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infer_little_image_15channels.py
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infer_little_image_15channels.py
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from __future__ import print_function
import numpy as np
import tensorflow as tf
from scipy.misc import imread, imsave
import tensor_utils_5_channels as utils
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")
tf.flags.DEFINE_string("model_dir", "../pretrained_models/imagenet-vgg-verydeep-19.mat", "Path to vgg model mat")
tf.flags.DEFINE_bool('debug', "False", "Debug mode: True/ False")
MAX_ITERATION = int(1e6 + 1)
NUM_OF_CLASSESS = 6
IMAGE_SIZE = 224
def vgg_net(weights, image):
layers = (
'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1',
'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2',
'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3',
'relu3_3', 'conv3_4', 'relu3_4', 'pool3',
'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3',
'relu4_3', 'conv4_4', 'relu4_4', 'pool4',
'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3',
'relu5_3', 'conv5_4', 'relu5_4'
)
net = {}
current = image
for i, name in enumerate(layers):
kind = name[:4]
if kind == 'conv':
kernels, bias = weights[i][0][0][0][0]
# matconvnet: weights are [width, height, in_channels, out_channels]
# tensorflow: weights are [height, width, in_channels, out_channels]
if name == 'conv1_1':
append_channels= np.random.normal(loc=0,scale=0.02,size=(3,3,12,64))
print(append_channels)
kernels = np.concatenate((kernels, append_channels), axis=2)
kernels = utils.get_variable(np.transpose(kernels, (0, 1, 2, 3)), name=name + "_w")
else:
kernels = utils.get_variable(np.transpose(kernels, (0, 1, 2, 3)), name=name + "_w")
bias = utils.get_variable(bias.reshape(-1), name=name + "_b")
current = utils.conv2d_basic(current, kernels, bias)
elif kind == 'relu':
current = tf.nn.relu(current, name=name)
if FLAGS.debug:
utils.add_activation_summary(current)
elif kind == 'pool':
current = utils.avg_pool_2x2(current)
net[name] = current
return net
def inference(image, keep_prob):
"""
Semantic segmentation network definition
:param image: input image. Should have values in range 0-255
:param keep_prob:
:return:
"""
print("setting up vgg initialized conv layers ...")
model_data = utils.get_model_data(FLAGS.model_dir)
mean = model_data['normalization'][0][0][0]
mean_pixel = np.mean(mean, axis=(0, 1))
mean_pixel = np.append(mean_pixel, [30.6986130799, 284.97018,106.314329243,124.171918054,109.260369903,182.615729022,
75.1762766769,84.3529895303,100.699252985,66.8837693324,98.6030061849,133.955897217])
weights = np.squeeze(model_data['layers'])
processed_image = utils.process_image(image, mean_pixel)
with tf.variable_scope("inference"):
image_net = vgg_net(weights, processed_image)
conv_final_layer = image_net["conv5_3"]
pool5 = utils.max_pool_2x2(conv_final_layer)
W6 = utils.weight_variable([7, 7, 512, 4096], name="W6")
b6 = utils.bias_variable([4096], name="b6")
conv6 = utils.conv2d_basic(pool5, W6, b6)
relu6 = tf.nn.relu(conv6, name="relu6")
if FLAGS.debug:
utils.add_activation_summary(relu6)
relu_dropout6 = tf.nn.dropout(relu6, keep_prob=keep_prob)
W7 = utils.weight_variable([1, 1, 4096, 4096], name="W7")
b7 = utils.bias_variable([4096], name="b7")
conv7 = utils.conv2d_basic(relu_dropout6, W7, b7)
relu7 = tf.nn.relu(conv7, name="relu7")
if FLAGS.debug:
utils.add_activation_summary(relu7)
relu_dropout7 = tf.nn.dropout(relu7, keep_prob=keep_prob)
W8 = utils.weight_variable([1, 1, 4096, NUM_OF_CLASSESS], name="W8")
b8 = utils.bias_variable([NUM_OF_CLASSESS], name="b8")
conv8 = utils.conv2d_basic(relu_dropout7, W8, b8)
# annotation_pred1 = tf.argmax(conv8, dimension=3, name="prediction1")
# now to upscale to actual image size
deconv_shape1 = image_net["pool4"].get_shape()
W_t1 = utils.weight_variable([4, 4, deconv_shape1[3].value, NUM_OF_CLASSESS], name="W_t1")
b_t1 = utils.bias_variable([deconv_shape1[3].value], name="b_t1")
conv_t1 = utils.conv2d_transpose_strided(conv8, W_t1, b_t1, output_shape=tf.shape(image_net["pool4"]))
fuse_1 = tf.add(conv_t1, image_net["pool4"], name="fuse_1")
deconv_shape2 = image_net["pool3"].get_shape()
W_t2 = utils.weight_variable([4, 4, deconv_shape2[3].value, deconv_shape1[3].value], name="W_t2")
b_t2 = utils.bias_variable([deconv_shape2[3].value], name="b_t2")
conv_t2 = utils.conv2d_transpose_strided(fuse_1, W_t2, b_t2, output_shape=tf.shape(image_net["pool3"]))
fuse_2 = tf.add(conv_t2, image_net["pool3"], name="fuse_2")
shape = tf.shape(image)
deconv_shape3 = tf.stack([shape[0], shape[1], shape[2], NUM_OF_CLASSESS])
W_t3 = utils.weight_variable([16, 16, NUM_OF_CLASSESS, deconv_shape2[3].value], name="W_t3")
b_t3 = utils.bias_variable([NUM_OF_CLASSESS], name="b_t3")
conv_t3 = utils.conv2d_transpose_strided(fuse_2, W_t3, b_t3, output_shape=deconv_shape3, stride=8)
annotation_pred = tf.argmax(conv_t3, axis=3, name="prediction")
return tf.expand_dims(annotation_pred, dim=3), conv_t3
def infer_little_img(input_image_path,patch_size=224,stride_ver=112,stride_hor=112):
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=[None, 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)
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_area38.tif",
infer_little_img("../ISPRS_semantic_labeling_Vaihingen/top/top_mosaic_09cm_area38.tif"))
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