Opencv Learning Notes 6 -- image mosaic

import numpy as np
import cv2
import matplotlib.pyplot as plt#Matplotlib yes RGB

class Stitcher:
    
    # Splicing function 
    def stitch(self, images, ratio=0.75, reprojThresh=4.0,showMatches=False):
        # Get input picture 
        (imageB, imageA) = images
        # testing A、B The image SIFT Key feature points , And calculate the feature descriptors 
        (kpsA, featuresA) = self.detectAndDescribe(imageA)
        (kpsB, featuresB) = self.detectAndDescribe(imageB)

        #  Match all the feature points of the two pictures , Return matching results 
        M = self.matchKeypoints(kpsA, kpsB, featuresA, featuresB, ratio, reprojThresh)

        #  If the returned result is empty , There is no feature point matching success , Exit algorithm 
        if M is None:
            return None

        #  otherwise , Extract matching results 
        # H yes 3x3 Perspective transformation matrix  
        (matches, H, status) = M
        #  The picture A Change the angle of view ,result It's the transformed picture 
        result = cv2.warpPerspective(imageA, H, (imageA.shape[1] + imageB.shape[1], imageA.shape[0]))# The third parameter is the image size 
        self.cv_show('result', result)
        #  The picture B Pass in result At the far left of the picture 
        result[0:imageB.shape[0], 0:imageB.shape[1]] = imageB
        self.cv_show('result', result)
        #  Check if you need to show a picture match 
        if showMatches:
            #  Generate matching pictures 
            vis = self.drawMatches(imageA, imageB, kpsA, kpsB, matches, status)
            #  Return results 
            return (result, vis)

        #  Return matching results 
        return result
    # Show function 
    def cv_show(self,name,img):
        b,g,r = cv2.split(img)
        img_rgb = cv2.merge((r,g,b))
        plt.imshow(img_rgb)
        plt.show()
    def cv_show1(self,name,img):
        plt.imshow(img)
        plt.show()
        cv2.imshow(name,img)
        cv2.waitKey()
        cv2.destroyAllWindows()



    # Calculate features and descriptions 
    def detectAndDescribe(self, image):
        #  Convert a color image to a grayscale image 
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

        #  establish SIFT generator 
        descriptor = cv2.xfeatures2d.SIFT_create()
        #  testing SIFT Characteristic point , And calculate the descriptors 
        (kps, features) = descriptor.detectAndCompute(image, None)

        #  Convert the result to NumPy Array 
        kps = np.float32([kp.pt for kp in kps])

        #  Return to feature point set , And corresponding description features 
        return (kps, features)
    
    # Matching key points 
    def matchKeypoints(self, kpsA, kpsB, featuresA, featuresB, ratio, reprojThresh):
        #  Build violence matchers 
        matcher = cv2.BFMatcher()
  
        #  Use KNN The test comes from A、B Of Graphs SIFT Feature matching ,K=2
        rawMatches = matcher.knnMatch(featuresA, featuresB, 2)#1 Yes 2 matching 

        matches = []
        for m in rawMatches:
            #  When the ratio of the nearest distance to the next nearest distance is less than ratio When the value of , Keep this match 
            if len(m) == 2 and m[0].distance < m[1].distance * ratio:
            #  Store two points in featuresA, featuresB Index value in 
                matches.append((m[0].trainIdx, m[0].queryIdx))

        #  When the filtered match pair is greater than 4 when , Calculate the angle transformation matrix 
        if len(matches) > 4:
            #  Get the point coordinates of the matched pair 
            ptsA = np.float32([kpsA[i] for (_, i) in matches])
            ptsB = np.float32([kpsB[i] for (i, _) in matches])

            #  Calculate the angle transformation matrix 
            (H, status) = cv2.findHomography(ptsA, ptsB, cv2.RANSAC, reprojThresh)

            #  Return results 
            return (matches, H, status)

        #  If the match pair is less than 4 when , return None
        return None
    #  Draw matching points 
    def drawMatches(self, imageA, imageB, kpsA, kpsB, matches, status):
        #  Initialize visualization image , take A、B Connect left and right 
        (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

        #  Joint traversal , Draw a match 
        for ((trainIdx, queryIdx), s) in zip(matches, status):
            #  When the point pair match is successful , Draw on the visualization 
            if s == 1:
                #  Draw a 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 visualization results 
        return vis

#  Read the mosaic 
imageA = cv2.imread("left_01.png")
imageB = cv2.imread("right_01.png")

#  Make a panorama of the picture 
stitcher = Stitcher()
(result, vis) = stitcher.stitch([imageA, imageB], showMatches=True)

#  Show all pictures 
stitcher.cv_show("Image A", imageA)
stitcher.cv_show("Image B", imageB)
stitcher.cv_show("Keypoint Matches", vis)
stitcher.cv_show("Result", result)