-
Notifications
You must be signed in to change notification settings - Fork 0
/
infer_little_image_fc_densenet.py
181 lines (166 loc) · 9.47 KB
/
infer_little_image_fc_densenet.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
from __future__ import print_function
from scipy.misc import imread, imsave
import tensor_utils_5_channels as utils
from layers_fc_densenet import *
FLAGS = tf.flags.FLAGS
tf.flags.DEFINE_integer("batch_size", "1", "batch size for training")
tf.flags.DEFINE_string("logs_dir", "../logs-vgg19/", "path to logs directory")
tf.flags.DEFINE_string("data_dir", "../ISPRS_semantic_labeling_Vaihingen", "path to dataset")
MAX_ITERATION = int(1e6 + 1)
NUM_OF_CLASSESS = 6
IMAGE_SIZE = 224
def inference(image, keep_prob):
n_filters_first_conv = 30
n_pool = 5
growth_rate = 12
n_layers_per_block = [5]*(2*n_pool+1)
n_classes = 6
mean_pixel = np.array([120.895239985, 81.9300816234, 81.2898876188, 30.6986130799, 284.97018, 106.314329243, 124.171918054, 109.260369903, 182.615729022,
75.1762766769, 84.3529895303, 100.699252985, 66.8837693324, 98.6030061849, 133.955897217])
processed_image = utils.process_image(image, mean_pixel)
print(np.shape(processed_image))
W_first = utils.weight_variable([3,3,processed_image.get_shape().as_list()[3],n_filters_first_conv], name='W_first')
b_first = utils.bias_variable([n_filters_first_conv], name= 'b_first')
conv_first = utils.conv2d_basic(processed_image, W_first, b_first)
stack = tf.nn.relu(conv_first)
n_filters = n_filters_first_conv
print("Before Downsample")
print(np.shape(stack))
#####################
# Downsampling path #
#####################
skip_connection_list = []
for i in range(n_pool):
# Dense Block
for j in range(n_layers_per_block[i]):
l = BN_ReLU_Conv(inputs=stack,n_filters= growth_rate,keep_prob=keep_prob, name="downsample_"+str(i)+"_"+str(j))
stack = tf.concat([stack,l], axis=3)
n_filters += growth_rate
skip_connection_list.append(stack)
stack = Transition_Down(inputs=stack, n_filters=n_filters, keep_prob=keep_prob, name='downsample_stack_'+str(i))
skip_connection_list = skip_connection_list[::-1]
#####################
# Bottleneck #
#####################
block_to_upsample = []
for j in range(n_layers_per_block[n_pool]):
l = BN_ReLU_Conv(inputs=stack, n_filters=growth_rate, keep_prob= keep_prob, name="bottleneck_"+str(j))
block_to_upsample.append(l)
stack = tf.concat([stack,l], axis=3)
#######################
# Upsampling path #
#######################
for i in range(n_pool):
n_filters_keep = growth_rate * n_layers_per_block[n_pool + i]
stack = Transition_Up(skip_connection=skip_connection_list[i], block_to_upsample=block_to_upsample, n_filters_keep = n_filters_keep, name="upsample_stack_"+str(i))
# Dense Block
block_to_upsample = []
for j in range(n_layers_per_block[n_pool + i + 1]):
l = BN_ReLU_Conv(inputs=stack, n_filters=growth_rate, keep_prob=keep_prob, name="upsample_"+str(i)+"_"+str(j))
block_to_upsample.append(l)
stack = tf.concat([stack, l], axis=3)
W_last = utils.weight_variable([1,1,stack.get_shape().as_list()[3],n_classes], name="W_last")
b_last = utils.bias_variable([n_classes], name="b_last")
conv_last = utils.conv2d_basic(stack,W_last,b_last)
print("Conv_last")
print(np.shape(conv_last))
annotation_pred = tf.argmax(conv_last, dimension=3, name="prediction")
return tf.expand_dims(annotation_pred, dim=3), conv_last
def infer_little_img(input_image_path,patch_size=224,stride_ver=56,stride_hor=56):
tf.reset_default_graph()
input_image= imread(input_image_path)
dsm_image= imread(input_image_path.replace('top','dsm').replace('_mosaic','').replace('area','matching_area'))
dsm_image = np.expand_dims(dsm_image, axis=2)
ndsm_image= imread(input_image_path.replace('top/','ndsm/').replace('top','dsm').replace('_mosaic','')
.replace('area','matching_area').replace('.tif','_normalized.jpg'))
ndsm_image= np.expand_dims(ndsm_image,axis=2)
A_image = imread(input_image_path.replace('top','A').replace('_mosaic_09cm_area',''))
A_image = np.expand_dims(A_image, axis=2)
azi_image = imread(input_image_path.replace('top', 'azi').replace('_mosaic_09cm_area', ''))
azi_image = np.expand_dims(azi_image, axis=2)
B_image = imread(input_image_path.replace('top', 'B').replace('_mosaic_09cm_area', ''))
B_image = np.expand_dims(B_image, axis=2)
ele_image = imread(input_image_path.replace('top', 'ele').replace('_mosaic_09cm_area', ''))
ele_image = np.expand_dims(ele_image, axis=2)
