def _plot_normals_entry(scene, disp_map, gt, mask_planes, mask_contin,
algo_name, metric_mae_contin, metric_mae_planes, idx,
grid, entries_per_row, n_vis_types, cols,
colorbar_args, fs):
add_ylabel = not idx % entries_per_row # is first column
add_colorbar = not (idx + 1) % entries_per_row # is last column
idx_row = (idx / entries_per_row) * n_vis_types
idx_col = idx % entries_per_row
idx = idx_row * cols + idx_col
# plot disparity map
plt.subplot(grid[idx])
cb = plt.imshow(disp_map, **settings.disp_map_args(scene))
plt.title(algo_name, fontsize=fs)
if add_ylabel:
plt.ylabel("DispMap", fontsize=fs)
if add_colorbar:
plotting.add_colorbar(grid[idx + 1], cb, **colorbar_args)
# plot normal map
plt.subplot(grid[idx + cols])
plt.imshow(scene.get_normal_vis_from_disp_map(disp_map))
if add_ylabel:
plt.ylabel("Normals", fontsize=fs)
# plot angular errors
plt.subplot(grid[idx + 2 * cols])
# compute angular errors
try:
score_contin = "%0.2f" % metric_mae_contin.get_score(
disp_map, gt, scene)
except IOError:
score_contin = "-"
try:
score_planes = "%0.2f" % metric_mae_planes.get_score(
disp_map, gt, scene)
except IOError:
score_planes = "-"
plt.title("%s / %s" % (score_contin, score_planes), fontsize=fs)
if add_ylabel:
plt.ylabel("Angular Error", fontsize=fs)
# get combined error visualization (if applicable)
mask = mask_contin + mask_planes
if np.sum(mask) > 0:
score, vis_normals = metric_mae_contin.get_score_from_mask(
disp_map, gt, scene, mask, with_visualization=True)
cb = plt.imshow(vis_normals, **settings.metric_args(metric_mae_contin))
if add_colorbar:
plotting.add_colorbar(grid[idx + 2 * cols + 1], cb,
**colorbar_args)
def plot_scene_difficulty(scenes, subdir="overview", fs=10):
n_scenes_per_row = 4
rows, cols = 6, n_scenes_per_row + 1
fig = plt.figure(figsize=(6, 9))
grid, cb_height, cb_width = plotting.get_grid_with_colorbar(
rows, cols, scenes[0])
colorbar_args = {
"height": cb_height,
"width": cb_width,
"colorbar_bins": 2,
"fontsize": fs
}
median_algo = PerPixMedianDiff()
best_algo = PerPixBest()
for idx_s, scene in enumerate(scenes):
# prepare data
gt = scene.get_gt()
median_result = misc.get_algo_result(median_algo, scene)
best_result = misc.get_algo_result(best_algo, scene)
idx_row = idx_s / n_scenes_per_row * 2
idx_col = (idx_s % n_scenes_per_row)
add_ylabel = not idx_s % n_scenes_per_row # is first column
add_colorbar = idx_col == (n_scenes_per_row - 1) # is last column
idx = idx_row * cols + idx_col
# plot errors for median result
plt.subplot(grid[idx])
plt.title(scene.get_display_name(), fontsize=fs)
cb = plt.imshow(np.abs(gt - median_result),
**settings.abs_diff_map_args())
if add_ylabel:
plt.ylabel("|GT - %s|" % median_algo.get_display_name(),
fontsize=fs - 2)
if add_colorbar:
plotting.add_colorbar(grid[idx + 1], cb, **colorbar_args)
# plot error for best result
plt.subplot(grid[idx + cols])
cb = plt.imshow(np.abs(gt - best_result),
**settings.abs_diff_map_args())
if add_ylabel:
plt.ylabel("|GT - %s|" % best_algo.get_display_name(),
fontsize=fs - 2)
if add_colorbar:
plotting.add_colorbar(grid[idx + cols + 1], cb, **colorbar_args)
fig_path = plotting.get_path_to_figure("scene_difficulty", subdir=subdir)
plotting.save_tight_figure(fig,
fig_path,
hide_frames=True,
hspace=0.08,
wspace=0.03)
def plot(algorithms,
scenes,
thresh=settings.BAD_PIX_THRESH,
subdir="error_heatmaps",
fs=18,
