diff --git a/assignment4/datam/dining_room_keypoints.png b/assignment4/datam/dining_room_keypoints.png
new file mode 100644
index 0000000..17fd33e
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diff --git a/assignment4/datam/dining_room_rotated.png b/assignment4/datam/dining_room_rotated.png
new file mode 100644
index 0000000..2acb585
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diff --git a/assignment4/datam/frames/0.jpg b/assignment4/datam/frames/0.jpg
new file mode 100644
index 0000000..9229c50
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diff --git a/assignment4/datam/frames/1.jpg b/assignment4/datam/frames/1.jpg
new file mode 100644
index 0000000..23e53df
Binary files /dev/null and b/assignment4/datam/frames/1.jpg differ
diff --git a/assignment4/solution.py b/assignment4/solution.py
index 2267d3c..896e7dd 100644
--- a/assignment4/solution.py
+++ b/assignment4/solution.py
@@ -54,7 +54,7 @@ def one_b() -> None:
 
 def ex2():
 	two_a()
-	#two_b()
+	two_b()
 
 def two_a() -> None:
 	"""
@@ -74,20 +74,15 @@ def two_a() -> None:
 	# Find the correspondences
 	matches_a = uz_image.find_correspondences(graph_a_descriptors, graph_b_descriptors)
 	matches_b = uz_image.find_correspondences(graph_b_descriptors, graph_a_descriptors)
-
+	
 	matches_a_coordinates = []
-	matche_b_coordinates = []
+	matches_b_coordinates = []
 	for i, match in enumerate(matches_a):
-		if i % 2 == 0: # plot every second one
-			if np.flip(match) in matches_b: # Check if the match is reciprocal
-				print(np.argwhere(matches_b == np.flip(match)))
-				matches_a_coordinates.append(np.flip(graph_a_keypoints[match[0]]))
-				matche_b_coordinates.append(np.flip(graph_b_keypoints[match[1]]))
-			else:
-				print("Not reciprocal")
+		matches_a_coordinates.append(np.flip(graph_a_keypoints[match[0]]))
+		matches_b_coordinates.append(np.flip(graph_b_keypoints[match[1]]))
 
 	# Plot the matches
-	uz_image.display_matches(graph_a_small, matches_a_coordinates, graph_b_small, matche_b_coordinates)
+	uz_image.display_matches(graph_a_small, matches_a_coordinates, graph_b_small, matches_b_coordinates)
 
 def two_b() -> None:
 	"""
@@ -157,10 +152,10 @@ def three_b() -> None:
 	"""
 	Hi
 	"""
-	image_a = uz_image.imread_gray("data/graf/graf_a.jpg", uz_image.ImageType.float64)
-	image_b = uz_image.imread_gray("data/graf/graf_b.jpg", uz_image.ImageType.float64)
-	#image_a = uz_image.imread_gray("datam/img1.jpg", uz_image.ImageType.float64)
-	#image_b = uz_image.imread_gray("datam/img2.jpg", uz_image.ImageType.float64)
+	#image_a = uz_image.imread_gray("data/graf/graf_a.jpg", uz_image.ImageType.float64)
+	#image_b = uz_image.imread_gray("data/graf/graf_b.jpg", uz_image.ImageType.float64)
+	image_a = uz_image.imread_gray("datam/img1.jpg", uz_image.ImageType.float64)
+	image_b = uz_image.imread_gray("datam/img2.jpg", uz_image.ImageType.float64)
 	#image_a = uz_image.imread_gray("data/newyork/newyork_a.jpg", uz_image.ImageType.float64)
 	#image_b = uz_image.imread_gray("data/newyork/newyork_b.jpg", uz_image.ImageType.float64)
 	# Does not work for newyork dataset, becouse the keypoints are not reciprocal
@@ -182,8 +177,17 @@ def three_b() -> None:
 		return np.array(a_points), np.array(b_points)
 	
 	a,b = map_keypoints(best_inliers)
+
 	uz_image.display_matches(image_a, a, image_b, b)
 
+	img_output = cv2.warpPerspective(image_a, hm, (image_b.shape[1], image_b.shape[0]))
+
+	fig, axs = plt.subplots(1, 2)
+	fig.suptitle("Transformation and rotation using homography")
+	axs[0].imshow(image_b, cmap="gray")
+	axs[1].imshow(img_output, cmap="gray")
+	plt.show()
+
 def three_lol():
 	"""
 	Hi
@@ -209,8 +213,8 @@ def three_lol():
 
 def main():
 	#ex1() 
-	#ex2()
-	ex3()
+	ex2()
+	#ex3()
 
 if __name__ == '__main__':
 	main()
diff --git a/assignment4/uz_framework/__init__.py b/assignment4/uz_framework/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/assignment4/uz_framework/image.py b/assignment4/uz_framework/image.py
index 6c5c8f9..72d60e2 100644
--- a/assignment4/uz_framework/image.py
+++ b/assignment4/uz_framework/image.py
@@ -1186,7 +1186,7 @@ def estimate_homography(keypoints: npt.NDArray[np.float64]) \
 
 def ransac(image_a: npt.NDArray[np.float64], correspondences_a: npt.NDArray[np.float64],
            image_b: npt.NDArray[np.float64], correspondences_b: npt.NDArray[np.float64],
-            iterations: int = 1000,
+            iterations: int = 5000,
             threshold: float = 3) \
     -> tuple[npt.NDArray[np.float64], npt.NDArray[np.float64]]:
     """
@@ -1207,7 +1207,7 @@ def ransac(image_a: npt.NDArray[np.float64], correspondences_a: npt.NDArray[np.f
         # Make it (x,y) -> (x,y) = x,y,x,y matrix
         keypoints = np.concatenate((sample_a, sample_b), axis=1)
         # Estimate homography
-        homography = estimate_homography(image_a, image_b, keypoints)
+        homography = estimate_homography(keypoints)
         # Compute the inliers
         inlier_indices = []
         # Calculate the distance between the transformed points and the actual points