# a = snaps[0]
# nans_imgshow(a)
# convert to greyscale
greysnaps = nanrgb2grey(snaps)
testimgs = nanrgb2grey(testimgs)
testimg = testimgs[35]
save_image('image-analysis/train.png', img)
save_image('image-analysis/test.png', testimg)
h1 = datamae['heading'][35]
print('heading by mae:', h1)
save_image('image-analysis/test-rotated-mae.png', rotate(h1, testimg))
heat = np.abs(testimgs[35] - greysnaps[127])
save_image('image-analysis/mae-heat.png', heat)
h2 = datacc['heading'][35]
print('heading by cc:', h2)
save_image('image-analysis/test-rotated-cc.png', rotate(h2, testimg))
# plt.imshow(heat, cmap='hot', interpolation='nearest')
# plt.show()
index_mae = datamae['best_index'][35]
def log_error_points(route,
traj,
nav,
thresh=0.5,
route_id=1,
target_path=None):
if target_path:
logs_path = os.path.join(target_path, 'route' + str(route_id))
else:
logs_path = 'route' + str(route_id)
os.makedirs(logs_path, exist_ok=True)
# the antworld agent
if not route.get('qimgs'):
agent = antworld2.Agent()
# get xy coords
traj_xy = np.column_stack((traj['x'], traj['y']))
route_xy = np.column_stack((route['x'], route['y']))
rsims_matrices = nav.get_rsims_log()
index_log = nav.get_index_log()
degrees = nav.degrees
# Loop through every test point
for i in range(len(traj['heading'])):
dist = np.squeeze(
cdist(np.expand_dims(traj_xy[i], axis=0), route_xy, 'euclidean'))
min_dist_i = np.argmin(dist)
min_dist = dist[min_dist_i]
route_match_i = index_log[i]
if min_dist > thresh:
point_path = os.path.join(logs_path, str(i))
os.mkdir(point_path)
# Save best match window (or non) images
if traj.get('window_log'):
w = traj.get('window_log')
for wi in range(w[0], w[1]):
imgfname = route['filename'][wi]
cv.imwrite(os.path.join(point_path, imgfname),
route['imgs'][wi])
else: # If perfect memory is used
imgfname = route['filename'][route_match_i]
cv.imwrite(os.path.join(point_path, imgfname),
route['imgs'][route_match_i])
# save the minimum distance image
imgfname = 'img{}-mindist.png'.format(min_dist_i)
cv.imwrite(os.path.join(point_path, imgfname),
route['imgs'][min_dist_i])
# Save the query image rotated to the extractred direction
h = traj['heading'][i]
if route.get('qimgs'):
rimg = rotate(h, route['qimgs'][i])
imgfname = 'rotated-grid-h' + str(h) + '.png'
cv.imwrite(os.path.join(point_path, imgfname), rimg)
imgfname = 'test-grid.png'
cv.imwrite(os.path.join(point_path, imgfname),
route['qimgs'][i])
else:
#TODO: Rotating the image to the recovered heading is nto enough here as it is also