max_per_row=4):
# prepare figure
n_scenes = len(scenes)
cols = min(n_scenes, max_per_row) + 1 # + 1 for colorbars
rows = int(np.ceil(n_scenes / float(cols - 1)))
fig = plt.figure(figsize=(2.7 * cols, 3 * rows))
grid, cbh, cbw = plotting.get_grid_with_colorbar(rows,
cols,
scenes[0],
hscale=1,
wscale=9)
colorbar_args = {
"height": cbh,
"width": cbw,
"colorbar_bins": 5,
"fontsize": 10,
"scale": 0.8
}
# plot heatmaps
idx_scene = 0
for idx in range(rows * cols):
if (idx + 1) % cols:
# plot error heatmap for scene
scene = scenes[idx_scene]
idx_scene += 1
plt.subplot(grid[idx])
bad_count = get_bad_count(scene,
algorithms,
thresh,
percentage=True)
cm = plt.imshow(bad_count, vmin=0, vmax=100, cmap="inferno")
plt.ylabel(scene.get_display_name(), fontsize=fs, labelpad=2.5)
else:
# plot colorbar
plotting.add_colorbar(grid[idx], cm, **colorbar_args)
if idx_scene >= n_scenes:
if idx % cols or n_scenes == 1:
plotting.add_colorbar(grid[idx + 1], cm, **colorbar_args)
break
plt.suptitle(
"Per Pixel: Percentage of %d Algorithms with abs(gt-algo) > %0.2f" %
(len(algorithms), thresh),
fontsize=fs)
fig_name = ("error_heatmaps_%.3f" % thresh).replace(".", "")
fig_path = plotting.get_path_to_figure(fig_name, subdir=subdir)
plotting.save_tight_figure(fig, fig_path, hide_frames=True, hspace=0.02)
def plot_normals_explanation(algorithm, scene, fs=14, subdir="overview"):
# prepare figure
rows, cols = 1, 4
fig = plt.figure(figsize=(10, 4))
grid, cb_height, cb_width = plotting.get_grid_with_colorbar(
rows, cols, scene)
# prepare metrics
normals_contin = MAEContinSurf()
normals_planes = MAEPlanes()
# prepare data
gt = scene.get_gt()
algo_result = misc.get_algo_result(algorithm, scene)
mask = normals_contin.get_evaluation_mask(
scene) + normals_planes.get_evaluation_mask(scene)
score_normals, vis_normals = normals_contin.get_score_from_mask(
algo_result, gt, scene, mask, with_visualization=True)
# plot ground truth normals
plt.subplot(grid[0])
plt.imshow(scene.get_normal_vis_from_disp_map(gt))
plt.title("Ground Truth Normals", fontsize=fs)
# plot algorithm normals
plt.subplot(grid[1])
plt.imshow(scene.get_normal_vis_from_disp_map(algo_result))
plt.title("Algorithm Normals", fontsize=fs)
# plot median angular error with colorbar
plt.subplot(grid[2])
cb = plt.imshow(vis_normals, **settings.metric_args(normals_contin))
plt.title("Median Angular Error: %0.1f" % score_normals, fontsize=fs)
plt.subplot(grid[3])
plotting.add_colorbar(grid[3],
cb,
cb_height,
cb_width,
colorbar_bins=4,
fontsize=fs)
# save figure
fig_name = "metrics_%s_%s" % (scene.get_name(), algorithm.get_name())
fig_path = plotting.get_path_to_figure(fig_name, subdir=subdir)
plotting.save_tight_figure(fig,
fig_path,
hide_frames=False,
hspace=0.04,
wspace=0.03)
def plot_discont_overview(algorithms,
scene,
n_rows=2,
fs=15,
subdir="overview",
xmin=150,
ymin=230,
ww=250):
# prepare figure grid
n_vis_types = 2
n_entries_per_row = int(np.ceil((len(algorithms) + 1) / float(n_rows)))
rows, cols = (n_vis_types * n_rows), n_entries_per_row + 1
fig = plt.figure(figsize=(cols * 1.7, 1.45 * rows * 1.5))
grid, cb_height, cb_width = plotting.get_grid_with_colorbar(
rows, cols, scene)
colorbar_args = {
"height": cb_height,
"width": cb_width,
"colorbar_bins": 7,
"fontsize": fs
}
# prepare data
median_algo = PerPixMedianDiff()
gt = scene.get_gt()
median_result = misc.get_algo_result(median_algo, scene)
