stitch.py

import cv2
import numpy as np
from matplotlib import pyplot as plt

left_image_address = '/home/deveshdatwani/Desktop/left.jpg'
right_image_address = '/home/deveshdatwani/Desktop/right.jpg'

left_image = cv2.imread(left_image_address, cv2.IMREAD_GRAYSCALE)
right_image = cv2.imread(right_image_address, cv2.IMREAD_GRAYSCALE)

left_image = cv2.resize(left_image, (448, 448))
right_image = cv2.resize(right_image, (448, 448))

assert (left_image.shape) and (right_image.shape)

def getFeatures(image):
    orb = cv2.ORB_create()
    features = {}
    keypoints = orb.detect(image)
    keypoints, descriptors = orb.compute(image, keypoints)
    features['keypoints'] = keypoints
    features['descriptors'] = descriptors
    return features

def drawKeypoints(image, features):
    new_image = cv2.drawKeypoints(image, features['keypoints'], (0,255,0), 0)
    plt.imshow(new_image)
    plt.show()
    return None

def matchFeatures(features_1, features_2):
    bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
    matches = bf.match(features_1['descriptors'], features_2['descriptors'])
    return matches

def drawMatches(img1, kp1, img2, kp2, matches):
    matches = sorted(matches, key=lambda x: x.distance)
    img3 = cv2.drawMatches(img1, kp1, img2, kp2, matches[:10], None, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
    plt.imshow(img3)
    plt.show()
    return None

def getHomography(points1, points2):
    T, mask = cv2.findHomography(points1, points2)
    return T

def merge(image1, image2, M):
    image3 = cv2

features_left_image = getFeatures(left_image) 
features_right_image = getFeatures(right_image)

matches = matchFeatures(features_left_image, features_right_image)

# drawMatches(left_image, features_left_image['keypoints'], right_image, features_right_image['keypoints'], matches)

matches = sorted(matches, key=lambda x: x.distance)

points1_idx = np.array([i.trainIdx for i in matches[:4]], dtype=np.uint32)
points2_idx = np.array([i.queryIdx for i in matches[:4]], dtype=np.uint32)

k_points1 = np.float32([i.pt for i in features_left_image['keypoints']])[points2_idx]
k_points2 = np.float32([i.pt for i in features_right_image['keypoints']])[points1_idx]

T = cv2.getPerspectiveTransform(k_points1, k_points2)

warp = cv2.warpPerspective(right_image, T, (800, 800))
plt.imshow(warp)
plt.show()