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#%%
# -*- coding: utf-8 -*-
from matplotlib.pyplot import imshow
import matplotlib.cm as cm
import matplotlib.pylab as plt
from keras.preprocessing.image import ImageDataGenerator
import numpy as np
import PIL
from PIL import ImageFilter
import cv2
import itertools
import random
from keras.preprocessing.image import img_to_array
from sklearn.model_selection import train_test_split
from tensorflow.keras.utils import to_categorical
from keras import callbacks
from keras.models import Sequential
from tensorflow.keras.layers import (
BatchNormalization
)
from keras.layers import Dense, Dropout, Flatten
import keras
import imutils
from imutils import paths
import os
from tensorflow.keras import optimizers
from keras.layers import Conv2D, MaxPooling2D , UpSampling2D ,Conv2DTranspose
from keras import backend as K
#%%
################# Define pil image method
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
################# define noise image method
def noise_image(pil_im):
# Adding Noise to image
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
################# Define blur image method
def blur_image(pil_im):
#Adding Blur to image
blur_img = pil_im.filter(ImageFilter.GaussianBlur(radius=3)) # output
imshow(blur_img)
blur_img = blur_img.resize((105, 105))
return blur_img
################# define affine rotation method
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
################# define gradient fill method
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
#%%
################# get path
data_path = "C:/Users/Lejla/Desktop/Dataset/test"
data=[]
labels=[]
imagePaths = sorted(list(paths.list_images(data_path)))
random.seed(42)
random.shuffle(imagePaths)
##print(imagePaths);
################# Define labels for model
def conv_label(label):
if label == 'Pacifico':
return 0
elif label == 'DroidSans':
return 1
elif label == 'Roboto-Regular':
return 2
elif label == 'Raleway-Medium':
return 3
################# Define methods
augument=["blur","noise","affine","gradient"]
a=itertools.combinations(augument, 4)
##for i in list(a):
## print(list(i))
################# Handling font_patch images with previously defined methods
counter=0
for imagePath in imagePaths:
label = imagePath.split(os.path.sep)[-2]
label = conv_label(label)
pil_img = pil_image(imagePath)
## imshow(pil_img)
# Adding original image
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)
## print(len(combinations))
temp_img = pil_img
for j in combinations:
if j == 'noise':
# Adding Noise image
temp_img = noise_image(temp_img)
elif j == 'blur':
# Adding Blur image
temp_img = blur_image(temp_img)
#imshow(blur_img)
elif j == 'affine':
open_cv_affine = np.array(pil_img)
# Adding affine rotation image
temp_img = affine_rotation(open_cv_affine)
elif j == 'gradient':
open_cv_gradient = np.array(pil_img)
# Adding gradient image
temp_img = gradient_fill(open_cv_gradient)
temp_img = img_to_array(temp_img)
data.append(temp_img)
labels.append(label)
################# Preparing data for model creation
data = np.asarray(data, dtype="float") / 255.0
labels = np.array(labels)
print("Success")
# 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)
nsamples, nx, ny, nz = trainX.shape
d2_trainX = trainX.reshape((nsamples, nx*ny*nz))
nsamples, nx, ny, nz = testX.shape
d2_testX = testX.reshape((nsamples, nx*ny*nz))
#################
# convert the labels from integers to vectors
##trainY = to_categorical(trainY, num_classes=4)
##testY = to_categorical(testY, num_classes=4)
################# augtment
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)
#%%
from sklearn.model_selection import cross_val_score, GridSearchCV, RandomizedSearchCV
import pandas as pd
import time
def search(pipeline, parameters, X_train, y_train, X_test, y_test, optimizer='grid_search', n_iter=None):
start = time.time()
if optimizer == 'grid_search':
grid_obj = GridSearchCV(estimator=pipeline,
param_grid=parameters,
cv=5,
refit=True,
return_train_score=False,
scoring = 'accuracy',
)
grid_obj.fit(X_train, y_train,)
elif optimizer == 'random_search':
grid_obj = RandomizedSearchCV(estimator=pipeline,
param_distributions=parameters,
cv=5,
n_iter=n_iter,
refit=True,
return_train_score=False,
scoring = 'accuracy',
random_state=1)
grid_obj.fit(X_train, y_train,)
else:
print('enter search method')
return
estimator = grid_obj.best_estimator_
cvs = cross_val_score(estimator, X_train, y_train, cv=5)
results = pd.DataFrame(grid_obj.cv_results_)
print("##### Results")
print("Score best parameters: ", grid_obj.best_score_)
print("Best parameters: ", grid_obj.best_params_)
print("Cross-validation Score: ", cvs.mean())
print("Test Score: ", estimator.score(X_test, y_test))
print("Time elapsed: ", time.time() - start)
print("Parameter combinations evaluated: ",results.shape[0])
return results, estimator
#%%
import lightgbm as lgb
import numpy as np
from sklearn import pipeline
from hyperopt import hp
pipe = pipeline.Pipeline([
('clf', lgb.LGBMClassifier())
])
param_gridsearch = {
'clf__learning_rate' : [0.01, 0.1, 1],
'clf__max_depth' : [5, 10, 15],
'clf__n_estimators' : [5, 20, 35],
'clf__num_leaves' : [5, 25, 50],
'clf__boosting_type': ['gbdt', 'dart'],
'clf__colsample_bytree' : [0.6, 0.75, 1],
'clf__reg_lambda': [0.01, 0.1, 1],
}
param_random = {
'clf__learning_rate': list(np.logspace(np.log(0.01), np.log(1), num = 500, base=3)),
'clf__max_depth': list(range(5, 15)),
'clf__n_estimators': list(range(5, 35)),
'clf__num_leaves': list(range(5, 50)),
'clf__boosting_type': ['gbdt', 'dart'],
'clf__colsample_bytree': list(np.linspace(0.6, 1, 500)),
'clf__reg_lambda': list(np.linspace(0, 1, 500)),
}
#%%
results_grid, estimator_grid = search(pipe, param_gridsearch, d2_trainX, trainY, d2_testX, testY, 'grid_search')
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