Stitching aerial images in python with open cv

Question

I'm trying to stitch drone images with Open CV ive put together the code below which adds padding to the image to avoid clipping the edges. The unpadded image should be overlayed over the padded one but I don't have a good enough understanding of the stitching code to make this happen. I have tried fiddling with things with no luck.

Any help would be greatly appreciated by myself and the Mussels we are surveying!

results

Code

import cv2
import numpy as np
from tkinter import Tk

# Read in images resize and convert a copy to grayscale

img1 = cv2.imread('Photos/Mussels/img1.jpg')
img2= cv2.imread('Photos/Mussels/img2.jpg')

img1 = cv2.resize(img1, (0,0), fx=0.5, fy=0.5)
img2 = cv2.resize(img2, (0,0), fx=0.5, fy=0.5)

img1_gs = cv2.cvtColor(img1,cv2.COLOR_BGR2GRAY)
img2_gs = cv2.cvtColor(img2,cv2.COLOR_BGR2GRAY)

# Add padding to prevent stitched image cutting out parts of the image 
padding = 1000
img2 = cv2.copyMakeBorder( img2, padding, padding, padding, padding, cv2.BORDER_CONSTANT, value=(0,0,0))
img2_gs = cv2.copyMakeBorder( img2_gs, padding, padding, padding, padding, cv2.BORDER_CONSTANT, value=(0,0,0))

preview_height = 500
preview_width = 500

# Find the key points and descriptors with SIFT -------------------------------#

sift = cv2.xfeatures2d.SIFT_create()
kp1, des1 = sift.detectAndCompute(img1_gs,None)
kp2, des2 = sift.detectAndCompute(img2_gs,None)

kp_show = cv2.drawKeypoints(img1,kp1,None)
kp_show_resize = cv2.resize(kp_show, (preview_width ,preview_height))
cv2.imshow('Image 1 keypoints',kp_show_resize)
# Generate matching keypoints in images
match = cv2.BFMatcher()
matches = match.knnMatch(des1,des2,k=2)

good = [] 
for m,n in matches: # Filter for good matches
    if m.distance < 0.7*n.distance:
        good.append(m)
draw_params = dict(matchColor = (0,255,0), # draw matches in green color
                   singlePointColor = None,
                   flags = 2)
img3 = cv2.drawMatches(img1,kp1,img2,kp2,good,None,**draw_params)

matches_resize = cv2.resize(img3, (preview_width * 2 ,preview_height))
cv2.imshow("DrawMatches.jpg", matches_resize)

# Stitching ------------------------------------------------------------------#

MIN_MATCH_COUNT = 10
if len(good) > MIN_MATCH_COUNT:
    src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2)
    dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2)
    M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
    h,w, _ = img1.shape
    pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2)
    dst = cv2.perspectiveTransform(pts, M)
    img2 = cv2.polylines(img2,[np.int32(dst)],True,255,3, cv2.LINE_AA)
    img2_preview = cv2.resize(img2, (preview_width ,preview_height))
    cv2.imshow("Overlapping.jpg", img2_preview)
else:
    print("Not enought matches are found - %d/%d", (len(good)/MIN_MATCH_COUNT))
dst = cv2.warpPerspective(img1,M,(img2.shape[1] + img1.shape[1], img2.shape[0]))
dst[0:img2.shape[0],0:img2.shape[1]] = img2

dst_resize = cv2.resize(dst, (preview_width ,preview_height))
cv2.imshow("Stitched.jpg", dst_resize)