-
Notifications
You must be signed in to change notification settings - Fork 28
/
Copy pathget_classification_maps.py
205 lines (168 loc) · 6.65 KB
/
get_classification_maps.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
import scipy.io as sio
import tensorflow as tf
from keras.utils.np_utils import to_categorical
from keras.optimizers import Adam, SGD, Adadelta, RMSprop, Nadam
from sklearn import preprocessing
from Utils import fdssc_model, extract_samll_cubic
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
nb_val = int(proportion * len(indexes))
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 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 our_model():
model = fdssc_model.fdssc_model.build_fdssc((1, img_rows, img_cols, img_channels), nb_classes)
rms = RMSprop(lr=0.0003)
model.compile(loss='categorical_crossentropy', optimizer=rms, metrics=['accuracy'])
return model
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.
return y
global Dataset
data_set = input('Please input the name of data set(IN, UP or KSC):')
Dataset = data_set.upper()
if Dataset == 'IN':
mat_data = sio.loadmat('datasets/Indian_pines_corrected.mat')
data_hsi = mat_data['indian_pines_corrected']
mat_gt = sio.loadmat('datasets/Indian_pines_gt.mat')
gt_hsi = mat_gt['indian_pines_gt']
TOTAL_SIZE = 10249
TRAIN_SIZE = 2055
VALIDATION_SPLIT = 0.8
if Dataset == 'UP':
uPavia = sio.loadmat('datasets/PaviaU.mat')
gt_uPavia = sio.loadmat('datasets/PaviaU_gt.mat')
data_hsi = uPavia['paviaU']
gt_hsi = gt_uPavia['paviaU_gt']
TOTAL_SIZE = 42776
TRAIN_SIZE = 4281
VALIDATION_SPLIT = 0.9
if Dataset == 'KSC':
KSC = sio.loadmat('datasets/KSC.mat')
gt_KSC = sio.loadmat('datasets/KSC_gt.mat')
data_hsi = KSC['KSC']
gt_hsi = gt_KSC['KSC_gt']
TOTAL_SIZE = 5211
TRAIN_SIZE = 1048
VALIDATION_SPLIT = 0.8
print(data_hsi.shape)
data = data_hsi.reshape(np.prod(data_hsi.shape[:2]), np.prod(data_hsi.shape[2:]))
gt = gt_hsi.reshape(np.prod(gt_hsi.shape[:2]),)
nb_classes = max(gt)
print('The class numbers of the HSI data is:', nb_classes)
print('-----Importing Setting Parameters-----')
batch_size = 32
nb_epoch = 80
ITER = 10
PATCH_LENGTH = 4
img_rows = 2*PATCH_LENGTH+1
img_cols = 2*PATCH_LENGTH+1
img_channels = data_hsi.shape[2]
INPUT_DIMENSION = data_hsi.shape[2]
ALL_SIZE = data_hsi.shape[0] * data_hsi.shape[1]
VAL_SIZE = int(0.5*TRAIN_SIZE)
TEST_SIZE = TOTAL_SIZE - TRAIN_SIZE
data = preprocessing.scale(data)
data_ = data.reshape(data_hsi.shape[0], data_hsi.shape[1], data_hsi.shape[2])
whole_data = data_
padded_data = np.lib.pad(whole_data, ((PATCH_LENGTH, PATCH_LENGTH), (PATCH_LENGTH, PATCH_LENGTH), (0, 0)),
'constant', constant_values=0)
num = input('Please enter the number of model:')
print('the model is:' + Dataset + '_FDSSC_' + str(num) + '.hdf5')
best_weights_path = 'models/' + Dataset + '_FDSSC_' + str(num) + '@1.hdf5'
seeds = [1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341]
for index_iter in range(ITER):
train_indices, test_indices = sampling(VALIDATION_SPLIT, gt)
y_train_raw = gt[train_indices]
y_train = to_categorical(np.asarray(y_train_raw))
y_test_raw = gt[test_indices]
y_test = to_categorical(np.asarray(y_test_raw))
all_data = extract_samll_cubic.select_small_cubic(ALL_SIZE, range(ALL_SIZE), whole_data,
PATCH_LENGTH, padded_data, INPUT_DIMENSION)
print('--------Load trained model----------')
model_fdssc = our_model()
model_fdssc.load_weights(best_weights_path)
print('-------Load best model successfully--------')
pred_test = model_fdssc.predict(all_data.reshape(all_data.shape[0], all_data.shape[1], all_data.shape[2],
all_data.shape[3], 1)).argmax(axis=1)
x = np.ravel(pred_test)
gt = gt_hsi.flatten()
for i in range(len(gt)):
if gt[i] == 0:
gt[i] = 17
gt = gt[:]-1
print('-------Save the result in mat format--------')
x_re = np.reshape(x, (gt_hsi.shape[0], gt_hsi.shape[1]))
sio.savemat('mat/' + Dataset + '_' + str(num) + '.mat', {Dataset: x_re})
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,
'classification_maps/'+Dataset+'_'+str(num)+'.png')
classification_map(gt_re, gt_hsi, 300,
'classification_maps/' + Dataset + '_gt.png')
print('------Get classification maps successful-------')