From e554e6537200cf2717ec529ddb166de3661b0a7a Mon Sep 17 00:00:00 2001
From: "Daniel.Frisinghelli" <daniel.frisinghelli@eurac.edu>
Date: Thu, 20 Aug 2020 15:57:43 +0200
Subject: [PATCH] Made the ground truth mask in plot_sample optional
---
pysegcnn/core/graphics.py | 80 +++++++++++++++++++++------------------
pysegcnn/core/predict.py | 4 +-
2 files changed, 45 insertions(+), 39 deletions(-)
diff --git a/pysegcnn/core/graphics.py b/pysegcnn/core/graphics.py
index 5303d97..dfb1034 100644
--- a/pysegcnn/core/graphics.py
+++ b/pysegcnn/core/graphics.py
@@ -32,8 +32,6 @@ from pysegcnn.core.trainer import accuracy_function
from pysegcnn.main.config import HERE
-# this function applies percentile stretching at the alpha level
-# can be used to increase constrast for visualization
def contrast_stretching(image, alpha=5):
"""Apply percentile stretching to an image to increase constrast.
@@ -85,9 +83,7 @@ def running_mean(x, w):
return (cumsum[w:] - cumsum[:-w]) / w
-# plot_sample() plots a false color composite of the scene/tile together
-# with the model prediction and the corresponding ground truth
-def plot_sample(x, y, use_bands, labels, y_pred=None, figsize=(10, 10),
+def plot_sample(x, use_bands, labels, y=None, y_pred=None, figsize=(10, 10),
bands=['nir', 'red', 'green'], state=None,
outpath=os.path.join(HERE, '_samples/'), alpha=0):
"""Plot false color composite (FCC), ground truth and model prediction.
@@ -96,8 +92,6 @@ def plot_sample(x, y, use_bands, labels, y_pred=None, figsize=(10, 10),
----------
x : `numpy.ndarray` or `torch.Tensor`, (b, h, w)
Array containing the raw data of the tile, shape=(bands, height, width)
- y : `numpy.ndarray` or `torch.Tensor`, (h, w)
- Array containing the ground truth of tile ``x``, shape=(height, width)
use_bands : `list` of `str`
List describing the order of the bands in ``x``.
labels : `dict` [`int`, `dict`]
@@ -107,9 +101,12 @@ def plot_sample(x, y, use_bands, labels, y_pred=None, figsize=(10, 10),
A named color (`str`).
``'label'``
The name of the class label (`str`).
- y_pred : `numpy.ndarray` or `None`, optional
+ y : `numpy.ndarray` or `torch.Tensor` or `None`, optional
+ Array containing the ground truth of tile ``x``, shape=(height, width).
+ The default is None.
+ y_pred : `numpy.ndarray` or `torch.Tensor` or `None`, optional
Array containing the prediction for tile ``x``, shape=(height, width).
- The default is None, i.e. only FCC and ground truth are plotted.
+ The default is None.
figsize : `tuple`, optional
The figure size in centimeters. The default is (10, 10).
bands : `list` [`str`], optional
@@ -119,7 +116,7 @@ def plot_sample(x, y, use_bands, labels, y_pred=None, figsize=(10, 10),
state file ending with '.pt'. The default is None, i.e. plot is not
saved to disk.
outpath : `str` or `pathlib.Path`, optional
- Output path. The default is os.path.join(HERE, '_samples/').
+ Output path. The default is 'pysegcnn/main/_samples'.
alpha : `int`, optional
The level of the percentiles to increase constrast in the FCC.
The default is 0, i.e. no stretching.
@@ -128,8 +125,8 @@ def plot_sample(x, y, use_bands, labels, y_pred=None, figsize=(10, 10),
-------
fig : `matplotlib.figure.Figure`
The figure handle.
- ax : `matplotlib.axes._subplots.AxesSubplot`
- The axes handle.
+ ax : `numpy.ndarray` [`matplotlib.axes._subplots.AxesSubplot`]
+ An array of the axes handles.
