frame = vid.read_next_frame()
return images.crop_polygon(frame)
def go(files, crop_result):
for i, file in tqdm(enumerate(files)):
# file = direc + '/' + file
name, ext = os.path.splitext(file)
if ext == '.MP4':
if i == 0 and crop_result is None:
print('cropping')
crop_result = get_crop_result(file)
data_file = name + '.hdf5'
if not os.path.exists(data_file):
tracker = tracking.ParticleTracker(
file, track.HoughManager(crop_result=crop_result), True)
tracker.track()
# tracker.link()
return crop_result
parent = "/media/data/Data/N32/PhaseDiagram_2021_07_06"
all_files = []
for direc in os.listdir(parent):
new_direc = parent + '/' + direc
print(new_direc)
files = filehandling.get_directory_filenames(new_direc + '/*.MP4')
all_files += files
print(all_files)
go(all_files, None)
import filehandling
from particletracking import dataframes
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
directory = filehandling.open_directory('/media/data/Data')
files = filehandling.get_directory_filenames(directory + '/*.hdf5')
duties = np.arange(750, 525, 25)
numbers = np.arange(1700, 2400, 50)
counts = {n: {d: 0 for d in duties} for n in numbers}
for file in files:
filename = os.path.splitext(os.path.split(file)[1])[0]
number, duty = filename.split('_')
number = int(number)
duty = int(duty)
data = dataframes.DataStore(file)
order = data.df.order
count = np.count_nonzero(order > 0.95) / len(order)
counts[number][duty] = count
# print(count)
counts = pd.DataFrame(counts)
counts.plot()
plt.show()
def run():
direc = "/media/data/Data/FirstOrder/Interfaces/RecordFluctuatingInterfaceJanuary2020/Quick/first_frames"
savename = f"{direc}/data_new.hdf5"
files = filehandling.get_directory_filenames(direc + '/*.png')
ims = [images.load(f, 0) for f in tqdm(files, 'Loading images')]
ims = [images.bgr_to_gray(im) for im in ims]
circles = [images.find_circles(im, 27, 200, 7, 16, 16)
for im in tqdm(ims, 'Finding Circles')]
data = dataframes.DataStore(savename, load=False)
for f, info in tqdm(enumerate(circles), 'Adding Circles'):
data.add_tracking_data(f, info, ['x', 'y', 'r'])
calc = statistics.PropertyCalculator(data)
calc.order()
lattice_spacing = 10
x = np.arange(0, ims[0].shape[1], lattice_spacing)
y = np.arange(0, ims[0].shape[0], lattice_spacing)
x, y = np.meshgrid(x, y)
# cgw = get_cgw(data.df.loc[0], 1.85) # k=1.85 looked the best
cgw = get_cgw(data.df.loc[0], 1.85)
fields = [coarse_order_field(data.df.loc[f], cgw, x, y)
for f in tqdm(range(len(ims)), 'Calculating Fields')]
field_threshold = get_field_threshold(fields, lattice_spacing, ims[0])
contours = [find_contours(f, field_threshold)
for f in tqdm(fields, 'Calculating contours')]
# Multiply the contours by the lattice spacing and squeeze
contours = [c.squeeze() * lattice_spacing for c in contours]
# Close contours
contours = [close_contour(c) for c in contours]
# Convert to LineString
contours = [LineString(c) for c in contours]
# Select the line to query the points across
print("Select the line to query points")
ls = LineSelector(ims[0])
p1, p2 = ls.points
centre_line = get_extended_centre_line(p1, p2)
# Distance between query points that determines one end of the frequency
dL = data.df.loc[0].r.mean() / 10
L = np.sqrt((p1[0]-p2[0])**2+(p1[1]-p2[1])**2)
N_query = int(L/dL)
xq, yq = np.linspace(p1[0], p2[0], N_query), np.linspace(p1[1], p2[1],
N_query)
dL = np.sqrt((xq[1] - xq[0]) ** 2 + (yq[1] - yq[0]) ** 2)
dists, crosses = zip(
*[get_dists(xq, yq, c, centre_line) for c in tqdm(contours)])
# plot_crosses(crosses, ims)
# Select points from inside red edge to inside red edge across the centre
# of the tray which is 200mm to convert pixels to mm
PIX_2_mm = get_pix_2_mm(ims[0])
plot_fft(dists, dL, PIX_2_mm, data.df.loc[0].r.mean(), cgw)
import numpy as np
import matplotlib.pyplot as plt
import filehandling
from labvision import images
from math import atan, sin, cos
