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UPDATE Vision 修正box输出

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This commit is contained in:
HJW
2024-09-19 16:05:43 +08:00
parent ba9f28406d
commit bd91bae88b
6 changed files with 15 additions and 61 deletions
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13
Vision/camera_coordinate_dete.py
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@ -18,7 +18,6 @@ from Vision.tool.utils import find_position
from Vision.tool.utils import class_names
from Vision.tool.utils import get_disk_space
from Vision.tool.utils import remove_nan
from Vision.tool.utils import compute_pca_direction
import time
import os
@ -77,7 +76,6 @@ class Detection:
uv = []
seg_point = []
box_list = []
pca_list = []
if Point_isVision==True:
pm2 = pm.copy()
pm2 = pm2.reshape(-1, 3)
@ -153,9 +151,6 @@ class Detection:
continue
# cv2.imshow('result', piont_result)
pca_list.append(compute_pca_direction(pm_seg))
'''
拟合平面，计算法向量
'''
@ -231,20 +226,20 @@ class Detection:
if save_img_piont == True:
save_img = cv2.resize(img, (720, 540))
cv2.imwrite(save_img_name, save_img)
return 1, img, xyz[_idx], nx_ny_nz[_idx], box_list[_idx], pca_list[_idx]
return 1, img, xyz[_idx], nx_ny_nz[_idx], box_list[_idx]
else:
if save_img_piont == True:
save_img = cv2.resize(img,(720, 540))
cv2.imwrite(save_img_name, save_img)
return 1, img, None, None, None, None
return 1, img, None, None, None
else:
print("RVC X Camera capture failed!")
return 0, None, None, None, None, None
return 0, None, None, None, None
else:
print("RVC X Camera is not opened!")
return 0, None, None, None, None, None
return 0, None, None, None, None
def release(self):
self.camera_rvc.release()

12
Vision/camera_coordinate_dete_img.py
View File

@ -19,8 +19,6 @@ from Vision.tool.utils import find_position
from Vision.tool.utils import class_names
from Vision.tool.utils import get_disk_space
from Vision.tool.utils import remove_nan
from Vision.tool.utils import compute_pca_direction
from Vision.tool.utils import pca
import os
@ -86,8 +84,6 @@ class Detection:
uv = []
seg_point = []
box_list = []
pca_list = []
pca_nx_ny_nz = []
if Point_isVision == True:
pm2 = pm.copy()
@ -163,8 +159,6 @@ class Detection:
print("分割点云数量较少，无法拟合平面")
continue
# cv2.imshow('result', piont_result)
pca_list.append(compute_pca_direction(pm_seg))
pca_nx_ny_nz.append(pca(pm_seg))
'''
拟合平面，计算法向量
'''
@ -240,17 +234,17 @@ class Detection:
save_img = cv2.resize(img,(720, 540))
cv2.imwrite(save_img_name, save_img)
return 1, img, xyz[_idx], nx_ny_nz[_idx], box_list[_idx], pca_list[_idx], pca_nx_ny_nz[_idx]
return 1, img, xyz[_idx], nx_ny_nz[_idx], box_list[_idx]
else:
if save_img_piont == True:
save_img = cv2.resize(img, (720, 540))
cv2.imwrite(save_img_name, save_img)
return 1, img, None, None, None, None, None
return 1, img, None, None, None
else:
print("RVC X Camera capture failed!")
return 0, None, None, None, None, None, None
return 0, None, None, None, None
def release(self):
self.model.clear()

2
Vision/camera_coordinate_dete_test.py
View File

@ -14,7 +14,7 @@ detection = Detection()
while True:
t1 = time.time()
ret, img, xyz, nx_ny_nz = detection.get_position()
ret, img, xyz, nx_ny_nz, box = detection.get_position()
if ret==1:
print('xyz点云坐标：', xyz)
print('nx_ny_nz法向量：', nx_ny_nz)

