一、全景影象拼接步驟

1、使用SIFT演算法尋找關鍵特徵點

2、建立BFMatcher匹配器將圖片特徵點進行匹配

3、特徵點多於4個則可以計算視角變換矩陣

4、將圖片經過變換矩陣變換

5、圖片變換過後進行拼接

二、參考程式碼

import numpy as np
import cv2

class Stitcher:
    # 拼接函式
    def stitch(self, images, ratio=0.75, reprojThresh=4.0, showMatches=False):
        # 獲取輸入圖片
        (imageB, imageA) = images
        
 
# 檢測A、B圖片的SIFT關鍵特徵點，並計算特徵描述子
        (kpsA, featuresA) = self.detectAndDescribe(imageA)
        (kpsB, featuresB) = self.detectAndDescribe(imageB)
        # 匹配兩張圖片的所有特徵點，返回匹配結果
        M = self.matchKeypoints(kpsA, kpsB, featuresA, featuresB, ratio, reprojThresh)
        # 如果返回結果為空，沒有匹配成功的特徵點，退出演算法
        if
 
 M is None:
            return None
        # 否則，提取匹配結果
        # H是3x3視角變換矩陣
        (matches, H, status) = M
        # 將圖片A進行視角變換，result是變換後圖片
        result = cv2.warpPerspective(imageA, H, (imageA.shape[1] + imageB.shape[1], imageA.shape[0]))
        self.cv_show('result1', result)
        # 將圖片B傳入result圖片最左端
 

        result[0:imageB.shape[0], 0:imageB.shape[1]] = imageB
        self.cv_show('result2', result)
        # 檢測是否需要顯示圖片匹配
        if showMatches:
            # 生成匹配圖片
            vis = self.drawMatches(imageA, imageB, kpsA, kpsB, matches, status)
            # 返回結果
            return (result, vis)
        # 返回匹配結果
        return result

    def cv_show(self, name, img):
        cv2.imshow(name, img)
        cv2.waitKey(0)
        cv2.destroyAllWindows()

    def detectAndDescribe(self, image):
        # 將彩色圖片轉換成灰度圖
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        # 建立SIFT生成器
        descriptor = cv2.xfeatures2d.SIFT_create()
        # 檢測SIFT特徵點，並計算描述子
        (kps, features) = descriptor.detectAndCompute(image, None)
        # 將結果轉換成NumPy陣列
        kps = np.float32([kp.pt for kp in kps])
        # 返回特徵點集，及對應的描述特徵
        return (kps, features)

    def matchKeypoints(self, kpsA, kpsB, featuresA, featuresB, ratio, reprojThresh):
        # 建立暴力匹配器
        matcher = cv2.BFMatcher()
        # 使用KNN檢測來自A、B圖的SIFT特徵匹配對，K=2
        rawMatches = matcher.knnMatch(featuresA, featuresB, 2)
        matches = []
        for m in rawMatches:
            # 當最近距離跟次近距離的比值小於ratio值時，保留此匹配對
            if len(m) == 2 and m[0].distance < m[1].distance * ratio:
                # 儲存兩個點在featuresA, featuresB中的索引值, 既是特徵點的索引值
                matches.append((m[0].trainIdx, m[0].queryIdx))
        # 當篩選後的匹配對大於4時，計算視角變換矩陣
        if len(matches) > 4:
            # 獲取匹配對的點座標
            ptsA = np.float32([kpsA[i] for (_, i) in matches])
            ptsB = np.float32([kpsB[i] for (i, _) in matches])
            # 計算視角變換矩陣
            (H, status) = cv2.findHomography(ptsA, ptsB, cv2.RANSAC, reprojThresh)
            # 返回結果
            return (matches, H, status)
        # 如果匹配對小於4時，返回None
        return None

    def drawMatches(self, imageA, imageB, kpsA, kpsB, matches, status):
        # 初始化視覺化圖片，將A、B圖左右連線到一起
        (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
        # 聯合遍歷，畫出匹配對
        for ((trainIdx, queryIdx), s) in zip(matches, status):
            # 當點對匹配成功時，畫到視覺化圖上
            if s == 1:
                # 畫出匹配對
                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)
        # 返回視覺化結果
        return vis

if __name__ == '__main__':
    imageA = cv2.imread("left_01.png")
    imageB = cv2.imread("right_01.png")
    stitcher = Stitcher()
    (result, vis) = stitcher.stitch([imageA, imageB], showMatches=True)
    cv2.imshow("Keypoint Matches", vis)
    cv2.imshow("Result", result)
    cv2.waitKey(0)
    cv2.destroyAllWindows()