Complete DIP assignments main

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diff --git a/opencv/code/align.py b/opencv/code/align.py
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+++ b/opencv/code/align.py
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+import cv2
+import numpy as np
+
+# Load the two images
+image1 = cv2.imread("T1.jpg")
+image2 = cv2.imread("T2.jpg")
+
+# Convert images to grayscale
+gray1 = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY)
+gray2 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)
+
+# Initialize SIFT detector
+sift = cv2.SIFT_create()
+
+# Find keypoints and descriptors
+keypoints1, descriptors1 = sift.detectAndCompute(gray1, None)
+keypoints2, descriptors2 = sift.detectAndCompute(gray2, None)
+
+# Initialize keypoint matcher
+matcher = cv2.BFMatcher()
+
+# Match descriptors
+matches = matcher.match(descriptors1, descriptors2)
+
+# Sort matches by distance
+matches = sorted(matches, key=lambda x: x.distance)
+
+# Extract matched keypoints
+points1 = np.float32([keypoints1[m.queryIdx].pt for m in matches])
+points2 = np.float32([keypoints2[m.trainIdx].pt for m in matches])
+
+# Estimate transformation matrix (homography) using RANSAC
+homography, _ = cv2.findHomography(points1, points2, cv2.RANSAC)
+
+# Warp image1 to align with image2
+aligned_image1 = cv2.warpPerspective(
+    image1, homography, (image2.shape[1], image2.shape[0])
+)
+
+# Display and save the aligned image
+cv2.imwrite("T1p.jpg", aligned_image1)
+cv2.waitKey(0)
+cv2.destroyAllWindows()