python

gen_pinv.py

import numpy as np
import matplotlib.pyplot as plt

#%%
def generateData(n = 30):
    # similar to peaks() function in MATLAB
    g = np.linspace(-2.0, 2.0, n)
    X, Y = np.meshgrid(g, g)
    X, Y = X.reshape(-1,1), Y.reshape(-1,1)
    #a, b, c, d , e = -5, -5, 1, 1, 10
    a, b, c, d , e = -1.29166667, -1.29166667, 0.41666667, 1.91666667, 135.77777778
    Z = a * X**2 + b* Y**2 +c*X+ d*Y + e + np.random.random(X.shape)
    return X, Y, Z

X, Y, Z = generateData()

ax = plt.figure().add_subplot(projection='3d')
ax.scatter(X, Y, Z, c='r')
plt.xlabel('X')
plt.ylabel('Y')
ax.set_zlabel('Z')
ax.axis('equal')
ax.axis('tight')
plt.show()

# %%
ONES = np.ones((9,1))
A = np.c_[X**2, Y**2, X, Y, ONES]

A_pinv = np.linalg.pinv(A)
# !!!代码中这里runtime calc

B = np.matmul(A_pinv, Z)

Z_new = np.matmul(A, B)
ax = plt.figure().add_subplot(projection='3d')
ax.scatter(X, Y, Z_new, c='g', s=5)
center_x = (-B[2]/(2*B[0]))
center_y = (-B[3]/(2*B[0]))

center_z = np.int32(B[0]*center_x**2 + B[1]*center_y**2 + B[2]*center_x+ B[3]*center_y + B[4])

ax.scatter(center_x, center_y, center_z, c='r', s=20)
plt.xlabel('X')
plt.ylabel('Y')
ax.set_zlabel('Z')
ax.axis('tight')
plt.show()

B, (center_x, center_y, center_z)


#%%
np.set_printoptions(suppress=True)
data = A_pinv
data.reshape(-1, 1).flatten()

# %%
Z_0 = np.array([127, 130, 122, 134, 136, 128, 150, 154, 144])
B_0 = np.matmul(A_pinv, Z_0)

ax = plt.figure().add_subplot(projection='3d')
ax.scatter(X, Y, Z_0, c='r')
plt.xlabel('X')
plt.ylabel('Y')
ax.set_zlabel('Z')
ax.axis('equal')
ax.axis('tight')
plt.show()

B_0, np.array(Z_0).reshape(-1, 3)
# %%
Z_1 = np.array([118, 127, 125, 130, 136, 131, 131, 134, 128])
B_1 = np.matmul(A_pinv, Z_1)

ax = plt.figure().add_subplot(projection='3d')
ax.scatter(X, Y, Z_1, c='r')
plt.xlabel('X')
plt.ylabel('Y')
ax.set_zlabel('Z')
ax.axis('equal')
ax.axis('tight')
plt.show()

B_1, np.array(Z_1).reshape(-1, 3)
# %%

main_tml.py

import argparse
import os
import sys
import numpy as np
import matplotlib.pyplot as plt
import cv2 as cv


def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("image", type=str, help="test image file")
    parser.add_argument("file_in", type=str, help="test image file in")
    parser.add_argument("file_exp", type=str, help="test result file")
    parser.add_argument("--result_cv", type=str, help="test result file")
    params = parser.parse_args()
    return params

def main(args): 
    
if __name__ == "__main__":
    args = parse_args()
    main(args)

plot.py

import matplotlib.pyplot as plt

# Lines
# 显示线的信息
x = np.linspace(int(list(dspse[0].keys())[0]), int(list(dspse[-1].keys())[0]), 50)  
p1, = plt.plot(x, mcs_old, color='r')
p2, = plt.plot(x, mcs, color='g')
plt.title('Compare: match counts')
plt.ylabel('Match counts')
plt.xlabel('Frame')
plt.legend([p1, p2], ['master','new'], loc='lower right')

#显示坐标点信息
plt.annotate("text", xy=(10,10), xytext=(12,12), color='r')

## image 将图片拼接显示
from PIL import Image
from IPython.display import display

image = Image.fromarray(np.column_stack([image0, image1]))
display(image)