pycharm读取文件夹的多幅图像配准拼接

要实现读取文件夹中的多幅图像进行配准和拼接，可以使用OpenCV库来进行操作。具体步骤如下：

1.导入必要的库

import cv2
import numpy as np
import glob

2.读取文件夹中的图像

images = []
for img_path in glob.glob("path/to/folder/*.jpg"):
    img = cv2.imread(img_path)
    images.append(img)

3.将图像转换为灰度图像

gray_images = []
for img in images:
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    gray_images.append(gray)

4.进行图像配准

# 创建ORB特征检测器和描述符
orb = cv2.ORB_create()

# 用ORB计算特征点和描述符
key_points = []
descriptors = []
for img in gray_images:
    kp, des = orb.detectAndCompute(img, None)
    key_points.append(kp)
    descriptors.append(des)

# 创建暴力匹配器
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)

# 进行匹配
matches = []
for i in range(len(descriptors)-1):
    matches.append(bf.match(descriptors[i], descriptors[i+1]))

# 对匹配结果进行筛选
good_matches = []
for match in matches:
    good = []
    for m in match:
        if m.distance < 100:
            good.append(m)
    good_matches.append(good)

# 获取匹配的图像点坐标
points1 = []
points2 = []
for i in range(len(good_matches)):
    for m in good_matches[i]:
        points1.append(key_points[i][m.queryIdx].pt)
        points2.append(key_points[i+1][m.trainIdx].pt)

# 将点坐标转换为numpy数组
points1 = np.float32(points1)
points2 = np.float32(points2)

# 计算变换矩阵
M, mask = cv2.findHomography(points2, points1, cv2.RANSAC, 5.0)

5.对图像进行透视变换

# 获取第一幅图像的大小
h, w = gray_images[0].shape

# 对除第一幅图像外的其他图像进行透视变换
aligned_images = []
for i in range(1, len(gray_images)):
    aligned_img = cv2.warpPerspective(gray_images[i], M, (w, h))
    aligned_images.append(aligned_img)

# 将第一幅图像和变换后的其他图像拼接
result = np.hstack([gray_images[0]] + aligned_images)

6.显示拼接结果

cv2.imshow("Result", result)
cv2.waitKey()
cv2.destroyAllWindows()