Reorganize Sift_Distance.py

* Put Shift_Distance code into sift_distance function
* Add import statements for numpy and opencv

src/Sift_Distance.py

@@ -1,24 +1,28 @@
-sift = cv.xfeatures2d.SIFT_create()
+import numpy as np
-original_kp, original_des = sift.detectAndCompute(left,None)
+import cv2
-new_kp, new_des = sift.detectAndCompute(right,None)
 
-bf = cv2.BFMatcher()
-matches = bf.knnMatch(original_des, new_des, k=2)
 
-# Apply ratio test
+def sift_distance():
-good = []
+    sift = cv2.xfeatures2d.SIFT_create()
-for m, n in matches:
+    original_kp, original_des = sift.detectAndCompute(left,None)
-    if m.distance < 0.3 * n.distance:
+    new_kp, new_des = sift.detectAndCompute(right,None)
-        good.append([m])
 
-# Featured matched keypoints from images 1 and 2
+    bf = cv2.BFMatcher()
-pts1 = np.float32([original_kp[m.queryIdx].pt for m in good])
+    matches = bf.knnMatch(original_des, new_des, k=2)
-pts2 = np.float32([new_kp[m.trainIdx].pt for m in good])
 
-#convert to complex number
+    # Apply ratio test
-z1 = np.array([[complex(c[0],c[1]) for c in pts1]])
+    good = []
-z2 = np.array([[complex(c[0],c[1]) for c in pts2]])
+    for m, n in matches:
+        if m.distance < 0.3 * n.distance:
+            good.append([m])
 
-# Distance between featured matched keypoints
+    # Featured matched keypoints from images 1 and 2
-FM_dist = abs(z2 - z1)
+    pts1 = np.float32([original_kp[m.queryIdx].pt for m in good])
+    pts2 = np.float32([new_kp[m.trainIdx].pt for m in good])
 
+    #convert to complex number
+    z1 = np.array([[complex(c[0],c[1]) for c in pts1]])
+    z2 = np.array([[complex(c[0],c[1]) for c in pts2]])
+
+    # Distance between featured matched keypoints
+    FM_dist = abs(z2 - z1)