diff --git a/README.md b/README.md
index f23a354..c8be2f8 100644
--- a/README.md
+++ b/README.md
@@ -6,6 +6,10 @@ This project explores the image morphing technique, particularly face image morp
 
 We perform image morphing in the frequency. Specifically, we transform the source and target images to frequency domain using DFT and interpolate them to generate the intermediate images.
 
+<p align="center">
+    <img src="assets/README/morph_slider.gif" width="50%"/>
+</p>
+
 
 This project consists of two major steps: Image Alignment and Transition Control.
 
@@ -15,6 +19,11 @@ The baseline paper on which this work is built: [image morphing in frequency dom
 
 The project introduces an automated approach to align images, significantly enhancing morphing quality. It uses the OpenCV Python module for optimized image processing and facial landmark recognition for feature extraction. Techniques such as rotation to align eyes parallel to the ground and affine transformations to align eyes of source and target images are employed for precise alignment.
 
+<p align="center">
+    <img src="/assets/README/landmarks.png"
+      width="70%">
+</p>
+
 ## Transition Control
 
 Transition control is achieved by using the Discrete Fourier Transform (DFT) for transforming images. The project innovates on transitioning images by interpolating their frequency domain representations. It details the approach of using polar interpolation of magnitude and phase of images, a method superior to linear interpolation in spatial domain.
@@ -81,7 +90,10 @@ show_images_slider(*morphs, path1=paths[0], path2=paths[1])
 python3 src/main.py
 ```
 
-![](assets/README/morph.gif)
+<p align="center">
+    <img src="/assets/README/morph_seperate.png"
+      width="50%">
+</p>
 
 ### Producing metrics
 To produce the metrics for both DFT and DCT based morphing.
diff --git a/assets/README/landmarks.png b/assets/README/landmarks.png
new file mode 100644
index 0000000..1ec3412
Binary files /dev/null and b/assets/README/landmarks.png differ
diff --git a/assets/README/morph_seperate.png b/assets/README/morph_seperate.png
new file mode 100644
index 0000000..7f47e2a
Binary files /dev/null and b/assets/README/morph_seperate.png differ
diff --git a/assets/README/morph.gif b/assets/README/morph_slider.gif
similarity index 100%
rename from assets/README/morph.gif
rename to assets/README/morph_slider.gif
diff --git a/src/main.py b/src/main.py
index 2bb26d5..100b97c 100644
--- a/src/main.py
+++ b/src/main.py
@@ -10,6 +10,10 @@
 
     # Set paths
     paths = random_files(emotion1, emotion2)
+    paths = [
+        "/home/subroot/Personal/university/dip/image-morphing/data/front/neutral_front/107_03.jpg",
+        "/home/subroot/Personal/university/dip/image-morphing/data/front/neutral_front/100_03.jpg",
+    ]
 
     # Load images
     img1, img2 = load_image(paths[0]), load_image(paths[1])
@@ -44,13 +48,14 @@
     face1 = resize_image(face1)
     face2 = resize_image(face2)
 
+    # landmarks1 = detect_landmarks_points(face1)
+    # landmarks2 = detect_landmarks_points(face2)
     # face1 = draw_landmarks(face1, landmarks1)
     # face2 = draw_landmarks(face2, landmarks2)
     # show_images(face1, face2, path1=paths[0], path2=paths[1])
 
     # Perform morph
-    morph_type = "dft"
-    morphs = morph(face1, face2, morph_type=morph_type)
+    morphs = morph(face1, face2, morph_type=configs.morph_type)
 
     # Combine the faces if both are detected
     # show_images(*morphs, path1=paths[0], path2=paths[1])