from matplotlib import pyplot as plt
from colloids import track
def draw_circles(xs, ys, rs, **kwargs):
for x, y, r in zip(xs, ys, rs):
circle = plt.Circle((x, y), radius=r, **kwargs)
plt.gca().add_patch(circle)
im = plt.imread('droplets.jpg')
finder = track.MultiscaleBlobFinder(im.shape, Octave0=False, nbOctaves=4)
centers = finder(im, k=2)
draw_circles(centers[:, 0], centers[:, 1], centers[:, 2], facecolor='none', edgecolor='g')
plt.imshow(im, 'hot')
plt.show()
pattern = filename
path, name = split(filename)
outpattern = join(path,'treated_'+name)
divpattern = splitext(join(path,'bgdiv_'+name))[0]+'.jpg'
filename = pattern%0
#read the first image
im = imread(filename)
if im.ndim ==0:
im = readTIFF16(filename)
cent = np.zeros_like(im)
if len(sys.argv) >2:
sigma = float(sys.argv[2])
else:
#estimate the best blurring radius
finder = track.MultiscaleBlobFinder(im.shape, nbOctaves=100)
centers = finder(im, k=1.6, Octave0=False, removeOverlap=False)
sigma = 1.6*2**(np.argmax([
oc.binary[1:-1].sum(-1).sum(-1)
for oc in finder.octaves[1:]
])/3.0)
#create a monoscale Crocker & Grier finder
finder = track.CrockerGrierFinder(im.shape)
#background
background = track.gaussian_filter(np.array(im, float), 10*sigma)
background[background==0] = 1.0
#drift or flow
overlap = 2**int(np.log(20*sigma)/np.log(2))
window_size = 2*overlap
#having 3 subwindows is not enought
use_subgrid_phasecor = 5*overlap < min(im.shape)
import numpy as np
def draw_circles(centres, z_chosen, **kwargs):
for (x, y, z, r) in centres[:, :4]:
dz = np.abs(z - z_chosen)
if dz < r:
r_real = np.sqrt(r**2 - dz**2)
circle = plt.Circle((x, y), radius=r_real, **kwargs)
plt.gca().add_patch(circle)
im = np.load('hard_sphere_volume.npy')
im = np.moveaxis(im, -1, 0)
finder = track.MultiscaleBlobFinder(im.shape,
Octave0=False,
nbOctaves=10,
nbLayers=5)
centers = finder(im, k=1.7)
print(centers.shape)
im = np.moveaxis(im, 0, -1)
z = 30
draw_circles(centers, z, facecolor='none', edgecolor='teal', lw=4)
plt.imshow(im[:, :, z], 'hot')
plt.show()
s = track.radius2sigma(centers[:, -2], dim=2)
bonds, dists = get_bonds(positions=centers[:, :-2],
radii=centers[:, -2],
