fredtest.py

# -*- coding: utf-8 -*-
"""
Spyder Editor

This is a temporary script file.
"""

import os,cv2
import numpy as np
import matplotlib.pyplot as plt

from sklearn.utils import shuffle
from sklearn.cross_validation import train_test_split

from keras import backend as K
K.set_image_dim_ordering('tf')

from keras.utils import np_utils
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.optimizers import SGD,RMSprop
from sklearn.metrics import classification_report,confusion_matrix
import itertools

PATH = os.getcwd()
# Define data path
data_path = PATH + '/dataset'
data_dir_list = os.listdir(data_path)

img_rows=128
img_cols=128
num_channel=1
num_epoch=20

# Define the number of classes
num_classes = 4

img_data_list=[]

for dataset in data_dir_list:
	img_list=os.listdir(data_path+'/'+ dataset)
	print ('Loaded the images of dataset-'+'{}\n'.format(dataset))
	for img in img_list:
		input_img=cv2.imread(data_path + '/'+ dataset + '/'+ img )
		input_img=cv2.cvtColor(input_img, cv2.COLOR_BGR2GRAY)
		input_img_resize=cv2.resize(input_img,(128,128))
		img_data_list.append(input_img_resize)

img_data = np.array(img_data_list)
img_data = img_data.astype('float32')
img_data /= 255
print (img_data.shape)

if num_channel==1:
	if K.image_dim_ordering()=='th':
		img_data= np.expand_dims(img_data, axis=1) 
		print (img_data.shape)
	else:
		img_data= np.expand_dims(img_data, axis=4) 
		print (img_data.shape)
		
else:
	if K.image_dim_ordering()=='th':
		img_data=np.rollaxis(img_data,3,1)
		print (img_data.shape)
        
        


names = ['anger','disgust','happy','neutral','surprise']
	  

#%%
# Defining the model
input_shape=img_data[0].shape

from keras.models import load_model
model=load_model('RUN9.hdf5')

model.summary()
model.get_config()

# Evaluating the model


# Testing a new image
test_image = cv2.imread('fredtest_data/h1.jpg')
test_image=cv2.cvtColor(test_image, cv2.COLOR_BGR2GRAY)
test_image=cv2.resize(test_image,(128,128))
test_image = np.array(test_image)
test_image = test_image.astype('float32')
test_image /= 255
print (test_image.shape)
   
if num_channel==1:
	if K.image_dim_ordering()=='th':
		test_image= np.expand_dims(test_image, axis=0)
		test_image= np.expand_dims(test_image, axis=0)
		print (test_image.shape)
	else:
		test_image= np.expand_dims(test_image, axis=3) 
		test_image= np.expand_dims(test_image, axis=0)
		print (test_image.shape)
		
else:
	if K.image_dim_ordering()=='th':
		test_image=np.rollaxis(test_image,2,0)
		test_image= np.expand_dims(test_image, axis=0)
		print (test_image.shape)
	else:
		test_image= np.expand_dims(test_image, axis=0)
		print (test_image.shape)
		
# Predicting the test image
print(model.predict(test_image))
print(model.predict_classes(test_image))

plt.imshow(test_image.squeeze())