpixel_peak_position( correlation[i, :, :], subpixel_method=subpixel_method) 'this loop is doing the displacment evaluation for each window ' disp = np.array(disp) - np.floor(np.array(correlation[0, :, :].shape) / 2) 'reshaping the interrogation window to vector field shape' shapes = np.array( pyprocess.get_field_shape(np.shape(frame_a), window_size, overlap)) u = disp[:, 1].reshape(shapes) v = -disp[:, 0].reshape(shapes) 'adding the recent displacment on to the displacment of the previous pass' u = u + u_pre v = v + v_pre 'validation using gloabl limits and local median' u, v, mask_g = validation.global_val(u, v, MinMaxU, MinMaxV) u, v, mask_s = validation.global_std(u, v, std_threshold=std_threshold) u, v, mask_m = validation.local_median_val(u, v, u_threshold=median_threshold, v_threshold=median_threshold, size=median_size) mask = mask_g + mask_m + mask_s 'adding masks to add the effect of alle the validations' #mask=np.zeros_like(u) 'filter to replace the values that where marked by the validation' if current_iteration != iterations: 'filter to replace the values that where marked by the validation' u, v = filters.replace_outliers(u, v, method=filter_method, max_iter=max_filter_iteration, kernel_size=filter_kernel_size) if do_sig2noise == True and current_iteration == iterations and iterations != 1: sig2noise_ratio = sig2noise_ratio_function( correlation, sig2noise_method=sig2noise_method, width=sig2noise_mask) sig2noise_ratio = sig2noise_ratio.reshape(shapes) else: sig2noise_ratio = np.full_like(u, np.nan) return x, y, u, v, sig2noise_ratio, mask
def func(args): """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 # counter2=str(counter2) ##################### # Here goes you code ##################### ' read images into numpy arrays' frame_a = tools.imread(os.path.join(settings.filepath_images, file_a)) frame_b = tools.imread(os.path.join(settings.filepath_images, file_b)) ## Miguel: I just had a quick look, and I do not understand the reason for this step. # I propose to remove it. #frame_a = (frame_a*1024).astype(np.int32) #frame_b = (frame_b*1024).astype(np.int32) ' 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.dynamic_masking_method == 'edge' or 'intensity': frame_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 = preprocess.dynamic_masking( frame_b, method=settings.dynamic_masking_method, filter_size=settings.dynamic_masking_filter_size, threshold=settings.dynamic_masking_threshold) '''%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%''' 'first pass' x, y, u, v, sig2noise_ratio = first_pass( frame_a, frame_b, settings.windowsizes[0], settings.overlap[0], settings.iterations, correlation_method=settings.correlation_method, subpixel_method=settings.subpixel_method, do_sig2noise=settings.extract_sig2noise, sig2noise_method=settings.sig2noise_method, sig2noise_mask=settings.sig2noise_mask, ) 'validation using gloabl limits and std and local median' '''MinMaxU : two elements tuple sets the limits of the u displacment component Used for validation. MinMaxV : two elements tuple sets the limits of the v displacment component Used for validation. std_threshold : float sets the threshold for the std validation median_threshold : float sets the threshold for the median validation filter_method : string the method used to replace the non-valid vectors Methods: 'localmean', 'disk', 'distance', max_filter_iteration : int maximum of filter iterations to replace nans filter_kernel_size : int size of the kernel used for the filtering''' mask = np.full_like(x, False) if settings.validation_first_pass == True: u, v, mask_g = validation.global_val(u, v, settings.MinMax_U_disp, settings.MinMax_V_disp) u, v, mask_s = validation.global_std( u, v, std_threshold=settings.std_threshold) u, v, mask_m = validation.local_median_val( u, v, u_threshold=settings.median_threshold, v_threshold=settings.median_threshold, size=settings.median_size) if settings.extract_sig2noise == True and settings.iterations == 1 and settings.do_sig2noise_validation == True: u, v, mask_s2n = validation.sig2noise_val( u, v, sig2noise_ratio, threshold=settings.sig2noise_threshold) mask = mask + mask_g + mask_m + mask_s + mask_s2n else: mask = mask + mask_g + mask_m + mask_s 'filter to replace the values that where marked by the validation' if settings.iterations > 1: 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 == True: 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) elif settings.iterations == 1 and