diff --git a/.gitignore b/.gitignore
index 723ef36..b435414 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1 +1,6 @@
-.idea
\ No newline at end of file
+.DS_Store
+.idea
+
+*venv/
+*.pkl
+*.avi
diff --git a/requirements.txt b/requirements.txt
index c41093f..4f9a42d 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,4 +2,5 @@ osqp==0.6.1
 cvxpy==1.2.1
 matplotlib==3.5.3
 numpy==1.23.3
-opencv-contrib-python==4.6.0.66
\ No newline at end of file
+opencv-contrib-python==4.6.0.66
+progressbar>=2.3
\ No newline at end of file
diff --git a/script.sh b/script.sh
old mode 100644
new mode 100755
index 54ab420..2e92428
--- a/script.sh
+++ b/script.sh
@@ -1,3 +1,4 @@
+#!/usr/bin/env bash
 echo "Preprocessing"
 python3 src/preproc.py $1
 
@@ -5,4 +6,4 @@ echo "Optimizing trajectory"
 python3 src/stabilize.py $2
 
 echo "Generating output"
-python3 src/generate.py $1 $2
\ No newline at end of file
+python3 src/generate.py $1 $2
diff --git a/src/preproc.py b/src/preproc.py
index 18e4c90..723a289 100644
--- a/src/preproc.py
+++ b/src/preproc.py
@@ -2,8 +2,11 @@
 import numpy as np
 import pickle
 import sys
+from progressbar import ProgressBar, Percentage, Bar, ETA
 
 sift = cv2.xfeatures2d.SIFT_create(200)
+pbar = ProgressBar(widgets=[Percentage(), Bar(), ETA()])
+
 
 def getAffMat(I1, I2):
     I1 = cv2.cvtColor(I1, cv2.COLOR_BGR2GRAY)
@@ -16,17 +19,17 @@ def getAffMat(I1, I2):
     # Finding good matches using ratio testing
     bf = cv2.BFMatcher()
     matches = bf.knnMatch(desc1, desc2, k=2)
-    
+
     good = []
-    for m,n in matches:
+    for m, n in matches:
         if m.distance < 0.7*n.distance:
             good.append(m)
 
     pts_src = []
     pts_dst = []
     for i in range(len(good)):
-    	pts_src.append([kp1[good[i].queryIdx].pt[0], kp1[good[i].queryIdx].pt[1]])
-    	pts_dst.append([kp2[good[i].trainIdx].pt[0], kp2[good[i].trainIdx].pt[1]])
+        pts_src.append([kp1[good[i].queryIdx].pt[0], kp1[good[i].queryIdx].pt[1]])
+        pts_dst.append([kp2[good[i].trainIdx].pt[0], kp2[good[i].trainIdx].pt[1]])
 
     pts_src = np.array(pts_src).astype(np.float32)
     pts_dst = np.array(pts_dst).astype(np.float32)
@@ -34,14 +37,17 @@ def getAffMat(I1, I2):
     # Computing affine matrix using the best matches
     return cv2.estimateAffinePartial2D(pts_src, pts_dst)[0]
 
+
 v = cv2.VideoCapture(sys.argv[1])
 n_frames = int(v.get(cv2.CAP_PROP_FRAME_COUNT))
+pbar.maxval = n_frames
+pbar.start()
 
 # Generating the Xdata and Ydata
 transforms = [[], [], [], []]
 count = 0
 while v.isOpened():
-    
+
     ret, frame = v.read()
 
     if ret == True:
@@ -54,19 +60,19 @@ def getAffMat(I1, I2):
                 transforms[2].append(np.arctan2(transMat[1][0], transMat[0][0]))
                 transforms[3].append(np.sqrt(transMat[1][0]**2 + transMat[0][0]**2))
             except:
-            	transforms[0].append(0)
-            	transforms[1].append(0)
-            	transforms[2].append(0)
-            	transforms[3].append(1)
-            
+                transforms[0].append(0)
+                transforms[1].append(0)
+                transforms[2].append(0)
+                transforms[3].append(1)
+
         count += 1
         prev = frame
-        print( str((count/n_frames)*100) + "% completed")
+        pbar.update(count)
     else:
-    	break
+        break
 
 v.release()
 
 # Storing the data
 with open('transforms.pkl', 'wb') as f:
-	pickle.dump(transforms, f)
\ No newline at end of file
+    pickle.dump(transforms, f)