import cv2 as cv        # 導(dǎo)入opencv包import numpy as np      # 導(dǎo)入numpy包，圖像處理中的矩陣運(yùn)算需要用到

# 檢測(cè)圖像的SIFT關(guān)鍵特征點(diǎn)def sift_keypoints_detect(image):    # 處理圖像一般很少用到彩色信息，通常直接將圖像轉(zhuǎn)換為灰度圖    gray_image = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
    # 獲取圖像特征sift-SIFT特征點(diǎn),實(shí)例化對(duì)象sift    sift = cv.xfeatures2d.SIFT_create()                
    # keypoints:特征點(diǎn)向量,向量?jī)?nèi)的每一個(gè)元素是一個(gè)KeyPoint對(duì)象，包含了特征點(diǎn)的各種屬性信息(角度、關(guān)鍵特征點(diǎn)坐標(biāo)等)    # features:表示輸出的sift特征向量，通常是128維的    keypoints, features = sift.detectAndCompute(image, None)
    # cv.drawKeyPoints():在圖像的關(guān)鍵特征點(diǎn)部位繪制一個(gè)小圓圈    # 如果傳遞標(biāo)志flags=cv.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS,它將繪制一個(gè)大小為keypoint的圓圈并顯示它的方向    # 這種方法同時(shí)顯示圖像的坐標(biāo)，大小和方向，是最能顯示特征的一種繪制方式    keypoints_image = cv.drawKeypoints(        gray_image, keypoints, None, flags=cv.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS)
    # 返回帶關(guān)鍵特征點(diǎn)的圖像、關(guān)鍵特征點(diǎn)和sift的特征向量    return keypoints_image, keypoints, features

# 使用KNN檢測(cè)來(lái)自左右圖像的SIFT特征，隨后進(jìn)行匹配def get_feature_point_ensemble(features_right, features_left):    # 創(chuàng)建BFMatcher對(duì)象解決匹配    bf = cv.BFMatcher()    # knnMatch()函數(shù)：返回每個(gè)特征點(diǎn)的最佳匹配k個(gè)匹配點(diǎn)    # features_right為模板圖，features_left為匹配圖    matches = bf.knnMatch(features_right, features_left, k=2)      # 利用sorted()函數(shù)對(duì)matches對(duì)象進(jìn)行升序(默認(rèn))操作    matches = sorted(matches, key=lambda x: x[0].distance / x[1].distance)    # x:x[]字母可以隨意修改，排序方式按照中括號(hào)[]里面的維度進(jìn)行排序，[0]按照第一維排序，[2]按照第三維排序
    # 建立列表good用于存儲(chǔ)匹配的點(diǎn)集    good = []    for m, n in matches:    # ratio的值越大，匹配的線條越密集，但錯(cuò)誤匹配點(diǎn)也會(huì)增多        ratio = 0.6        if m.distance < ratio * n.distance:            good.append(m)
    # 返回匹配的關(guān)鍵特征點(diǎn)集    return good

