添加图像拼接代码

.vscode/settings.json

+{
+    "python.pythonPath": "E:\\Python36\\python.exe"
+}
\ No newline at end of file

1.py

+import numpy as np
+import cv2 as cv
+from matplotlib import pyplot as plt
+
+if __name__ == '__main__':
+    top, bot, left, right = 100, 100, 0, 500
+    img1 = cv.imread('1.jpg')
+    img2 = cv.imread('2.jpg')
+    srcImg = cv.copyMakeBorder(img1, top, bot, left, right, cv.BORDER_CONSTANT, value=(0, 0, 0))
+    testImg = cv.copyMakeBorder(img2, top, bot, left, right, cv.BORDER_CONSTANT, value=(0, 0, 0))
+    img1gray = cv.cvtColor(srcImg, cv.COLOR_BGR2GRAY)
+    img2gray = cv.cvtColor(testImg, cv.COLOR_BGR2GRAY)
+    sift = cv.xfeatures2d_SIFT().create()
+    # find the keypoints and descriptors with SIFT
+    kp1, des1 = sift.detectAndCompute(img1gray, None)
+    kp2, des2 = sift.detectAndCompute(img2gray, None)
+    # FLANN parameters
+    FLANN_INDEX_KDTREE = 1
+    index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
+    search_params = dict(checks=50)
+    flann = cv.FlannBasedMatcher(index_params, search_params)
+    matches = flann.knnMatch(des1, des2, k=2)
+
+    # Need to draw only good matches, so create a mask
+    matchesMask = [[0, 0] for i in range(len(matches))]
+
+    good = []
+    pts1 = []
+    pts2 = []
+    # ratio test as per Lowe's paper
+    for i, (m, n) in enumerate(matches):
+        if m.distance < 0.7*n.distance:
+            good.append(m)
+            pts2.append(kp2[m.trainIdx].pt)
+            pts1.append(kp1[m.queryIdx].pt)
+            matchesMask[i] = [1, 0]
+
+    draw_params = dict(matchColor=(0, 255, 0),
+                       singlePointColor=(255, 0, 0),
+                       matchesMask=matchesMask,
+                       flags=0)
+    img3 = cv.drawMatchesKnn(img1gray, kp1, img2gray, kp2, matches, None, **draw_params)
+    plt.imshow(img3, ), plt.show()
+
+    rows, cols = srcImg.shape[:2]
+    MIN_MATCH_COUNT = 10
+    if len(good) > MIN_MATCH_COUNT:
+        src_pts = np.float32([kp1[m.queryIdx].pt for m in good]).reshape(-1, 1, 2)
+        dst_pts = np.float32([kp2[m.trainIdx].pt for m in good]).reshape(-1, 1, 2)
+        M, mask = cv.findHomography(src_pts, dst_pts, cv.RANSAC, 5.0)
+        warpImg = cv.warpPerspective(testImg, np.array(M), (testImg.shape[1], testImg.shape[0]), flags=cv.WARP_INVERSE_MAP)
+
+        for col in range(0, cols):
+            if srcImg[:, col].any() and warpImg[:, col].any():
+                left = col
+                break
+        for col in range(cols-1, 0, -1):
+            if srcImg[:, col].any() and warpImg[:, col].any():
+                right = col
+                break
+
+        res = np.zeros([rows, cols, 3], np.uint8)
+        for row in range(0, rows):
+            for col in range(0, cols):
+                if not srcImg[row, col].any():
+                    res[row, col] = warpImg[row, col]
+                elif not warpImg[row, col].any():
+                    res[row, col] = srcImg[row, col]
+                else:
+                    srcImgLen = float(abs(col - left))
+                    testImgLen = float(abs(col - right))
+                    alpha = srcImgLen / (srcImgLen + testImgLen)
+                    res[row, col] = np.clip(srcImg[row, col] * (1-alpha) + warpImg[row, col] * alpha, 0, 255)
+
+        # opencv is bgr, matplotlib is rgb
+        res = cv.cvtColor(res, cv.COLOR_BGR2RGB)
+        # show the result
+        plt.figure()
+        plt.imshow(res)
+        plt.show()
+    else:
+        print("Not enough matches are found - {}/{}".format(len(good), MIN_MATCH_COUNT))
+        matchesMask = None
\ No newline at end of file

