diff --git a/build_csv.py b/build_csv.py
index ac3b10e..dc2df26 100644
--- a/build_csv.py
+++ b/build_csv.py
@@ -12,14 +12,15 @@
 if __name__== "__main__":
 	# output csv file where we save paths to out images
 	write_file= open("data.csv", "w")
-	data_dir= "/home/suryateja/Documents/GitHub/FaceRecognizer/output_images"
+	data_dir= "C:/Users/jaraa/OneDrive/Documentos/GitHub/FaceRecognizer/sorted_output_images"
 
 	# folders for each subject in root images folder
 	subjects= listdir(data_dir)
-	#print(subjects)
+	print(subjects)
 
 	for i in range(len(subjects)):
-		# all pictures in a particular folder/subject
+        # all pictures in a particular folder/subject
+		print(subjects[i])
 		photos= listdir(data_dir+ "/"+ subjects[i])
 		for photo in photos:
 			out_line= data_dir+ "/"+ subjects[i]+ "/"+ photo+ ";"+ str(i)
diff --git a/detect_save_images.py b/detect_save_images.py
index 759cf37..cc85a9a 100644
--- a/detect_save_images.py
+++ b/detect_save_images.py
@@ -7,11 +7,14 @@
 If face(s) is(are) found, it crops and saves them at "output_path".
 '''
 
-import cv2
-import cv2.cv as cv
+
+import cv2 as cv
 from os import listdir
 import time
 
+
+
+
 def cropImage(img, box):
 	[p, q, r, s]= box
 	# crop and save the image provided with the co-ordinates of bounding box
@@ -20,28 +23,28 @@ def cropImage(img, box):
 
 # save the cropped image at specified location
 def saveCropped(img, name):
-	cv2.imwrite(output_path+ name+ ".jpg", img)
+	cv.imwrite(output_path+ name+ ".jpg", img)
 
 if __name__== "__main__":
 	# paths to input and output images
-	input_path= "old/input_images/"
-	output_path= "old/output_images/"
+	input_path= "input_images/"
+	output_path= "output_images/"
 
 	# load pre-trained frontalface cascade classifier
-	frontal_face= cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
-	input_names= listdir("/home/suryateja/Documents/GitHub/FaceRecognizer/"+ input_path)
+	frontal_face= cv.CascadeClassifier("haarcascade_frontalface_default.xml")
+	input_names= listdir("C:/Users/jaraa/OneDrive/Documentos/GitHub/FaceRecognizer/"+ input_path)
 
 	print("Starting to detect faces in images and save the cropped images to output file...")
 	sttime= time.clock()
 	i= 1
 	for name in input_names:
 		print(input_path+name)
-		color_img= cv2.imread(input_path+ name)
+		color_img= cv.imread(input_path+ name)
 		# converting color image to grayscale image
-		gray_img= cv2.cvtColor(color_img, cv2.COLOR_BGR2GRAY)
+		gray_img= cv.cvtColor(color_img, cv.COLOR_BGR2GRAY)
 
 		# find the bounding boxes around detected faces in images
-		bBoxes= frontal_face.detectMultiScale(gray_img, scaleFactor=1.3, minNeighbors=5, minSize=(30, 30), flags = cv.CV_HAAR_SCALE_IMAGE)
+		bBoxes= frontal_face.detectMultiScale(gray_img, scaleFactor=1.3, minNeighbors=5, minSize=(30, 30), flags = cv.CASCADE_SCALE_IMAGE)
 		#print(bBoxes)
 
 		for box in bBoxes:
diff --git a/face_detect_recognition.py b/face_detect_recognition.py
index 2483a25..724af5c 100644
--- a/face_detect_recognition.py
+++ b/face_detect_recognition.py
@@ -18,8 +18,7 @@
 		Note: Images in here can be full sized images. (no need to crop faces)
 '''
 
-import cv2
-import cv2.cv as cv
+import cv2 as cv2
 import numpy as np
 from os import listdir
 import sys, time
@@ -67,7 +66,7 @@ def get_images(path, size):
 	# initializing eigen_model and training
 	print("Initializing eigen FaceRecognizer and training...")
 	sttime= time.clock()
-	eigen_model= cv2.createEigenFaceRecognizer()
+	eigen_model= cv2.face.EigenFaceRecognizer_create()
 	eigen_model.train(images, labels)
 	print("\tSuccessfully completed training in "+ str(time.clock()- sttime)+ " Secs!")
 
@@ -90,7 +89,7 @@ def get_images(path, size):
 		#starting to detect face in given image
 		frontal_face= cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
 		#bBoxes= frontal_face.detectMultiScale(pre_image, scaleFactor=1.3, minNeighbors=5, minSize=(30, 30), flags = cv.CV_HAAR_SCALE_IMAGE)
-		bBoxes= frontal_face.detectMultiScale(pre_image, scaleFactor=1.3, minNeighbors=4, minSize=(30, 30), flags = cv.CV_HAAR_SCALE_IMAGE)
+		bBoxes= frontal_face.detectMultiScale(pre_image, scaleFactor=1.3, minNeighbors=4, minSize=(30, 30), flags = cv2.CASCADE_SCALE_IMAGE)
 
 		for bBox in bBoxes:
 			(p,q,r,s)= bBox