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utils_HSI.py
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utils_HSI.py
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# -*- coding: utf-8 -*-
import random
import numpy as np
from sklearn.metrics import confusion_matrix
import sklearn.model_selection
import itertools
import spectral
import matplotlib.pyplot as plt
from scipy import io
import imageio
import os
import re
import torch
import numpy as np
def get_device(ordinal):
# Use GPU ?
if ordinal < 0:
print("Computation on CPU")
device = torch.device('cpu')
elif torch.cuda.is_available():
print("Computation on CUDA GPU device {}".format(ordinal))
device = torch.device('cuda:{}'.format(ordinal))
else:
print("/!\\ CUDA was requested but is not available! Computation will go on CPU. /!\\")
device = torch.device('cpu')
return device
def seed_worker(seed):
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed) # if you are using multi-GPU.
np.random.seed(seed) # Numpy module.
random.seed(seed) # Python random module.
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
def open_file(dataset):
_, ext = os.path.splitext(dataset)
ext = ext.lower()
if ext == '.mat':
# Load Matlab array
# matlab v5.0 files using "io.loadmat"
# return io.loadmat(dataset)
# Solve bug: NotImplementedError: Please use HDF reader for matlab v7.3 files, e.g. h5py
import h5py
return h5py.File(dataset)
elif ext == '.tif' or ext == '.tiff':
# Load TIFF file
return imageio.imread(dataset)
elif ext == '.hdr':
img = spectral.open_image(dataset)
return img.load()
else:
raise ValueError("Unknown file format: {}".format(ext))
def convert_to_color_(arr_2d, palette=None):
"""Convert an array of labels to RGB color-encoded image.
Args:
arr_2d: int 2D array of labels
palette: dict of colors used (label number -> RGB tuple)
Returns:
arr_3d: int 2D images of color-encoded labels in RGB format
"""
arr_3d = np.zeros((arr_2d.shape[0], arr_2d.shape[1], 3), dtype=np.uint8)
if palette is None:
raise Exception("Unknown color palette")
for c, i in palette.items():
m = arr_2d == c
arr_3d[m] = i
return arr_3d
def convert_from_color_(arr_3d, palette=None):
"""Convert an RGB-encoded image to grayscale labels.
Args:
arr_3d: int 2D image of color-coded labels on 3 channels
palette: dict of colors used (RGB tuple -> label number)
Returns:
arr_2d: int 2D array of labels
"""
if palette is None:
raise Exception("Unknown color palette")
arr_2d = np.zeros((arr_3d.shape[0], arr_3d.shape[1]), dtype=np.uint8)
for c, i in palette.items():
m = np.all(arr_3d == np.array(c).reshape(1, 1, 3), axis=2)
arr_2d[m] = i
return arr_2d
def display_predictions(pred, vis, gt=None, caption=""):
if gt is None:
vis.images([np.transpose(pred, (2, 0, 1))],
opts={'caption': caption})
else:
vis.images([np.transpose(pred, (2, 0, 1)),
np.transpose(gt, (2, 0, 1))],
nrow=2,
opts={'caption': caption})
def display_dataset(img, gt, bands, labels, palette, vis):
"""Display the specified dataset.
Args:
img: 3D hyperspectral image
gt: 2D array labels
bands: tuple of RGB bands to select
labels: list of label class names
palette: dict of colors
display (optional): type of display, if any
"""
print("Image has dimensions {}x{} and {} channels".format(*img.shape))
rgb = spectral.get_rgb(img, bands)
rgb /= np.max(rgb)
rgb = np.asarray(255 * rgb, dtype='uint8')
# Display the RGB composite image
caption = "RGB (bands {}, {}, {})".format(*bands)
# send to visdom server
vis.images([np.transpose(rgb, (2, 0, 1))],
opts={'caption': caption})
def explore_spectrums(img, complete_gt, class_names, vis,
ignored_labels=None):
"""Plot sampled spectrums with mean + std for each class.
