structure from motion

import cv2

# 示例代码：使用SIFT进行特征提取和匹配
sift = cv2.SIFT_create()
kp1, des1 = sift.detectAndCompute(img1, None)
kp2, des2 = sift.detectAndCompute(img2, None)

bf = cv2.BFMatcher()
matches = bf.knnMatch(des1, des2, k=2)

good = []
for m, n in matches:
    if m.distance < 0.75 * n.distance:
        good.append([m])

img3 = cv2.drawMatchesKnn(img1, kp1, img2, kp2, good, None, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)

import numpy as np
from scipy.optimize import least_squares

# 示例代码：Bundle Adjustment优化
def bundle_adjustment(points_3d, points_2d, camera_params):
    def error_function(params):
        # 计算重投影误差
        pass

    initial_guess = np.concatenate([camera_params, points_3d.flatten()])
    result = least_squares(error_function, initial_guess)
    return result.x

import multiprocessing as mp

# 示例代码：使用多进程加速特征匹配
def match_features(img1, img2):
    # 特征匹配逻辑
    pass

pool = mp.Pool(mp.cpu_count())
results = pool.starmap(match_features, [(img1, img2), (img3, img4)])