settings.replace_vectors == True: 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 == True: u, v = filters.replace_outliers( u, v, method=settings.filter_method, max_iter=settings.max_filter_iteration, kernel_size=settings.filter_kernel_size) 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) i = 1 'all the following passes' for i in range(2, settings.iterations + 1): x, y, u, v, sig2noise_ratio, mask = multipass_img_deform( frame_a, frame_b, settings.windowsizes[i - 1], settings.overlap[i - 1], settings.iterations, i, x, y, u, v, correlation_method=settings.correlation_method, subpixel_method=settings.subpixel_method, do_sig2noise=settings.extract_sig2noise, sig2noise_method=settings.sig2noise_method, sig2noise_mask=settings.sig2noise_mask, MinMaxU=settings.MinMax_U_disp, MinMaxV=settings.MinMax_V_disp, std_threshold=settings.std_threshold, median_threshold=settings.median_threshold, median_size=settings.median_size, filter_method=settings.filter_method, max_filter_iteration=settings.max_filter_iteration, filter_kernel_size=settings.filter_kernel_size, interpolation_order=settings.interpolation_order) # If the smoothing is active, we do it at each pass if settings.smoothn == True: 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.extract_sig2noise == True and i == settings.iterations and settings.iterations != 1 and settings.do_sig2noise_validation == True: u, v, mask_s2n = validation.sig2noise_val( u, v, sig2noise_ratio, threshold=settings.sig2noise_threshold) mask = mask + mask_s2n if settings.replace_vectors == True: u, v = filters.replace_outliers( u, v, method=settings.filter_method, max_iter=settings.max_filter_iteration, kernel_size=settings.filter_kernel_size) 'pixel/frame->pixel/sec' u = u / settings.dt v = v / settings.dt 'scales the results pixel-> meter' x, y, u, v = scaling.uniform(x, y, u, v, scaling_factor=settings.scaling_factor) 'save to a file' save(x, y, u, v, sig2noise_ratio, mask, os.path.join(save_path, 'field_A%03d.txt' % counter), delimiter='\t') 'some messages to check if it is still alive' 'some other stuff that one might want to use' if settings.show_plot == True or settings.save_plot == True: plt.close('all') plt.ioff() Name = os.path.join(save_path, 'Image_A%03d.png' % counter) display_vector_field(os.path.join(save_path, 'field_A%03d.txt' % counter), scale=settings.scale_plot) if settings.save_plot == True: plt.savefig(Name) if settings.show_plot == True: plt.show() print('Image Pair ' + str(counter + 1))
status_message(u) # In[33]: # "filter to replace the values that where marked by the validation" # if settings.iterations > 1: u, v = filters.replace_outliers( u, v, method=settings.filter_method, max_iter=settings.max_filter_iteration, kernel_size=settings.filter_kernel_size, ) # In[34]: # mask the velocity maps tmp = np.zeros_like(x,dtype=bool) tmp.flat[xymask] = 1 u = np.ma.masked_array(u, mask = tmp) v = np.ma.masked_array(v, mask = tmp)
dt = DeltaFrame * 1. / fps # piexels u0, v0, sig2noise = process.extended_search_area_piv( frame_a.astype(np.int32), frame_b.astype(np.int32), window_size=winsize, overlap=overlap, dt=dt, search_area_size=searchsize, sig2noise_method='peak2peak') x, y = process.get_coordinates(image_size=frame_a.shape, window_size=winsize, overlap=overlap) u1, v1, mask = validation.sig2noise_val(u0, v0, sig2noise, threshold=1.3) u2, v2 = filters.replace_outliers(u1, v1, method='localmean', max_iter=5, kernel_size=5) u3, v3, mask1 = validation.local_median_val(u2, v2, 3, 3, 1) u4, v4 = filters.replace_outliers(u3, v3, method='localmean', max_iter=5, kernel_size=5) tools.save(x, y, u4, v4, mask1, '../testResult/test.txt') tools.display_vector_field('../testResult/test.txt', scale=500, width=0.0025) #%% define node def process_node(i):
def piv_frames(self, topn=-1, show=True): from openpiv.validation import sig2noise_val from openpiv.filters import replace_outliers frames = self._frames[::2] topn = np.min([len(frames), topn]) frames = frames[:topn] (ut, vt, s2nt) = self.piv_frame(frame=0, show=False) for i in tqdm.tqdm(frames[1:]): (u, v, s2n) = self.piv_frame(frame=i, show=False) ut += u vt += v s2nt += s2n #print(np.max(u), np.min(u), u.size) #print(np.max(v), np.max(v), v.size) ut /= len(frames) vt /= len(frames) s2nt /= len(frames) ut, vt, mask = sig2noise_val(ut, vt, s2nt, threshold=self._piv_threshold) ut, vt = replace_outliers(ut, vt, method='localmean', max_iter=10, kernel_size=2) self._pivu = ut self._pivv = vt self._pivs2n = s2nt self.save_piv() if