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our_func_cvpr18.py
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our_func_cvpr18.py
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from __future__ import division
import os,time,cv2
import scipy.io as sio
import tensorflow as tf
import tensorflow.contrib.slim as slim
import numpy as np
from numpy import *
import scipy.linalg
from copy import copy, deepcopy
from scipy import ndimage
def compIoU(im1, im2):
im1_mask = (im1>0.5)
im2_mask = (im2>0.5)
iou = np.sum(im1_mask&im2_mask)/np.sum(im1_mask|im2_mask)
return iou
def lrelu(x):
return tf.maximum(x*0.2,x)
def identity_initializer():
def _initializer(shape, dtype=tf.float32, partition_info=None):
array = np.zeros(shape, dtype=float)
cx, cy = shape[0]//2, shape[1]//2
for i in range(min(shape[2],shape[3])):
array[cx, cy, i, i] = 1
return tf.constant(array, dtype=dtype)
return _initializer
def nm(x):
w0=tf.Variable(1.0,name='w0')
w1=tf.Variable(0.0,name='w1')
return w0*x+w1*slim.batch_norm(x)
MEAN_VALUES = np.array([123.6800, 116.7790, 103.9390]).reshape((1,1,1,3))
def build_net(ntype,nin,nwb=None,name=None):
if ntype=='conv':
return tf.nn.relu(tf.nn.conv2d(nin,nwb[0],strides=[1,1,1,1],padding='SAME',name=name)+nwb[1])
elif ntype=='pool':
return tf.nn.avg_pool(nin,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')
def get_weight_bias(vgg_layers,i):
weights=vgg_layers[i][0][0][2][0][0]
weights=tf.constant(weights)
bias=vgg_layers[i][0][0][2][0][1]
bias=tf.constant(np.reshape(bias,(bias.size)))
return weights,bias
def build_vgg19(input,reuse=False):
if reuse:
tf.get_variable_scope().reuse_variables()
net={}
vgg_rawnet=scipy.io.loadmat('Models/imagenet-vgg-verydeep-19.mat')
vgg_layers=vgg_rawnet['layers'][0]
net['input']=input-MEAN_VALUES
net['conv1_1']=build_net('conv',net['input'],get_weight_bias(vgg_layers,0),name='vgg_conv1_1')
net['conv1_2']=build_net('conv',net['conv1_1'],get_weight_bias(vgg_layers,2),name='vgg_conv1_2')
net['pool1']=build_net('pool',net['conv1_2'])
net['conv2_1']=build_net('conv',net['pool1'],get_weight_bias(vgg_layers,5),name='vgg_conv2_1')
net['conv2_2']=build_net('conv',net['conv2_1'],get_weight_bias(vgg_layers,7),name='vgg_conv2_2')
net['pool2']=build_net('pool',net['conv2_2'])
net['conv3_1']=build_net('conv',net['pool2'],get_weight_bias(vgg_layers,10),name='vgg_conv3_1')
net['conv3_2']=build_net('conv',net['conv3_1'],get_weight_bias(vgg_layers,12),name='vgg_conv3_2')
net['conv3_3']=build_net('conv',net['conv3_2'],get_weight_bias(vgg_layers,14),name='vgg_conv3_3')
net['conv3_4']=build_net('conv',net['conv3_3'],get_weight_bias(vgg_layers,16),name='vgg_conv3_4')
net['pool3']=build_net('pool',net['conv3_4'])
net['conv4_1']=build_net('conv',net['pool3'],get_weight_bias(vgg_layers,19),name='vgg_conv4_1')