5
Vision/get_position_test.py
View File

@ -13,13 +13,14 @@ import cv2
detection = Detection()
while True:
ret, img, xyz, nx_ny_nz = detection.get_position()
ret, img, xyz, nx_ny_nz, box = detection.get_position()
if ret==1:
print('xyz点云坐标：', xyz)
print('nx_ny_nz法向量：', nx_ny_nz)
print('矩形框四顶点：', box)
img = cv2.resize(img,(720, 540))
transformation_matrix = R_matrix(521.534, 0.705, 850.03, 0.0, 90.0, 0.0) # (x, y, z, u, v, w)
target_position, noraml_base = getPosition(xyz[0], xyz[1], xyz[2], nx_ny_nz[0], nx_ny_nz[1], nx_ny_nz[2], transformation_matri,box)
target_position, noraml_base = getPosition(xyz[0], xyz[1], xyz[2], nx_ny_nz[0], nx_ny_nz[1], nx_ny_nz[2], transformation_matrix, box)
print("target_position:", target_position)
print("noraml_base", noraml_base)
cv2.imshow('img', img)

31
Vision/tool/utils.py
View File

@ -12,37 +12,6 @@ import numpy as np
import cv2
import psutil
from psutil._common import bytes2human
from sklearn.decomposition import PCA
def pca(points):
# 计算PCA
mean = np.mean(points, axis=0)
cov = np.cov(points, rowvar=False)
eigenvalues, eigenvectors = np.linalg.eigh(cov)
# 对特征值进行排序并找到对应的特征向量
sorted_indices = np.argsort(eigenvalues)[::-1]
sorted_eigenvalues = eigenvalues[sorted_indices]
sorted_eigenvectors = eigenvectors[:, sorted_indices]
# # 打印PCA结果
# print("PCA Mean:", mean)
# print("PCA Eigenvalues:", sorted_eigenvalues)
# print("PCA Eigenvectors:\n", sorted_eigenvectors)
# 通常，最小特征值对应的特征向量是平面的法线
normal_vector = sorted_eigenvectors[:, 0] if sorted_eigenvalues[0] < sorted_eigenvalues[
1] else sorted_eigenvectors[:, 1]
# print("法向量：", normal_vector)
return normal_vector
def compute_pca_direction(points):
# points 是物体点云的坐标
pca = PCA(n_components=3)
pca.fit(points)
primary_direction = pca.components_[0] # 第一主成分对应的方向向量
return primary_direction
def remove_nan(pm, y, x):
piont_x, piont_y, piont_z = pm[y, x]

13
get_position_test.py
View File

@ -14,20 +14,15 @@ import cv2
detection = Detection()
while True:
ret, img, xyz, nx_ny_nz = detection.get_position()
ret, img, xyz, nx_ny_nz, box = detection.get_position()
if ret==1:
print('xyz点云坐标：', xyz)
print('nx_ny_nz法向量：', nx_ny_nz)
print('矩形框四顶点：', box)
img = cv2.resize(img,(720, 540))
transformation_matrix = R_matrix(521.534, 0.705, 850.03, 0.0, 90.0, 0.0) # (x, y, z, u, v, w)
target_position, noraml_base = getPosition(xyz[0], xyz[1], xyz[2], nx_ny_nz[0], nx_ny_nz[1], nx_ny_nz[2], transformation_matrix)
target_position, noraml_base = getPosition(xyz[0], xyz[1], xyz[2], nx_ny_nz[0], nx_ny_nz[1], nx_ny_nz[2], transformation_matrix, box)
print("target_position:", target_position)
print("noraml_base", noraml_base)
cv2.imshow('img', img)
cv2.waitKey(0)
# sysstr = platform.system()
# if sysstr == "Windows":
# space_free = get_disk_space(path=os.getcwd())
# else:
# space_free = get_disk_space(path = "D:")
# print('剩余空间: ', space_free)
cv2.waitKey(0)