center_view = scene.get_center_view()
# center view
plt.subplot(grid[0])
plt.imshow(center_view[ymin:ymin + ww, xmin:xmin + ww])
plt.title("Center View", fontsize=fs)
plt.ylabel("DispMap", fontsize=fs)
plt.subplot(grid[cols])
plt.ylabel("MedianDiff", fontsize=fs)
for idx_a, algorithm in enumerate(algorithms):
algo_result = misc.get_algo_result(algorithm, scene)
idx = idx_a + 1
add_ylabel = not idx % n_entries_per_row # is first column
add_colorbar = not (idx + 1) % n_entries_per_row # is last column
idx_row = (idx / n_entries_per_row) * n_vis_types
idx_col = idx % n_entries_per_row
idx = idx_row * cols + idx_col
# top row with algorithm disparity map
plt.subplot(grid[idx])
algo_result_crop = algo_result[ymin:ymin + ww, xmin:xmin + ww]
cb_depth = plt.imshow(algo_result_crop,
**settings.disp_map_args(scene))
plt.title(algorithm.get_display_name(), fontsize=fs)
if add_ylabel:
plt.ylabel("DispMap", fontsize=fs)
if add_colorbar:
plotting.add_colorbar(grid[idx + 1], cb_depth, **colorbar_args)
# second row with median diff
plt.subplot(grid[idx + cols])
diff = (np.abs(median_result - gt) -
np.abs(algo_result - gt))[ymin:ymin + ww, xmin:xmin + ww]
cb_error = plt.imshow(diff,
interpolation="none",
cmap=cm.RdYlGn,
vmin=-.05,
vmax=.05)
if add_ylabel:
plt.ylabel("MedianDiff", fontsize=fs)
if add_colorbar:
plotting.add_colorbar(grid[idx + cols + 1], cb_error,
**colorbar_args)
fig_path = plotting.get_path_to_figure("discont_%s" % scene.get_name(),
subdir=subdir)
plotting.save_tight_figure(fig,
fig_path,
hide_frames=True,
hspace=0.03,
wspace=0.03,
dpi=100)
def plot_general_overview(algorithms,
scenes,
metrics,
fig_name=None,
subdir=SUBDIR,
fs=11):
n_vis_types = len(metrics)
# prepare figure grid
rows, cols = len(scenes) * n_vis_types, len(algorithms) + 1
fig = plt.figure(figsize=(cols * 1.4, 1.15 * rows * 1.6))
grid, cb_height, cb_width = plotting.get_grid_with_colorbar(
rows, cols, scenes[0])
for idx_s, scene in enumerate(scenes):
gt = scene.get_gt()
applicable_metrics = scene.get_applicable_metrics(metrics)
for idx_a, algorithm in enumerate(algorithms):
algo_result = misc.get_algo_result(algorithm, scene)
for idx_m, metric in enumerate(metrics):
idx = (n_vis_types * idx_s + idx_m) * cols + idx_a
ylabel = metric.get_display_name()
plt.subplot(grid[idx])
if metric in applicable_metrics:
score, vis = metric.get_score(algo_result,
gt,
scene,
with_visualization=True)
cb = plt.imshow(vis, **settings.metric_args(metric))
# add algorithm name and metric score on top row
if idx_s == 0 and idx_m == 0:
plt.title("%s\n%0.2f" %
(algorithm.get_display_name(), score),
fontsize=fs)
else:
plt.title("%0.2f" % score, fontsize=fs)
# add colorbar to last column
if idx_a == len(algorithms) - 1:
plotting.add_colorbar(
grid[idx + 1],
cb,
cb_height,
cb_width,
colorbar_bins=metric.colorbar_bins,
fontsize=fs)
# add metric name to first column
if idx_a == 0:
plt.ylabel(ylabel)
else:
if idx_a == 0:
log.warning("Metric %s not applicable for scene %s." %
(metric.get_display_name(),
scene.get_display_name()))
plt.ylabel(ylabel + "\n(not applicable)")
# save figure
if fig_name is None:
fig_name = "metric_overview_%s_%s" % ("_".join(
metric.get_id()
for metric in metrics), "_".join(scene.get_name()
for scene in scenes))
fig_path = plotting.get_path_to_figure(fig_name, subdir=subdir)
plotting.save_tight_figure(fig,
fig_path,
