def score_state_full(self, state, img):
assert len(img.shape)== 2
x = state['x']
y = state['y']
theta = state['theta']
phi = state['phi']
img_thold = img.copy()
img_thold[img_thold < self.params['pix-threshold']] = 0
if self.img_cache == None or (self.img_cache != img_thold).any():
self.img_cache = img_thold.copy()
coordinates = skimage.feature.peak_local_max(img,
min_distance=30,
threshold_rel=0.8)
frame_regions = filters.label_regions(img_thold)
filtered_regions = filters.filter_regions(frame_regions,
max_width = 30,
max_height=30)
fc = filters.points_in_mask(filtered_regions > 0,
coordinates)
self.coordinates = fc
# pylab.imshow(img, interpolation='nearest', cmap=pylab.cm.gray)
# pylab.plot([p[1] for p in self.coordinates],
# [p[0] for p in self.coordinates], 'r.')
# pylab.show()
# get the points
coordinates = self.coordinates
if len(coordinates) == 0:
return 0
x_pix, y_pix = self.env.gc.real_to_image(x, y)
x_pix = int(x_pix)
y_pix = int(y_pix)
front_pos_pix, back_pos_pix = util.compute_pos(self.template_obj.length,
x_pix, y_pix, phi, theta)
front_deltas = np.abs((coordinates - np.array(front_pos_pix)[:2][::-1]))
front_dists = np.sqrt(np.sum(front_deltas**2, axis=1))
assert len(front_dists) == len(coordinates)
back_deltas = np.abs((coordinates - np.array(back_pos_pix)[:2][::-1]))
back_dists = np.sqrt(np.sum(back_deltas**2, axis=1))
dist_thold = self.params.get('dist-thold', None)
if dist_thold != None:
front_deltas= front_deltas[front_deltas < dist_thold]
back_deltas = back_deltas[back_deltas < dist_thold]
closest_n = self.params.get('closest-n', None)
if closest_n != None:
front_deltas = np.sort(front_deltas)[::-1]
back_deltas = np.sort(back_deltas)[::-1]
front_deltas= front_deltas[:closest_n]
back_deltas = back_deltas[:closest_n]
power = self.params.get('power', 1)
delta_sum = np.sum(front_dists**power) + np.sum(back_dists**power)
if self.params.get('normalize', True):
delta_sum = delta_sum / len(coordinates)
if len(coordinates) == 0:
score = -1e10
elif self.params.get('log', False):
score = -np.log(delta_sum)
elif self.params.get('exp', False):
score = -np.exp(delta_sum)
else:
score = -delta_sum
return score
def score_state_full(self, state, img):
assert len(img.shape) == 2
x = state['x']
y = state['y']
theta = state['theta']
phi = state['phi']
img_thold = img.copy()
img_thold[img_thold < self.params['pix-threshold']] = 0
if self.img_cache == None or (self.img_cache != img_thold).any():
self.img_cache = img_thold.copy()
coordinates = skimage.feature.peak_local_max(img,
min_distance=30,
threshold_rel=0.8)
frame_regions = filters.label_regions(img_thold)
filtered_regions = filters.filter_regions(frame_regions,
max_width=30,
max_height=30)
fc = filters.points_in_mask(filtered_regions > 0, coordinates)
self.coordinates = fc
# pylab.imshow(img, interpolation='nearest', cmap=pylab.cm.gray)
# pylab.plot([p[1] for p in self.coordinates],
# [p[0] for p in self.coordinates], 'r.')
# pylab.show()
# get the points
coordinates = self.coordinates
if len(coordinates) == 0:
return 0
x_pix, y_pix = self.env.gc.real_to_image(x, y)
x_pix = int(x_pix)
y_pix = int(y_pix)
front_pos_pix, back_pos_pix = util.compute_pos(
self.template_obj.length, x_pix, y_pix, phi, theta)
front_deltas = np.abs(
(coordinates - np.array(front_pos_pix)[:2][::-1]))
front_dists = np.sqrt(np.sum(front_deltas**2, axis=1))
assert len(front_dists) == len(coordinates)
back_deltas = np.abs((coordinates - np.array(back_pos_pix)[:2][::-1]))
back_dists = np.sqrt(np.sum(back_deltas**2, axis=1))
dist_thold = self.params.get('dist-thold', None)
if dist_thold != None:
front_deltas = front_deltas[front_deltas < dist_thold]
back_deltas = back_deltas[back_deltas < dist_thold]
closest_n = self.params.get('closest-n', None)
if closest_n != None:
front_deltas = np.sort(front_deltas)[::-1]
back_deltas = np.sort(back_deltas)[::-1]
front_deltas = front_deltas[:closest_n]
back_deltas = back_deltas[:closest_n]
power = self.params.get('power', 1)
delta_sum = np.sum(front_dists**power) + np.sum(back_dists**power)
if self.params.get('normalize', True):
delta_sum = delta_sum / len(coordinates)
if len(coordinates) == 0:
score = -1e10
elif self.params.get('log', False):
score = -np.log(delta_sum)
elif self.params.get('exp', False):
score = -np.exp(delta_sum)
else:
score = -delta_sum
return score
FRAMEN = len(frames)
coordinates = []
regions = np.zeros((FRAMEN, frames[0].shape[0], frames[0].shape[1]),
dtype=np.uint8)
point_est_track_data = []
for fi, frame in enumerate(frames):
abs_frame_index = frame_pos[fi]
coordinates.append(skimage.feature.peak_local_max(frame,
min_distance=30,
threshold_abs=220))
frame_regions = filters.label_regions(frame, mark_min=120, mark_max=220)
regions[fi] = frame_regions
point_est_track_data.append(dettrack.point_est_track2(frame, env, eo_params))
pickle.dump({'frame_pos' : frame_pos,
'coordinates' : coordinates,
'regions' : regions,
'eo_params' : eo_params,
'point_est_track' : point_est_track_data,
'truth' : truth,
'missing' : missing,
'truth_interp' : truth_interp,
'derived_truth' : derived_truth,
'epoch' : epoch,
'frame_start' : frame_start},
open(outfile, 'w'))
coordinates = []
regions = np.zeros((FRAMEN, frames[0].shape[0], frames[0].shape[1]),
dtype=np.uint8)
point_est_track_data = []
for fi, frame in enumerate(frames):
abs_frame_index = frame_pos[fi]
coordinates.append(
skimage.feature.peak_local_max(frame,
min_distance=30,
threshold_abs=220))
frame_regions = filters.label_regions(frame,
mark_min=120,
mark_max=220)
regions[fi] = frame_regions
point_est_track_data.append(
dettrack.point_est_track2(frame, env, eo_params))
pickle.dump(
{
'frame_pos': frame_pos,
'coordinates': coordinates,
'regions': regions,
'eo_params': eo_params,
'point_est_track': point_est_track_data,
'truth': truth,
'missing': missing,
'truth_interp': truth_interp,
