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FER2013_Input_Keras_Second_Scale.py
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import csv
import numpy as np
import tensorflow as tf
from PIL import Image
from numpy import array
from scipy.misc import toimage
from resizeimage import resizeimage
class FER2013_Input_Keras_Second_Scale:
Training_labels = []
Training_Images = []
Validation_labels = []
Validation_Images = []
Testing_labels = []
Testing_Images = []
Batch_Size = 128
path = ""
def __init__(self, path):
self.path = path
[self.Training_labels, self.Training_Images] = self.FER2013_Training_Set()
[self.Validation_labels, self.Validation_Images] = self.FER2013_Validation_Set()
[self.Testing_labels, self.Testing_Images] = self.FER2013_Testing_Set()
def FER2013_Training_Set (self):
path = self.path + 'FER2013-Training.csv'
Training_labels = []
Training_Images = []
with open(path) as csvfile:
readCSV = csv.reader(csvfile, delimiter = ',')
#row[0] = Labels, row[1] = Image Pixels
for row in readCSV:
Label = int(row[0])
#Label = [0] * 7
#Label[int(row[0])] = 1
Training_labels.append(Label)
image = row[1].split()
#Cast strings to integer
image = [int(i) for i in image]
#Convert 1D array to 2D array 48x48
Image48x48 = np.reshape(image, (48, 48))
#Convert 2D array to Image
Image84x84 = Image.fromarray(np.uint8(Image48x48))
#Resize Image to 84x84
Image84x84 = resizeimage.resize_contain(Image84x84, [84, 84])
#Convert Image to 2D array
Image84x84 = np.uint8(Image84x84.convert('L'))
Training_Images.append(Image84x84)
return Training_labels, np.array(Training_Images)
def FER2013_Validation_Set (self):
Validation_labels = []
Validation_Images = []
path = self.path + 'FER2013-Validation.csv'
with open(path) as csvfile:
readCSV = csv.reader(csvfile, delimiter = ',')
#row[0] = Labels, row[1] = Image Pixels
for row in readCSV:
Label = int(row[0])
#Label = [0] * 7
#Label[int(row[0])] = 1
Validation_labels.append(Label)
image = row[1].split()
#Cast strings to integer
image = [int(i) for i in image]
#Convert 1D array to 2D array 48x48
Image48x48 = np.reshape(image, (48, 48))
#Convert 2D array to Image
Image84x84 = Image.fromarray(np.uint8(Image48x48))
#Resize Image to 84x84
Image84x84 = resizeimage.resize_contain(Image84x84, [84, 84])
#Convert Image to 2D array
Image84x84 = np.uint8(Image84x84.convert('L'))
Validation_Images.append(Image84x84)
return Validation_labels, np.array(Validation_Images)
def FER2013_Testing_Set (self):
Testing_labels = []
Testing_Images = []
path = self.path + 'FER2013-Testing.csv'
with open(path) as csvfile:
readCSV = csv.reader(csvfile, delimiter = ',')
#row[0] = Labels, row[1] = Image Pixels
c = 0
for row in readCSV:
Label = int(row[0])
#Label = [0] * 7
#Label[int(row[0])] = 1
Testing_labels.append(Label)
image = row[1].split()
#Cast strings to integer
image = [int(i) for i in image]
#Convert 1D array to 2D array 48x48
Image48x48 = np.reshape(image, (48, 48))
#Convert 2D array to Image
Image84x84 = Image.fromarray(np.uint8(Image48x48))
#Resize Image to 84x84
Image84x84 = resizeimage.resize_contain(Image84x84, [84, 84])
#Convert Image to 2D array
Image84x84 = np.uint8(Image84x84.convert('L'))
Testing_Images.append(Image84x84)
c = c + 1
#if c>=400:
# break
return Testing_labels, np.array(Testing_Images)
#Batch_Number 0 based
def Get_batch(self, Batch_Number, Mode):
begin = Batch_Number * self.Batch_Size
end = begin + self.Batch_Size
Labels = []
Images = []
if(Mode == 'Training'):
end = min(end, len(self.Training_labels) + 1)
Labels = self.Training_labels[begin:end]
Images = self.Training_Images[begin:end]
elif (Mode == 'Validation'):
end = min(end, len(self.Validation_labels) + 1)
Labels = self.Validation_labels[begin:end]
Images = self.Validation_Images[begin:end]
elif (Mode == 'Testing'):
end = min(end, len(self.Testing_labels) + 1)
Labels = self.Testing_labels[begin:end]
Images = self.Testing_Images[begin:end]
return Labels, Images