show: fig = plt.figure(figsize=(12, 6)) ax1 = fig.add_subplot(121) ax1.imshow(self.mean()) ax1.quiver(self._pivx, self._pivy, ut, vt, pivot='mid', color='w') ax2 = fig.add_subplot(122) n_u, bins, patches = ax2.hist(ut.flatten() * self._mpp, bins=20, normed=1, facecolor='blue', alpha=0.75, label='u') n_v, bins, patches = ax2.hist(vt.flatten() * self._mpp, bins=20, normed=1, facecolor='green', alpha=0.75, label='v') ax2.annotate(np.mean(ut) * self._mpp, xy=(np.mean(ut) * self._mpp, np.max(n_u))) ax2.annotate(np.mean(vt) * self._mpp, xy=(np.mean(vt) * self._mpp, np.max(n_v))) plt.legend(loc='best') plt.tight_layout() plt.savefig(self._fname[:-4] + '_piv_t.png', dpi=150) print("... frames: {}, {}".format(frames[0], frames[-1])) print("... mean velocity [um/sec]: ({:4.2f}, {:4.2f})".format( np.mean(ut) * self._umtopixel, np.mean(vt) * self._umtopixel)) print("... mean velocity [pixel/frame]: ({:4.2f}, {:4.2f})".format( np.mean(ut) * self._dt, np.mean(vt) * self._dt))
def process(self, args): """ Process chain as configured in the GUI. Parameters ---------- args : tuple Tuple as expected by the inherited run method: file_a (str) -- image file a file_b (str) -- image file b counter (int) -- index pointing to an element of the filename list """ file_a, file_b, counter = args frame_a = piv_tls.imread(file_a) frame_b = piv_tls.imread(file_b) # Smoothning script borrowed from openpiv.windef s = self.p['smoothn_val'] def smoothn(u, s): s = s u, _, _, _ = piv_smt.smoothn(u, s=s, isrobust=self.p['robust']) return (u) # delimiters placed here for safety delimiter = self.p['separator'] if delimiter == 'tab': delimiter = '\t' if delimiter == 'space': delimiter = ' ' # preprocessing print('\nPre-pocessing image pair: {}'.format(counter + 1)) if self.p['background_subtract'] \ and self.p['background_type'] == 'minA - minB': self.background = gen_background(self.p, frame_a, frame_b) frame_a = frame_a.astype(np.int32) frame_a = process_images(self, frame_a, self.GUI.preprocessing_methods, background=self.background) frame_b = frame_b.astype(np.int32) frame_b = process_images(self, frame_b, self.GUI.preprocessing_methods, background=self.background) print('Evaluating image pair: {}'.format(counter + 1)) # evaluation first pass start = time.time() passes = 1 # setup custom windowing if selected if self.parameter['custom_windowing']: corr_window_0 = self.parameter['corr_window_1'] overlap_0 = self.parameter['overlap_1'] for i in range(2, 8): if self.parameter['pass_%1d' % i]: passes += 1 else: break else: passes = self.parameter['coarse_factor'] if self.parameter['grid_refinement'] == 'all passes' \ and self.parameter['coarse_factor'] != 1: corr_window_0 = self.parameter['corr_window'] * \ 2**(self.parameter['coarse_factor'] - 1) overlap_0 = self.parameter['overlap'] * \ 2**(self.parameter['coarse_factor'] - 1) # Refine all passes after first when there are more than 1 pass. elif self.parameter['grid_refinement'] == '2nd pass on' \ and self.parameter['coarse_factor'] != 1: corr_window_0 = self.parameter['corr_window'] * \ 2**(self.parameter['coarse_factor'] - 2) overlap_0 = self.parameter['overlap'] * \ 2**(self.parameter['coarse_factor'] - 2) # If >>none<< is selected or something goes wrong, the window # size would remain the same. else: corr_window_0 = self.parameter['corr_window'] overlap_0 = self.parameter['overlap'] overlap_percent = overlap_0 / corr_window_0 sizeX = corr_window_0 u, v, sig2noise = piv_wdf.extended_search_area_piv( frame_a.astype(np.int32), frame_b.astype(np.int32), window_size=corr_window_0, overlap=overlap_0, search_area_size=corr_window_0, width=self.parameter['s2n_mask'], subpixel_method=self.parameter['subpixel_method'], sig2noise_method=self.parameter['sig2noise_method'], correlation_method=self.parameter['corr_method'], normalized_correlation=self.parameter['normalize_correlation']) x, y = piv_prc.get_coordinates(frame_a.shape, corr_window_0, overlap_0) # validating first pass mask = np.full_like(x, 0) if self.parameter['fp_vld_global_threshold']: u, v, Mask = piv_vld.global_val( u, v, u_thresholds=(self.parameter['fp_MinU'], self.parameter['fp_MaxU']), v_thresholds=(self.parameter['fp_MinV'], self.parameter['fp_MaxV'])) # consolidate effects of mask mask += Mask if self.parameter['fp_local_med']: u, v, Mask = piv_vld.local_median_val( u, v, u_threshold=self.parameter['fp_local_med'], v_threshold=self.parameter['fp_local_med'], size=self.parameter['fp_local_med_size']) mask += Mask if self.parameter['adv_repl']: u, v = piv_flt.replace_outliers( u, v, method=self.parameter['adv_repl_method'], max_iter=self.parameter['adv_repl_iter'], kernel_size=self.parameter['adv_repl_kernel']) print('Validated first pass result of image pair: {}.'