The second part of the implemented classifier is listed here:

from scipy import misc 
import numpy as np 
import matplotlib.cm as cm 
import tensorflow as tf 
from matplotlib import pyplot as plt 
import matplotlib.image as mpimg 
import EmotionDetectorUtils 
from EmotionDetectorUtils import testResult 
 
 
def rgb2gray(rgb): 
    return np.dot(rgb[...,:3], [0.299, 0.587, 0.114]) 
 
img = mpimg.imread('author_image.jpg')      
gray = rgb2gray(img) 
plt.imshow(gray, cmap = plt.get_cmap('gray')) 
plt.show() 
 
sess = tf.InteractiveSession() 
new_saver = tf.train.import_meta_graph('logs/model.ckpt-1000.meta') 
new_saver.restore(sess, 'logs/model.ckpt-1000') 
tf.get_default_graph().as_graph_def() 
x = sess.graph.get_tensor_by_name("input:0") 
y_conv = sess.graph.get_tensor_by_name("output:0") 
 
image_test = np.resize(gray,(1,48,48,1)) 
tResult = testResult() 
num_evaluations = 1000 
for i in range(0,num_evaluations): 
    result = sess.run(y_conv, feed_dict={x:image_test}) 
    label = sess.run(tf.argmax(result, 1)) 
    label = label[0] 
    label = int(label) 
    tResult.evaluate(label) 
 
tResult.display_result(num_evaluations) 

We implement the testResult Python class to display the resulting percentages. It can be found in the EmotionDetectorUtils.py file.

Here we report the implementation of this class:

class testResult: 
 
    def __init__(self): 
        self.anger = 0 
        self.disgust = 0 
        self.fear = 0 
        self.happy = 0 
        self.sad = 0 
        self.surprise = 0 
        self.neutral = 0 
         
    def evaluate(self,label): 
         
        if (0 == label): 
            self.anger = self.anger+1 
        if (1 == label): 
            self.disgust = self.disgust+1 
        if (2 == label): 
            self.fear = self.fear+1 
        if (3 == label): 
            self.happy = self.happy+1 
        if (4 == label): 
            self.sad = self.sad+1 
        if (5 == label): 
            self.surprise = self.surprise+1 
        if (6 == label): 
            self.neutral = self.neutral+1 
 
    def display_result(self,evaluations): 
        print("anger = "    + 
              str((self.anger/float(evaluations))*100)    + "%") 
        print("disgust = "  + 
              str((self.disgust/float(evaluations))*100)  + "%") 
        print("fear = "     + 
              str((self.fear/float(evaluations))*100)     + "%") 
        print("happy = "    + 
              str((self.happy/float(evaluations))*100)    + "%") 
        print("sad = "      + 
              str((self.sad/float(evaluations))*100)      + "%") 
        print("surprise = " + 
              str((self.surprise/float(evaluations))*100) + "%") 
        print("neutral = "  + 
              str((self.neutral/float(evaluations))*100)  + "%")