import h5py

import numpy as np

import os

import glob

import cv2

import warnings

from matplotlib import pyplot

from sklearn.model_selection import train_test_split, cross_val_score

from sklearn.model_selection import KFold, StratifiedKFold

from sklearn.metrics import confusion_matrix, accuracy_score, classification_report

from sklearn.linear_model import LogisticRegression

from sklearn.tree import DecisionTreeClassifier

from sklearn.ensemble import RandomForestClassifier

from sklearn.neighbors import KNeighborsClassifier

from sklearn.discriminant_analysis import LinearDiscriminantAnalysis

from sklearn.naive_bayes import GaussianNB

from sklearn.svm import SVC

from sklearn.externals import joblib

warnings.filterwarnings('ignore')

num_trees = 100

test_size = 0.10

seed = 9

test_path = "dataset/test"

h5_data = 'output/data.h5'

h5_labels = 'output/labels.h5'

scoring = "accuracy"

if not os.path.exists(test_path):

os.makedirs(test_path)

models = []

models.append(('LR', LogisticRegression(random_state=seed)))

models.append(('LDA', LinearDiscriminantAnalysis()))

models.append(('KNN', KNeighborsClassifier()))

models.append(('CART', DecisionTreeClassifier(random_state=seed)))

models.append(('RF', RandomForestClassifier(n_estimators=num_trees, random_state=seed)))

models.append(('NB', GaussianNB()))

models.append(('SVM', SVC(random_state=seed)))

results = []

names = []

h5f_data = h5py.File(h5_data, 'r')

h5f_label = h5py.File(h5_labels, 'r')

global_features_string = h5f_data['dataset_1']

global_labels_string = h5f_label['dataset_1']

global_features = np.array(global_features_string)

global_labels = np.array(global_labels_string)

h5f_data.close()

h5f_label.close()

print("[STATUS] features shape: {}".format(global_features.shape))

print("[STATUS] labels shape: {}".format(global_labels.shape))

print("[STATUS] training started...")

(trainDataGlobal, testDataGlobal, trainLabelsGlobal, testLabelsGlobal) = train_test_split(np.array(global_features),

np.array(global_labels),

test_size=test_size,

random_state=seed)

print("[STATUS] splitted train and test data...")

print("Train data : {}".format(trainDataGlobal.shape))

print("Test data : {}".format(testDataGlobal.shape))

print("Train labels: {}".format(trainLabelsGlobal.shape))

print("Test labels : {}".format(testLabelsGlobal.shape))

for name, model in models:

kfold = KFold(n_splits=10, random_state=seed)

cv_results = cross_val_score(model, trainDataGlobal, trainLabelsGlobal, cv=kfold, scoring=scoring)

results.append(cv_results)

names.append(name)

msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())

print(msg)

fig = pyplot.figure()

fig.suptitle('Machine Learning algorithm comparison')

ax = fig.add_subplot(111)

pyplot.boxplot(results)

ax.set_xticklabels(names)

pyplot.show()

import matplotlib.pyplot as plt

clf = RandomForestClassifier(n_estimators=num_trees, random_state=seed)

clf.fit(trainDataGlobal, trainLabelsGlobal)

for file in glob.glob(test_path + "/*.jpg"):

# read the image

image = cv2.imread(file)

# resize the image

image = cv2.resize(image, fixed_size)

####################################

# Global Feature extraction

####################################

fv_hu_moments = fd_hu_moments(image)

fv_haralick = fd_haralick(image)

fv_histogram = fd_histogram(image)

###################################

# Concatenate global features

###################################

global_feature = np.hstack([fv_histogram, fv_haralick, fv_hu_moments])

# scale features in the range (0-1)

scaler = MinMaxScaler(feature_range=(0, 1))

rescaled_feature = scaler.fit_transform(global_feature)

# predict label of test image

prediction = clf.predict(rescaled_feature.reshape(1,-1))[0]

# show predicted label on image

cv2.putText(image, train_labels[prediction], (20,30), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0,255,255), 3)

# display the output image

plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))

plt.show()