entpy_image = imread(input_image_path.replace('top', 'entpy').replace('_mosaic_09cm_area', ''))
entpy_image = np.expand_dims(entpy_image, axis=2)
entpy2_image = imread(input_image_path.replace('top', 'entpy2').replace('_mosaic_09cm_area', ''))
entpy2_image = np.expand_dims(entpy2_image, axis=2)
L_image = imread(input_image_path.replace('top', 'L').replace('_mosaic_09cm_area', ''))
L_image = np.expand_dims(L_image, axis=2)
ndvi_image = imread(input_image_path.replace('top', 'ndvi').replace('_mosaic_09cm_area', ''))
ndvi_image = np.expand_dims(ndvi_image, axis=2)
sat_image = imread(input_image_path.replace('top', 'sat').replace('_mosaic_09cm_area', ''))
sat_image = np.expand_dims(sat_image, axis=2)
texton_image = imread(input_image_path.replace('top', 'texton').replace('_mosaic_09cm_area', ''))
texton_image = np.expand_dims(texton_image, axis=2)
height = np.shape(input_image)[0]
width = np.shape(input_image)[1]
output_image = np.zeros_like(input_image)
input_image= np.concatenate((input_image,ndsm_image,dsm_image,A_image,azi_image,B_image,ele_image,
entpy_image, entpy2_image, L_image, ndvi_image, sat_image, texton_image),axis=2)
output_map = np.zeros((height, width, 6), dtype=np.float32)
number_of_vertical_points = (height - patch_size) // stride_ver + 1
number_of_horizontial_points = (width - patch_size) // stride_hor + 1
sess= tf.Session()
keep_probability = tf.placeholder(tf.float32, name="keep_probabilty")
image = tf.placeholder(tf.float32, shape=[1, IMAGE_SIZE, IMAGE_SIZE, 15], name="input_image")
_, logits = inference(image, keep_probability)
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
ckpt = tf.train.get_checkpoint_state(FLAGS.logs_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print("Model restored...")
input_image= np.expand_dims(input_image,axis=0)
print(np.shape(input_image))
for i in range(number_of_vertical_points):
for j in range(number_of_horizontial_points):
current_patch = input_image[:,i * stride_ver:i * stride_ver + patch_size,
j * stride_hor:j * stride_hor + patch_size, :]
logits_result = sess.run(logits, feed_dict={image: current_patch, keep_probability: 1.0})
logits_result = tf.squeeze(logits_result)
patch_result= sess.run(logits_result)
output_map[i * stride_ver:i * stride_ver + patch_size, j * stride_hor:j * stride_hor + patch_size,
:] += patch_result
print('stage 1: i='+str(i)+"; j="+str(j))
for i in range(number_of_vertical_points):
current_patch= input_image[:,i*stride_ver:i*stride_ver+patch_size,width-patch_size:width,:]
logits_result = sess.run(logits, feed_dict={image: current_patch, keep_probability: 1.0})
logits_result = tf.squeeze(logits_result)
patch_result = sess.run(logits_result)
output_map[i*stride_ver:i*stride_ver+patch_size,width-patch_size:width,:]+=patch_result
print('stage 2: i=' + str(i) + "; j=" + str(j))
for i in range(number_of_horizontial_points):
current_patch= input_image[:,height-patch_size:height,i*stride_hor:i*stride_hor+patch_size,:]
logits_result = sess.run(logits, feed_dict={image: current_patch, keep_probability: 1.0})
logits_result = tf.squeeze(logits_result)
patch_result = sess.run(logits_result)
output_map[height-patch_size:height,i*stride_hor:i*stride_hor+patch_size,:]+=patch_result
print('stage 3: i=' + str(i) + "; j=" + str(j))
current_patch = input_image[:,height - patch_size:height, width - patch_size:width, :]
logits_result = sess.run(logits, feed_dict={image: current_patch, keep_probability: 1.0})
logits_result = tf.squeeze(logits_result)
patch_result = sess.run(logits_result)
output_map[height - patch_size:height, width - patch_size:width, :] += patch_result
predict_annotation_image = np.argmax(output_map, axis=2)
print(np.shape(predict_annotation_image))
for i in range(height):
for j in range(width):
if predict_annotation_image[i,j]==0:
output_image[i,j,:]=[255,255,255]
elif predict_annotation_image[i,j]==1:
output_image[i,j,:]=[0,0,255]
elif predict_annotation_image[i,j]==2:
output_image[i,j,:]=[0,255,255]
elif predict_annotation_image[i,j]==3:
output_image[i,j,:]=[0,255,0]
elif predict_annotation_image[i,j]==4:
output_image[i,j,:]=[255,255,0]
elif predict_annotation_image[i,j]==5:
output_image[i,j,:]=[255,0,0]
return output_image
if __name__ == "__main__":
#tf.app.run()
imsave("top_mosaic_09cm_area35.tif",
infer_little_img("/home/khmt/sangdv/duytv/Thesis_15channels/ISPRS_semantic_labeling_Vaihingen/top/top_mosaic_09cm_area35.tif"))