如何使用opencv缝合多个图像？

Question

我想创建一个全景使用多个图像。因此，基本上，相机将在平面上线性平移，并将连续拍摄约20-30幅图像。我必须为这些图像创建一个全景图。我尝试过使用缝合功能和传统的方法来查找特征并匹配它们，然后进行翘曲。但我不确定我是否做得很好。我该如何进行

import imutils
import cv2
import glob

input_path = "/Users/akshayacharya/Desktop/Panorama/Raw 
Data/riverside/*.jpg"

# input and sort the images
list_images = glob.glob(input_path)
list_sorted = sorted(list_images)
print(list_sorted)

# output path definition
output_path = "/Users/akshayacharya/Desktop/Panorama/Final 
Panorama/finalpanoex1.jpg"

# initialize empty list and fill all images
images = []
for image in list_sorted:
    image1 = cv2.imread(image)
    image1 = cv2.resize(image1, (720, 480))
    images.append(image1)

print("Read the images")
# this is the final list to stitch
final = [images[0]]
flag = True
print(len(images))
temp = [images[0]]
print(type(temp))

stitcher = cv2.createStitcher() if imutils.is_cv3() else 
cv2.Stitcher_create()

i = 0
while(i < len(images)-1):
    (status, stitched) = stitcher.stitch([temp[0], images[i+1]])
    if status == 0:
        final.append(images[i+1])
        print(f"Succesfully stitch {i} to {i+1}")
        i = i+1

        temp[0] = stitched

        continue
    if status != 0:
        print(f"Succesfully could not stitch {i} to {i + 1}")
        for j in range(i+2, len(images)):
            print(f"now trying {i} to {j}")
            (status, stitchedd) = stitcher.stitch([temp[0], images[j]])
            if status == 0:
                print(f"Succesfully managed to stitch {i} to {j}")
                final.append(images[j])
                i=j
                temp[0] = stitchedd
                break
            if status != 0:
                print(f"Oops could not stitch {i} to {j}")
                print(f"Will now see compatibility between {i} and {j+1}")

            continue

        i += 1
    continue

cv2.imwrite(output_path, temp[0])

I have attached the output image```

Answer 1

您可以查看我的SIFT笔记本，了解有关使用opencvhere的功能和全景缝合的代码

Answer 2

全景图像构造流水线可以是关键点检测和局部不变描述子；关键点匹配；兰萨克；和透视扭曲

使用RANSAC算法使用匹配的特征向量估计单应矩阵。使用从RANSAC获得的单应矩阵应用扭曲变换。RANSAC改进了匹配过程检测。 最近的教程可在OpenCV documentation和here上找到