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
coordinates = data['coordinates']
regions = data['regions']
eo_params = data['eo_params']
filtered_regions = np.zeros_like(data['regions'])
point_est_track = data['point_est_track']
region_props = []
max_dim = (eo_params[1] + eo_params[2])*2*1.2
for fi, f in enumerate(frame_pos):
filtered_regions[fi] = filters.filter_regions(regions[fi],
max_width = max_dim,
max_height = max_dim)
fc = filters.points_in_mask(filtered_regions[fi] > 0,
coordinates[fi])
region_props.append(skimage.measure.regionprops((filtered_regions[fi]>0).astype(int)))
### how many coordinate points
#ax_point_cnt.plot(frame_pos, [len(x) for x in filtered_coordinates])
ax_point_cnt.plot(frame_pos, [len(x) for x in coordinates])
ax_phi_theta.plot(frame_pos, derived_truth['phi'][frame_pos] % (2*np.pi))
coord = point_est_track
cand_pts_per_frame = np.array([p.shape[0] for p in coord])
have_est = cand_pts_per_frame > 0
def expand_index(inarray, cnt):
assert len(inarray) == len(cnt)
N_idx = np.sum(cnt)
ax_truth.plot(frame_pos, truth_interp[frame_pos]['x'], label='x')
ax_truth.plot(frame_pos, truth_interp[frame_pos]['y'], label='y')
coordinates = data['coordinates']
regions = data['regions']
eo_params = data['eo_params']
filtered_regions = np.zeros_like(data['regions'])
point_est_track = data['point_est_track']
region_props = []
max_dim = (eo_params[1] + eo_params[2]) * 2 * 1.2
for fi, f in enumerate(frame_pos):
filtered_regions[fi] = filters.filter_regions(regions[fi],
max_width=max_dim,
max_height=max_dim)
fc = filters.points_in_mask(filtered_regions[fi] > 0, coordinates[fi])
region_props.append(
skimage.measure.regionprops(
(filtered_regions[fi] > 0).astype(int)))
### how many coordinate points
#ax_point_cnt.plot(frame_pos, [len(x) for x in filtered_coordinates])
ax_point_cnt.plot(frame_pos, [len(x) for x in coordinates])
ax_phi_theta.plot(frame_pos, derived_truth['phi'][frame_pos] % (2 * np.pi))
coord = point_est_track
cand_pts_per_frame = np.array([p.shape[0] for p in coord])
have_est = cand_pts_per_frame > 0
def expand_index(inarray, cnt):
