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CNN.py
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CNN.py
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# -*- coding: utf-8 -*-
"""
Created on Mon Dec 2 11:41:44 2019
@author: Mohammad
"""
from skimage import io
from skimage.filters import gaussian
from skimage.transform import resize
import matplotlib.pyplot as plt
import numpy as np
from keras.models import Sequential
from keras.layers import Convolution2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
from keras.preprocessing.image import ImageDataGenerator
from sklearn.utils import shuffle
from sklearn.metrics import confusion_matrix, classification_report
from sklearn.model_selection import train_test_split
class DataSet:
def __init__(self, inputs=None, targets=None):
self.inputs = [] if inputs is None else inputs
self.targets = [] if targets is None else targets
if __name__ == '__main__':
dataset = DataSet()
print('Loading Training Data...')
labels = ['01_palm', '02_l', '03_fist', '04_fist_moved', '05_thumb',
'06_index', '07_ok', '08_palm_moved', '09_c', '10_down', ]
# 75% to train
for i in range(10):
for label in labels:
for j in range(1, 151): # 201):
label.split()
image = io.imread('data/leapGestRecog/0' +
str(i) + '/' +
label + '/frame_0' +
str(i) + '_' +
label[0:2] + '_' +
str(j).zfill(4) + '.png')
img_blurred = gaussian(image, sigma=1.65)
dataset.inputs.append(resize(img_blurred, (30, 80), preserve_range=True))
dataset.targets.append(int(label[1]))
x_data_org = np.array(dataset.inputs, dtype='float32')
y_data_org = np.array(dataset.targets)
y_data_org = y_data_org.reshape(150 * 100, 1) # Reshape to be the correct size
# 25% to test
test_data = DataSet()
for i in range(10):
for label in labels:
for j in range(151, 201): # 1, 201):
label.split()
image = io.imread('data/leapGestRecog/0' +
str(i) + '/' +
label + '/frame_0' +
str(i) + '_' +
label[0:2] + '_' +
str(j).zfill(4) + '.png')
img_blurred = gaussian(image, sigma=1.65)
test_data.inputs.append(resize(img_blurred, (30, 80), preserve_range=True))
test_data.targets.append(int(label[1]))
x_test_org = np.array(test_data.inputs, dtype='float32')
y_test_org = np.array(test_data.targets)
y_test_org = y_test_org.reshape(50 * 100, 1) # Reshape to be the correct size
# shuffle data
x_data, y_data = shuffle(x_data_org, y_data_org)
x_validate, x_data, y_validate, y_data = train_test_split(x_data, y_data, test_size=0.75)
# initializing the CNN
classifier = Sequential()
# Convolution
classifier.add(Convolution2D(16, 3, 3, input_shape=(30, 80, 1), activation='relu'))
# Pooling
classifier.add(MaxPooling2D(pool_size=(2, 2)))
# add a second layer
classifier.add(Convolution2D(32, 3, 3, activation='relu'))
classifier.add(MaxPooling2D(pool_size=(2, 2)))
# Flattening
classifier.add(Flatten())
# densing
classifier.add(Dense(output_dim=64, activation='relu'))
classifier.add(Dense(output_dim=10, activation='softmax'))
# compiling CNN
classifier.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
from keras.utils import to_categorical
y_data = to_categorical(y_data)
x_data = x_data.reshape((len(x_data), 30, 80, 1))
# x_data /= 255
y_validate = to_categorical(y_validate)
x_validate = x_validate.reshape((len(x_validate), 30, 80, 1))
# x_validate /= 255
y_test = to_categorical(y_test_org)
x_test = x_test_org.reshape((len(x_test_org), 30, 80, 1))
H = classifier.fit(x_data, y_data,
epochs=5, batch_size=32, verbose=1,
validation_data=(x_validate, y_validate))
print('Testing...')
predicted = classifier.predict(x_test)
predicted_classes = np.argmax(predicted, axis=1)
print("\nClassification Report")
print(classification_report(np.argmax(y_test, axis=1), predicted_classes))
print("\nConfusion Matrix")
print(confusion_matrix(np.argmax(y_test, axis=1), predicted_classes))
print(classifier.summary())
plt.plot(H.history['loss'])
plt.plot(H.history['val_loss'])
plt.title('Model Loss')
plt.ylabel('Loss')
plt.xlabel('Epochs')
plt.legend(['train', 'test'])
plt.show()
plt.plot(H.history['accuracy'])
plt.plot(H.history['val_accuracy'])
plt.title('Model Accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epochs')
plt.legend(['train', 'test'])
plt.show()