hide_frames=True,
hspace=0.01,
wspace=0.01)
def plot(algorithms,
scenes,
meta_algo,
subdir="meta_algo_comparisons",
fig_name=None,
with_gt_row=False,
fs=12):
# prepare figure
rows, cols = len(algorithms) + int(with_gt_row), len(scenes) * 3 + 1
fig = plt.figure(figsize=(cols * 1.3, rows * 1.5))
grid, cb_height, cb_width = plotting.get_grid_with_colorbar(
rows, cols, scenes[0])
colorbar_args = {
"height": cb_height * 0.8,
"width": cb_width,
"colorbar_bins": 4,
"fontsize": fs
}
for idx_s, scene in enumerate(scenes):
gt = scene.get_gt()
meta_algo_result = misc.get_algo_result(meta_algo, scene)
add_label = idx_s == 0 # is first column
add_colorbar = idx_s == len(scenes) - 1 # is last column
# plot one row per algorithm
for idx_a, algorithm in enumerate(algorithms):
algo_result = misc.get_algo_result(algorithm, scene)
add_title = idx_a == 0 # is top row
idx = idx_a * cols + 3 * idx_s
# disparity map
plt.subplot(grid[idx])
plt.imshow(algo_result, **settings.disp_map_args(scene))
if add_title:
plt.title("DispMap", fontsize=fs)
if add_label:
plt.ylabel(algorithm.get_display_name(), fontsize=fs)
# error map: gt - algo
plt.subplot(grid[idx + 1])
cb1 = plt.imshow(gt - algo_result,
**settings.diff_map_args(vmin=-.1, vmax=.1))
if add_title:
plt.title("GT-Algo", fontsize=fs)
# error map: |meta-gt| - |algo-gt|
plt.subplot(grid[idx + 2])
median_diff = np.abs(meta_algo_result - gt) - np.abs(algo_result -
gt)
cb2 = plt.imshow(median_diff,
interpolation="none",
cmap=cm.RdYlGn,
vmin=-.05,
vmax=.05)
if add_title:
plt.title(meta_algo.get_display_name().replace("PerPix", ""),
fontsize=fs)
if add_colorbar:
if idx_a % 2 == 0:
plotting.add_colorbar(grid[idx + 3], cb1, **colorbar_args)
else:
plotting.add_colorbar(grid[idx + 3], cb2, **colorbar_args)
if with_gt_row:
idx = len(algorithms) * cols + 3 * idx_s
plt.subplot(grid[idx])
plt.imshow(gt, **settings.disp_map_args(scene))
plt.xlabel("GT", fontsize=fs)
if add_label:
plt.ylabel("Reference")
plt.subplot(grid[idx + 1])
cb1 = plt.imshow(np.abs(gt - meta_algo_result),
**settings.abs_diff_map_args())
plt.xlabel("|GT-%s|" % meta_algo.get_display_name(),
fontsize=fs - 2)
if add_colorbar:
plotting.add_colorbar(grid[idx + 3], cb1, **colorbar_args)
if fig_name is None:
scene_names = "_".join(s.get_name() for s in scenes)
fig_name = "%s_comparison_%s" % (meta_algo.get_name(), scene_names)
fig_path = plotting.get_path_to_figure(fig_name, subdir=subdir)
plotting.save_tight_figure(fig,
fig_path,
hide_frames=True,
hspace=0.02,
wspace=0.0)
def plot_algo_overview(self,
algorithms,
with_metric_vis=True,
subdir="algo_overview",
fs=14):
self.set_scale_for_algo_overview()
metrics = self.get_scene_specific_metrics()
n_metrics = len(metrics)
if not with_metric_vis:
rows, cols = 2 + n_metrics, len(algorithms) + 2
fig = plt.figure(figsize=(2.6 * len(algorithms), 4.9))
offset = 0
else:
rows, cols = 2 + 2 * n_metrics, len(algorithms) + 2
fig = plt.figure(figsize=(2.6 * len(algorithms), rows + 3))
offset = n_metrics
labelpad = -15
hscale, wscale = 7, 5
width_ratios = [wscale] * (len(algorithms) + 1) + [1]
height_ratios = [hscale] * (rows - n_metrics) + [1] * n_metrics
gs = gridspec.GridSpec(rows,
cols,
height_ratios=height_ratios,
width_ratios=width_ratios)
gt = self.get_gt()
dummy = np.ones((self.get_height() / hscale, self.get_width()))
cb_height, w = np.shape(gt)
cb_width = w / float(wscale)