.format(counter + 1)) # smoothning before deformation if 'each pass' is selected if self.parameter['smoothn_each_pass']: if self.parameter['smoothn_first_more']: s *= 2 u = smoothn(u, s) v = smoothn(v, s) print('Smoothned pass 1 for image pair: {}.'.format(counter + 1)) s = self.parameter['smoothn_val1'] print('Finished pass 1 for image pair: {}.'.format(counter + 1)) print("window size: " + str(corr_window_0)) print('overlap: ' + str(overlap_0), '\n') # evaluation of all other passes if passes != 1: iterations = passes - 1 for i in range(2, passes + 1): # setting up the windowing of each pass if self.parameter['custom_windowing']: corr_window = self.parameter['corr_window_%1d' % i] overlap = int(corr_window * overlap_percent) else: if self.parameter['grid_refinement'] == 'all passes' or \ self.parameter['grid_refinement'] == '2nd pass on': corr_window = self.parameter['corr_window'] * \ 2**(iterations - 1) overlap = self.parameter['overlap'] * \ 2**(iterations - 1) else: corr_window = self.parameter['corr_window'] overlap = self.parameter['overlap'] sizeX = corr_window # translate settings to windef settings object piv_wdf_settings = piv_wdf.Settings() piv_wdf_settings.correlation_method = \ self.parameter['corr_method'] piv_wdf_settings.normalized_correlation = \ self.parameter['normalize_correlation'] piv_wdf_settings.windowsizes = (corr_window, ) * (passes + 1) piv_wdf_settings.overlap = (overlap, ) * (passes + 1) piv_wdf_settings.num_iterations = passes piv_wdf_settings.subpixel_method = \ self.parameter['subpixel_method'] piv_wdf_settings.deformation_method = \ self.parameter['deformation_method'] piv_wdf_settings.interpolation_order = \ self.parameter['interpolation_order'] piv_wdf_settings.sig2noise_validate = True, piv_wdf_settings.sig2noise_method = \ self.parameter['sig2noise_method'] piv_wdf_settings.sig2noise_mask = self.parameter['s2n_mask'] # do the correlation x, y, u, v, sig2noise, mask = piv_wdf.multipass_img_deform( frame_a.astype(np.int32), frame_b.astype(np.int32), i, # current iteration x, y, u, v, piv_wdf_settings) # validate other passes if self.parameter['sp_vld_global_threshold']: u, v, Mask = piv_vld.global_val( u, v, u_thresholds=(self.parameter['sp_MinU'], self.parameter['sp_MaxU']), v_thresholds=(self.parameter['sp_MinV'], self.parameter['sp_MaxV'])) mask += Mask # consolidate effects of mask if self.parameter['sp_vld_global_threshold']: u, v, Mask = piv_vld.global_std( u, v, std_threshold=self.parameter['sp_std_threshold']) mask += Mask if self.parameter['sp_local_med_validation']: u, v, Mask = piv_vld.local_median_val( u, v, u_threshold=self.parameter['sp_local_med'], v_threshold=self.parameter['sp_local_med'], size=self.parameter['sp_local_med_size']) mask += Mask if self.parameter['adv_repl']: u, v = piv_flt.replace_outliers( u, v, method=self.parameter['adv_repl_method'], max_iter=self.parameter['adv_repl_iter'], kernel_size=self.parameter['adv_repl_kernel']) print('Validated pass {} of image pair: {}.'.format( i, counter + 1)) # smoothning each individual pass if 'each pass' is selected if self.parameter['smoothn_each_pass']: u = smoothn(u, s) v = smoothn(v, s) print('Smoothned pass {} for image pair: {}.'.format( i, counter + 1)) print('Finished pass {} for image pair: {}.'.format( i, counter + 1)) print("window size: " + str(corr_window)) print('overlap: ' + str(overlap), '\n') iterations -= 1 if self.p['flip_u']: u = np.flipud(u) if self.p['flip_v']: v = np.flipud(v) if self.p['invert_u']: u *= -1 if self.p['invert_v']: v *= -1 # scaling u = u / self.parameter['dt'] v = v / self.parameter['dt'] x, y, u, v = piv_scl.uniform(x, y, u, v, scaling_factor=self.parameter['scale']) end = time.time() # save data to file. out = np.vstack([m.ravel() for m in [x, y, u, v, mask, sig2noise]]) np.savetxt(self.save_fnames[counter], out.T, fmt='%8.4f', delimiter=delimiter) print('Processed image pair: {}'.format(counter + 1)) sizeY = sizeX sizeX = ((int(frame_a.shape[0] - sizeX) // (sizeX - (sizeX * overlap_percent))) + 1) sizeY = ((int(frame_a.shape[1] - sizeY) // (sizeY - (sizeY * overlap_percent))) + 1) time_per_vec = _round((((end - start) * 1000) / ((sizeX * sizeY) - 1)), 3) print('Process time: {} second(s)'.format((_round((end - start), 3)))) print('Number of vectors: {}'.format(int((sizeX * sizeY) - 1))) print('Time per vector: {} millisecond(s)'.format(time_per_vec))
