Image data creator from FER-2013 dataset (files merged seperately)

Author: jaydeepthik

create_data.py

@@ -0,0 +1,156 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Nov  2 15:27:26 2019
+
+@authors: jaydeep thik , Vasudev Purandare
+"""
+
+import pandas as pd
+import numpy as np
+from PIL import Image
+
+def generate():
+    data_folder = "./hackdataset"
+    df = pd.read_csv("./fer2013/fer2013.csv")
+    
+    train_samples = df[df['Usage']=="Training"]
+    validation_samples = df[df["Usage"]=="PublicTest"]
+    test_samples = df[df["Usage"]=="PrivateTest"]
+    
+    y_train = train_samples.emotion.astype(np.int32).values
+    y_valid = validation_samples.emotion.astype(np.int32).values
+    y_test = test_samples.emotion.astype(np.int32).values
+    i=0
+    for image, label in zip(train_samples.pixels, y_train):
+        #print(label)
+        img_array = np.fromstring(image, np.uint8, sep=" ").reshape((48,48))
+        if label==0:
+            
+            im = Image.fromarray(img_array, 'L')
+            im.save(data_folder+'/train/Angry/A_'+str(i)+'.jpg')
+            i+=1
+    
+        elif label==1:
+            
+            im = Image.fromarray(img_array, 'L')
+            im.save(data_folder+'/train/Disgust/D_'+str(i)+'.jpg')
+            i+=1
+            
+        elif label==2:
+            
+            im = Image.fromarray(img_array, 'L')
+            im.save(data_folder+'/train/Fear/F_'+str(i)+'.jpg')
+            i+=1
+        elif label==3:
+            
+            im = Image.fromarray(img_array, 'L')
+            im.save(data_folder+'/train/Happy/H_'+str(i)+'.jpg')
+            i+=1
+        
+        elif label==4:
+            
+            im = Image.fromarray(img_array, 'L')
+            im.save(data_folder+'/train/Sad/S_'+str(i)+'.jpg')
+            i+=1
+        elif label==5:
+            
+            im = Image.fromarray(img_array, 'L')
+            im.save(data_folder+'/train/Surprise/S_'+str(i)+'.jpg')
+            i+=1
+        elif label==6:
+            
+            im = Image.fromarray(img_array, 'L')
+            im.save(data_folder+'/train/Tran/T_'+str(i)+'.jpg')
+            i+=1
+        print(i)  
+            
+    for image, label in zip(test_samples.pixels, y_test):
+        #print(label)
+        img_array = np.fromstring(image, np.uint8, sep=" ").reshape((48,48))
+        if label==0:
+            
+            im = Image.fromarray(img_array, 'L')
+            im.save(data_folder+'/test/Angry/A_'+str(i)+'.jpg')
+            i+=1
+    
+        elif label==1:
+            
+            im = Image.fromarray(img_array, 'L')
+            im.save(data_folder+'/test/Disgust/D_'+str(i)+'.jpg')
+            i+=1
+            
+        elif label==2:
+            
+            im = Image.fromarray(img_array, 'L')
+            im.save(data_folder+'/test/Fear/F_'+str(i)+'.jpg')
+            i+=1
+        elif label==3:
+            
+            im = Image.fromarray(img_array, 'L')
+            im.save(data_folder+'/test/Happy/H_'+str(i)+'.jpg')
+            i+=1
+        
+        elif label==4:
+            
+            im = Image.fromarray(img_array, 'L')
+            im.save(data_folder+'/test/Sad/S_'+str(i)+'.jpg')
+            i+=1
+        elif label==5:
+            
+            im = Image.fromarray(img_array, 'L')
+            im.save(data_folder+'/test/Surprise/S_'+str(i)+'.jpg')
+            i+=1
+        elif label==6:
+            
+            im = Image.fromarray(img_array, 'L')
+            im.save(data_folder+'/test/Tran/T_'+str(i)+'.jpg')
+            i+=1
+        print(i)
+    
+    for image, label in zip(validation_samples.pixels, y_valid):
+       # print(label)
+        img_array = np.fromstring(image, np.uint8, sep=" ").reshape((48,48))
+        if label==0:
+            
+            im = Image.fromarray(img_array, 'L')
+            im.save(data_folder+'/valid/Angry/A_'+str(i)+'.jpg')
+            i+=1
+    
+        elif label==1:
+            
+            im = Image.fromarray(img_array, 'L')
+            im.save(data_folder+'/valid/Disgust/D_'+str(i)+'.jpg')
+            i+=1
+            
+        elif label==2:
+            
+            im = Image.fromarray(img_array, 'L')
+            im.save(data_folder+'/valid/Fear/F_'+str(i)+'.jpg')
+            i+=1
+        elif label==3:
+            
+            im = Image.fromarray(img_array, 'L')
+            im.save(data_folder+'/valid/Happy/H_'+str(i)+'.jpg')
+            i+=1
+        
+        elif label==4:
+            
+            im = Image.fromarray(img_array, 'L')
+            im.save(data_folder+'/valid/Sad/S_'+str(i)+'.jpg')
+            i+=1
+        elif label==5:
+            
+            im = Image.fromarray(img_array, 'L')
+            im.save(data_folder+'/valid/Surprise/S_'+str(i)+'.jpg')
+            i+=1
+        elif label==6:
+            
+            im = Image.fromarray(img_array, 'L')
+            im.save(data_folder+'/valid/Tran/T_'+str(i)+'.jpg')
+            i+=1
+    #X_train =np.array([ np.fromstring(image, np.uint8, sep=" ").reshape((48,48)) for image in train_samples.pixels])
+    #X_valid =np.array([ np.fromstring(image, np.uint8, sep=" ").reshape((48,48)) for image in validation_samples.pixels])
+    #X_test =np.array([ np.fromstring(image, np.uint8, sep=" ").reshape((48,48)) for image in test_samples.pixels])
+    
+        print(i)
+    #return X_train, y_train, X_valid, y_valid, X_test, y_test