"""
# check whether to apply constrast stretching
@@ -145,35 +142,46 @@ def plot_sample(x, y, use_bands, labels, y_pred=None, figsize=(10, 10),
boundaries = [*labels.keys(), cmap.N]
norm = BoundaryNorm(boundaries, cmap.N)
- # create figure: check whether to plot model prediction
- if y_pred is not None:
-
- # compute accuracy
- acc = accuracy_function(y_pred, y)
-
- # plot model prediction
- fig, ax = plt.subplots(1, 3, figsize=figsize)
- ax[2].imshow(y_pred, cmap=cmap, interpolation='nearest', norm=norm)
- ax[2].set_title('Prediction ({:.2f}%)'.format(acc * 100), pad=15)
+ # create a patch (proxy artist) for every color
+ patches = [mpatches.Patch(color=c, label=l) for c, l in
+ zip(colors, ulabels)]
- else:
- fig, ax = plt.subplots(1, 2, figsize=figsize)
+ # initialize figure
+ fig, ax = plt.subplots(1, 3, figsize=figsize)
# plot false color composite
ax[0].imshow(rgb)
ax[0].set_title('R = {}, G = {}, B = {}'.format(*bands), pad=15)
- # plot ground thruth mask
- ax[1].imshow(y, cmap=cmap, interpolation='nearest', norm=norm)
- ax[1].set_title('Ground truth', pad=15)
+ # check whether to plot ground truth
+ acc = None
+ if y is None:
+ # remove axis to plot ground truth from figure
+ fig.delaxes(ax[1])
+ else:
+ # plot ground thruth mask
+ ax[1].imshow(y, cmap=cmap, interpolation='nearest', norm=norm)
+ ax[1].set_title('Ground truth', pad=15)
+
+ # check whether to plot model prediction
+ if y_pred is None:
+ # remove axis to plot model prediction from figure
+ fig.delaxes(ax[2])
+ else:
+ # plot model prediction
+ ax[2].imshow(y_pred, cmap=cmap, interpolation='nearest', norm=norm)
- # create a patch (proxy artist) for every color
- patches = [mpatches.Patch(color=c, label=l) for c, l in
- zip(colors, ulabels)]
+ # set title
+ title = 'Prediction'
+ if y is not None:
+ acc = accuracy_function(y_pred, y)
+ title += ' ({:.2f}%)'.format(acc * 100)
+ ax[2].set_title(title, pad=15)
- # plot patches as legend
- plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2,
- frameon=False)
+ # if a ground truth or a model prediction is plotted, add legend
+ if len(fig.axes) > 1:
+ plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2,
+ frameon=False)
# save figure
if state is not None:
@@ -184,8 +192,6 @@ def plot_sample(x, y, use_bands, labels, y_pred=None, figsize=(10, 10),
return fig, ax
-# plot_confusion_matrix() plots the confusion matrix of the validation/test
-# set returned by the pytorch.predict function
def plot_confusion_matrix(cm, labels, normalize=True,
figsize=(10, 10), cmap='Blues', state=None,
outpath=os.path.join(HERE, '_graphics/')):
@@ -213,7 +219,7 @@ def plot_confusion_matrix(cm, labels, normalize=True,
state file ending with '.pt'. The default is None, i.e. plot is not
saved to disk.
outpath : `str` or `pathlib.Path`, optional
- Output path. The default is os.path.join(HERE, '_graphics/').
+ Output path. The default is 'pysegcnn/main/_graphics/'.
Returns
-------
@@ -306,7 +312,7 @@ def plot_loss(state_file, figsize=(10, 10), step=5,
A list of four named colors supported by `matplotlib`.
The default is ['lightgreen', 'green', 'skyblue', 'steelblue'].
outpath : `str` or `pathlib.Path`, optional
- Output path. The default is os.path.join(HERE, '_graphics/').
+ Output path. The default is 'pysegcnn/main/_graphics/'.
Returns
-------
diff --git a/pysegcnn/core/predict.py b/pysegcnn/core/predict.py
index 9405d7c..6403a7a 100644
--- a/pysegcnn/core/predict.py
+++ b/pysegcnn/core/predict.py
@@ -168,9 +168,9 @@ def predict_samples(ds, model, cm=False, plot=False, **kwargs):
# plot inputs, ground truth and model predictions
sname = fname + '_{}_{}.pt'.format(ds.name, batch)
fig, ax = plot_sample(inputs.numpy().clip(0, 1),
- labels,
ds.dataset.use_bands,
ds.dataset.labels,
+ y=labels,
y_pred=prd,
state=sname,
**kwargs)
@@ -298,9 +298,9 @@ def predict_scenes(ds, model, scene_id=None, cm=False, plot=False, **kwargs):
# plot current scene
if plot:
fig, ax = plot_sample(inputs.clip(0, 1),
- labels,
ds.dataset.use_bands,
ds.dataset.labels,
+ y=labels,
y_pred=prdtcn,
state=sname,
**kwargs)
--
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