图像拼接最佳缝合线算法python代码

图像拼接中最佳缝合线算法常用的是SIFT算法。以下是Python代码的示例：

import cv2
import numpy as np

# Load the images to be stitched
img1 = cv2.imread('image1.jpg')
img2 = cv2.imread('image2.jpg')

# Convert the images to grayscale
gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)

# Detect the keypoints and compute the descriptors using SIFT
sift = cv2.xfeatures2d.SIFT_create()
kp1, des1 = sift.detectAndCompute(gray1, None)
kp2, des2 = sift.detectAndCompute(gray2, None)

# Match the descriptors
bf = cv2.BFMatcher()
matches = bf.knnMatch(des1, des2, k=2)

# Apply ratio test to filter out false matches
good_matches = []
for m, n in matches:
    if m.distance < 0.75 * n.distance:
        good_matches.append(m)

# Find the homography matrix
src_pts = np.float32([kp1[m.queryIdx].pt for m in good_matches]).reshape(-1, 1, 2)
dst_pts = np.float32([kp2[m.trainIdx].pt for m in good_matches]).reshape(-1, 1, 2)
M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)

# Compute the size of the stitched image
h, w = img1.shape[:2]
h2, w2 = img2.shape[:2]
pts = np.float32([[0, 0], [0, h], [w, h], [w, 0]]).reshape(-1, 1, 2)
dst = cv2.perspectiveTransform(pts, M)
dst = np.concatenate((pts, dst), axis=0)
x, y, w, h = cv2.boundingRect(dst)
max_x = max(w, w2)
max_y = max(h, h2)

# Create the stitched image
stitched = np.zeros((max_y, max_x, 3), dtype=np.uint8)
stitched[y:h+y, x:w+x] = img1
stitched[:h2, :w2] = img2

# Find the best seam line
seam_mask = np.zeros((max_y, max_x, 3), dtype=np.uint8)
seam_mask[y:h+y, x:w+x] = 1
gray = cv2.cvtColor(stitched, cv2.COLOR_BGR2GRAY)
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=3)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=3)
mag = np.sqrt(sobelx**2 + sobely**2)
mag[mag == 0] = 1e-10
gradient = sobely/mag
gradient[np.isnan(gradient)] = 0
gradient[np.isinf(gradient)] = 0
gradient = np.abs(gradient)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3,3))
gradient = cv2.morphologyEx(gradient, cv2.MORPH_CLOSE, kernel)
seam_mask = cv2.cvtColor(seam_mask, cv2.COLOR_BGR2GRAY)
gradient = np.uint8(gradient*255)
gradient = cv2.bitwise_and(gradient, gradient, mask=seam_mask)
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(gradient)
if max_loc[1] > h:
    max_loc = (max_loc[0], h-1)
if max_loc[1] < 0:
    max_loc = (max_loc[0], 0)
seam = (max_loc[0], max_loc[1]-y)

# Blend the images along the seam line
mask1 = np.zeros((h, w), dtype=np.float32)
mask2 = np.zeros((h2, w2), dtype=np.float32)
mask1[:, :seam[0]] = 1
mask2[:, seam[0]:] = 1
mask = np.zeros((max_y, max_x), dtype=np.float32)
mask[y:h+y, x:w+x] = mask1
mask[:h2, :w2] += mask2
blend = cv2.seamlessClone(stitched, img2, mask, seam, cv2.MIXED_CLONE)

# Show the stitched image
cv2.imshow('Stitched Image', blend)
cv2.waitKey(0)
cv2.destroyAllWindows()

该代码包括了SIFT特征点检测、特征点匹配、计算单应性矩阵、拼接图像、找到最佳缝合线以及沿缝合线混合图像的过程。需要注意的是，SIFT算法需要OpenCV的contrib模块支持，因此需要确保安装了该模块。