Download full data set from http://benchmark.ini.rub.de/?section=gtsdb&subsection=dataset
In [5]:
!wget -c http://benchmark.ini.rub.de/Dataset_GTSDB/FullIJCNN2013.zip
!unzip FullIJCNN2013.zip
--2019-03-23 02:07:39-- http://benchmark.ini.rub.de/Dataset_GTSDB/FullIJCNN2013.zip
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2019-03-23 02:13:13 (4.76 MB/s) - ‘FullIJCNN2013.zip’ saved [1661866983/1661866983]
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In [0]:
import numpy as np
import cv2
IMG_HEIGHT = 600
SIGN_SIZE = (224, 224)
# Function for reading the images
def readImages(rootpath, images_range, signs_range):
'''Reads traffic sign data for German Traffic Sign Recognition Benchmark.
Arguments: path to the traffic sign data, for example 'FullIJCNN2013'
Returns: list of images, list of corresponding labels'''
images = {} # original image
scales = {} # original scale
for num in images_range:
filename = rootpath + '/' + "{:05d}".format(num) + '.ppm'
img = cv2.imread(filename, cv2.IMREAD_COLOR)
scale = IMG_HEIGHT / float(img.shape[0])
img_resized = cv2.resize(img, (int(img.shape[1]*scale),int(img.shape[0]*scale)))
images.setdefault(filename,[]).append(img_resized)
scales.setdefault(filename,[]).append(scale)
files = [] # filenames
signs = [] # traffic sign image
bboxes = [] # corresponding box detection
labels = [] # traffic sign type
data = np.genfromtxt(rootpath + '/' + 'gt.txt', delimiter=';', dtype=str, usecols=range(0, 6))
for elem in signs_range:
filename = rootpath + '/' + data[elem][0]
img = images.get(filename)[0]
scale = scales.get(filename)[0]
bbox = np.array([int(data[elem][1]), int(data[elem][2]), int(data[elem][3]), int(data[elem][4])]) * scale
sign = img[int(bbox[1]):int(bbox[3]), int(bbox[0]):int(bbox[2])]
sign_resized = cv2.resize(sign, SIGN_SIZE)
files.append(filename)
signs.append(sign_resized)
bboxes.append(bbox)
labels.append(data[elem][5])
return images, files, signs, bboxes, labels
In [0]:
# The German Traffic Sign Recognition Benchmark
train_images, train_files, train_signs, train_bboxes, train_labels = readImages('FullIJCNN2013', range(0,600), range(0,852))
test_images, test_files, test_signs, test_bboxes, test_labels = readImages('FullIJCNN2013', range(600,900), range(852,1213))
In [10]:
import matplotlib.pyplot as plt
%matplotlib inline
# Show examples from each class
class_names = np.unique(train_labels)
num_classes = len(class_names)
fig = plt.figure(figsize=(8,8))
for i in range(num_classes):
ax = fig.add_subplot(6, 9, 1 + i, xticks=[], yticks=[])
ax.set_title(class_names[i])
indices = np.where(np.isin(train_labels, class_names[i]))[0]
plt.imshow(cv2.cvtColor(train_signs[int(np.random.choice(indices, 1))], cv2.COLOR_BGR2RGB))
plt.show()
In [0]:
from sklearn.utils import shuffle
train_files, train_signs, train_bboxes, train_labels = shuffle(train_files, train_signs, train_bboxes, train_labels)
# plt.imshow(cv2.cvtColor(train_images.get(train_files[0])[0], cv2.COLOR_BGR2RGB))
# plt.show()
# plt.imshow(cv2.cvtColor(train_signs[0], cv2.COLOR_BGR2RGB))
# plt.show()
# print(train_bboxes[0])
# print(train_labels[0])
# Data pre-processing
tr_signs = np.array(train_signs)[0:600]
tr_labels = np.array(train_labels)[0:600]
va_signs = np.array(train_signs)[600:852]
va_labels = np.array(train_labels)[600:852]
te_signs = np.array(test_signs)
te_labels = np.array(test_labels)
tr_signs = tr_signs.astype('float32')
va_signs = va_signs.astype('float32')
te_signs = te_signs.astype('float32')
tr_signs /= 255.0
