计算机视觉学习8：照相机模型与增强现实

最新推荐文章于 2024-09-22 18:29:03 发布

Charon_L

最新推荐文章于 2024-09-22 18:29:03 发布

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本文链接：https://blog.youkuaiyun.com/qq_38090541/article/details/89074623

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该博客介绍了计算机视觉的学习，包括环境配置，如OpenGL和PyGame的设置，以及如何使用平面和标记物进行姿态估计。在增强现实部分，讲述了如何在图像和视频中放置虚拟物体，详细阐述了相机标定矩阵的测定过程，并分享了实验结果。

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环境配置

一、OpenGL配置
参考博客：https://blog.youkuaiyun.com/xufive/article/details/86565130#2__pyopengl_60
安装完成运行后会报错：freeglut ERROR: Function called without first calling ‘glutInit’.
这个错误是freeglut和glut共存的缘故，它们俩定义了相同的方法，这个是动态链接库的重叠问题，将python文件夹下\site-packages\OpenGL\DLLS文件夹里面的freeglut64.vc9.dll删掉就行了。
二、PｙGame配置
我用的是PｙCharm进行实验的，在PｙCharm中选择File菜单栏中的Set ing
在这里插入图片描述
点击＋号可进行搜索配置。

以平面和标记物进行姿态估计

from pylab import *
from PIL import Image

# If you have PCV installed, these imports should work
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 = []
    # bottom
    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

    # vertical sides
    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


# compute features
sift.process_image('../images/jingshen_1.jpg', '1.sift')
l0, d0 = sift.read_features_from_file('1.sift')

sift.process_image('../images/jingshen_2.jpg', 'im2.sift')
l1, d1 = sift.read_features_from_file('im2.sift')

# match features and estimate homography
matches = sift.match_twosided(d0, d1)
ndx = matches.nonzero()[0]
fp = homography.make_homog(l0[ndx, :2].T)
ndx2 = [int(matches[i]) for i in ndx]
tp = homography.make_homog(l1[ndx2, :2].T)

model = homography.RansacModel()
H, inliers = homography.H_from_ransac(fp, tp, model)

# camera calibration
K = my_calibration((747, 1000))

# 3D points at plane z=0 with sides of length 0.2
box = cube_points([0, 0, 0.1], 0.1)

# project bottom square in first image
cam1 = camera.Camera(hstack((K, dot(K, array([[0], [0], [-1]])))))
# first points are the bottom square
box_cam1 = cam1.project(homography.make_homog(box[:, :5]))

# use H to transfer points to the second image
box_trans = homography.normalize(dot(H, box_cam1))

# compute second camera matrix from cam1 and H
cam2 = camera.Camera(dot(H, cam1.P))
A = dot(linalg.inv(K), cam2.P[:, :3])
A = array([A[:, 0], A[:, 1], cross(A[:, 0], A[:, 1