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demo.py
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demo.py
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import numpy as np
import theano
import theano.tensor as T
import lasagne
#import skimage.transform
import pickle
import os
import re
import Image
##build the vgg model
from lasagne.layers import InputLayer, DenseLayer, NonlinearityLayer,DropoutLayer
from lasagne.layers.dnn import Conv2DDNNLayer as ConvLayer
#from lasagne.layers import Conv2DLayer as ConvLayer
from lasagne.layers import Pool2DLayer as PoolLayer
from lasagne.nonlinearities import softmax
from lasagne.utils import floatX
def build_model():
net = {}
net['input'] = InputLayer((None, 3, 224, 224))
net['conv1_1'] = ConvLayer(net['input'], 64, 3, pad=1)
net['conv1_2'] = ConvLayer(net['conv1_1'], 64, 3, pad=1)
net['pool1'] = PoolLayer(net['conv1_2'], 2)
net['conv2_1'] = ConvLayer(net['pool1'], 128, 3, pad=1)
net['conv2_2'] = ConvLayer(net['conv2_1'], 128, 3, pad=1)
net['pool2'] = PoolLayer(net['conv2_2'], 2)
net['conv3_1'] = ConvLayer(net['pool2'], 256, 3, pad=1)
net['conv3_2'] = ConvLayer(net['conv3_1'], 256, 3, pad=1)
net['conv3_3'] = ConvLayer(net['conv3_2'], 256, 3, pad=1)
net['pool3'] = PoolLayer(net['conv3_3'], 2)
net['conv4_1'] = ConvLayer(net['pool3'], 512, 3, pad=1)
net['conv4_2'] = ConvLayer(net['conv4_1'], 512, 3, pad=1)
net['conv4_3'] = ConvLayer(net['conv4_2'], 512, 3, pad=1)
net['pool4'] = PoolLayer(net['conv4_3'], 2)
net['conv5_1'] = ConvLayer(net['pool4'], 512, 3, pad=1)
net['conv5_2'] = ConvLayer(net['conv5_1'], 512, 3, pad=1)
net['conv5_3'] = ConvLayer(net['conv5_2'], 512, 3, pad=1)
net['pool5'] = PoolLayer(net['conv5_3'], 2)
net['fc6'] = DenseLayer(net['pool5'], num_units=4096)
net['fc7_2048'] = DenseLayer(net['fc6'], num_units=2048)
net['fc8_7'] = DenseLayer(net['fc7_2048'], num_units=2, nonlinearity=None)
net['prob'] = NonlinearityLayer(net['fc8_7'], softmax)
return net
#with np.load('/home/robolab/project/deepProjectBridge/YL_50iter.npz') as f:
# param_values = [f['arr_%d' % i] for i in range(len(f.files))]
# print(range(len(param_values)))
#print len(param_values)
BATCH_SIZE = 1
net = build_model()
#half_feature_layer=DenseLayer(net['fc6'], num_units=2048)
#half_feature_layer_dp=DropoutLayer(half_feature_layer,p=0.)
#output_layer=DenseLayer(half_feature_layer_dp,num_units=2,nonlinearity=softmax)
#final_prob=NonlinearityLayer(output_layer, softmax)
#lasagne.layers.set_all_param_values(net['fc7_2048'], param_values[:30])
half_feature_layer=DenseLayer(net['fc6'],num_units=2048)
half_feature_layer_dp=DropoutLayer(half_feature_layer,p=0.)
