From cadc3f2815abba7e97cddadfa66ca54774fef72a Mon Sep 17 00:00:00 2001
From: Maciej Wielgosz <maciej.wielgosz@nibio.no>
Date: Wed, 21 Dec 2022 16:58:16 +0100
Subject: [PATCH] update in sem seg metric + helper scripts
---
.../get_terrestial_validation_few_samples.sh | 10 +++
.../get_val_corrected_few_samples.sh | 12 ++++
metrics/metrics_sem_seg.py | 65 ++++++++++---------
3 files changed, 58 insertions(+), 29 deletions(-)
create mode 100755 bash_helper_scripts/get_terrestial_validation_few_samples.sh
create mode 100755 bash_helper_scripts/get_val_corrected_few_samples.sh
diff --git a/bash_helper_scripts/get_terrestial_validation_few_samples.sh b/bash_helper_scripts/get_terrestial_validation_few_samples.sh
new file mode 100755
index 0000000..d7466f7
--- /dev/null
+++ b/bash_helper_scripts/get_terrestial_validation_few_samples.sh
@@ -0,0 +1,10 @@
+#!/bin/bash
+
+TARGET_FOLDER=/home/nibio/mutable-outside-world/code/gitlab_fsct/instance_segmentation_classic/maciek
+# clean the folder
+rm -rf $TARGET_FOLDER/*
+
+cp -r /home/nibio/mutable-outside-world/data/terestial_data_for_training_sem_seg_model/validation/burum_2_tile_-100_0.las $TARGET_FOLDER
+cp -r /home/nibio/mutable-outside-world/data/terestial_data_for_training_sem_seg_model/validation/plot82_tile_-25_0.las $TARGET_FOLDER
+cp -r /home/nibio/mutable-outside-world/data/terestial_data_for_training_sem_seg_model/validation/plot72_tile_-25_0.las $TARGET_FOLDER
+
diff --git a/bash_helper_scripts/get_val_corrected_few_samples.sh b/bash_helper_scripts/get_val_corrected_few_samples.sh
new file mode 100755
index 0000000..cc31bc1
--- /dev/null
+++ b/bash_helper_scripts/get_val_corrected_few_samples.sh
@@ -0,0 +1,12 @@
+#!/bin/bash
+
+TARGET_FOLDER=/home/nibio/mutable-outside-world/code/gitlab_fsct/instance_segmentation_classic/maciek
+# clean the folder
+rm -rf $TARGET_FOLDER/*
+
+cp -r /home/nibio/mutable-outside-world/data/corrected/validation/burum2_tile_-100_0.las $TARGET_FOLDER
+cp -r /home/nibio/mutable-outside-world/data/corrected/validation/Plot89_tile_-25_0.las $TARGET_FOLDER
+cp -r /home/nibio/mutable-outside-world/data/corrected/validation/plot82_tile_-25_0.las $TARGET_FOLDER
+# cp -r /home/nibio/mutable-outside-world/data/corrected/validation/Plot104_tile_-25_0.las $TARGET_FOLDER
+
+
diff --git a/metrics/metrics_sem_seg.py b/metrics/metrics_sem_seg.py
index 19a737a..b93e7e8 100644
--- a/metrics/metrics_sem_seg.py
+++ b/metrics/metrics_sem_seg.py
@@ -4,9 +4,7 @@ from joblib import Parallel, delayed
import laspy
from matplotlib import pyplot as plt
import numpy as np
-# from sklearn.neighbors import NearestNeighbors
from sklearn.neighbors import KDTree
-from tqdm import tqdm
from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix, f1_score, precision_score, recall_score
@@ -47,41 +45,40 @@ class MetricSemSeg:
# sort the list in place
file_name_list_original.sort()
- # get the list of the core names of the original point clouds
- # file_name_list_original_core = []
- # for file_name in file_name_list_original:
- # file_name_list_original_core.append(os.path.basename(file_name).split('.')[0])
+ # get the files which have the same core name as the original point clouds
+ file_name_list_original_core = []
+ for file_name in file_name_list_original:
+ file_name_list_original_core.append(os.path.basename(file_name).split('.')[0])
# get list of the predicted point clouds
file_name_list_predicted = []
for file in os.listdir(self.pred_folder):
- if file.endswith(".las"):
- file_name_list_predicted.append(os.path.join(self.pred_folder, file))
+ # get files which have the same core name as the original point clouds and suffix of '.segmented'
+ if os.path.basename(file).split('.')[0] in file_name_list_original_core and os.path.basename(file).split('.')[1] == 'segmented':