net['conv4_2']=build_net('conv',net['conv4_1'],get_weight_bias(vgg_layers,21),name='vgg_conv4_2')
net['conv4_3']=build_net('conv',net['conv4_2'],get_weight_bias(vgg_layers,23),name='vgg_conv4_3')
net['conv4_4']=build_net('conv',net['conv4_3'],get_weight_bias(vgg_layers,25),name='vgg_conv4_4')
net['pool4']=build_net('pool',net['conv4_4'])
net['conv5_1']=build_net('conv',net['pool4'],get_weight_bias(vgg_layers,28),name='vgg_conv5_1')
net['conv5_2']=build_net('conv',net['conv5_1'],get_weight_bias(vgg_layers,30),name='vgg_conv5_2')
#net['conv5_3']=build_net('conv',net['conv5_2'],get_weight_bias(vgg_layers,32),name='vgg_conv5_3')
#net['conv5_4']=build_net('conv',net['conv5_3'],get_weight_bias(vgg_layers,34),name='vgg_conv5_4')
#net['pool5']=build_net('pool',net['conv5_4'])
return net
def build(input,sz):
vgg19_features=build_vgg19(input[:,:,:,0:3])
for layer_id in range(1,6):
vgg19_f = vgg19_features['conv%d_2'%layer_id]
input = tf.concat([input, tf.image.resize_bilinear(vgg19_f,sz)], axis=3)
input = input/255.0
net=slim.conv2d(input,64,[1,1],rate=1,activation_fn=lrelu,normalizer_fn=nm,weights_initializer=identity_initializer(),scope='g_conv0')
net=slim.conv2d(net,64,[3,3],rate=1,activation_fn=lrelu,normalizer_fn=nm,weights_initializer=identity_initializer(),scope='g_conv1')
net=slim.conv2d(net,64,[3,3],rate=2,activation_fn=lrelu,normalizer_fn=nm,weights_initializer=identity_initializer(),scope='g_conv2')
net=slim.conv2d(net,64,[3,3],rate=4,activation_fn=lrelu,normalizer_fn=nm,weights_initializer=identity_initializer(),scope='g_conv3')
net=slim.conv2d(net,64,[3,3],rate=8,activation_fn=lrelu,normalizer_fn=nm,weights_initializer=identity_initializer(),scope='g_conv4')
net=slim.conv2d(net,64,[3,3],rate=16,activation_fn=lrelu,normalizer_fn=nm,weights_initializer=identity_initializer(),scope='g_conv5')
net=slim.conv2d(net,64,[3,3],rate=32,activation_fn=lrelu,normalizer_fn=nm,weights_initializer=identity_initializer(),scope='g_conv6')
net=slim.conv2d(net,64,[3,3],rate=64,activation_fn=lrelu,normalizer_fn=nm,weights_initializer=identity_initializer(),scope='g_conv7')
net=slim.conv2d(net,64,[3,3],rate=128,activation_fn=lrelu,normalizer_fn=nm,weights_initializer=identity_initializer(),scope='g_conv8')
net=slim.conv2d(net,64,[3,3],rate=1,activation_fn=lrelu,normalizer_fn=nm,weights_initializer=identity_initializer(),scope='g_conv9')
net=slim.conv2d(net,6,[1,1],rate=1,activation_fn=None,scope='g_conv_last')
return tf.tanh(net)
def our_func(usrId, imIdx, im_path, cnt, pn, clk):
if not os.path.isdir("res/%d/Ours/%05d" % (usrId, imIdx)):
os.makedirs("res/%d/Ours/%05d/ints" % (usrId, imIdx))
os.makedirs("res/%d/Ours/%05d/segs" % (usrId, imIdx))
os.makedirs("res/%d/Ours/%05d/tmps" % (usrId, imIdx))
sess=tf.Session()
if cnt == 0 and imIdx == 0:
global network,input,output,sz
input = tf.placeholder(tf.float32, shape=[None, None, None, 7])