import cv2
direc = "/media/data/Data/January2020/RecordFluctuatingInterface/Quick/first_frames/"
pixels_to_mms = 0.11797
x = np.loadtxt(direc + 'x.txt') / pixels_to_mms
hs = np.loadtxt(direc + 'hs.txt') / pixels_to_mms
h = hs[20]
files = filehandling.get_directory_filenames(direc + '/*.png')
im = images.load(files[20])
def rotate_points(points, center, a):
rot = np.array(((cos(a), -sin(a)), (sin(a), cos(a))))
a1 = points - center
a2 = rot @ a1.T
a3 = a2.T + center
return a3
p1 = [333, 1318]
p2 = [1784, 528]
midpoint = np.array([p1[0] - p2[0], p1[1] - p2[1]])
h = h + midpoint[1]
from particletracking import statistics, dataframes
import filehandling
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
direc = "/media/data/Data/August2020/Hysterisis/RedTray2"
save_direc = "/media/data/Data/August2020/Hysterisis/RedTray2/Results_boxed_density_frame"
files = filehandling.get_directory_filenames(direc + '/*.hdf5')
#
# for file in files:
# print(file)
# filename = os.path.splitext(os.path.split(file)[1])[0]
# rate, direction, trial = filename.split('_')
# trial = int(trial)
# df = dataframes.DataStore(file).df
# if file == files[0]:
# r = df.r.mean()
# df = df.loc[(df.x > 750) * (df.x < 1250) * (df.y > 750) * (df.y < 1250)]
# # df = df.loc[df.edge_distance > 5*r]
# df.Duty = df.Duty.round()
# # xmean = df.x.mean()
# # df = df.loc[df.x > xmean]
#
# result = df.groupby(df.index)['density'].mean()
# result.to_csv(save_direc+'/{}_{}_{}'.format(rate, direction, trial))
data_files = filehandling.get_directory_filenames(save_direc + '/*')
results = {}
def run(direc, lattice_spacing=5):
files = filehandling.get_directory_filenames(direc + '/*.png')
print(files)
savename = direc + '/data.hdf5'
N = len(files)
# load images
ims = [images.load(f, 0) for f in tqdm(files, 'Loading images')]
images.display(ims[0])
# Find Circles
ims = [images.bgr_to_gray(im) for im in ims]
circles = [
images.find_circles(im, 27, 200, 7, 16, 16)
for im in tqdm(ims, 'Finding Circles')
]
# Save data
data = dataframes.DataStore(savename, load=False)
for f, info in tqdm(enumerate(circles), 'Adding Circles'):
data.add_tracking_data(f, info, ['x', 'y', 'r'])
# Calculate order parameter
calc = statistics.PropertyCalculator(data)
calc.order()
# Get the course graining width
cgw = get_cgw(data.df.loc[0]) / 2
# Create the lattice points
x = np.arange(0, max(data.df.x), lattice_spacing)
y = np.arange(0, max(data.df.y), lattice_spacing)
x, y = np.meshgrid(x, y)
# Calculate the coarse order fields
fields = [
coarse_order_field(data.df.loc[f], cgw, x, y)
for f in tqdm(range(N), 'Calculating Fields')
]
# Calculate the field threshold
field_threshold = get_field_threshold(fields, lattice_spacing, ims[0])
# Find the contours representing the boundary in each frame
contours = [
find_contours(f, field_threshold)
for f in tqdm(fields, 'Calculating contours')
]
# Multiply the contours by the lattice spacing
contours = [c * lattice_spacing for c in contours]
# Find the angle of the image to rotate the boundary to the x-axis
a, c, p1, p2 = get_angle(ims[0])
print(p1)
print(p2)
# Rotate the selection points and the contours by the angle
p1 = rotate_points(np.array(p1), c, a)
p2 = rotate_points(np.array(p2), c, a)
contours = [rotate_points(contour.squeeze(), c, a) for contour in contours]
xmin = int(p1[0])
xmax = int(p2[0])
h = int(p1[1])
# Get the heights of the fluctuations from the straight boundary
hs = [
get_h(contour, ims[0].shape, xmin, xmax, h)
for contour in tqdm(contours, 'Calculating heights')
]
# Calculate the fourier transforms for all the frames
L = xmax - xmin
pixels_to_mms = 195 / L
print("pixels_to_mms = ", pixels_to_mms)
# convert to mm
hs = [h * pixels_to_mms for h in hs]
L = L * pixels_to_mms
x = np.linspace(0, L, len(hs[0]))
np.savetxt(direc + '/x.txt', x)
np.savetxt(direc + '/hs.txt', hs)
# k, yplot = get_fourier(hs, L)
return k, yplot