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train.py
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train.py
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# from matplotlib.pyplot import imshow
# import matplotlib.cm as cm
# import matplotlib.pylab as plt
import os
import random
import PIL
import cv2
import argparse
import itertools
import numpy as np
from imutils import paths
from tensorflow.keras.utils import img_to_array
from sklearn.model_selection import train_test_split
from keras.utils import to_categorical
from keras import callbacks, optimizers
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.layers import BatchNormalization
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D , UpSampling2D ,Conv2DTranspose
from keras import backend as K
def pil_image(img_path):
pil_im =PIL.Image.open(img_path).convert('L')
pil_im=pil_im.resize((105,105))
#imshow(np.asarray(pil_im))
return pil_im
def noise_image(pil_im):
img_array = np.asarray(pil_im)
mean = 0.0 # some constant
std = 5 # some constant (standard deviation)
noisy_img = img_array + np.random.normal(mean, std, img_array.shape)
noisy_img_clipped = np.clip(noisy_img, 0, 255)
noise_img = PIL.Image.fromarray(np.uint8(noisy_img_clipped)) # output
#imshow((noisy_img_clipped ).astype(np.uint8))
noise_img=noise_img.resize((105,105))
return noise_img
def blur_image(pil_im):
blur_img = pil_im.filter(PIL.ImageFilter.GaussianBlur(radius=3)) # ouput
#imshow(blur_img)
blur_img=blur_img.resize((105,105))
return blur_img
def affine_rotation(img):
#img=cv2.imread(img_path,0)
rows, columns = img.shape
point1 = np.float32([[10, 10], [30, 10], [10, 30]])
point2 = np.float32([[20, 15], [40, 10], [20, 40]])
A = cv2.getAffineTransform(point1, point2)
output = cv2.warpAffine(img, A, (columns, rows))
affine_img = PIL.Image.fromarray(np.uint8(output)) # affine rotated output
#imshow(output)
affine_img=affine_img.resize((105,105))
return affine_img
def gradient_fill(image):
#image=cv2.imread(img_path,0)
laplacian = cv2.Laplacian(image,cv2.CV_64F)
laplacian = cv2.resize(laplacian, (105, 105))
return laplacian
def conv_label(label):
if label == 'AvenirNext':
return 0
elif label == 'Keyboard':
return 1
elif label == 'SFCompactRounded-Bold':
return 2
elif label == 'Times':
return 3
def create_model():
model=Sequential()
model.add(Conv2D(64, kernel_size=(48, 48), activation='relu', input_shape=(105,105,1)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(128, kernel_size=(24, 24), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2DTranspose(128, (24,24), strides = (2,2), activation = 'relu', padding='same', kernel_initializer='uniform'))
model.add(UpSampling2D(size=(2, 2)))
model.add(Conv2DTranspose(64, (12,12), strides = (2,2), activation = 'relu', padding='same', kernel_initializer='uniform'))
model.add(UpSampling2D(size=(2, 2)))
model.add(Conv2D(256, kernel_size=(12, 12), activation='relu'))
model.add(Conv2D(256, kernel_size=(12, 12), activation='relu'))
model.add(Conv2D(256, kernel_size=(12, 12), activation='relu'))
model.add(Flatten())
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(4096,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(2383,activation='relu'))
model.add(Dense(5, activation='softmax'))
return model
def main(batch_size=128,epochs=25,data_path="train_data/"):
data=[]
labels=[]
imagePaths = sorted(list(paths.list_images(data_path)))
random.seed(42)
random.shuffle(imagePaths)
augument=["blur","noise","affine","gradient"]
a=itertools.combinations(augument, 4)
for imagePath in imagePaths:
label = imagePath.split(os.path.sep)[-2]
label = conv_label(label)
pil_img = pil_image(imagePath)
#imshow(pil_img)
org_img = img_to_array(pil_img)
#print(org_img.shape)
data.append(org_img)
labels.append(label)
augument=["noise","blur","affine","gradient"]
for l in range(0,len(augument)):
a=itertools.combinations(augument, l+1)
for i in list(a):
combinations=list(i)
temp_img = pil_img
for j in combinations:
if j == 'noise':
temp_img = noise_image(temp_img)
elif j == 'blur':
temp_img = blur_image(temp_img)
elif j == 'affine':
open_cv_affine = np.array(pil_img)
temp_img = affine_rotation(open_cv_affine)
elif j == 'gradient':
open_cv_gradient = np.array(pil_img)
temp_img = gradient_fill(open_cv_gradient)
temp_img = img_to_array(temp_img)
data.append(temp_img)
labels.append(label)
data = np.asarray(data, dtype="float") / 255.0
labels = np.array(labels)
# partition the data into training and testing splits using 75% of
# the data for training and the remaining 25% for testing
(trainX, testX, trainY, testY) = train_test_split(data, labels, test_size=0.25, random_state=42)
# convert the labels from integers to vectors
trainY = to_categorical(trainY, num_classes=5)
testY = to_categorical(testY, num_classes=5)
aug = ImageDataGenerator(rotation_range=30, width_shift_range=0.1,height_shift_range=0.1, shear_range=0.2, zoom_range=0.2,horizontal_flip=True)
K.set_image_data_format('channels_last')
model= create_model()
sgd = optimizers.SGD(learning_rate=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='mean_squared_error', optimizer=sgd, metrics=['accuracy'])
early_stopping=callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=0, mode='min')
filepath="top_model.h5"
checkpoint = callbacks.ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [early_stopping,checkpoint]
model.fit(trainX,
trainY,
shuffle=True,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(testX, testY),
callbacks=callbacks_list)
score = model.evaluate(testX, testY, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Put training parameters')
parser.add_argument('--epochs','-e',required=True)
args = parser.parse_args()
main(epochs=int(args.epochs))