From c87eea8b88501d41e503ca4046d8c5d11aedcd39 Mon Sep 17 00:00:00 2001
From: Maciej Wielgosz <maciej.wielgosz@nibio.no>
Date: Fri, 17 Feb 2023 13:51:55 +0100
Subject: [PATCH] instance segmentation labels updated with rmse
---
metrics/instance_segmentation_metrics.py | 15 ++++++++++++---
.../instance_segmentation_metrics_in_folder.py | 3 ++-
2 files changed, 14 insertions(+), 4 deletions(-)
diff --git a/metrics/instance_segmentation_metrics.py b/metrics/instance_segmentation_metrics.py
index bd7e9cf..e10ecee 100644
--- a/metrics/instance_segmentation_metrics.py
+++ b/metrics/instance_segmentation_metrics.py
@@ -9,7 +9,7 @@ from sklearn.neighbors import KDTree
logging.basicConfig(level=logging.INFO)
class InstanceSegmentationMetrics:
- GT_LABEL_NAME = 'treeID' #GT_LABEL_NAME = 'StemID'
+ GT_LABEL_NAME = 'treeID' #GT_LABEL_NAME = 'StemID', 'treeID'
TARGET_LABEL_NAME = 'instance_nr'
def __init__(
self,
@@ -290,7 +290,9 @@ class InstanceSegmentationMetrics:
hight_of_tree_pred = (self.instance_segmented_las[self.Y_labels == label].z).max() - (self.instance_segmented_las[self.Y_labels == label].z).min()
# get abs resiudal of the hight of the tree in the prediction
- residual_hight_of_tree_pred = abs(hight_of_tree_gt - hight_of_tree_pred)
+ residual_hight_of_tree_pred = hight_of_tree_gt - hight_of_tree_pred
+
+ rmse_hight = np.square(residual_hight_of_tree_pred)
# create tmp dict
tmp_dict = {
@@ -299,6 +301,7 @@ class InstanceSegmentationMetrics:
'high_of_tree_gt': hight_of_tree_gt,
'high_of_tree_pred': hight_of_tree_pred,
'residual_hight(gt_minus_pred)': residual_hight_of_tree_pred,
+ 'rmse_hight': rmse_hight,
'sum_all': sum_all,
'true_positive': true_positive,
'false_positive': false_positive,
@@ -312,7 +315,7 @@ class InstanceSegmentationMetrics:
metric_dict[str(label)] = tmp_dict
# list of interesting metrics
- interesting_parameters = ['precision', 'recall', 'f1_score', 'IoU', 'residual_hight(gt_minus_pred)']
+ interesting_parameters = ['precision', 'recall', 'f1_score', 'IoU', 'residual_hight(gt_minus_pred)', 'rmse_hight']
# weight the metrics by tree hight
metric_dict_weighted_by_tree_hight = {}
@@ -328,6 +331,9 @@ class InstanceSegmentationMetrics:
# divide by the sum of the hights of the trees
for parameter in interesting_parameters:
metric_dict_weighted_by_tree_hight[parameter] /= sum([metric_dict[label]['high_of_tree_gt'] for label in metric_dict.keys()])
+ if parameter == 'rmse_hight':
+ # compute sqrt of the residual hight (we are computing RMSE)
+ metric_dict_weighted_by_tree_hight[parameter] = metric_dict_weighted_by_tree_hight[parameter] ** 0.5
# compute the mean of the metrics
metric_dict_mean = {}
@@ -342,6 +348,9 @@ class InstanceSegmentationMetrics:
for parameter in interesting_parameters:
metric_dict_mean[parameter] = metric_dict_mean[parameter] / len(metric_dict)
+ if parameter == 'rmse_hight':
+ # compute sqrt of the residual hight (we are computing RMSE)
+ metric_dict_mean[parameter] = metric_dict_mean[parameter] ** 0.5
# compute tree level metrics
if metric_dict:
diff --git a/metrics/instance_segmentation_metrics_in_folder.py b/metrics/instance_segmentation_metrics_in_folder.py
index 9e3e423..f22b6b6 100644
--- a/metrics/instance_segmentation_metrics_in_folder.py
+++ b/metrics/instance_segmentation_metrics_in_folder.py
@@ -9,7 +9,7 @@ from metrics.instance_segmentation_metrics import InstanceSegmentationMetrics
from nibio_postprocessing.attach_labels_to_las_file_pred2gt import AttachLabelsToLasFilePred2Gt
class InstanceSegmentationMetricsInFolder():
- GT_LABEL_NAME = 'treeID'
+ GT_LABEL_NAME = 'treeID' # 'StemID' or 'treeID'
TARGET_LABEL_NAME = 'instance_nr'
def __init__(
@@ -116,6 +116,7 @@ class InstanceSegmentationMetricsInFolder():
# use a dirty hack to compute the mean of the metrics (accounts for broken point clouds caused by phils code)
mean_f1_score = sum(f1_scores_weighted_list) / len(gt_las_file_paths)
+ # mean_f1_score = sum(f1_scores_weighted_list) / 16
print('numer of files: ' + str(len(gt_las_file_paths)))
--
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