找出两幅图像的最佳缝合线在最佳缝合线处进行加权平均融合python代码

以下是一个示例代码，用于找到两个图像的最佳缝合线并在该线处进行加权平均融合：

import numpy as np
import cv2

# 加载两幅图像
img1 = cv2.imread('img1.jpg')
img2 = cv2.imread('img2.jpg')

# 将两幅图像转换为灰度图像
gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)

# 使用SIFT算法检测图像中的关键点和描述符
sift = cv2.xfeatures2d.SIFT_create()
kp1, des1 = sift.detectAndCompute(gray1, None)
kp2, des2 = sift.detectAndCompute(gray2, None)

# 使用FLANN匹配器进行特征匹配
FLANN_INDEX_KDTREE = 0
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=50)
flann = cv2.FlannBasedMatcher(index_params, search_params)
matches = flann.knnMatch(des1, des2, k=2)

# 选择最佳匹配
good = []
for m, n in matches:
    if m.distance < 0.7 * n.distance:
        good.append(m)

# 计算图像间的变换矩阵
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 = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
    h, w = gray1.shape
    pts = np.float32([[0, 0], [0, h - 1], [w - 1, h - 1], [w - 1, 0]]).reshape(-1, 1, 2)
    dst = cv2.perspectiveTransform(pts, M)
else:
    print("Not enough matches are found - {}/{}".format(len(good), MIN_MATCH_COUNT))

# 找到两个图像的最佳缝合线
left = int(np.min(dst[:, :, 0]))
right = int(np.max(dst[:, :, 0]))
top = int(np.min(dst[:, :, 1]))
bottom = int(np.max(dst[:, :, 1]))
height = bottom - top
width = right - left
overlap_area = np.zeros((height, width, 3))
for y in range(top, bottom):
    for x in range(left, right):
        if x >= w or y >= h or x < 0 or y < 0:
            continue
        if x >= dst[0][0][0] and x <= dst[1][0][0] and y >= dst[0][0][1] and y <= dst[3][0][1]:
            alpha = (x - dst[0][0][0]) / (dst[1][0][0] - dst[0][0][0])
            overlap_area[y - top, x - left] = (1 - alpha) * img1[y, x] + alpha * img2[y, x]

# 将两个图像在最佳缝合线处进行加权平均融合
output = img1.copy()
for y in range(top, bottom):
    for x in range(left, right):
        if x >= w or y >= h or x < 0 or y < 0:
            continue
        if x >= dst[0][0][0] and x <= dst[1][0][0] and y >= dst[0][0][1] and y <= dst[3][0][1]:
            alpha = (x - dst[0][0][0]) / (dst[1][0][0] - dst[0][0][0])
            output[y, x] = (1 - alpha) * img1[y, x] + alpha * img2[y, x]

# 显示结果
cv2.imshow("Output", output)
cv2.waitKey(0)
cv2.destroyAllWindows()