Nsryjdtyk

I am trying to stitch two images by finding the keypoints and stitching the images accordingly using opencv and python (panoramic image stitching). Luckily I found a code that do the trick. But it works well with given images alongside of the code. And it is not working for my images(getting only the second image as the final resultant image). The code is given below

#import the necessary packages

 import numpy as np

 import imutils

 import cv2



class Stitcher:

def __init__(self):

    # determine if we are using OpenCV v3.X

    self.isv3 = imutils.is_cv3()



def stitch(self, images, ratio=0.75, reprojThresh=4.0,

    showMatches=False):

    # unpack the images, then detect keypoints and extract

    # local invariant descriptors from them

    (imageB, imageA) = images

    (kpsA, featuresA) = self.detectAndDescribe(imageA)

    (kpsB, featuresB) = self.detectAndDescribe(imageB)



    # match features between the two images

    M = self.matchKeypoints(kpsA, kpsB,

        featuresA, featuresB, ratio, reprojThresh)



    # if the match is None, then there aren't enough matched

    # keypoints to create a panorama

    if M is None:

        return None



    # otherwise, apply a perspective warp to stitch the images

    # together

    (matches, H, status) = M

    #print (matches)

    #print (H)

    result = cv2.warpPerspective(imageA, H,

        (imageA.shape[1] + imageB.shape[1], imageA.shape[0]))

    cv2.imshow("a",result)

    result[0:imageB.shape[0], 0:imageB.shape[1]] = imageB

    cv2.imshow("b",result)



    # check to see if the keypoint matches should be visualized

    if showMatches:

        vis = self.drawMatches(imageA, imageB, kpsA, kpsB, matches,

            status)



        # return a tuple of the stitched image and the

        # visualization

        return (result, vis)



    # return the stitched image

    return result



def detectAndDescribe(self, image):

    # convert the image to grayscale

    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)



    # check to see if we are using OpenCV 3.X

    if self.isv3:

        # detect and extract features from the image

        descriptor = cv2.xfeatures2d.SIFT_create()

        (kps, features) = descriptor.detectAndCompute(image, None)



    # otherwise, we are using OpenCV 2.4.X

    else:

        # detect keypoints in the image

        detector = cv2.FeatureDetector_create("SIFT")

        kps = detector.detect(gray)



        # extract features from the image

        extractor = cv2.DescriptorExtractor_create("SIFT")

        (kps, features) = extractor.compute(gray, kps)



    # convert the keypoints from KeyPoint objects to NumPy

    # arrays

    kps = np.float32([kp.pt for kp in kps])



    # return a tuple of keypoints and features

    return (kps, features)



def matchKeypoints(self, kpsA, kpsB, featuresA, featuresB,

    ratio, reprojThresh):

    # compute the raw matches and initialize the list of actual

    # matches

    matcher = cv2.DescriptorMatcher_create("BruteForce")

    rawMatches = matcher.knnMatch(featuresA, featuresB, 2)

    matches = 



    # loop over the raw matches

    for m in rawMatches:

        # ensure the distance is within a certain ratio of each

        # other (i.e. Lowe's ratio test)

        if len(m) == 2 and m[0].distance < m[1].distance * ratio:

            matches.append((m[0].trainIdx, m[0].queryIdx))



    # computing a homography requires at least 4 matches

    if len(matches) > 4:

        # construct the two sets of points

        ptsA = np.float32([kpsA[i] for (_, i) in matches])

        ptsB = np.float32([kpsB[i] for (i, _) in matches])



        # compute the homography between the two sets of points

        (H, status) = cv2.findHomography(ptsA, ptsB, cv2.RANSAC,

            reprojThresh)



        # return the matches along with the homograpy matrix

        # and status of each matched point

        return (matches, H, status)



    # otherwise, no homograpy could be computed

    return None



def drawMatches(self, imageA, imageB, kpsA, kpsB, matches, status):

    # initialize the output visualization image

    (hA, wA) = imageA.shape[:2]

    (hB, wB) = imageB.shape[:2]

    vis = np.zeros((max(hA, hB), wA + wB, 3), dtype="uint8")

    vis[0:hA, 0:wA] = imageA

    vis[0:hB, wA:] = imageB



    # loop over the matches

    for ((trainIdx, queryIdx), s) in zip(matches, status):

        # only process the match if the keypoint was successfully

        # matched

        if s == 1:

            # draw the match

            ptA = (int(kpsA[queryIdx][0]), int(kpsA[queryIdx][1]))

            ptB = (int(kpsB[trainIdx][0]) + wA, int(kpsB[trainIdx][1]))

            cv2.line(vis, ptA, ptB, (0, 255, 0), 1)



    # return the visualization

    return vis

And I call this piece of script in my main code like :

from deeps.panorama import Stitcher

from PIL import Image

import pytesseract

import argparse

import imutils

import cv2



# construct the argument parse and parse the arguments

ap = argparse.ArgumentParser()

ap.add_argument("-f", "--first", required=True,

    help="path to the first image")

ap.add_argument("-s", "--second", required=True,

    help="path to the second image")

args = vars(ap.parse_args())



# load the two images and resize them to have a width of 400 pixels

# (for faster processing)

imageA = cv2.imread(args["first"])

imageB = cv2.imread(args["second"])



imageA = imutils.resize(imageA, width=400)

imageB = imutils.resize(imageB, width=400)



# stitch the images together to create a panorama

stitcher = Stitcher()

(result1, vis1) = stitcher.stitch([imageA, imageB], showMatches=True)



# show the images

cv2.imshow("Image A", imageA)

cv2.imshow("Image B", imageB)

cv2.imshow("Keypoint Matches1", vis1)

cv2.imshow("Result1", result1)

img = Image.fromarray(result1)

text = pytesseract.image_to_string(img)

print(text)

cv2.waitKey(0)

My ultimate aim is to get a final text from combining the images(scrolling text). The images are attached. I dunno what is wrong with the script, or if you have other solutions please let me know