hcaculate.py

# -*- coding: utf-8 -*-
# @Time : 2021/06/11 10:09
# @Author : yunshan
# @File : hcaculate.py
import cv2
import numpy as np
import rrcvutils


top_points = np.array(
    [
        [371, 439],
        [1207, 377],
        [2157, 404],
        [2183, 966],
        [1322, 1024],
        [315, 1000],
        [365, 1605],
        [1335, 1652],
        [2150, 1586],
    ]
)
bot_points = np.array(
    [
        [455, 1509],
        [1148, 1561],
        [1938, 1542],
        [1962, 1075],
        [1245, 1024],
        [409, 1042],
        [451, 539],
        [1258, 502],
        [1936, 559],
    ]
)

# 计算两个平面的转换矩阵
# Homography 是一个3*3的变换矩阵，将一张图中的点映射到另一张图中对应的点
result_H, status = cv2.findHomography(srcPoints=top_points, dstPoints=bot_points)
print(result_H)
# 验证
rep_bot_points = rrcvutils.project_pts(top_points.T, result_H)
h = rep_bot_points.T - bot_points

print(h)

"""
 九个点对的变换矩阵
 1 2 3
 6 5 4
 7 8 9
 result_H:
 
 [[ 8.28625828e-01  6.12449060e-04  1.46553810e+02]
 [ 1.68468357e-03 -8.31006218e-01  1.87093418e+03]
 [-6.50402462e-07 -1.41868511e-06  1.00000000e+00]]
 
 
 验证结果：
[[-0.36428833 -0.94946289]
 [ 0.4519043   0.73950195]
 [-0.02331543 -0.11157227]
 [-0.49121094 -0.14099121]
 [ 0.5045166   0.58032227]
 [-0.15283203  0.15063477]
 [ 0.11950684  0.13970947]
 [-0.17895508 -0.02670288]
 [ 0.13452148 -0.38153076]]
"""