2.py

+import numpy as np
+import cv2
+ 
+leftgray = cv2.imread('1.jpg')
+rightgray = cv2.imread('2.jpg')
+ 
+hessian=400
+surf=cv2.xfeatures2d.SURF_create(hessian) #将Hessian Threshold设置为400,阈值越大能检测的特征就越少
+kp1,des1=surf.detectAndCompute(leftgray,None)  #查找关键点和描述符
+kp2,des2=surf.detectAndCompute(rightgray,None)
+ 
+ 
+FLANN_INDEX_KDTREE=0   #建立FLANN匹配器的参数
+indexParams=dict(algorithm=FLANN_INDEX_KDTREE,trees=5) #配置索引，密度树的数量为5
+searchParams=dict(checks=50)    #指定递归次数
+#FlannBasedMatcher：是目前最快的特征匹配算法（最近邻搜索）
+flann=cv2.FlannBasedMatcher(indexParams,searchParams)  #建立匹配器
+matches=flann.knnMatch(des1,des2,k=2)  #得出匹配的关键点
+ 
+good=[]
+#提取优秀的特征点
+for m,n in matches:
+    if m.distance < 0.7*n.distance: #如果第一个邻近距离比第二个邻近距离的0.7倍小，则保留
+        good.append(m)
+ 
+src_pts = np.array([ kp1[m.queryIdx].pt for m in good])    #查询图像的特征描述子索引
+dst_pts = np.array([ kp2[m.trainIdx].pt for m in good])    #训练(模板)图像的特征描述子索引
+H=cv2.findHomography(src_pts,dst_pts)         #生成变换矩阵
+
+h,w=leftgray.shape[:2]
+h1,w1=rightgray.shape[:2]
+shft=np.array([[1.0,0,w],[0,1.0,0],[0,0,1.0]])
+M=np.dot(shft,H[0])            #获取左边图像到右边图像的投影映射关系
+dst_corners=cv2.warpPerspective(leftgray,M,(w,h*2))#透视变换，新图像可容纳完整的两幅图
+cv2.imshow('tiledImg1',dst_corners)   #显示，第一幅图已在标准位置
+dst_corners[h:h*2,0:w]=rightgray  #将第二幅图放在右侧
+#cv2.imwrite('tiled.jpg',dst_corners)
+ 
+ 
+cv2.imshow('tiledImg',dst_corners)
+cv2.imshow('leftgray',leftgray)
+cv2.imshow('rightgray',rightgray)
+cv2.waitKey()
+cv2.destroyAllWindows()

main.py

+import numpy as np
+import cv2
+import os
+
+
+def img_add(img_1, img_2):
+    upgray = cv2.imread(img_1)
+    downgray = cv2.imread(img_2)
+
+    points1 = np.float32([[0,0],[1,0],[0,1],[1,1]])
+    points2 = np.float32([[0,0],[1,0],[0,1],[1,1]])
+
+    matrix = cv2.getPerspectiveTransform(points1,points2)
+
+    h,w=upgray.shape[:2]
+    h1,w1=downgray.shape[:2]
+    if w == w1 and h == h1:
+        dst_corners=cv2.warpPerspective(upgray,matrix,(w,h*2))
+        dst_corners[h:h*2,0:w]=downgray
+
+        cv2.imwrite(img_1, dst_corners)
+        return 1
+    return 0
+
+def read_directory(directory_name):
+    img_lyst = []
+    img_names = os.listdir(directory_name)
+    Extension = img_names[0].split('.')[-1]
+    print(len(img_names))
+    for i in range(len(img_names)):
+        a = f'{directory_name}/{i+1}.{Extension}'
+        img_lyst.append(a)
+    return img_lyst
+
+def img_merge(dir):
+    imgs = read_directory(dir)
+    m = 0
+    for i in range(len(imgs)):
+        if i+1 >= len(imgs):
+            break
+        m = img_add(imgs[i], imgs[i+1])
+    if m == 1:
+        os.remove(imgs[-1])
+    print(f'{dir}图片拼合完成！')
+
+img_merge(r'anhui\d9540653-52ed-442e-bda5-2f888c4e16a1')
+
+
+

requirements.txt

 cycler==0.10.0