Args:
img: 3D hyperspectral image
complete_gt: 2D array of labels
class_names: list of class names
ignored_labels (optional): list of labels to ignore
vis : Visdom display
Returns:
mean_spectrums: dict of mean spectrum by class
"""
mean_spectrums = {}
for c in np.unique(complete_gt):
if c in ignored_labels:
continue
mask = complete_gt == c
class_spectrums = img[mask].reshape(-1, img.shape[-1])
step = max(1, class_spectrums.shape[0] // 100)
fig = plt.figure()
plt.title(class_names[c])
# Sample and plot spectrums from the selected class
for spectrum in class_spectrums[::step, :]:
plt.plot(spectrum, alpha=0.25)
mean_spectrum = np.mean(class_spectrums, axis=0)
std_spectrum = np.std(class_spectrums, axis=0)
lower_spectrum = np.maximum(0, mean_spectrum - std_spectrum)
higher_spectrum = mean_spectrum + std_spectrum
# Plot the mean spectrum with thickness based on std
plt.fill_between(range(len(mean_spectrum)), lower_spectrum,
higher_spectrum, color="#3F5D7D")
plt.plot(mean_spectrum, alpha=1, color="#FFFFFF", lw=2)
vis.matplot(plt)
mean_spectrums[class_names[c]] = mean_spectrum
return mean_spectrums
def plot_spectrums(spectrums, vis, title=""):
"""Plot the specified dictionary of spectrums.
Args:
spectrums: dictionary (name -> spectrum) of spectrums to plot
vis: Visdom display
"""
win = None
for k, v in spectrums.items():
n_bands = len(v)
update = None if win is None else 'append'
win = vis.line(X=np.arange(n_bands), Y=v, name=k, win=win, update=update,
opts={'title': title})
def build_dataset(mat, gt, ignored_labels=None):
"""Create a list of training samples based on an image and a mask.
Args:
mat: 3D hyperspectral matrix to extract the spectrums from
gt: 2D ground truth
ignored_labels (optional): list of classes to ignore, e.g. 0 to remove
unlabeled pixels
return_indices (optional): bool set to True to return the indices of
the chosen samples
"""
samples = []
labels = []
# Check that image and ground truth have the same 2D dimensions
assert mat.shape[:2] == gt.shape[:2]
for label in np.unique(gt):
if label in ignored_labels:
continue
else:
indices = np.nonzero(gt == label)
samples += list(mat[indices])
labels += len(indices[0]) * [label]
return np.asarray(samples), np.asarray(labels)
def get_random_pos(img, window_shape):
""" Return the corners of a random window in the input image
Args:
img: 2D (or more) image, e.g. RGB or grayscale image
window_shape: (width, height) tuple of the window
Returns:
xmin, xmax, ymin, ymax: tuple of the corners of the window
"""
w, h = window_shape
W, H = img.shape[:2]
x1 = random.randint(0, W - w - 1)
x2 = x1 + w
y1 = random.randint(0, H - h - 1)
y2 = y1 + h
return x1, x2, y1, y2
def sliding_window(image, step=10, window_size=(20, 20), with_data=True):
"""Sliding window generator over an input image.
Args:
image: 2D+ image to slide the window on, e.g. RGB or hyperspectral
step: int stride of the sliding window
window_size: int tuple, width and height of the window
with_data (optional): bool set to True to return both the data and the
corner indices
Yields:
([data], x, y, w, h) where x and y are the top-left corner of the
window, (w,h) the window size
"""
# slide a window across the image
w, h = window_size
W, H = image.shape[:2]
offset_w = (W - w) % step
offset_h = (H - h) % step
for x in range(0, W - w + offset_w, step):
if x + w > W:
x = W - w
for y in range(0, H - h + offset_h, step):
if y + h > H:
y = H - h
if with_data:
yield image[x:x + w, y:y + h], x, y, w, h
else:
yield x, y, w, h
def count_sliding_window(top, step=10, window_size=(20, 20)):
""" Count the number of windows in an image.