# correcting stage drift between the field of views u -= np.nanmean(u) v -= np.nanmean(v) w -= np.nanmean(w) # %% # filtering uf, vf, wf, mask = sig2noise_val(u, v, w=w, sig2noise=sig2noise, threshold=signoise_filter) uf, vf, wf = replace_outliers(uf, vf, wf, max_iter=1, tol=100, kernel_size=2, method='disk') # %% # plotting # representation of the image stacks by maximums projections. The red circle marks the position of the cell def update_plot(i, ims, ax): a1 = ax.imshow(ims[i]) a2 = ax.add_patch(plt.Circle((330, 140), 100, color="red", fill=False)) return [a1, a2] # %%
u, v, sig2noise = process.extended_search_area_piv( frame_a.astype(np.int32), frame_b.astype(np.int32), window_size=32, overlap=8, dt=.1, sig2noise_method='peak2peak') x, y = process.get_coordinates(image_size=frame_a.shape, window_size=32, overlap=8) u, v, mask = validation.sig2noise_val(u, v, sig2noise, threshold=1.3) u, v = filters.replace_outliers(u, v, method='localmean', max_iter=10, kernel_size=2) x, y, u, v = scaling.uniform(x, y, u, v, scaling_factor=96.52) tools.save(x, y, u, v, mask, 'Y4-S3_Camera000398_a.txt') # %% # Use Python version, pyprocess: u, v, sig2noise = pyprocess.extended_search_area_piv( frame_a.astype(np.int32), frame_b.astype(np.int32), window_size=32, overlap=8, dt=.1,
def func(args): file_a, file_b, counter = args # read the iamges frame_a = tools.imread(os.path.join(settings.filepath_images, file_a)) frame_b = tools.imread(os.path.join(settings.filepath_images, file_b)) if counter == settings.fall_start: settings.ROI[1] = frame_a.shape[0] """Here we check if the interface has reached the top of the roi yet by comparing it to the index in the observation_periods file. If it has not reached the roi yet we skip this part, if it did then we shift the roi for each pair after the initial one """ if counter >= settings.roi_shift_start: # set the roi to the image height for the first frame # if counter == settings.roi_shift_start : # settings.current_pos = 0 # shift the roi for each pair (this is not done for the first one) settings.ROI[0] = int(settings.current_pos) # 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.dynamic_masking_method == 'edge' or settings.dynamic_masking_method == 'intensity': frame_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 = preprocess.dynamic_masking( frame_b, method=settings.dynamic_masking_method, filter_size=settings.dynamic_masking_filter_size, threshold=settings.dynamic_masking_threshold) #%% """ Here we do the first pass of the piv interrogation """ x, y, u, v, sig2noise_ratio = first_pass( frame_a, frame_b, settings.window_width[0], settings.window_height[0], settings.overlap_width[0], settings.overlap_height[0], settings.iterations, correlation_method=settings.correlation_method, subpixel_method=settings.subpixel_method, do_sig2noise=settings.extract_sig2noise, sig2noise_method=settings.sig2noise_method, sig2noise_mask=settings.sig2noise_mask, ) mask = np.full_like(x, False) if settings.validation_first_pass == True: u, v, mask_g = validation.global_val(u, v, settings.MinMax_U_disp, settings.MinMax_V_disp) u, v, mask_s = validation.global_std( u, v, std_threshold=settings.std_threshold) u, v, mask_m = validation.local_median_val( u, v, u_threshold=settings.median_threshold, v_threshold=settings.median_threshold, size=settings.median_size) if settings.extract_sig2noise == True and settings.iterations == 1 and settings.do_sig2noise_validation == True: u, v, mask_s2n = validation.sig2noise_val( u, v, sig2noise_ratio, threshold=settings.sig2noise_threshold) mask = mask + mask_g + mask_m + mask_s + mask_s2n else: mask = mask + mask_g + mask_m + mask_s 'filter to replace the values that where marked by the validation' if settings.iterations > 1: 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 == True: 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) elif settings.iterations == 1 and settings.replace_vectors == True: 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 == True: u, v = filters.replace_outliers( u, v, method=settings.filter_method, max_iter=settings.max_filter_iteration, kernel_size=settings.filter_kernel_size) 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) #%% i = 1 """ Do the multipass until the maximum iterations are reached """ for i in range(2, settings.iterations + 1): x, y, u, v, sig2noise_ratio, mask = multipass_img_deform( frame_a, frame_b, settings.window_width[i - 1], settings.window_height[i - 1], settings.overlap_width[i - 1], settings.overlap_height[i - 1], settings.iterations, i, x, y, u, v, correlation_method=settings.correlation_method, subpixel_method=settings.subpixel_method, do_sig2noise=settings.extract_sig2noise, sig2noise_method=settings.sig2noise_method, sig2noise_mask=settings.sig2noise_mask, MinMaxU=settings.MinMax_U_disp, MinMaxV=settings.MinMax_V_disp, std_threshold=settings.std_threshold, median_threshold=settings.median_threshold, median_size=settings.median_size, filter_method=settings.filter_method, max_filter_iteration=settings.max_filter_iteration, filter_kernel_size=settings.filter_kernel_size, interpolation_order=settings.interpolation_order) # smooth on each pass in case this is wanted if settings.smoothn == True: 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) # extract the sig2noise ratio in case it is desired and replace the vectors if settings.extract_sig2noise == True and i == settings.iterations and settings.iterations != 1 and settings.do_sig2noise_validation == True: u, v, mask_s2n = validation_patch.sig2noise_val( u, v, sig2noise_ratio, threshold_low=settings.sig2noise_threshold) mask = mask + mask_s2n if settings.replace_vectors == True: u, v = filters.replace_outliers( u, v, method=settings.filter_method, max_iter=settings.max_filter_iteration, kernel_size=settings.filter_kernel_size) if counter >= settings.roi_shift_start: settings.current_pos = settings.current_pos - calc_disp( x, v, frame_b.shape[1]) if ((settings.ROI[1] - settings.current_pos) < 300): return settings.current_pos, True # scale the result timewise and lengthwise u = u / settings.dt v = v / settings.dt x, y, u, v = scaling.uniform(x, y, u, v, scaling_factor=settings.scaling_factor) # save the result save(x, y, u, v, sig2noise_ratio, mask, os.path.join(save_path_txts, 'field_%06d.txt' % (counter)), delimiter='\t') # disable the grid in the rcParams file plt.rcParams['axes.grid'] = False # show and save the plot if it is desired if settings.show_plot == True or settings.save_plot == True: plt.ioff() Name = os.path.join(save_path_images, 'Image_%06d.png' % (counter)) display_vector_field(os.path.join(save_path_txts, 'field_%06d.txt' % (counter)), scale=settings.scale_plot) if settings.save_plot == True: plt.savefig(Name, dpi=100) if settings.show_plot == True: plt.show() plt.close('all') print('Image Pair ' + str(counter) + ' of ' + settings.save_folder_suffix) if settings.current_pos == np.nan: return settings.current_pos, True return settings.current_pos, False
def quick_piv(self, search_dict, index_a=100, index_b=101, folder=None): self.show_piv_param() ns = Namespace(**self.piv_param) if folder == None: img_a, img_b = self.read_two_images(search_dict, index_a=index_a, index_b=index_b) location_path = [ x['path'] for x in self.piv_dict_list if search_dict.items() <= x.items() ] results_path = os.path.join(self.results_path, *location_path) try: os.makedirs(results_path) except FileExistsError: pass else: try: file_a_path = os.path.join(self.path, folder, 'frame_%06d.tiff' % index_a) file_b_path = os.path.join(self.path, folder, 'frame_%06d.tiff' % index_b) img_a = np.array(Image.open(file_a_path)) img_b = np.array(Image.open(file_b_path)) except: return None # crop img_a = img_a[ns.crop[0]:-ns.crop[1] - 1, ns.crop[2]:-ns.crop[3] - 1] img_b = img_b[ns.crop[0]:-ns.crop[1] - 1, ns.crop[2]:-ns.crop[3] - 1] u0, v0, sig2noise = pyprocess.extended_search_area_piv( img_a.astype(np.int32), img_b.astype(np.int32), window_size=ns.winsize, overlap=ns.overlap, dt=ns.dt, search_area_size=ns.searchsize, sig2noise_method='peak2peak') x, y = pyprocess.get_coordinates(image_size=img_a.shape, search_area_size=ns.searchsize, overlap=ns.overlap) x, y, u0, v0 = scaling.uniform( x, y, u0, v0, scaling_factor=ns.pixel_density) # no. pixel per distance u0, v0, mask = validation.global_val( u0, v0, (ns.u_lower_bound, ns.u_upper_bound), (ns.v_lower_bound, ns.v_upper_bound)) u1, v1, mask = validation.sig2noise_val(u0, v0, sig2noise, threshold=1.01) u3, v3 = filters.replace_outliers(u1, v1, method='localmean', max_iter=500, kernel_size=3) #save in the simple ASCII table format tools.save(x, y, u3, v3, sig2noise, mask, os.path.join(results_path, ns.text_export_name)) if ns.image_check == True: fig, ax = plt.subplots(2, 1, figsize=(24, 12)) ax[0].imshow(img_a) ax[1].imshow(img_b) io.imwrite(os.path.join(results_path, ns.figure_export_name), img_a) if