def from_image(self,
image,
sigma=1,
absolute_threshold=None,
threshold_factor=0.95,
ncontour=100,
delta=0.3,
smooth=1.0,
nneighbours=4,
verbose=False,
save=None):
success, center, left, right, width = wormgeo.shape_from_image(
image,
sigma=sigma,
absolute_threshold=absolute_threshold,
threshold_factor=threshold_factor,
ncontour=ncontour,
delta=delta,
smooth=smooth,
nneighbours=nneighbours,
npoints=self.npoints,
verbose=verbose,
save=save)
if success:
#self.from_lines(shape[1], shape[2], shape[3]);
self.from_center(center, width)
else:
raise RuntimeWarning('failed inferring worm from image')
def analyze_shape(fid):
#load memmaps
fid2 = min(fid+frame_batch, n_frames_total);
#images = np.lib.format.open_memmap(data_image_file, mode = 'r');
images = turn;
#shape = pmm.open_memmap(data_shape_file, arange = (fid, fid2), mode = 'r+');
#shape_info = pmm.open_memmap(data_shape_info_file, arange = (fid, fid2), mode = 'r+');
#smooth
print('processing %d / %d' % (fid, n_frames_total));
for i,f in enumerate(range(fid, fid2)):
#print('success status: %d' % shape_info[i]['success']);
blur = cv2.GaussianBlur(images[f], ksize = (5,5), sigmaX = 0);
if verbose:
plt.clf();
try:
res = wgeo.shape_from_image(blur, absolute_threshold = 135, sigma = None, verbose = verbose,
smooth = 20, smooth_center = 2.5, npoints = n_points, ncontour = 80, center_offset = 3)
#print(res[0]);
except:
print('something went wrong');
#res = (-1, np.zeros((n_points,2)), 0, 0, np.zeros(n_points));
if verbose:
plt.draw(); plt.pause(0.5);
def from_image(self, image, sigma = 1, absolute_threshold = None, threshold_factor = 0.95,
ncontour = 100, delta = 0.3, smooth = 1.0, nneighbours = 4,
verbose = False, save = None):
success, center, left, right, width = wormgeo.shape_from_image(image,
sigma = sigma, absolute_threshold = absolute_threshold,
threshold_factor = threshold_factor, ncontour = ncontour,
delta = delta, smooth = smooth, nneighbours = nneighbours,
npoints = self.npoints,
verbose = verbose, save = save);
if success:
#self.from_lines(shape[1], shape[2], shape[3]);
self.from_center(center, width);
else:
raise RuntimeWarning('failed inferring worm from image');
img = exp.load_img(t=t0 + i)
plt.figure(71)
plt.clf()
plt.imshow(img[0])
plr.figure(100)
plt.clf()
w.from_image(img[0], verbose=True)
import worm.geometry as wgeo
reload(wgeo)
plt.figure(71)
plt.clf()
wgeo.shape_from_image(img[3], verbose=True)
from multiprocessing import Pool, cpu_count
pool = Pool(processes=cpu_count() - 2)
pool.map(process, range(ntimes))
data = [
np.load(os.path.join(data_path, 'analysis_2016_10_25_' + n + '.npy'),
mmap_mode='r+') for n in data_names
]
### Failed frames
nfailed = np.sum(data[3] == -1)
print 'percentage failed %f' % (100.0 * nfailed / data[3].shape[0])
#% # detect worm shape n_contours = len(contours); #%% f_id = 80 import worm.geometry as wgeo reload(wgeo); plt.clf() res = wgeo.shape_from_image(blur[f_id], threshold_factor = 0.975, sigma = None, verbose = True, smooth = 15, smooth_center = 2.5, npoints = 45, ncontour = 80, center_offset = 3) #%% import worm.model as wmod wm = wmod.WormModel(npoints = 21) f_id = 5; plt.figure(1); plt.clf(); wm.from_image(blur[f_id], sigma = None, verbose = True, smooth = 10) plt.figure(19); plt.clf(); plt.subplot(1,2,1); wm.plot(blur[f_id])
fid = 31;
n_points = 45;
cnts, hrchy = wgeo.detect_contour(blur[fid], 135, with_hierarchy=True)
plt.figure(13); plt.clf();
plt.imshow(blur[fid], interpolation = 'none');
for c in cnts:
plt.plot(c[:,0], c[:,1]);
#%% get previous center line / shape
status_prev, center_prev, _, _, width_prev = wgeo.shape_from_image(blur[fid-1], absolute_threshold = 135, sigma = None, verbose = True,
