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Utils.py
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Utils.py
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import numpy as np
from sklearn import metrics, preprocessing
from sklearn.preprocessing import MinMaxScaler
from sklearn.decomposition import PCA
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, accuracy_score, classification_report, cohen_kappa_score
from operator import truediv
import matplotlib.pyplot as plt
import scipy.io as sio
import os
import spectral
import torch
import cv2
from operator import truediv
def sampling(proportion, ground_truth):
train = {}
test = {}
labels_loc = {}
m = max(ground_truth)
for i in range(m):
indexes = [
j for j, x in enumerate(ground_truth.ravel().tolist())
if x == i + 1
]
np.random.shuffle(indexes)
labels_loc[i] = indexes
if proportion != 1:
nb_val = max(int((1 - proportion) * len(indexes)), 3)
else:
nb_val = 0
train[i] = indexes[:nb_val]
test[i] = indexes[nb_val:]
train_indexes = []
test_indexes = []
for i in range(m):
train_indexes += train[i]
test_indexes += test[i]
np.random.shuffle(train_indexes)
np.random.shuffle(test_indexes)
return train_indexes, test_indexes
def set_figsize(figsize=(3.5, 2.5)):
display.set_matplotlib_formats('svg')
plt.rcParams['figure.figsize'] = figsize
def classification_map(map, ground_truth, dpi, save_path):
fig = plt.figure(frameon=False)
fig.set_size_inches(ground_truth.shape[1] * 2.0 / dpi,
ground_truth.shape[0] * 2.0 / dpi)
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
fig.add_axes(ax)
ax.imshow(map)
fig.savefig(save_path, dpi=dpi)
return 0
def list_to_colormap(x_list):
y = np.zeros((x_list.shape[0], 3))
for index, item in enumerate(x_list):
if item == 0:
y[index] = np.array([255, 0, 0]) / 255.
if item == 1:
y[index] = np.array([0, 255, 0]) / 255.
if item == 2:
y[index] = np.array([0, 0, 255]) / 255.
if item == 3:
y[index] = np.array([255, 255, 0]) / 255.
if item == 4:
y[index] = np.array([0, 255, 255]) / 255.
if item == 5:
y[index] = np.array([255, 0, 255]) / 255.
if item == 6:
y[index] = np.array([192, 192, 192]) / 255.
if item == 7:
y[index] = np.array([128, 128, 128]) / 255.
if item == 8:
y[index] = np.array([128, 0, 0]) / 255.
if item == 9:
y[index] = np.array([128, 128, 0]) / 255.
if item == 10:
y[index] = np.array([0, 128, 0]) / 255.
if item == 11:
y[index] = np.array([128, 0, 128]) / 255.
if item == 12:
y[index] = np.array([0, 128, 128]) / 255.
if item == 13:
y[index] = np.array([0, 0, 128]) / 255.
if item == 14:
y[index] = np.array([255, 165, 0]) / 255.
if item == 15:
y[index] = np.array([255, 215, 0]) / 255.
if item == 16:
y[index] = np.array([0, 0, 0]) / 255.
if item == 17:
y[index] = np.array([215, 255, 0]) / 255.
if item == 18:
y[index] = np.array([0, 255, 215]) / 255.
if item == -1:
y[index] = np.array([0, 0, 0]) / 255.
return y
def generate_png(all_iter, net, gt_hsi, Dataset, device, total_indices, path):
pred_test = []
for X, y in all_iter:
# X = X.permute(0, 3, 1, 2)
X = X.to(device)
net.eval()
pred_test.extend(net(X).cpu().argmax(axis=1).detach().numpy())
gt = gt_hsi.flatten()
x_label = np.zeros(gt.shape)
for i in range(len(gt)):
if gt[i] == 0:
gt[i] = 17
x_label[i] = 16
gt = gt[:] - 1
x_label[total_indices] = pred_test
x = np.ravel(x_label)
y_list = list_to_colormap(x)
y_gt = list_to_colormap(gt)
y_re = np.reshape(y_list, (gt_hsi.shape[0], gt_hsi.shape[1], 3))
gt_re = np.reshape(y_gt, (gt_hsi.shape[0], gt_hsi.shape[1], 3))
classification_map(y_re, gt_hsi, 300,
path + '.png')
classification_map(gt_re, gt_hsi, 300,
path + '_gt.png')
print('------Get classification maps successful-------')