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# import modules
%matplotlib inline
import random
import pylab
import pandas as pd
import numpy as np
import cPickle as pkl
from PIL import Image
from lasagne import layers
from lasagne.updates import nesterov_momentum
from theano.tensor.nnet import softmax
from nolearn.lasagne import NeuralNet, BatchIterator
from sklearn.cross_validation import train_test_split
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# test image
from scipy.misc import imread as ims
img = ims('/home/faizy/workspace/project/project/datasets/svt/svt1/img/00_13.jpg')#[292:450, 176:850, :]#img -> 00_12
print img.shape
# image[y:y + image_height, x:x + image_width, :]
pylab.imshow(img)
pylab.show()
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# extra functions
def unpickle(filer):
f = open(filer, 'rb')
d_dict = pkl.load(f)
f.close()
return d_dict
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def shiftup(dataset):
shifted_dataset = np.zeros(dataset.shape)
# loop for images
for i in range(dataset.shape[0]):
# loop for shift up
for j in range(16):
shifted_dataset[i, 0, j:j+1, :] = dataset[i, 0, 16 + j : 16 + j + 1, :]
for j in range(16, 32):
shifted_dataset[i, 0, j:j+1, :] = shifted_dataset[i, :, 15, :]
return shifted_dataset
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def shiftdown(dataset):
shifted_dataset = np.zeros(dataset.shape)
# loop for images
for i in range(dataset.shape[0]):
# loop for shift up
for j in range(16, 32):
shifted_dataset[i, 0, j:j+1, :] = dataset[i, 0, j - 16 : j + 1 - 16, :]
for j in range(16):
shifted_dataset[i, 0, j:j+1, :] = shifted_dataset[i, :, 16, :]
return shifted_dataset
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def shiftleft(dataset):
shifted_dataset = np.zeros(dataset.shape)
# loop for images
for i in range(dataset.shape[0]):
for j in range(16):
shifted_dataset[i, 0, :, j:j+1] = dataset[i, 0, :, 16 + j: 16 + j + 1]
for j in range(16, 32):
shifted_dataset[i, :, :, j] = shifted_dataset[i, :, :, 15]
return shifted_dataset
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def shiftright(dataset):
shifted_dataset = np.zeros(dataset.shape)
# loop for images
for i in range(dataset.shape[0]):
for j in range(16, 32):
shifted_dataset[i, 0, :, j : j + 1] = dataset[i, 0, :, j - 16 : j + 1 - 16]
for j in range(16):
shifted_dataset[i, 0, :, j] = dataset[i, 0, :, 15]
return shifted_dataset
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# load train_test set
# cifar
train_dict = unpickle('/home/faizy/workspace/cifar/cifar-10-batches-py/data_batch_1')
train2_images = train_dict['data'].astype('float32')
train2_y = np.zeros((10000, )).astype('int')
test_dict = unpickle('/home/faizy/workspace/cifar/cifar-10-batches-py/test_batch')
test2_images = test_dict['data'].astype('float32')
# chars74k
data = pd.read_csv('/home/faizy/workspace/project/project/scripts/LISTFILE.txt', sep = ' ', header = None)
root = '/home/faizy/workspace/project/project/datasets/English/'
data_x = np.zeros((data.shape[0], 1, 32, 32))
data_y = np.ones((data.shape[0], )).astype('int32')
from scipy.misc import imread, imresize
for idx, path in enumerate(data[0]):
img = imread(root + path)
img = imresize(img, (32, 32))
if len(img.shape) == 3:
data_x[idx, ...] = img.dot([0.299, 0.587, 0.144])
else:
data_x[idx, ...] = img
data_x = data_x.astype('float32')
train1_x, test1_x, train1_y, test1_y = train_test_split(data_x, data_y, test_size = 0.2)
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# Augmented data
train3_x = shiftup(train1_x)
train4_x = shiftdown(train1_x)
train5_x = shiftleft(train1_x)
train6_x = shiftright(train1_x)
train3_y = np.zeros((train3_x.shape[0], )).astype('int')
train4_y = np.zeros((train4_x.shape[0], )).astype('int')
train5_y = np.zeros((train5_x.shape[0], )).astype('int')
train6_y = np.zeros((train6_x.shape[0], )).astype('int')
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print train1_x.shape, train2_images.shape, train3_x.shape, train4_x.shape, train5_x.shape, train6_x.shape
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# preprocess
# cifar
train2_images /= train2_images.std(axis = None)
train2_images -= train2_images.mean()
test2_images /= test2_images.std(axis = None)
test2_images -= test2_images.mean()
# chars74k
train1_x /= train1_x.std(axis = None)
train1_x -= train1_x.mean()
test1_x /= test1_x.std(axis = None)