va_signs /= 255.0
te_signs /= 255.0
from keras.utils import np_utils
tr_labels = np_utils.to_categorical(tr_labels, num_classes)
va_labels = np_utils.to_categorical(va_labels, num_classes)
te_labels = np_utils.to_categorical(te_labels, num_classes)
In [0]:
In [0]:
# Tensorboard
from time import time
from keras.callbacks import TensorBoard
tensorboard = TensorBoard(log_dir='logs/{}'.format(time()))
In [49]:
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras import optimizers
mlp = Sequential()
mlp.add(Dense(16, input_shape=(SIGN_SIZE[0], SIGN_SIZE[1], 3)))
mlp.add(Flatten())
mlp.add(Activation('relu'))
mlp.add(Dropout(0.2))
mlp.add(Dense(num_classes))
mlp.add(Activation('softmax'))
opt = optimizers.SGD(lr=0.001, decay=1e-6, momentum=0.9, nesterov=True)
mlp.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])
mlp.summary()
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_25 (Dense) (None, 224, 224, 16) 64
_________________________________________________________________
flatten_13 (Flatten) (None, 802816) 0
_________________________________________________________________
activation_25 (Activation) (None, 802816) 0
_________________________________________________________________
dropout_13 (Dropout) (None, 802816) 0
_________________________________________________________________
dense_26 (Dense) (None, 8) 6422536
_________________________________________________________________
activation_26 (Activation) (None, 8) 0
_________________________________________________________________
dropout_14 (Dropout) (None, 8) 0
_________________________________________________________________
dense_27 (Dense) (None, 43) 387
_________________________________________________________________
activation_27 (Activation) (None, 43) 0
=================================================================
Total params: 6,422,987
Trainable params: 6,422,987
Non-trainable params: 0
_________________________________________________________________
In [0]:
In [0]:
In [50]:
data = mlp.fit(tr_signs, tr_labels, batch_size=16, epochs=50, verbose=2, validation_data=(va_signs, va_labels), callbacks=[tensorboard])
start = time()
loss, acc = mlp.evaluate(te_signs, te_labels, verbose=0)
end = time()
print('MLP took ' + str(end - start) + ' seconds')
print('Test loss: ' + str(loss) + ' - Accuracy: ' + str(acc))
Train on 600 samples, validate on 252 samples
Epoch 1/50
- 3s - loss: 3.7695 - acc: 0.0417 - val_loss: 3.7555 - val_acc: 0.0833
Epoch 2/50
- 3s - loss: 3.7525 - acc: 0.0550 - val_loss: 3.7488 - val_acc: 0.0833
Epoch 3/50
- 3s - loss: 3.7456 - acc: 0.0700 - val_loss: 3.7416 - val_acc: 0.0833
Epoch 4/50
- 3s - loss: 3.7387 - acc: 0.0567 - val_loss: 3.7351 - val_acc: 0.0437
Epoch 5/50
- 3s - loss: 3.7321 - acc: 0.0700 - val_loss: 3.7283 - val_acc: 0.0833
Epoch 6/50
- 3s - loss: 3.7255 - acc: 0.0700 - val_loss: 3.7218 - val_acc: 0.0833
Epoch 7/50
- 3s - loss: 3.7190 - acc: 0.0700 - val_loss: 3.7154 - val_acc: 0.0833
Epoch 8/50
- 3s - loss: 3.7127 - acc: 0.0700 - val_loss: 3.7094 - val_acc: 0.0833
Epoch 9/50
- 3s - loss: 3.7065 - acc: 0.0700 - val_loss: 3.7032 - val_acc: 0.0833
Epoch 10/50
- 3s - loss: 3.7004 - acc: 0.0700 - val_loss: 3.6972 - val_acc: 0.0833
Epoch 11/50
- 3s - loss: 3.6944 - acc: 0.0700 - val_loss: 3.6913 - val_acc: 0.0833
Epoch 12/50
- 3s - loss: 3.6885 - acc: 0.0700 - val_loss: 3.6854 - val_acc: 0.0833
Epoch 13/50
- 3s - loss: 3.6827 - acc: 0.0700 - val_loss: 3.6798 - val_acc: 0.0833
Epoch 14/50
- 3s - loss: 3.6771 - acc: 0.0700 - val_loss: 3.6741 - val_acc: 0.0833