output_layer=DenseLayer(half_feature_layer_dp,num_units=2,nonlinearity=softmax)
final_prob=NonlinearityLayer(output_layer, softmax)
with np.load('/home/robolab/project/deepProjectBridge/YL_50iterV2.npz') as f:
param_values = [f['arr_%d' % i] for i in range(len(f.files))]
lasagne.layers.set_all_param_values(output_layer, param_values)
print 'successfully..., from new model 500'
###############################################################
# begin to load images and do the test
# Define loss function and metrics, and get an updates dictionary
# 11111111111111111
# define the function and cost stuff
X_sym = T.tensor4()
y_sym = T.ivector()
prediction = lasagne.layers.get_output(final_prob, X_sym)
loss = lasagne.objectives.categorical_crossentropy(prediction, y_sym)
loss = loss.mean()
acc = T.mean(T.eq(T.argmax(prediction, axis=1), y_sym),
dtype=theano.config.floatX)
params = lasagne.layers.get_all_params(final_prob, trainable=True)
updates = lasagne.updates.nesterov_momentum(
loss, params, learning_rate=0.0001, momentum=0.9)
# Compile functions for training, validation and prediction
train_fn = theano.function([X_sym, y_sym], loss, updates=updates)
val_fn = theano.function([X_sym, y_sym], [loss, acc])
pred_fn = theano.function([X_sym], prediction)
##################################################################
# generator splitting an iterable into chunks of maximum length N
def batches(iterable, N):
chunk = []
for item in iterable:
chunk.append(item)
if len(chunk)==N:
rst=chunk
chunk=[]
yield rst
if chunk:
yield chunk
#####################################################################
# defines functions for pre-process the image data
def train_batch():
trdata,trlb=imdata(imglist)
trdata/=256
#trdata=trdata-MEAN_IMAGE
return train_fn(trdata,trlb)
def test_batch():
tsdata,tslb=imdata(ixx)
tsdata/=256
#tsdata=tsdata-MEAN_IMAGE
return val_fn(tsdata,tslb)
def val_batch():
ix = range(len(y_val))
np.random.shuffle(ix)
ix = ix[:BATCH_SIZE]
return val_fn(X_val[ix], y_val[ix])
#####################################################################
# print trdata[0:5,:,:,:], trlb
##things to change
# print 'begin training'
loss_tot = 0.
acc_tot = 0.
# loss,acc=test_batch()
# print 'Test loss and acc:',loss,acc
import math
import cv2
import random
print 'direct testing'
regionSize = 200;
for kk in range(1):
img=cv2.imread('bridgeFlaws/%03d.jpg'%(232))
#print('bridgeFlaws/%3d.jpg',kk+1)
#img=cv2.imread('imageSource/'+s)
height, width, channels = img.shape
widthLevel = int(width/regionSize);
heightLevel = int(height/regionSize);
imgShow = np.zeros((height,width,3))
imgShow = np.copy(img)
for ii in range(widthLevel-1):
for jj in range(heightLevel-1):
#xRange = width - 304
#yRange = height - 304
#randXCut = random.random()
#randYCut = random.random()
#rangeXForCut = [1+ math.floor(xRange*randXCut), math.floor(xRange*randXCut) + 300]
#rangeYForCut = [1+ math.floor(yRange*randYCut), math.floor(yRange*randYCut) + 300]
rangeXForCut = [(ii-1)*regionSize +1, (ii-1)*regionSize + regionSize*2]
rangeYForCut = [(jj-1)*regionSize +1, (jj-1)*regionSize + regionSize*2]
print(int(rangeXForCut[0]), int(rangeXForCut[1]), int(rangeYForCut[0]), int(rangeYForCut[1]))
#tempImg = img[int(rangeXForCut[0]):int(rangeXForCut[1]), 100:200, :]
tempImg = img[int(rangeYForCut[0]):int(rangeYForCut[1]), int(rangeXForCut[0]):int(rangeXForCut[1]), :]
height1, width1, channels1 = tempImg.shape
if height1 != 0 and width1 != 0:
im224=np.zeros((3,224,224))
for t in range(3):
im224[t,:,:]=cv2.resize(tempImg[:,:,t],(224,224))
datablob=np.ndarray((1,3,224,224))
datalb=np.zeros((1,))
datablob[0,:,:,:]=im224
datalb[0]=int(1.0)
datablob=datablob.astype('float32')
datablob/=256
datalb=datalb.astype('int32')
loss, acc = val_fn(datablob, datalb)
prediction = pred_fn(datablob)
#print('prediction')
#print prediction[0][0]
#print datalb
if prediction[0][0] < 0.5:
cv2.rectangle(imgShow,(int(rangeXForCut[1]), int(rangeYForCut[1])), (int(rangeXForCut[0]), int(rangeYForCut[0])), (0,255,0),3)
#else:
# cv2.rectangle(imgShow,(int(rangeXForCut[1]), int(rangeYForCut[1])), (int(rangeXForCut[0]), int(rangeYForCut[0])), (255,0,0),1)
cv2.imwrite('generated/'+str(232)+'.jpg',imgShow)
#####################################################################