output = tf.placeholder(tf.float32, shape=[None, None, None, 1])
sz = tf.placeholder(tf.int32, shape=[2])
network=build(input,sz)
saver = tf.train.Saver(var_list=[var for var in tf.trainable_variables() if var.name.startswith('g_')])
sess.run(tf.initialize_all_variables())
ckpt=tf.train.get_checkpoint_state("Models/ours_cvpr18")
if ckpt:
# print('loaded '+ckpt.model_checkpoint_path)
saver.restore(sess,ckpt.model_checkpoint_path)
input_image = cv2.imread(im_path, -1)
iH, iW, _ = input_image.shape
if cnt == 0:
int_pos = np.uint8(255*np.ones([iH,iW]))
int_neg = np.uint8(255*np.ones([iH,iW]))
tmp_clk = cv2.imread(im_path, -1)
else:
int_pos = cv2.imread('res/%d/Ours/%05d/ints/pos_dt_%03d.png' % (usrId, imIdx, cnt - 1), -1)
int_neg = cv2.imread('res/%d/Ours/%05d/ints/neg_dt_%03d.png' % (usrId, imIdx, cnt - 1), -1)
tmp_clk = cv2.imread('res/%d/Ours/%05d/tmps/clk_%03d.png' % (usrId, imIdx, cnt - 1), -1)
clk_pos = (int_pos==0)
clk_neg = (int_neg==0)
if pn == 1:
clk_pos[clk.y,clk.x] = 1
int_pos = ndimage.distance_transform_edt(1-clk_pos)
int_pos = np.uint8(np.minimum(np.maximum(int_pos, 0.0), 255.0))
cv2.imwrite('res/%d/Ours/%05d/ints/pos_dt_%03d.png' % (usrId, imIdx, cnt), int_pos)
cv2.imwrite('res/%d/Ours/%05d/ints/neg_dt_%03d.png' % (usrId, imIdx, cnt), int_neg)
cv2.circle(tmp_clk, (clk.x, clk.y), 5, (0, 255, 0), -1)
else:
clk_neg[clk.y,clk.x] = 1
int_neg = ndimage.distance_transform_edt(1-clk_neg)
int_neg = np.uint8(np.minimum(np.maximum(int_neg, 0.0), 255.0))
cv2.imwrite('res/%d/Ours/%05d/ints/pos_dt_%03d.png' % (usrId, imIdx, cnt), int_pos)
cv2.imwrite('res/%d/Ours/%05d/ints/neg_dt_%03d.png' % (usrId, imIdx, cnt), int_neg)
cv2.circle(tmp_clk, (clk.x, clk.y), 5, (0, 0, 255), -1)
input_pos_clks = deepcopy(int_pos)
input_neg_clks = deepcopy(int_neg)
input_pos_clks[int_pos != 0] = 255
input_neg_clks[int_neg != 0] = 255
input_ = np.expand_dims(np.float32(np.concatenate([input_image, np.expand_dims(int_pos, axis=2), np.expand_dims(int_neg, axis=2),
np.expand_dims(input_pos_clks, axis=2), np.expand_dims(input_neg_clks, axis=2)],axis=2)), axis=0)
output_image = sess.run([network],feed_dict={input:input_,sz:[iH,iW]})
output_image = np.minimum(np.maximum(output_image, 0.0), 1.0)
output_image[np.where(output_image>0.5)]=1
output_image[np.where(output_image<=0.5)]=0
res_path = 'res/%d/Ours/%05d/segs/%03d.png' % (usrId, imIdx, cnt)
segmask = np.uint8(output_image[0, 0, :, :, 0] * 255.0)
cv2.imwrite(res_path, segmask)
tmp_ol = cv2.imread(im_path, -1)
tmp_ol[:,:,0] = 0.5*tmp_ol[:,:,0] + 0.5*segmask
tmp_ol[:,:,1] = 0.5*tmp_ol[:,:,1] + 0.5*segmask
tmp_ol[:,:,2] = 0.5*tmp_ol[:,:,2] + 0.5*segmask
tmp_clk_path = 'res/%d/Ours/%05d/tmps/clk_%03d.png' % (usrId, imIdx, cnt)
tmp_ol_path = 'res/%d/Ours/%05d/tmps/ol_%03d.png' % (usrId, imIdx, cnt)
cv2.imwrite(tmp_clk_path, tmp_clk)
cv2.imwrite(tmp_ol_path, tmp_ol)