+ print("processing file {}".format(file))
+ # check is the file is a las file
+ if file.endswith(".las"):
+ file_name_list_predicted.append(os.path.join(self.pred_folder, file))
+ # check if the file is a ply file and there is no las file with the same core name
+ elif file.endswith(".ply") and (os.path.basename(file).split('.')[0] + '.segmented.las' not in os.listdir(self.pred_folder)):
+ # if not convert it to las file
+ file_name = os.path.join(self.pred_folder, file)
+ file_name_las = os.path.basename(file).split('.')[0] + '.segmented.las'
+ file_name_las = os.path.join(self.pred_folder, file_name_las)
+ if self.verbose:
+ print("converting {} to las file".format(file))
+ os.system("pdal translate {} {} --writers.las.dataformat_id=3 --writers.las.extra_dims=all".format(file_name, file_name_las))
+ file_name_list_predicted.append(file_name_las)
+
+ # remove double files
+ file_name_list_predicted = list(set(file_name_list_predicted))
# sort the list in place
file_name_list_predicted.sort()
# check if the number of files in the two folders is the same
if len(file_name_list_original) != len(file_name_list_predicted):
- raise Exception('The number of files in the two folders is not the same.')
-
- # get core names of ground truth point clouds
- file_name_list_original_core = []
- for file_name in file_name_list_original:
- file_name_list_original_core.append(os.path.basename(file_name).split('.')[0])
-
- # get core names of predicted point clouds
- file_name_list_predicted_core = []
- for file_name in file_name_list_predicted:
- file_name_list_predicted_core.append(os.path.basename(file_name).split('.')[0])
-
- # remove the suffix of '.segmented' from the predicted point clouds
- file_name_list_predicted_core = [file_name.split('.')[0] for file_name in file_name_list_predicted_core]
-
- # compare the two lists and check if they are the same
- if file_name_list_original_core != file_name_list_predicted_core:
- raise ValueError("The two lists of point clouds are not the same")
-
+ raise ValueError("The two folders do not have the same number of files")
# zip the two lists
file_name_list = list(zip(file_name_list_original, file_name_list_predicted))
@@ -166,7 +163,7 @@ class MetricSemSeg:
labels_original_closest = labels_original[indices]
# get the confusion matrix
- conf_matrix = np.round(confusion_matrix(labels_original_closest, labels_predicted, normalize='false'), decimals=2)
+ conf_matrix = np.round(confusion_matrix(labels_original_closest, labels_predicted, normalize='true'), decimals=2)
# if conf_matrix.shape[0] == 3 add diagonal elements to make it 4x4 at dimension 2
if conf_matrix.shape[0] == 3:
@@ -187,7 +184,7 @@ class MetricSemSeg:
class_names = ['terrain', 'vegetation', 'CWD', 'stem']
disp = ConfusionMatrixDisplay(confusion_matrix=conf_matrix, display_labels=class_names)
disp.plot()
- plt.savefig('confusion_matrix.png')
+ plt.savefig(file_name_original + '_confusion_matrix.png')
# compute precision, recall and f1 score using sklearn.metrics
precision = precision_score(labels_original_closest, labels_predicted, average='weighted')
@@ -260,6 +257,16 @@ class MetricSemSeg:
# compute the mean of the confusion matrix
conf_matrix_mean = np.mean(conf_matrix_list, axis=0)
+ # save the confusion matrix
+ if self.plot_confusion_matrix:
+ if conf_matrix_mean.shape[0] == 3:
+ class_names = ['terrain', 'vegetation', 'stem']
+ elif conf_matrix_mean.shape[0] == 4:
+ class_names = ['terrain', 'vegetation', 'CWD', 'stem']
+ disp = ConfusionMatrixDisplay(confusion_matrix=conf_matrix_mean, display_labels=class_names)
+ disp.plot()
+ plt.savefig('confusion_matrix_mean.png')
+
# compute the mean of the precision, recall and f1 score
precision_mean = np.mean(precision_list)
recall_mean = np.mean(recall_list)
--
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