# dependent on the heading of the previous lo9cation or in other word the location where the
# test image was sampled.
img = agent.get_img(traj_xy[i], h)
# rimg = rotate(h, img)
imgfname = str(h) + '.png'
cv.imwrite(os.path.join(point_path, imgfname), img)
# Save ridf
rsim = rsims_matrices[i][route_match_i]
fig = plt.figure()
plt.plot(degrees, rsim)
fig.savefig(os.path.join(point_path, 'rsim.png'))
plt.close(fig)
# Save window or full memory rsims heatmap
fig = plt.figure()
plt.imshow(rsims_matrices[i], cmap='hot')
fig.savefig(os.path.join(point_path, 'heat.png'))
plt.close(fig)
path = os.path.join(point_path, 'map.png')
plot_route_errors(route,
traj,
route_i=route_match_i,
error_i=i,
path=path)
route_id = 1 path = 'new-antworld/exp1/route' + str(route_id) + '/' route = load_route_naw(path, route_id=1, imgs=True) imgs = route['imgs'] im = cv.resize(imgs[0], (256, 35)) im = im.astype(np.int32) imm = ma.masked_array(im, mask) # plt.imshow(rotate(90, imm), cmap='gray') # plt.show() # Original image sims = rmf(im, im, d_range=(-180, 180)) imr = rotate(90, im) print(np.mean(np.abs(im - imr))) # Masked image masked_sims = rmf(imm, imm, d_range=(-180, 180)) print(np.ma.mean(imm - rotate(90, imm))) # Nan's image imn = imm.astype(np.float64) imn = imn.filled(np.nan) #nans_imgshow(rotate(90, imn)) print(np.nanmean(imn - rotate(90, imn))) nan_sims = rmf(imn, imn, matcher=nanmae, d_range=(-180, 180)) sims = np.stack([sims, masked_sims, nan_sims], axis=0) plot_multiline(sims, labels=['original', 'masked', 'nan-masked'])
imgs = []
for imgfile in filenames:
img = cv.imread(os.path.join(data_path, imgfile))
imgs.append(cv.cvtColor(img, cv.COLOR_BGR2RGB))
imgs = rgb02nan(imgs)
imgs = nanrgb2grey(imgs)
# get the headings of eahc of the images
sims = rmf(imgs[0], imgs, matcher=nanmae)
headings = np.argmin(sims, axis=1)
# Rotational similarity between the first image and itself
rsims = []
for h in headings:
rim = rotate(h, imgs[0])
rsims.append(nanmae(imgs[0], rim))
plt.scatter(headings, rsims, label='in-silica')
minsims = np.min(sims, axis=1)
plt.scatter(headings, minsims, label='actual rotation')
# linking lines
# for i, sim in enumerate(minsims):
# plt.plot([headings[i], headings[i]], [minsims[i], rsims[i]])
plt.legend()
plt.show()
# masked = np.ma.masked_invalid(imgs[0])
# path = os.path.join(fwd, 'odk-mask.pickle')
# alternative way to convert to greyscale
greysnaps2 = nanrbg2greyweighted(snaps)
# a = greysnaps2[0]
# nans_imgshow(a)
sims = rmf(testimgs[35], greysnaps[127], matcher=nanmae, d_range=(-180, 180))
save_image('test.png', testimgs[35])
save_image('train.png', greysnaps[127])
index = np.argmin(sims)
deg_range = (-180, 180)
degrees = np.arange(*deg_range)
h = int(degrees[index])
test_rotated = rotate(h, testimgs[35])
save_image('test rotated.png', test_rotated)
plot_multiline(sims, xlabel='Degrees', ylabel='Image Difference')
# plot_3d(sims, show=True)
heat = np.abs(testimgs[35] - greysnaps[127])
plt.imshow(heat, cmap='hot', interpolation='nearest')
plt.show()
deg_range = (-90, 90)
degrees = np.arange(*deg_range)
mindiff = []
best_index = []
heading = []
data = {'best_index': [], 'mindiff': [], 'heading': [], 'rsims': []}
img = route.get_imgs()[0]
deg_range = (-180, 180)
degrees = np.arange(*deg_range)
sims = []
for i, r in enumerate(degrees):
fig = plt.figure(figsize=(10, 7))
plt.suptitle('deg={}'.format(r))
rows = 3
cols = 1
ax = fig.add_subplot(rows, cols, 1)
ax.set_title('train')
plt.imshow(img, cmap='gray')
plt.axis('off')
ax = fig.add_subplot(rows, cols, 2)
ax.set_title('test')
plt.imshow(rotate(r, img), cmap='gray')
plt.axis('off')
ax = fig.add_subplot(rows, cols, 3)
ax.set_title('RMF')
sims.append(mae(img, rotate(r, img)))
plt.plot(degrees[:i + 1], sims)
plt.xlim(-180, 180)
fig.savefig('{}.png'.format(r + 180))
plt.close(fig)