diff --git a/assets/aparna_front.jpg b/assets/aparna_front.jpg
new file mode 100755
index 0000000..5d79364
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diff --git a/assets/aparna_side.jpg b/assets/aparna_side.jpg
new file mode 100755
index 0000000..b117add
Binary files /dev/null and b/assets/aparna_side.jpg differ
diff --git a/assets/sonali_front.jpg b/assets/sonali_front.jpg
new file mode 100755
index 0000000..1ad63aa
Binary files /dev/null and b/assets/sonali_front.jpg differ
diff --git a/assets/sonali_side.jpg b/assets/sonali_side.jpg
new file mode 100755
index 0000000..82eae59
Binary files /dev/null and b/assets/sonali_side.jpg differ
diff --git a/backup.py b/backup.py
index be444e7..b0c2dfa 100644
--- a/backup.py
+++ b/backup.py
@@ -254,7 +254,7 @@ class Landmarker:
                 int(center_point[1] * self.front_image_resized.shape[0]),
             )
             xc, yc = center_point
-            logging.warning(f"xc: {xc}")
+            logging.warning("xc: ", xc)
             logging.warning(f"yc: {yc}")
             self.circle(self.front_image_keypoints, xc, yc)
 
diff --git a/landmarks.py b/landmarks.py
index be444e7..83e14a1 100644
--- a/landmarks.py
+++ b/landmarks.py
@@ -91,7 +91,7 @@ class Landmarker:
         )
         parser.add_argument(
             "--pixel_height",
-            default=255,
+            #    default=216,
             dest="pixel_height",
             type=int,
             help="pixel height of person",
@@ -151,6 +151,7 @@ class Landmarker:
         return real_distance
 
     def pixel_to_metric_ratio(self):
+        self.pixel_height = self.pixel_distance * 2
         pixel_to_metric_ratio = self.person_height / self.pixel_height
         logging.warning("pixel_to_metric_ratio", pixel_to_metric_ratio)
         return pixel_to_metric_ratio
@@ -184,6 +185,8 @@ class Landmarker:
 
         print("Distance between center and top point:", self.distance)
 
+        print("Height of person:", self.distance * 2)
+
     def calculate_distance_betn_landmarks(self, front_results):
         if not front_results.pose_landmarks:
             return
@@ -222,11 +225,11 @@ class Landmarker:
         cv2.imshow("edges", self.edges)
         cv2.waitKey(0)
 
-    def get_center_top_point(self, front_results):
-        gray_image = cv2.cvtColor(self.front_image_keypoints,
+    def get_center_top_point(self, side_results):
+        gray_image = cv2.cvtColor(self.side_image_keypoints,
                                   cv2.COLOR_BGR2GRAY)
         blurred_image = cv2.GaussianBlur(gray_image, (5, 5), 0)
-        roi = blurred_image[0:int(self.front_image_resized.shape[0] / 2), :]
+        roi = blurred_image[0:int(self.side_image_resized.shape[0] / 2), :]
         self.edges = cv2.Canny(roi, 50, 150)
         contours, _ = cv2.findContours(self.edges, cv2.RETR_EXTERNAL,
                                        cv2.CHAIN_APPROX_SIMPLE)
@@ -238,11 +241,14 @@ class Landmarker:
             self.topmost_point = tuple(
                 largest_contour[largest_contour[:, :, 1].argmin()][0])
             xt, yt = self.topmost_point
-            self.circle(self.front_image_keypoints, xt, yt)
+            self.circle(self.side_image_keypoints, xt, yt)
 
+            logging.warning("xt: ", xt)
+            logging.warning(f"yt: ")
         xc, yc = None, None
-        landmarks = front_results.pose_landmarks.landmark
-        if front_results.pose_landmarks:
+        landmarks = side_results.pose_landmarks.landmark
+
+        if side_results.pose_landmarks:  # type: ignore
             left_hip = landmarks[pose.PoseLandmark.LEFT_HIP.value]
             right_hip = landmarks[pose.PoseLandmark.RIGHT_HIP.value]
             center_point = (
@@ -250,17 +256,17 @@ class Landmarker:
                 (left_hip.y + right_hip.y) / 2,
             )
             center_point = (
-                int(center_point[0] * self.front_image_resized.shape[1]),
-                int(center_point[1] * self.front_image_resized.shape[0]),
+                int(center_point[0] * self.side_image_resized.shape[1]),
+                int(center_point[1] * self.side_image_resized.shape[0]),
             )
             xc, yc = center_point
-            logging.warning(f"xc: {xc}")
+            logging.warning("xc: ", xc)
             logging.warning(f"yc: {yc}")
-            self.circle(self.front_image_keypoints, xc, yc)
-
-        self.distance = self.euclidean_distance(
-            xc, xt, yc, yt) * self.pixel_to_metric_ratio()
+            self.circle(self.side_image_keypoints, xc, yc)
 
+            self.pixel_distance = self.euclidean_distance(xc, xt, yc, yt)
+            self.distance = self.euclidean_distance(
+                xc, xt, yc, yt) * self.pixel_to_metric_ratio()
         return self.distance