コンピュータビジョンにおける姿勢推定問題

import cv2

# 加载图像
image = cv2.imread("image.jpg")

# 创建SIFT对象
sift = cv2.xfeatures2d.SIFT_create()

# 检测特征点
keypoints, descriptors = sift.detectAndCompute(image, None)

# 绘制特征点
image_with_keypoints = cv2.drawKeypoints(image, keypoints, None)

# 显示图像
cv2.imshow("Image with keypoints", image_with_keypoints)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2

# 加载图像1和图像2
image1 = cv2.imread("image1.jpg")
image2 = cv2.imread("image2.jpg")

# 创建SIFT对象
sift = cv2.xfeatures2d.SIFT_create()

# 检测特征点和计算描述子
keypoints1, descriptors1 = sift.detectAndCompute(image1, None)
keypoints2, descriptors2 = sift.detectAndCompute(image2, None)

# 创建FLANN匹配器对象
matcher = cv2.FlannBasedMatcher_create()

# 特征点匹配
matches = matcher.match(descriptors1, descriptors2)

# 绘制匹配结果
matched_image = cv2.drawMatches(image1, keypoints1, image2, keypoints2, matches[:10], None, flags=2)

# 显示图像
cv2.imshow("Matched image", matched_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2
import numpy as np

# 3D物体坐标
object_points = np.array([[0, 0, 0], [0, 1, 0], [1, 0, 0], [1, 1, 0]], np.float32)

# 2D图像坐标
image_points = np.array([[10, 20], [30, 40], [50, 60], [70, 80]], np.float32)

# 相机内参矩阵
camera_matrix = np.array([[500, 0, 320], [0, 500, 240], [0, 0, 1]], np.float32)

# 畸变系数
dist_coeffs = np.array([0, 0, 0, 0, 0], np.float32)

# 姿态估计
success, rvec, tvec = cv2.solvePnP(object_points, image_points, camera_matrix, dist_coeffs)

# 输出结果
print("Rotation vector: ", rvec)
print("Translation vector: ", tvec)