Args:
image: 2D+ image to slide the window on, e.g. RGB or hyperspectral, ...
step: int stride of the sliding window
window_size: int tuple, width and height of the window
Returns:
int number of windows
"""
sw = sliding_window(top, step, window_size, with_data=False)
return sum(1 for _ in sw)
def grouper(n, iterable):
""" Browse an iterable by grouping n elements by n elements.
Args:
n: int, size of the groups
iterable: the iterable to Browse
Yields:
chunk of n elements from the iterable
"""
it = iter(iterable)
while True:
chunk = tuple(itertools.islice(it, n))
if not chunk:
return
yield chunk
def metrics(prediction, target, ignored_labels=[], n_classes=None):
"""Compute and print metrics (accuracy, confusion matrix and F1 scores).
Args:
prediction: list of predicted labels
target: list of target labels
ignored_labels (optional): list of labels to ignore, e.g. 0 for undef
n_classes (optional): number of classes, max(target) by default
Returns:
accuracy, F1 score by class, confusion matrix
"""
ignored_mask = np.zeros(target.shape[:2], dtype=np.bool)
for l in ignored_labels:
ignored_mask[target == l] = True
ignored_mask = ~ignored_mask
#target = target[ignored_mask] -1
# target = target[ignored_mask]
# prediction = prediction[ignored_mask]
results = {}
n_classes = np.max(target) + 1 if n_classes is None else n_classes
cm = confusion_matrix(
target,
prediction,
labels=range(n_classes))
results["Confusion_matrix"] = cm
FP = cm.sum(axis=0) - np.diag(cm)
FN = cm.sum(axis=1) - np.diag(cm)
TP = np.diag(cm)
TN = cm.sum() - (FP + FN + TP)
FP = FP.astype(float)
FN = FN.astype(float)
TP = TP.astype(float)
TN = TN.astype(float)
# Sensitivity, hit rate, recall, or true positive rate
TPR = TP/(TP+FN)
results["TPR"] = TPR
# Compute global accuracy
total = np.sum(cm)
accuracy = sum([cm[x][x] for x in range(len(cm))])
accuracy *= 100 / float(total)
results["Accuracy"] = accuracy
# Compute F1 score
F1scores = np.zeros(len(cm))
for i in range(len(cm)):
try:
F1 = 2 * cm[i, i] / (np.sum(cm[i, :]) + np.sum(cm[:, i]))
except ZeroDivisionError:
F1 = 0.
F1scores[i] = F1
results["F1_scores"] = F1scores
# Compute kappa coefficient
pa = np.trace(cm) / float(total)
pe = np.sum(np.sum(cm, axis=0) * np.sum(cm, axis=1)) / \
float(total * total)
kappa = (pa - pe) / (1 - pe)
results["Kappa"] = kappa
results["prediction"] = prediction
results["label"] = target
return results
def show_results(results, vis, label_values=None, agregated=False):
text = ""
if agregated:
accuracies = [r["Accuracy"] for r in results]
kappas = [r["Kappa"] for r in results]
F1_scores = [r["F1_scores"] for r in results]
F1_scores_mean = np.mean(F1_scores, axis=0)
F1_scores_std = np.std(F1_scores, axis=0)
cm = np.mean([r["Confusion_matrix"] for r in results], axis=0)
text += "Agregated results :\n"
else:
cm = results["Confusion_matrix"]
accuracy = results["Accuracy"]
F1scores = results["F1_scores"]
kappa = results["Kappa"]
#label_values = label_values[1:]
vis.heatmap(cm, opts={'title': "Confusion_matrix",
'marginbottom': 150,
'marginleft': 150,
'width': 500,
'height': 500,
'rownames': label_values, 'columnnames': label_values})
text += "Confusion_matrix :\n"
text += str(cm)
text += "---\n"
if agregated:
text += ("Accuracy: {:.03f} +- {:.03f}\n".format(np.mean(accuracies),
np.std(accuracies)))
else:
text += "Accuracy : {:.03f}%\n".format(accuracy)
text += "---\n"
text += "F1_scores :\n"
if agregated:
for label, score, std in zip(label_values, F1_scores_mean,
F1_scores_std):
text += "\t{}: {:.03f} +- {:.03f}\n".format(label, score, std)
else:
for label, score in zip(label_values, F1scores):
text += "\t{}: {:.03f}\n".format(label, score)
text += "---\n"
if agregated:
text += ("Kappa: {:.03f} +- {:.03f}\n".format(np.mean(kappas),
np.std(kappas)))
else:
text += "Kappa: {:.03f}\n".format(kappa)
vis.text(text.replace('\n', '<br/>'))
print(text)
def sample_gt(gt, train_size, mode='random'):
"""Extract a fixed percentage of samples from an array of labels.