Epoch 15/50
- 3s - loss: 3.6715 - acc: 0.0700 - val_loss: 3.6686 - val_acc: 0.0833
Epoch 16/50
- 3s - loss: 3.6660 - acc: 0.0700 - val_loss: 3.6632 - val_acc: 0.0833
Epoch 17/50
- 3s - loss: 3.6606 - acc: 0.0700 - val_loss: 3.6580 - val_acc: 0.0833
Epoch 18/50
- 3s - loss: 3.6554 - acc: 0.0700 - val_loss: 3.6527 - val_acc: 0.0833
Epoch 19/50
- 3s - loss: 3.6503 - acc: 0.0700 - val_loss: 3.6475 - val_acc: 0.0833
Epoch 20/50
- 3s - loss: 3.6452 - acc: 0.0700 - val_loss: 3.6427 - val_acc: 0.0833
Epoch 21/50
- 3s - loss: 3.6403 - acc: 0.0700 - val_loss: 3.6379 - val_acc: 0.0833
Epoch 22/50
- 3s - loss: 3.6354 - acc: 0.0700 - val_loss: 3.6332 - val_acc: 0.0833
Epoch 23/50
- 3s - loss: 3.6306 - acc: 0.0700 - val_loss: 3.6284 - val_acc: 0.0833
Epoch 24/50
- 3s - loss: 3.6260 - acc: 0.0700 - val_loss: 3.6237 - val_acc: 0.0833
Epoch 25/50
- 3s - loss: 3.6213 - acc: 0.0700 - val_loss: 3.6192 - val_acc: 0.0833
Epoch 26/50
- 3s - loss: 3.6169 - acc: 0.0700 - val_loss: 3.6147 - val_acc: 0.0833
Epoch 27/50
- 3s - loss: 3.6124 - acc: 0.0700 - val_loss: 3.6103 - val_acc: 0.0833
Epoch 28/50
- 3s - loss: 3.6081 - acc: 0.0700 - val_loss: 3.6059 - val_acc: 0.0833
Epoch 29/50
- 3s - loss: 3.6039 - acc: 0.0700 - val_loss: 3.6020 - val_acc: 0.0833
Epoch 30/50
- 3s - loss: 3.5998 - acc: 0.0700 - val_loss: 3.5979 - val_acc: 0.0833
Epoch 31/50
- 3s - loss: 3.5957 - acc: 0.0700 - val_loss: 3.5940 - val_acc: 0.0833
Epoch 32/50
- 3s - loss: 3.5918 - acc: 0.0700 - val_loss: 3.5901 - val_acc: 0.0833
Epoch 33/50
- 3s - loss: 3.5878 - acc: 0.0700 - val_loss: 3.5862 - val_acc: 0.0833
Epoch 34/50
- 3s - loss: 3.5841 - acc: 0.0700 - val_loss: 3.5826 - val_acc: 0.0833
Epoch 35/50
- 3s - loss: 3.5804 - acc: 0.0700 - val_loss: 3.5787 - val_acc: 0.0833
Epoch 36/50
- 3s - loss: 3.5767 - acc: 0.0700 - val_loss: 3.5754 - val_acc: 0.0833
Epoch 37/50
- 3s - loss: 3.5731 - acc: 0.0700 - val_loss: 3.5717 - val_acc: 0.0833
Epoch 38/50
- 3s - loss: 3.5696 - acc: 0.0700 - val_loss: 3.5684 - val_acc: 0.0833
Epoch 39/50
- 3s - loss: 3.5662 - acc: 0.0700 - val_loss: 3.5649 - val_acc: 0.0833
Epoch 40/50
- 3s - loss: 3.5628 - acc: 0.0700 - val_loss: 3.5616 - val_acc: 0.0833
Epoch 41/50
- 3s - loss: 3.5595 - acc: 0.0700 - val_loss: 3.5584 - val_acc: 0.0833
Epoch 42/50
- 3s - loss: 3.5563 - acc: 0.0700 - val_loss: 3.5551 - val_acc: 0.0833
Epoch 43/50
- 3s - loss: 3.5531 - acc: 0.0700 - val_loss: 3.5520 - val_acc: 0.0833
Epoch 44/50
- 3s - loss: 3.5501 - acc: 0.0700 - val_loss: 3.5489 - val_acc: 0.0833
Epoch 45/50
- 3s - loss: 3.5470 - acc: 0.0700 - val_loss: 3.5460 - val_acc: 0.0833
Epoch 46/50
- 3s - loss: 3.5440 - acc: 0.0700 - val_loss: 3.5432 - val_acc: 0.0833
Epoch 47/50
- 3s - loss: 3.5412 - acc: 0.0700 - val_loss: 3.5404 - val_acc: 0.0833
Epoch 48/50
- 3s - loss: 3.5384 - acc: 0.0700 - val_loss: 3.5374 - val_acc: 0.0833
Epoch 49/50
- 3s - loss: 3.5355 - acc: 0.0700 - val_loss: 3.5350 - val_acc: 0.0833
Epoch 50/50
- 3s - loss: 3.5329 - acc: 0.0700 - val_loss: 3.5322 - val_acc: 0.0833
MLP took 0.33611178398132324 seconds
Test loss: 3.5112366095144 - Accuracy: 0.04709141274238227
In [40]:
acc
Out[40]:
0.8144044324631836
In [29]:
plt.plot(data.history['acc'])
plt.plot(data.history['val_acc'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()
In [30]:
plt.plot(data.history['loss'])
plt.plot(data.history['val_loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()
Content source: Matthew-Ye/Matthew-Ye.github.io
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