# 計(jì)算視角變換矩陣H，用H對(duì)右圖進(jìn)行變換并返回全景拼接圖像def Panorama_stitching(image_right, image_left):    _, keypoints_right, features_right = sift_keypoints_detect(image_right)    _, keypoints_left, features_left = sift_keypoints_detect(image_left)    goodMatch = get_feature_point_ensemble(features_right, features_left)
    # 當(dāng)篩選項(xiàng)的匹配對(duì)大于4對(duì)(因?yàn)閔omography單應(yīng)性矩陣的計(jì)算需要至少四個(gè)點(diǎn))時(shí),計(jì)算視角變換矩陣    if len(goodMatch) > 4:        # 獲取匹配對(duì)的點(diǎn)坐標(biāo)        ptsR = np.float32(            [keypoints_right[m.queryIdx].pt for m in goodMatch]).reshape(-1, 1, 2)        ptsL = np.float32(            [keypoints_left[m.trainIdx].pt for m in goodMatch]).reshape(-1, 1, 2)
        # ransacReprojThreshold：將點(diǎn)對(duì)視為內(nèi)點(diǎn)的最大允許重投影錯(cuò)誤閾值(僅用于RANSAC和RHO方法時(shí)),若srcPoints和dstPoints是以像素為單位的，該參數(shù)通常設(shè)置在1到10的范圍內(nèi)        ransacReprojThreshold = 4
        # cv.findHomography():計(jì)算多個(gè)二維點(diǎn)對(duì)之間的最優(yōu)單映射變換矩陣 H(3行x3列),使用最小均方誤差或者RANSAC方法        # 函數(shù)作用:利用基于RANSAC的魯棒算法選擇最優(yōu)的四組配對(duì)點(diǎn)，再計(jì)算轉(zhuǎn)換矩陣H(3*3)并返回,以便于反向投影錯(cuò)誤率達(dá)到最小        Homography, status = cv.findHomography(            ptsR, ptsL, cv.RANSAC, ransacReprojThreshold)
        # cv.warpPerspective()：透視變換函數(shù)，用于解決cv2.warpAffine()不能處理視場(chǎng)和圖像不平行的問(wèn)題        # 作用：就是對(duì)圖像進(jìn)行透視變換，可保持直線不變形，但是平行線可能不再平行        Panorama = cv.warpPerspective(            image_right, Homography, (image_right.shape[1] + image_left.shape[1], image_right.shape[0]))
        cv.imshow("扭曲變換后的右圖", Panorama)        cv.waitKey(0)        cv.destroyAllWindows()        # 將左圖加入到變換后的右圖像的左端即獲得最終圖像        Panorama[0:image_left.shape[0], 0:image_left.shape[1]] = image_left
        # 返回全景拼接的圖像        return Panorama

if __name__ == '__main__':
    # 讀取需要拼接的圖像,需要注意圖像左右的順序    image_left = cv.imread("./Left.jpg")    image_right = cv.imread("./Right.jpg")
    # 通過(guò)調(diào)用cv2.resize()使用插值的方式來(lái)改變圖像的尺寸，保證左右兩張圖像大小一致    # cv.resize()函數(shù)中的第二個(gè)形參dsize表示輸出圖像大小尺寸，當(dāng)設(shè)置為0(None)時(shí)，則表示按fx與fy與原始圖像大小相乘得到輸出圖像尺寸大小    image_right = cv.resize(image_right, None, fx=0.4, fy=0.24)    image_left = cv.resize(image_left, (image_right.shape[1], image_right.shape[0]))
    # 獲取檢測(cè)到關(guān)鍵特征點(diǎn)后的圖像的相關(guān)參數(shù)    keypoints_image_right, keypoints_right, features_right = sift_keypoints_detect(image_right)    keypoints_image_left, keypoints_left, features_left = sift_keypoints_detect(image_left)
    # 利用np.hstack()函數(shù)同時(shí)將原圖和繪有關(guān)鍵特征點(diǎn)的圖像沿著豎直方向(水平順序)堆疊起來(lái)    cv.imshow("左圖關(guān)鍵特征點(diǎn)檢測(cè)", np.hstack((image_left, keypoints_image_left)))    # 一般在imshow后設(shè)置 waitKey(0) , 代表按任意鍵繼續(xù)    cv.waitKey(0)    # 刪除先前建立的窗口    cv.destroyAllWindows()    cv.imshow("右圖關(guān)鍵特征點(diǎn)檢測(cè)", np.hstack((image_right, keypoints_image_right)))    cv.waitKey(0)    cv.destroyAllWindows()    goodMatch = get_feature_point_ensemble(features_right, features_left)
    # cv.drawMatches():在提取兩幅圖像特征之后，畫(huà)出匹配點(diǎn)對(duì)連線    # matchColor – 匹配的顏色（特征點(diǎn)和連線),若matchColor==Scalar::all(-1),顏色隨機(jī)    all_goodmatch_image = cv.drawMatches(        image_right, keypoints_right, image_left, keypoints_left, goodMatch, None, None, None, None, flags=2)    cv.imshow("所有匹配的SIFT關(guān)鍵特征點(diǎn)連線", all_goodmatch_image)    cv.waitKey(0)    cv.destroyAllWindows()
    # 把圖片拼接成全景圖并保存    Panorama = Panorama_stitching(image_right, image_left)    cv.namedWindow("全景圖", cv.WINDOW_AUTOSIZE)    cv.imshow("全景圖", Panorama)    cv.imwrite("./全景圖.jpg", Panorama)    cv.waitKey(0)????cv.destroyAllWindows()