# first column (gt, center view, ...)
plt.subplot(gs[0])
plt.imshow(gt, **settings.disp_map_args(self))
plt.title("Ground Truth", fontsize=fs)
plt.ylabel("Disparity Map", fontsize=fs)
plt.subplot(gs[cols])
plt.imshow(self.get_center_view())
plt.ylabel("diff: GT - Algo", fontsize=fs)
for idx_m, metric in enumerate(metrics):
plt.subplot(gs[(2 + idx_m + offset) * cols])
plt.xlabel(metric.get_short_name(), labelpad=labelpad, fontsize=fs)
plt.imshow(dummy, cmap="gray_r")
# algorithm columns
for idx_a, algorithm in enumerate(algorithms):
log.info("Processing algorithm: %s" % algorithm)
algo_result = misc.get_algo_result(algorithm, self)
# algorithm disparities
plt.subplot(gs[idx_a + 1])
plt.title(algorithm.get_display_name(), fontsize=fs)
cm1 = plt.imshow(algo_result, **settings.disp_map_args(self))
# algorithm diff map
plt.subplot(gs[cols + idx_a + 1])
cm2 = plt.imshow(gt - algo_result, **settings.diff_map_args())
# add colorbar if last column
if idx_a == (len(algorithms) - 1):
plotting.add_colorbar(gs[idx_a + 2],
cm1,
cb_height,
cb_width,
colorbar_bins=5,
fontsize=fs - 4)
plotting.add_colorbar(gs[cols + idx_a + 2],
cm2,
cb_height,
cb_width,
colorbar_bins=5,
fontsize=fs - 4)
# score and background color for metrics
for idx_m, metric in enumerate(metrics):
if with_metric_vis:
plt.subplot(gs[(2 + idx_m) * cols + idx_a + 1])
score, vis = metric.get_score(algo_result,
gt,
self,
with_visualization=True)
cm3 = plt.imshow(vis, **settings.metric_args(metric))
if idx_a == 0:
plt.ylabel(metric.get_short_name(), fontsize=fs)
elif idx_a == (len(algorithms) - 1):
plotting.add_colorbar(
gs[(2 + idx_m) * cols + idx_a + 2],
cm3,
cb_height,
cb_width,
colorbar_bins=metric.colorbar_bins,
fontsize=fs - 4)
else:
score = metric.get_score(algo_result, gt, self)
plt.subplot(gs[(2 + idx_m + offset) * cols + idx_a + 1])
plt.imshow(
dummy * score,
**settings.score_color_args(vmin=metric.vmin,
vmax=metric.vmax))
plt.xlabel(metric.format_score(score),
labelpad=labelpad,
fontsize=fs)
fig_name = "algo_overview_" + self.get_name(
) + with_metric_vis * "_vis"
fig_path = plotting.get_path_to_figure(fig_name, subdir=subdir)
plotting.save_tight_figure(fig,
fig_path,
wspace=0.04,
hide_frames=True)