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UAV-image-stitching.py

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+# -*- coding: utf-8 -*-
+#Import library
+#import libraries
+import sys
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+def warpImages(img1, img2, H):
+  rows1, cols1 = img1.shape[:2]
+  rows2, cols2 = img2.shape[:2]
+
+  list_of_points_1 = np.float32([[0,0], [0, rows1],[cols1, rows1], [cols1, 0]]).reshape(-1, 1, 2) #coordinates of a reference image
+  temp_points = np.float32([[0,0], [0,rows2], [cols2,rows2], [cols2,0]]).reshape(-1,1,2) #coordinates of second image
+
+  # When we have established a homography we need to warp perspective
+  # Change field of view
+  list_of_points_2 = cv2.perspectiveTransform(temp_points, H)#calculate the transformation matrix
+
+  list_of_points = np.concatenate((list_of_points_1,list_of_points_2), axis=0)
+
+  [x_min, y_min] = np.int32(list_of_points.min(axis=0).ravel() - 0.5)
+  [x_max, y_max] = np.int32(list_of_points.max(axis=0).ravel() + 0.5)
+
+  translation_dist = [-x_min,-y_min]
+
+  H_translation = np.array([[1, 0, translation_dist[0]], [0, 1, translation_dist[1]], [0, 0, 1]])
+
+  output_img = cv2.warpPerspective(img2, H_translation.dot(H), (x_max-x_min, y_max-y_min))
+  output_img[translation_dist[1]:rows1+translation_dist[1], translation_dist[0]:cols1+translation_dist[0]] = img1
+
+  return output_img
+#folfer containing images from drones, sorted by name 
+import glob
+path = sorted(glob.glob("*.jpg"))
+img_list = []
+for img in path:
+    n = cv2.imread(img)
+    img_list.append(n)
+"""Functions for stitching"""
+
+#Use ORB detector to extract keypoints
+orb = cv2.ORB_create(nfeatures=2000)
+while True:
+  img1=img_list.pop(0)
+  img2=img_list.pop(0)
+# Find the key points and descriptors with ORB
+  keypoints1, descriptors1 = orb.detectAndCompute(img1, None)#descriptors are arrays of numbers that define the keypoints
+  keypoints2, descriptors2 = orb.detectAndCompute(img2, None)
+
+
+# Create a BFMatcher object to match descriptors
+# It will find all of the matching keypoints on two images
+  bf = cv2.BFMatcher_create(cv2.NORM_HAMMING)#NORM_HAMMING specifies the distance as a measurement of similarity between two descriptors
+
+# Find matching points
+  matches = bf.knnMatch(descriptors1, descriptors2,k=2)
+
+  all_matches = []
+  for m, n in matches:
+    all_matches.append(m)
+# Finding the best matches
+  good = []
+  for m, n in matches:
+    if m.distance < 0.6 * n.distance:#Threshold
+        good.append(m)
+
+# Set minimum match condition
+  MIN_MATCH_COUNT = 5
+
+  if len(good) > MIN_MATCH_COUNT:
+
+    # Convert keypoints to an argument for findHomography
+    src_pts = np.float32([ keypoints1[m.queryIdx].pt for m in good]).reshape(-1,1,2)
+    dst_pts = np.float32([ keypoints2[m.trainIdx].pt for m in good]).reshape(-1,1,2)
+
+    # Establish a homography
+    M, _ = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC,5.0)
+
+    result = warpImages(img2, img1, M)
+
+    img_list.insert(0,result)
+
+    if len(img_list)==1:
+      break
+result = cv2.cvtColor(result, cv2.COLOR_BGR2RGB )  
+plt.imshow(result)
+plt.show()
+