smooth = 20, smooth_center = 2.5, npoints = n_points, ncontour = 80, center_offset = 3)
#%% decide inner vs outer
n_cnts = len(cnts);
print('Number of contours: %d' % n_cnts);
# check if inner outer contour
# find children of each contour
children = [];
for cid in range(n_cnts):
children.append(np.where(hrchy[:,-1]==cid)[0]);
# outer vs inner
nc = [len(c) for c in children]
npoints = 21;
nobs = npoints*2-2; #d differences from the sides / head tail only once
worm = wmod.WormModel(npoints = npoints);
nparameter = worm.nparameter; #full number of parameter
nobs = worm.ndistances;
t0 = 500000;
threshold_factor = 0.9;
absolute_threshold = None;
img = exp.load_img(wid = 80, t = t0);
imgs = filters.gaussian_filter(np.asarray(img, float), 1.0);
worm.from_image(img, absolute_threshold = absolute_threshold, threshold_factor = threshold_factor, verbose = False);
success, center, left, right, width = wgeo.shape_from_image(img,
absolute_threshold = absolute_threshold,
threshold_factor = threshold_factor, npoints = npoints,
verbose = False, save = False);
plt.figure(1); plt.clf();
#worm.plot(image = imgs)
worm.plot(image = imgs)
plot(center, 'r');
### Compare Worm to Contour
cntrs = wgeo.contour_from_image(img, sigma = 1, absolute_threshold = absolute_threshold, threshold_factor = threshold_factor,
verbose = False, save = None);
cntr = resample_curve(cntrs[0], 100); #assume outer contour is first, only match to this
contour = Curve(cntr, nparameter = 50);
contour.plot(with_points=False);
u2 = np.linspace(us[imax[0]], us[imax[1]]-1, npoints);
u2 = np.mod(u2,1);
x2, y2 = splev(u2, cinterp, der = 0);
right = np.vstack([x2,y2]).T;
fig = plt.figure(5); plt.clf();
plt.imshow(img, cmap = 'gray');
plt.plot(left[:,0], left[:,1], c = 'r', linewidth = 1.5);
plt.plot(right[:,0], right[:,1], c = 'g', linewidth = 1.5);
plt.xlim(0,151); plt.ylim(0,151)
plt.axis('off')
fig.savefig('pipeline_left_right.png', facecolor = 'white')
s,center,l,r,width = wgeo.shape_from_image(img)
fig = plt.figure(6); plt.clf();
plt.imshow(img, cmap = 'gray');
plt.plot(left[:,0], left[:,1], c = 'r', linewidth = 1.5);
plt.plot(right[:,0], right[:,1], c = 'g', linewidth = 1.5);
plt.plot(center[:,0], center[:,1], c = 'b', linewidth = 2);
plt.xlim(0,151); plt.ylim(0,151)
plt.axis('off')
fig.savefig('pipeline_center.png', facecolor = 'white')
# theta angles , width profile etc
theta = wgeo.theta_from_center(center)
plt.plot(dd) i = np.where(dd < 5)[0]; img = exp.load_img(t = t0 + i); plt.figure(71); plt.clf(); plt.imshow(img[0]) plr.figure(100); plt.clf(); w.from_image(img[0], verbose = True) import worm.geometry as wgeo reload(wgeo) plt.figure(71); plt.clf(); wgeo.shape_from_image(img[3], verbose = True); from multiprocessing import Pool, cpu_count; pool = Pool(processes = cpu_count()-2); pool.map(process, range(ntimes)); data = [np.load(os.path.join(data_path, 'analysis_2016_10_25_' + n + '.npy'), mmap_mode = 'r+') for n in data_names]; ### Failed frames nfailed = np.sum(data[3] == -1);
t0 = 500000
threshold_factor = 0.9
absolute_threshold = None
img = exp.load_img(wid=80, t=t0)
imgs = filters.gaussian_filter(np.asarray(img, float), 1.0)
worm.from_image(img,
absolute_threshold=absolute_threshold,
threshold_factor=threshold_factor,
verbose=False)
success, center, left, right, width = wgeo.shape_from_image(
img,
absolute_threshold=absolute_threshold,
threshold_factor=threshold_factor,
npoints=npoints,
verbose=False,
save=False)
plt.figure(1)
plt.clf()
#worm.plot(image = imgs)
worm.plot(image=imgs)
plot(center, 'r')
### Compare Worm to Contour
cntrs = wgeo.contour_from_image(img,
sigma=1,
absolute_threshold=absolute_threshold,