python
复制代码
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import pandas as pd
from matplotlib import cm
width = 3088
height = 2064
def gen_data():
x = np.linspace(-10, 10,21)
y = np.linspace(-10, 10, 21)
z = np.linspace(15, 25, 21)
X,Y,Z = np.meshgrid(x,y,z)
coors = np.concatenate((X[:, :, :, None], Y[:, :, :, None], Z[:, :, :, None]), axis=-1)
pt3ds = 0.01*np.float32(coors).reshape(-1,3)
data = []
for pt3d in pt3ds:
pix_left = reproject2_left(pt3d) #np.float32([[1196.6746, 796.03284]])
pix_right = reproject2_right(pt3d) # np.float32( [[1125.056 , 1051.7277]])
pix_left = reproject2_left(pt3d)
pix_right = reproject2_right(pt3d)
# 改变x值
distx = []
for i in range(11):
step = i/10
pl = pix_left.copy()
pl[0][0]+=step
pt3d_c = triangle3d(pl, pix_right)
distx.append(1000*np.linalg.norm(pt3d_c-pt3d))
disty=[]
for i in range(11):
step = i / 10
pl = pix_left.copy()
pl[0][1] += step
pt3d_c = triangle3d(pl, pix_right)
disty.append(1000*np.linalg.norm(pt3d_c - pt3d))
out = [*pt3d, *pix_left[0], *pix_right[0], distx[5], max(distx), disty[5], max(disty), np.linalg.norm(pt3d) ]
data.append(out)
df = pd.DataFrame(data)
df.columns = ['x','y','z', 'xl', 'yl', 'xr', 'yr', 'dist_x0.5', 'dist_x1', 'dist_y0.5', 'dist_y1', 'dist_cam']
df_filter = df.loc[(df['xl'] >= 0) & (df['yl'] >= 0) & (df['xr'] >= 0) & (df['yr'] >= 0)]
df_filter.to_csv(r'C:\Users\31408\Desktop\datamat\cube_model_8000.csv', index=False, sep=',')
return df_filter
def viz_cube3d_effect():
df = pd.read_csv(r'C:\Users\31408\Desktop\datamat\cube_model_8000.csv')
df_filter = df.loc[(df['xl'] <= width) & (df['yl'] <= height) & (df['xr'] < width) & (df['yr'] < height)]
data = np.array(df_filter.values)
layersz = list(set(data[:, 2]))
layersz.sort()
layersz.pop()
rows, cols = 1, 1
x = np.linspace(-10, 10, 21) / 100
y = np.linspace(-10, 10, 21) / 100
for ii, layerz in enumerate(layersz):
fig = plt.figure(figsize=(1200, 1000))
ax = fig.add_subplot(rows, cols, 1, projection='3d')
ax.set_title(f'z={np.round(layerz, 3)}')
ax.set_xlabel('x')
ax.set_ylabel('y')
vizd = np.float32([item for item in data if item[2] == layerz])
ax.stem(vizd[:, 0], vizd[:, 1], vizd[:, -5])
X, Y = np.meshgrid(x, y)
# R = np.sqrt(X ** 2 + Y ** 2)
Z = np.ones((21, 21)) * 0.1
surf = ax.plot_surface(X, Y, Z, cmap='rainbow', linewidth=0, antialiased=False)
plt.show()
def viz_image_effect():
df = pd.read_csv(r'C:\Users\31408\Desktop\datamat\cube_model_8000.csv')
df_filter = df.loc[(df['xl'] <= width) & (df['yl'] <= height) & (df['xr'] < width) & (df['yr'] < height)]
data = np.array(df_filter.values)
leftx = data[:,3]
lefty = data[:,4]
err = data[:,7]
fig = plt.figure(figsize=(1200, 1000))
ax = fig.add_subplot(1, 1, 1, projection='3d')
ax.set_title(f'left')
ax.stem(leftx, lefty, err)
x = np.linspace(0, 3088, 20)
y = np.linspace(0, 2064, 20)
X, Y = np.meshgrid( x, y)
Z = np.ones((len(X), len(Y))) * 0.1
surf = ax.plot_surface(X, Y, Z, cmap='rainbow', linewidth=0, antialiased=False)
plt.show()
viz_cube3d_effect()
