(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