def visualize(data_root, result_root, scenes, daytimes, approaches,
evaluations, weathers, visibilities, rainfall_rates):
d = Dataset(data_root)
e = Evaluator(data_root)
for scene in scenes:
if 'intermetric' in evaluations:
depth = e.load_depth_groundtruth(scene,
frame='rgb_left',
gt_type='intermetric')
depth_color = colorize_depth(depth,
min_distance=e.clip_min,
max_distance=e.clip_max)
intermetric_path = os.path.join(
result_root, 'intermetric',
'{}_{}.jpg'.format('intermetric', scene))
if not os.path.exists(os.path.split(intermetric_path)[0]):
os.makedirs(os.path.split(intermetric_path)[0])
cv2.imwrite(intermetric_path, depth_color)
# top_view, top_view_color = e.create_top_view(e.load_depth_groundtruth, scene)
top_view, top_view_color = e.create_top_view(scene, 'intermetric')
intermetric_top_view_file = os.path.join(
result_root, 'intermetric',
'{}_{}_topview.jpg'.format('intermetric', scene))
cv2.imwrite(intermetric_top_view_file, top_view_color)
for daytime in daytimes:
for weather in weathers:
samples = []
if weather == 'fog':
for visibility in visibilities:
samples.append(
d.get_fog_sequence(scene, daytime, visibility)[0])
if weather == 'rain':
for rainfall_rate in rainfall_rates:
samples.append(
d.get_rain_sequence(scene, daytime,
rainfall_rate)[0])
if weather == 'clear':
samples.append(d.get_clear_sequence(scene, daytime)[0])
for i, sample in enumerate(samples):
print(sample)
if 'rgb' in evaluations:
rgb = e.load_rgb(sample)
if weather == 'fog':
rgb_path = os.path.join(
result_root, 'rgb', '{}_{}_{}_{}_{}'.format(
'rgb', scene, daytime, weather,
visibilities[i]))
elif weather == 'rain':
rgb_path = os.path.join(
result_root, 'rgb', '{}_{}_{}_{}_{}'.format(
'rgb', scene, daytime, weather,
rainfall_rates[i]))
elif weather == 'clear':
rgb_path = os.path.join(
result_root, 'rgb',
'{}_{}_{}_{}'.format('rgb', scene, daytime,
weather))
if not os.path.exists(os.path.split(rgb_path)[0]):
os.makedirs(os.path.split(rgb_path)[0])
cv2.imwrite(rgb_path + '.jpg', rgb)
clahe = cv2.createCLAHE(clipLimit=2.0,
tileGridSize=(8, 8))
rgb[:, :, 0] = clahe.apply(rgb[:, :, 0])
rgb[:, :, 1] = clahe.apply(rgb[:, :, 1])
rgb[:, :, 2] = clahe.apply(rgb[:, :, 2])
cv2.imwrite(rgb_path + '_clahe.jpg', rgb)
if 'lidar_raw' in evaluations:
depth = e.load_depth(sample,
'lidar_hdl64_rgb_left',
interpolate=False)
depth_color = colorize_pointcloud(
depth,
min_distance=e.clip_min,
max_distance=e.clip_max,
radius=5)
if weather == 'fog':
lidar_path = os.path.join(
result_root, 'lidar_raw',
'{}_{}_{}_{}_{}'.format(
'lidar_raw', scene, daytime, weather,
visibilities[i]))
elif weather == 'rain':
lidar_path = os.path.join(
result_root, 'lidar_raw',
'{}_{}_{}_{}_{}'.format(
'lidar_raw', scene, daytime, weather,
rainfall_rates[i]))
elif weather == 'clear':
lidar_path = os.path.join(
result_root, 'lidar_raw',
'{}_{}_{}_{}'.format('lidar_raw', scene,
daytime, weather))
if not os.path.exists(os.path.split(lidar_path)[0]):
os.makedirs(os.path.split(lidar_path)[0])
cv2.imwrite(lidar_path + '.jpg', depth_color)
if 'gated' in evaluations:
for t in [0, 17, 31]:
gated_img = e.load_gated(sample, t)
if weather == 'fog':
gated_path = os.path.join(
result_root, 'gated{}'.format(t),
'{}_{}_{}_{}_{}'.format(
'gated{}'.format(t), scene, daytime,
weather, visibilities[i]))
elif weather == 'rain':
gated_path = os.path.join(
result_root, 'gated{}'.format(t),
'{}_{}_{}_{}_{}'.format(
'gated{}'.format(t), scene, daytime,
weather, rainfall_rates[i]))
elif weather == 'clear':
gated_path = os.path.join(
result_root, 'gated{}'.format(t),
'{}_{}_{}_{}'.format(
'gated{}'.format(t), scene, daytime,
weather))
if not os.path.exists(
os.path.split(gated_path)[0]):
os.makedirs(os.path.split(gated_path)[0])
cv2.imwrite(gated_path + '.jpg', gated_img)
clahe = cv2.createCLAHE(clipLimit=2.0,
tileGridSize=(8, 8))
gated_img[:, :, 0] = clahe.apply(gated_img[:, :,
0])
gated_img[:, :, 1] = clahe.apply(gated_img[:, :,
1])
gated_img[:, :, 2] = clahe.apply(gated_img[:, :,
2])
cv2.imwrite(gated_path + '_clahe.jpg', gated_img)
for approach in approaches:
if weather == 'fog':
sample_path = os.path.join(
result_root, approach, '{}_{}_{}_{}_{}'.format(
approach, scene, daytime, weather,
visibilities[i]))
elif weather == 'rain':
sample_path = os.path.join(
result_root, approach, '{}_{}_{}_{}_{}'.format(
approach, scene, daytime, weather,
rainfall_rates[i]))
elif weather == 'clear':
sample_path = os.path.join(
result_root, approach,
'{}_{}_{}_{}'.format(approach, scene, daytime,
weather))
if not os.path.exists(os.path.split(sample_path)[0]):
os.makedirs(os.path.split(sample_path)[0])
if 'depth_map' in evaluations:
depth = e.load_depth(sample, approach)
depth_color = colorize_depth(
depth,
min_distance=e.clip_min,
max_distance=e.clip_max)
depth_map_path = sample_path + '_depth_map.jpg'
cv2.imwrite(depth_map_path, depth_color)
if 'error_image' in evaluations:
error_image = e.error_image(sample,
approach,
gt_type='intermetric')
error_image_path = sample_path + '_error_image.jpg'
cv2.imwrite(error_image_path, error_image)
if 'top_view' in evaluations:
top_view, top_view_color = e.create_top_view(
sample, approach)
top_view_path = sample_path + '_top_view.jpg'
cv2.imwrite(top_view_path, top_view_color)