Args:
gt: a 2D array of int labels
percentage: [0, 1] float
Returns:
train_gt, test_gt: 2D arrays of int labels
"""
indices = np.nonzero(gt)
X = list(zip(*indices)) # x,y features
y = gt[indices].ravel() # classes
train_gt = np.zeros_like(gt)
test_gt = np.zeros_like(gt)
if train_size > 1:
train_size = int(train_size)
train_label = []
test_label = []
if mode == 'random':
if train_size == 1:
random.shuffle(X)
train_indices = [list(t) for t in zip(*X)]
[train_label.append(i) for i in gt[tuple(train_indices)]]
train_set = np.column_stack((train_indices[0],train_indices[1],train_label))
train_gt[tuple(train_indices)] = gt[tuple(train_indices)]
test_gt = []
test_set = []
else:
train_indices, test_indices = sklearn.model_selection.train_test_split(X, train_size=train_size, stratify=y, random_state=23)
train_indices = [list(t) for t in zip(*train_indices)]
test_indices = [list(t) for t in zip(*test_indices)]
train_gt[tuple(train_indices)] = gt[tuple(train_indices)]
test_gt[tuple(test_indices)] = gt[tuple(test_indices)]
[train_label.append(i) for i in gt[tuple(train_indices)]]
train_set = np.column_stack((train_indices[0],train_indices[1],train_label))
[test_label.append(i) for i in gt[tuple(test_indices)]]
test_set = np.column_stack((test_indices[0],test_indices[1],test_label))
elif mode == 'disjoint':
train_gt = np.copy(gt)
test_gt = np.copy(gt)
for c in np.unique(gt):
mask = gt == c
for x in range(gt.shape[0]):
first_half_count = np.count_nonzero(mask[:x, :])
second_half_count = np.count_nonzero(mask[x:, :])
try:
ratio = first_half_count / second_half_count
if ratio > 0.9 * train_size and ratio < 1.1 * train_size:
break
except ZeroDivisionError:
continue
mask[:x, :] = 0
train_gt[mask] = 0
test_gt[train_gt > 0] = 0
else:
raise ValueError("{} sampling is not implemented yet.".format(mode))
return train_gt, test_gt, train_set, test_set
def compute_imf_weights(ground_truth, n_classes=None, ignored_classes=[]):
""" Compute inverse median frequency weights for class balancing.
For each class i, it computes its frequency f_i, i.e the ratio between
the number of pixels from class i and the total number of pixels.
Then, it computes the median m of all frequencies. For each class the
associated weight is m/f_i.
Args:
ground_truth: the annotations array
n_classes: number of classes (optional, defaults to max(ground_truth))
ignored_classes: id of classes to ignore (optional)
Returns:
numpy array with the IMF coefficients
"""
n_classes = np.max(ground_truth) if n_classes is None else n_classes
weights = np.zeros(n_classes)
frequencies = np.zeros(n_classes)
for c in range(0, n_classes):
if c in ignored_classes:
continue
frequencies[c] = np.count_nonzero(ground_truth == c)
# Normalize the pixel counts to obtain frequencies
frequencies /= np.sum(frequencies)
# Obtain the median on non-zero frequencies
idx = np.nonzero(frequencies)
median = np.median(frequencies[idx])
weights[idx] = median / frequencies[idx]
weights[frequencies == 0] = 0.
return weights
def camel_to_snake(name):
s = re.sub('(.)([A-Z][a-z]+)', r'\1_\2', name)
return re.sub('([a-z0-9])([A-Z])', r'\1_\2', s).lower()