def f1(route_id):
matcher = 'rmse'
pre_proc = {'blur': True, 'shape': (180, 50)}
window = 14
dist = 100
w, x_inlimit, y_inlimit, world_grid_imgs, x_route, y_route, \
route_heading, route_images = load_route(route_id=route_id, grid_pos_limit=dist)
pre_world_grid_imgs = pre_process(world_grid_imgs, pre_proc)
pre_route_images = pre_process(route_images, pre_proc)
nav = spm.SequentialPerfectMemory(pre_route_images, matcher, window=window)
recovered_heading, window_log = nav.navigate(pre_world_grid_imgs)
print(mean_degree_error(x_inlimit, y_inlimit, x_route, y_route, route_heading, recovered_heading))
def mean_residual_image(ref_img, current_img, d):
current_img = rotate(d, current_img)
current_img = current_img.flatten()
ref_img = ref_img.flatten()
current_mean = np.mean(current_img)
ref_mean = np.mean(ref_img)
res_img = ((ref_img - ref_mean) *
(current_img - current_mean)) / len(ref_img)
res_img = np.reshape(res_img, (75, 360))
fig = plt.figure(figsize=(15, 5))
ax = sns.heatmap(res_img)
# ax.figure.savefig('idf_v_correlation_figures/mean residual image ' + str(d) + '.png')
# save_image('idf_v_correlation_figures/mean residual image ' + str(d) + '.png', res_img)
route_img = pre_process([route_img], pre_proc)[0]
grid_img = pre_process([grid_img], pre_proc)[0]
logs = ridf(route_img, grid_img, 360, 1)
print(logs.index(min(logs)))
fig = plt.figure()
plt.plot(range(len(logs)), logs)
fig.suptitle('RIDF')
plt.xlabel('Degrees')
plt.ylabel('IDF')
plt.show()
# Save residual images
residual_image(route_img, grid_img, 134)
residual_image(route_img, grid_img, 190)
logs = r_cor_coef(route_img, grid_img, 360, 1)
window = 15
dist = 100
figures_path = 'Figures/'
check_for_dir_and_create(figures_path)
w, x_inlimit, y_inlimit, world_grid_imgs, x_route, y_route, \
route_heading, route_images = load_loop_route(directory, route_id=route_id, grid_pos_limit=dist)
zoom = [np.mean(x_route), np.mean(y_route)]
plot_map(w, [x_route, y_route], [x_inlimit, y_inlimit],
size=(15, 15),
route_headings=route_heading,
scale=40,
zoom=zoom)
pre_world_grid_imgs = pre_process(world_grid_imgs, pre_proc)
pre_route_images = pre_process(route_images, pre_proc)
nav = spm.SequentialPerfectMemory(pre_route_images, matcher, window=window)
recovered_heading, window_log = nav.navigate(pre_world_grid_imgs)
print(
mean_degree_error(x_inlimit, y_inlimit, x_route, y_route, route_heading,
recovered_heading))
plot_map(w, [x_route, y_route], [x_inlimit, y_inlimit],
size=(15, 15),
route_headings=route_heading,
grid_headings=recovered_heading,
scale=40,
zoom=(3000, 5000),
save=True)
check_for_dir_and_create(figures_path)
# w, x_inlimit, y_inlimit, grid_imgs, x_route, y_route, \
# route_heading, route_images = load_route(route_id=route, grid_pos_limit=dist)
w, x_inlimit, y_inlimit, grid_imgs, x_route, y_route, \
route_heading, route_images = load_loop_route(directory, route_id=route, grid_pos_limit=dist)
img_path = figures_path + 'Route.png'
plot_map(w, [x_route, y_route], [x_inlimit, y_inlimit],
size=(6, 6),
path=img_path,
route_headings=route_heading,
scale=40,
route_zoom=False,
save=True)
pre_grid_imgs = pre_process(grid_imgs, pre_proc)
pre_route_images = pre_process(route_images, pre_proc)
nav = spm.SequentialPerfectMemory(pre_route_images, matcher, window=window)
recovered_heading, window_log = nav.navigate(pre_grid_imgs)
# nav = pm.PerfectMemory(pre_route_images, matcher)
# recovered_heading = nav.navigate(pre_grid_imgs)
print(
mean_degree_error(x_inlimit, y_inlimit, x_route, y_route, route_heading,
recovered_heading))
zoom = [np.mean(x_route), np.mean(y_route)]
img_path = figures_path + 'errorRoute.png'
plot_map(w, [x_route, y_route], [x_inlimit, y_inlimit],
size=(6, 6),
path=img_path,
route_id = 3
path = '../new-antworld/exp1/route' + str(route_id) + '/'
# path = '../test_data/route'+ str(route_id) + '/'
route = load_route_naw(path,
route_id=route_id,
imgs=True,
query=True,
max_dist=0.2)
plot_route(route)
window = 20
matcher = 'mae'
sets = {'shape': (180, 50)} #, 'edge_range': (180, 200)}
route_imgs = pre_process(route['imgs'], sets)
test_imgs = pre_process(route['qimgs'], sets)
nav = seqnav.SequentialPerfectMemory(route_imgs, matcher, window=window)
recovered_heading, window_log = nav.navigate(test_imgs)
traj = {'x': route['qx'], 'y': route['qy'], 'heading': recovered_heading}
traj['heading'] = np.array(traj['heading'])
plot_route(route, traj)
errors, min_dist_index = angular_error(route, traj)
print(np.mean(errors))
xs = route['x'][min_dist_index]
ys = route['y'][min_dist_index]
# matplotlib.use( 'tkagg' )
# _, x_inlimit, y_inlimit, world_grid_imgs, x_route, y_route, \
# route_heading, route_images = load_route(1)
# original_img = route_images[img_indx]
route_id = 3
path = '../new-antworld/exp1/route' + str(route_id) + '/'
route = load_route_naw(path, route_id=route_id, imgs=True)
img_indx = 98
original_img = route['imgs'][img_indx]
display_image(original_img)
pre_proc = {'edge_range': (220, 240)}
edges_img = pre_process(original_img, pre_proc)
display_image(edges_img)
fig = plt.figure()
logs = rmf(original_img,
original_img,
matcher=cor_dist,
d_range=(-180, 180),
d_step=1)
plt.plot(range(len(logs)), logs, label='original')
low_bounds = list(range(150, 240, 20))
for bound in low_bounds:
pre_proc = {'edge_range': (bound, bound + 20)}
edges_img = pre_process(original_img, pre_proc)
from source2 import load_route_naw, pre_process, animated_window
from source2 import seqnav
from source2 import antworld2 as aw
agent = aw.Agent()
route_id = 1
preprocessing = {'shape': (180, 50)}
path = '../new-antworld/exp1/route' + str(route_id) + '/'
# path = '../test_data/route' + str(route_id) + '/'
route = load_route_naw(path, route_id=route_id, imgs=True)
# plot_route(route)
nav = seqnav.SequentialPerfectMemory
# nav = perfect_memory.PerfectMemory
# set up the navigator
route['imgs'] = pre_process(route['imgs'], preprocessing)
nav = nav(route['imgs'], 'mae', window=10, deg_range=(-180, 180))
traj, nav = agent.segment_test(route,
nav,
segment_length=2,
t=20,
r=0.1,
preproc=preprocessing)
window_log = nav.get_window_log()
path = 'window-plots/'
animated_window(route, window_log, traj=traj, path=path)