test1_x -= test1_x.mean()
# augmented data
train3_x /= train3_x.std(axis = None)
train3_x -= train3_x.mean()
train4_x /= train4_x.std(axis = None)
train4_x -= train4_x.mean()
train5_x /= train5_x.std(axis = None)
train5_x -= train5_x.mean()
train6_x /= train6_x.std(axis = None)
train6_x -= train6_x.mean()
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# reshape dataset
# cifar
# grayscaling and cropping to size
train2_x_rgb = train2_images.reshape(-1, 3, 32, 32).transpose(0, 2, 3, 1)
train2_x = np.zeros((10000, 1, 32, 32))
for i in range(10000):
train2_x[i, :, :, :] = np.dot(train2_x_rgb[i, :, :, :], [0.299, 0.587, 0.144])
test2_x_rgb = test2_images.reshape(-1, 3, 32, 32).transpose(0, 2, 3, 1)
test2_x = np.zeros((10000, 1, 32, 32))
for i in range(10000):
test2_x[i, :, :, :] = np.dot(test2_x_rgb[i, :, :, :], [0.299, 0.587, 0.144])
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# finally
#train_x = np.vstack((train1_x, train2_x))
train_x = np.vstack((train1_x, train3_x))
train_x = np.vstack((train_x, train4_x))
train_x = np.vstack((train_x, train5_x))
train_x = np.vstack((train_x, train6_x))
train_y = np.concatenate([train1_y, train3_y, train4_y, train5_y, train6_y])#train2_y,
test_x = test1_x#np.vstack((test1_x, test2_x))
test_y = np.concatenate([test1_y, np.zeros((10000,))])
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# check again
train_x = train_x.astype('float32')
train_y = train_y.astype('int32')
test_x = test_x.astype('float32')
test_y = test_y.astype('int32')
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# setting nn
net = NeuralNet(
layers = [
('input', layers.InputLayer),
('conv1', layers.Conv2DLayer),
('pool1', layers.MaxPool2DLayer),
('dropout1', layers.DropoutLayer),
('conv2', layers.Conv2DLayer),
('pool2', layers.MaxPool2DLayer),
('dropout2', layers.DropoutLayer),
('conv3', layers.Conv2DLayer),
('hidden4', layers.DenseLayer),
('output', layers.DenseLayer),
],
input_shape = (None, 1, 32, 32),
conv1_num_filters = 32, conv1_filter_size = (5, 5),
pool1_pool_size = (2, 2),
dropout1_p = 0.2,
conv2_num_filters = 64, conv2_filter_size = (5, 5),
pool2_pool_size = (2, 2),
dropout2_p = 0.2,
conv3_num_filters = 128, conv3_filter_size = (5, 5),
hidden4_num_units = 128,
output_num_units = 2, output_nonlinearity = softmax,
batch_iterator_train = BatchIterator(batch_size = 1000),
batch_iterator_test = BatchIterator(batch_size = 1000),
update=nesterov_momentum,
update_learning_rate = 0.01,
update_momentum = 0.9,
use_label_encoder = True,
regression = False,
max_epochs = 50,
verbose = 1,
)
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# train and test nn
net.fit(train_x, train_y)
pred = net.predict(test_x)
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f = open('/home/faizy/workspace/project/project/models/detector_2.pkl', 'wb')
pkl.dump(net, f)
f.close()
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from sklearn.metrics import accuracy_score, classification_report
print classification_report(test_y, pred)
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from sklearn.feature_extraction import image
patches = image.extract_patches(img, (100, 60, 3), extraction_step = 5)
print patches.shape
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from scipy.misc import imresize
new_lst = []
for i in range(patches.shape[0]):
for j in range(patches.shape[1]):
new_lst.append(imresize(patches[i, j, 0, :, :, :], (32, 32)))
print len(new_lst)
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new_list = np.stack(new_lst)
new_list = new_list.dot([0.299, 0.587, 0.144])
tester = new_list.reshape(patches.shape[0]*patches.shape[1], 1, 32, 32)
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tester /= tester.std(axis = None)
tester -= tester.mean()
tester = tester.astype('float32')
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print tester.shape
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preder = net.predict_proba(tester)
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heatmap = preder[:, 1].reshape((patches.shape[0], patches.shape[1]))
print heatmap.shape
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pylab.pcolor(heatmap[::-1])
pylab.axis('off')
pylab.show()
pylab.imshow(img)
pylab.axis('off')
pylab.show()
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preder.shape
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