From e30dbfac1078d60c231cc7973353f677fa2fba77 Mon Sep 17 00:00:00 2001
From: "Daniel.Frisinghelli" <daniel.frisinghelli@eurac.edu>
Date: Wed, 27 Jan 2021 18:15:04 +0100
Subject: [PATCH] Implemented a more efficient way to calculate class
distribution.
---
pysegcnn/core/dataset.py | 38 ++++++++++++++++++++++++++------------
1 file changed, 26 insertions(+), 12 deletions(-)
diff --git a/pysegcnn/core/dataset.py b/pysegcnn/core/dataset.py
index 2366ecf..c04103c 100644
--- a/pysegcnn/core/dataset.py
+++ b/pysegcnn/core/dataset.py
@@ -632,14 +632,18 @@ class ImageDataset(Dataset):
def class_distribution(self):
+ # initialize dictionary of class spectral distribution
+ cls_ds = {k: np.empty(shape=(0, len(self.use_bands))) for k, _ in
+ self.labels.items()}
+
# initialize class distribution dataframe
- columns = [band.capitalize() for band in self.use_bands] + ['Class']
- cls_df = pd.DataFrame(columns=columns)
+ # columns = [band.capitalize() for band in self.use_bands] + ['Class']
+ # cls_df = pd.DataFrame(columns=columns)
# create the lookup table to replace the class identifiers by their
# corresponding labels
- lookup = np.array(list({k: v['label'] for k, v in self.labels.items()}
- .items())).astype(object)
+ # lookup = np.array(list({k: v['label'] for k, v in self.labels.items()}
+ # .items())).astype(object)
# iterate over the samples of the dataset
for i in range(len(self)):
@@ -647,17 +651,27 @@ class ImageDataset(Dataset):
LOGGER.info('Sample: {}/{}'.format(i + 1, len(self)))
x, y = self[i]
- # reshape the current sample
- data = np.hstack([x.flatten(start_dim=1).T, np.expand_dims(
- array_replace(y.flatten(), lookup), axis=1)])
+ # iterate over the different classes
+ for k, v in self.labels.items():
+ # get values equal to the current class
+ mask = np.where(y == k)
+
+ # subset input to current class
+ cls_ds[k] = np.vstack([cls_ds[k], x[:, mask[0], mask[1]].T])
+
+ return cls_ds
+
+ # reshape the current sample
+ # data = np.hstack([x.flatten(start_dim=1).T, np.expand_dims(
+ # array_replace(y.flatten(), lookup), axis=1)])
- # the pixels of the current sample to the dataframe
- df = pd.DataFrame(data, columns=columns)
+ # # the pixels of the current sample to the dataframe
+ # df = pd.DataFrame(data, columns=columns)
- # update class distribution dataframe
- cls_df = cls_df.append(df)
+ # # update class distribution dataframe
+ # cls_df = cls_df.append(df)
- return cls_df
+ # return cls_df
def __repr__(self):
"""Dataset representation.
--
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