计算机视觉学习（五）：照相机标定及增强现实

最新推荐文章于 2024-09-02 22:55:22 发布

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最新推荐文章于 2024-09-02 22:55:22 发布 · 746 阅读

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本文介绍了使用平面和标记物进行照相机标定的过程，通过计算单应性矩阵确定照相机姿态。接着探讨了增强现实的应用，详细讲解如何在图像中放置虚拟物体，并分享了解决运行时闪退问题的方法。

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（一）照相机标定之以平面和标记物进行姿态估计

使用平面物体作为标记物，来计算用于新视图投影矩阵的例子。将图像的特征和对齐后的标记匹配，计算出单应性矩阵，然后用于计算照相机的姿态。

下面是运行的代码：

from pylab import *
from PIL import Image

from PCV.geometry import homography, camera
from PCV.localdescriptors import sift

"""
This is the augmented reality and pose estimation cube example from Section 4.3.
"""


def cube_points(c, wid):
    """ Creates a list of points for plotting
        a cube with plot. (the first 5 points are
        the bottom square, some sides repeated). """
    p = []
    # 底部
    p.append([c[0]-wid, c[1]-wid, c[2]-wid])
    p.append([c[0]-wid, c[1]+wid, c[2]-wid])
    p.append([c[0]+wid, c[1]+wid, c[2]-wid])
    p.append([c[0]+wid, c[1]-wid, c[2]-wid])
    p.append([c[0]-wid, c[1]-wid, c[2]-wid]) #same as first to close plot
    
    # top
    p.append([c[0]-wid, c[1]-wid, c[2]+wid])
    p.append([c[0]-wid, c[1]+wid, c[2]+wid])
    p.append([c[0]+wid, c[1]+wid, c[2]+wid])
    p.append([c[0]+wid, c[1]-wid, c[2]+wid])
    p.append([c[0]-wid, c[1]-wid, c[2]+wid]) #same as first to close plot
    
    # 垂直的侧面
    p.append([c[0]-wid, c[1]-wid, c[2]+wid])
    p.append([c[0]-wid, c[1]+wid, c[2]+wid])
    p.append([c[0]-wid, c[1]+wid, c[2]-wid])
    p.append([c[0]+wid, c[1]+wid, c[2]-wid])
    p.append([c[0]+wid, c[1]+wid, c[2]+wid])
    p.append([c[0]+wid, c[1]-wid, c[2]+wid])
    p.append([c[0]+wid, c[1]-wid, c[2]-wid])
    
    return array(p).T


def my_calibration(sz):
    """
    Calibration function for the camera (iPhone4) used in this example.
    """
    row, col = sz
    fx = 2555*col/2592
    fy = 2586*row/1936
    K = diag([fx, fy, 1])
    K[0, 2] = 0.5*col
    K[1, 2] = 0.5*row
    return K



# 计算
sift.process_image('D:\pythonxy\PCV-book-data\data/book_frontal.jpg', 'im0.sift')
l0, d0 = sift.read_features_from_file('im0.sift')

sift.process_image('D:\pythonxy\PCV-book-data\data/book_perspective.jpg', 'im1.sift')
l1, d1 = sift.read_featur