ns.show_result == True: fig, ax = plt.subplots(figsize=(24, 12)) tools.display_vector_field( os.path.join(results_path, ns.text_export_name), ax=ax, scaling_factor=ns.pixel_density, scale=ns.scale_factor, # scale defines here the arrow length width=ns.arrow_width, # width is the thickness of the arrow on_img=True, # overlay on the image image_name=os.path.join(results_path, ns.figure_export_name)) fig.savefig(os.path.join(results_path, ns.figure_export_name)) if ns.show_vertical_profiles: field_shape = pyprocess.get_field_shape( image_size=img_a.shape, search_area_size=ns.searchsize, overlap=ns.overlap) vertical_profiles(ns.text_export_name, field_shape) print('Mean of u: %.3f' % np.mean(u3)) print('Std of u: %.3f' % np.std(u3)) print('Mean of v: %.3f' % np.mean(v3)) print('Std of v: %.3f' % np.std(v3)) output = np.array([np.mean(u3), np.std(u3), np.mean(v3), np.std(v3)]) # if np.absolute(np.mean(v3)) < 50: # output = self.quick_piv(search_dict,index_a = index_a + 1, index_b = index_b + 1) return x, y, u3, v3
def run_piv( frame_a, frame_b, winsize=16, # pixels, interrogation window size in frame A searchsize=20, # pixels, search in image B overlap=8, # pixels, 50% overlap dt=0.0001, # sec, time interval between pulses image_check=False, show_vertical_profiles=False, figure_export_name='_results.png', text_export_name="_results.txt", scale_factor=1, pixel_density=36.74, arrow_width=0.001, show_result=True, u_bounds=(-100, 100), v_bounds=(-100, 100)): u0, v0, sig2noise = pyprocess.extended_search_area_piv( frame_a.astype(np.int32), frame_b.astype(np.int32), window_size=winsize, overlap=overlap, dt=dt, search_area_size=searchsize, sig2noise_method='peak2peak') x, y = pyprocess.get_coordinates(image_size=frame_a.shape, search_area_size=searchsize, overlap=overlap) x, y, u0, v0 = scaling.uniform( x, y, u0, v0, scaling_factor=pixel_density) # no. pixel per distance u0, v0, mask = validation.global_val(u0, v0, u_bounds, v_bounds) u1, v1, mask = validation.sig2noise_val(u0, v0, sig2noise, threshold=1.05) u3, v3 = filters.replace_outliers(u1, v1, method='localmean', max_iter=10, kernel_size=3) #save in the simple ASCII table format if np.std(u3) < 480: tools.save(x, y, u3, v3, sig2noise, mask, text_export_name) if image_check == True: fig, ax = plt.subplots(2, 1, figsize=(24, 12)) ax[0].imshow(frame_a) ax[1].imshow(frame_b) io.imwrite(figure_export_name, frame_a) if show_result == True: fig, ax = plt.subplots(figsize=(24, 12)) tools.display_vector_field( text_export_name, ax=ax, scaling_factor=pixel_density, scale=scale_factor, # scale defines here the arrow length width=arrow_width, # width is the thickness of the arrow on_img=True, # overlay on the image image_name=figure_export_name) fig.savefig(figure_export_name) if show_vertical_profiles: field_shape = pyprocess.get_field_shape(image_size=frame_a.shape, search_area_size=searchsize, overlap=overlap) vertical_profiles(text_export_name, field_shape) print('Std of u3: %.3f' % np.std(u3)) print('Mean of u3: %.3f' % np.mean(u3)) return np.std(u3)
def run(self): self.is_to_stop = False self.piv.piv_results_list = [] for i in range(0, len(self.frames_list) - 1, abs(self.jump)): if self.piv.xy_zoom[0][0] and self.piv.xy_zoom[1][0]: """try:""" frame_a = self.frames_list[i][2][ int(self.piv.xy_zoom[1][0]):int(self.piv.xy_zoom[1][1]), int(self.piv.xy_zoom[0][0]):int(self.piv.xy_zoom[0][1])] frame_b = self.frames_list[i + 1][2][ int(self.piv.xy_zoom[1][0]):int(self.piv.xy_zoom[1][1]), int(self.piv.xy_zoom[0][0]):int(self.piv.xy_zoom[0][1])] """ except ValueError: frame_a = self.frames_list[i][2][int(self.frames_list[i][2].shape[1] - self.piv.xy_zoom[1][0]): int( self.frames_list[i][2].shape[1] - self.piv.xy_zoom[1][1]), int(self.piv.xy_zoom[0][0]): int( self.piv.xy_zoom[0][1])] frame_b = self.frames_list[i + 1][2][ int(self.frames_list[i + 1][2].shape[1] - self.piv.xy_zoom[1][0]): int( self.frames_list[i + 1][2].shape[1] - self.piv.xy_zoom[1][1]), int(self.piv.xy_zoom[0][0]): int( self.piv.xy_zoom[0][1])] """ else: frame_a = self.frames_list[i][2] frame_b = self.frames_list[i + 1][2] try: self.u, self.v, self.sig2noise = extended_search_area_piv( frame_a.astype(np.int32), frame_b.astype(np.int32), window_size=self.winsize, overlap=self.overlap, dt=self.dt, search_area_size=self.searchsize, sig2noise_method='peak2peak') self.x, self.y = get_coordinates(image_size=frame_a.shape, window_size=self.winsize, overlap=self.overlap) self.u, self.v, self.mask = sig2noise_val(self.u, self.v, self.sig2noise, threshold=1.0) self.u, self.v = replace_outliers(self.u, self.v, method='localmean', max_iter=10, kernel_size=2) # self.x, self.y, self.u, self.v = uniform(self.x, self.y, self.u, self.v, scaling_factor=5) if self.piv.xy_zoom[0][0] and self.piv.xy_zoom[1][0]: self.x += int(self.piv.xy_zoom[0][0]) self.y += int(self.piv.xy_zoom[1][0]) self.y = np.flip(self.y, 0) self.x = np.flip(self.x, 0) except ValueError: if self.searchsize < self.winsize: print("0") self.error_message.setText( "the search size cannot be smaller than the window size" ) elif self.overlap > self.winsize: print("1") self.error_message.setText( "Overlap has to be smaller than the window_size") else: print("2") self.error_message.setText("ROI window to small") self.error_message.exec() break self.piv.piv_results_list.append( [self.x, self.y, self.u, self.v, self.mask]) self.piv.piv_images_list[i][3] = self.piv.piv_results_list[ i // abs(self.jump)] # data = np.zeros((len(np.ravel(self.u)), 5)) # res_list = [np.ravel(self.x), np.ravel(self.y), np.ravel(self.u), np.ravel(self.v), np.ravel(self.mask)] # for j in range(0, 4): # for k in range(len(res_list[j])): # data[k][j] = res_list[j][k] # self.piv.piv_images_list[i][4] = data # save_openpiv_vec(self.piv.piv_images_list[i][1].split('.')[0], data, 'pix', 'dt', # len(data[0]), len(data)) self.piv.show_plot(i, self.piv.bit, True) if i == len(self.frames_list) - 2 and self.jump == 1: self.piv.piv_results_list.append( [self.x, self.y, self.u, self.v, self.mask]) self.piv.piv_images_list[i + 1][3] = self.piv.piv_results_list[i + 1] # self.piv.piv_images_list[i + 1][4] = data self.piv.show_plot(i + 1, self.piv.bit, True) if self.is_to_stop: break self.piv_finished_signal.emit()
ax[0].imshow(frame_a,cmap=plt.cm.gray) ax[1].imshow(frame_b,cmap=plt.cm.gray) # %% winsize = 24 # pixels searchsize = 64 # pixels, search in image B overlap = 12 # pixels dt = 0.02 # sec u0, v0, sig2noise = process.extended_search_area_piv( frame_a.astype(np.int32), frame_b.astype(np.int32), window_size=winsize, overlap=overlap, dt=dt, search_area_size=searchsize, sig2noise_method='peak2peak' ) # %% x, y = process.get_coordinates( image_size=frame_a.shape, window_size=winsize, overlap=overlap ) # %% u1, v1, mask = validation.sig2noise_val( u0, v0, sig2noise, threshold = 1.3 ) # %% u2, v2 = filters.replace_outliers( u1, v1, method='localmean', max_iter=10, kernel_size=2) # %% x, y, u3, v3 = scaling.uniform(x, y, u2, v2, scaling_factor = 96.52 ) # %% tools.save(x, y, u3, v3, mask, 'exp1_001.txt' ) # %% tools.display_vector_field('exp1_001.txt', scale=100, width=0.0025)
def calc_piv_2_images(frame_a, frame_b, idx, dir_name): ''' Performs Particle Image Velocimetry (PIV) of two images, and saves an image with PIV on it. :param frame_a: first image :param frame_b: consecutive image :param idx: index of the first frame, for saving and ordering the images :param dir_name: directory to save the image to :return: - ''' u0, v0, sig2noise = pyprocess.extended_search_area_piv( frame_a.astype(np.int32), frame_b.astype(np.int32), window_size=winsize, overlap=overlap, dt=dt, search_area_size=searchsize, sig2noise_method='peak2peak') x, y = pyprocess.get_coordinates(image_size=frame_a.shape, search_area_size=searchsize, overlap=overlap) u1, v1, mask = validation.sig2noise_val(u0, v0, sig2noise, threshold=1.05) # to see where is a reasonable limit filter out # outliers that are very different from the neighbours u2, v2 = filters.replace_outliers(u1, v1, method='localmean', max_iter=3, kernel_size=3) # convert x,y to mm; convert u,v to mm/sec x, y, u3, v3 = scaling.uniform( x, y, u2, v2, scaling_factor=scaling_factor) # 96.52 microns/pixel # 0,0 shall be bottom left, positive rotation rate is counterclockwise x, y, u3, v3 = tools.transform_coordinates(x, y, u3, v3) fig, ax = plt.subplots() im = np.negative(frame_a) # plot negative of the image for more clarity xmax = np.amax(x) + winsize / (2 * scaling_factor) ymax = np.amax(y) + winsize / (2 * scaling_factor) ax.imshow(im, cmap="Greys_r", extent=[0.0, xmax, 0.0, ymax]) invalid = mask.astype("bool") valid = ~invalid plt.quiver(x[invalid], y[invalid], u3[invalid], v3[invalid], color="r", width=width) plt.quiver(x[valid], y[valid], u3[valid], v3[valid], color="b", width=width) ax.set_aspect(1.) plt.title(r'Velocity Vectors Field (Frame #%d) $(\frac{\mu m}{hour})$' % idx) plt.savefig(dir_name + "/" + "vec_page%d.png" % idx, dpi=200) plt.show() plt.close()