def get_best_segmentation(groundtruth_segmentation, inferred_segmentation):
"""Remap inferred segmentation onto ground truth segmentation so that it
matches the groundtruth_segmentation as good as possible
Parameters
----------
groundtruth_segmentation : np.ndarray
Ground truth segmentation label map[N, H, W]
inferred_segmentation : np.ndarray
Inferred segmentation label map [N, H, W]
Returns
-------
np.ndarray
remapped inferred segmentation
"""
best_remapping = denseposelib.compute_best_iou_remapping(
inferred_segmentation, groundtruth_segmentation)
remapped_inferred = denseposelib.remap_parts(inferred_segmentation,
best_remapping)
return remapped_inferred
def make_figure_1(data: dict, root: str, config: dict, global_step: int):
figure01_options = config.get("figure01_options")
dp_semantic_remap_dict = config.get("dp_semantic_remap_dict")
dp_new_part_list = sorted(list(dp_semantic_remap_dict.keys()))
dp_remap_dict = denseposelib.semantic_remap_dict2remap_dict(
dp_semantic_remap_dict, dp_new_part_list)
inferred_segmentation = (
data["outputs"][figure01_options["inferred_segmentation_key"]] + 1
) # +1 because the visualizer code uses + 1
sampled_segmentation = data["outputs"][
figure01_options["sampled_mask_key"]]
images = data["inputs"][figure01_options["input_view_key"]]
generated = data["outputs"][figure01_options["generated_image_key"]]
groundtruth_segmentation = data["batches"][
figure01_options["gt_segmentation_key"]]
groundtruth_segmentation = denseposelib.resize_labels(
groundtruth_segmentation, (128, 128))
remapped_gt_segmentation = denseposelib.remap_parts(
groundtruth_segmentation, dp_remap_dict)
best_remapping = denseposelib.compute_best_iou_remapping(
inferred_segmentation, remapped_gt_segmentation)
remapped_inferred = denseposelib.remap_parts(inferred_segmentation,
best_remapping)
ncols = 7
n_inferred_parts = config.get("n_inferred_parts", 10)
colors = make_mask_colors(len(set(dp_new_part_list)), background_id=1)
df = pd.DataFrame(columns=["global_step", "batch_idx"] + dp_new_part_list)
for i in range(
len(inferred_segmentation)
): # TODO: maybe replace this amount of plots by parameters in the config file
image_container = []
# remap inferred segmentation
old_inferred = inferred_segmentation[i]
current_sampled_segmentation = np.argmax(sampled_segmentation[i], -1)
old_inferred_colors = make_mask_colors(n_inferred_parts,
background_id=1)
image_container.append(old_inferred_colors[old_inferred - 1])
image_container.append(
old_inferred_colors[current_sampled_segmentation])
new_inferred = remapped_inferred[i]
current_gt_segmentation = remapped_gt_segmentation[i]
# remap GT segmentation
iou, iou_labels = denseposelib.compute_iou(new_inferred,
current_gt_segmentation)
# filter out background
iou_filter = np.ones_like(iou) == 1.0
iou_filter[iou_labels == dp_new_part_list.index("background")] = False
df_update = {p: -1.0 for p in dp_new_part_list}
df_update.update({
p: float(np.squeeze(iou[pi == iou_labels]))
for pi, p in enumerate(dp_new_part_list) if pi in iou_labels
})
df_update.update({"batch_idx": i, "global_step": global_step})
df = df.append(df_update, ignore_index=True)
filtered_iou = iou[iou_filter]
mean_iou = np.mean(filtered_iou)
image_container.append(colors[new_inferred])
image_container.append(colors[current_gt_segmentation])
legend_labels = []
for pi, p in enumerate(dp_new_part_list):
if pi in iou_labels:
p_iou = np.squeeze(iou[np.argwhere(iou_labels == pi)])
else:
p_iou = 0.0
legend_labels.append(p + " - IOU : {:.03f}".format(p_iou))
legend_labels.append("mIOU (no BG) : {:.03f}".format(mean_iou))
colors = np.concatenate([colors, np.reshape([0, 0, 0], (1, 3))],
axis=0)
text_colors = [1, 1, 1] * len(colors)
legend_image = utils.make_legend_image(legend_labels, colors,
text_colors, (128, 128), 1)
image_container.append(legend_image)
current_image = images[i]
current_generated = generated[i]
image_container.append(
imageutils.convert_range(current_image, [-1, 1], [0, 1]))
image_container.append(
imageutils.convert_range(current_generated, [-1, 1], [0, 1]))
# write files
out_path = os.path.join(root, "figure_01")
os.makedirs(out_path, exist_ok=True)
out_image = np.stack(image_container)
out_image = imageutils.convert_range(out_image, [0, 1], [-1, 1])
plot_batch(out_image,
os.path.join(out_path, "{:06d}.png".format(i)),
cols=ncols)
df.to_csv(os.path.join(root, "part_ious.csv"), index=False, header=True)
df_mean = df[df != -1].mean().to_frame().transpose()
with open(os.path.join(root, "mean_part_ios.csv"), "w") as f:
print(
tabulate(df_mean,
headers="keys",
tablefmt="psql",
showindex="never"),
file=f,
)