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UPDATE Vision 新增box[]输出

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HJW
2024-09-18 18:31:08 +08:00
parent 0ddc0c15e8
commit 5f093a2551
3 changed files with 84 additions and 14 deletions
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26
Vision/camera_coordinate_dete.py
View File

@ -17,6 +17,8 @@ from Vision.yolo.yolov8_openvino import yolov8_segment_openvino
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
@ -74,6 +76,8 @@ class Detection:
Depth_Z = []
uv = []
seg_point = []
box_list = []
pca_list = []
if Point_isVision==True:
pm2 = pm.copy()
pm2 = pm2.reshape(-1, 3)
@ -150,6 +154,8 @@ class Detection:
# cv2.imshow('result', piont_result)
pca_list.append(compute_pca_direction(pm_seg))
'''
拟合平面，计算法向量
'''
@ -180,7 +186,10 @@ class Detection:
# 在原图上画出预测的外接矩形
box = box.reshape((-1, 1, 2)).astype(np.int32)
box = box + [[[box_x1, box_y1]], [[box_x1, box_y1]], [[box_x1, box_y1]], [[box_x1, box_y1]]]
box_piont_x1, box_piont_y1, box_piont_z1 = remove_nan(pm, box[0][0][1], box[0][0][0])
box_piont_x2, box_piont_y2, box_piont_z2 = remove_nan(pm, box[1][0][1], box[1][0][0])
box_piont_x3, box_piont_y3, box_piont_z3 = remove_nan(pm, box[2][0][1], box[2][0][0])
box_piont_x4, box_piont_y4, box_piont_z4 = remove_nan(pm, box[3][0][1], box[3][0][0])
x_rotation_center = int((box[0][0][0] + box[1][0][0] + box[2][0][0] + box[3][0][0]) / 4)
y_rotation_center = int((box[0][0][1] + box[1][0][1] + box[2][0][1] + box[3][0][1]) / 4)
point_x, point_y, point_z = pm[y_rotation_center, x_rotation_center]
@ -188,6 +197,11 @@ class Detection:
if np.isnan(point_x): # 点云值为无效值
continue
else:
box_list.append(
[[box_piont_x1, box_piont_y1, box_piont_z1],
[box_piont_x2, box_piont_y2, box_piont_z2],
[box_piont_x3, box_piont_y3, box_piont_z3],
[box_piont_x4, box_piont_y4, box_piont_z4]])
xyz.append([point_x*1000, point_y*1000, point_z*1000])
Depth_Z.append(point_z*1000)
nx_ny_nz.append([a, b, c])
@ -201,7 +215,7 @@ class Detection:
if _idx == None:
return 1, img, None, None
else:
cv2.circle(img, (uv[_idx][0], uv[_idx][1]), 30, (255, 0, 0), 20) # 标出中心点
cv2.circle(img, (uv[_idx][0], uv[_idx][1]), 30, (0, 0, 255), 20) # 标出中心点
if Point_isVision==True:
pcd = o3d.geometry.PointCloud()
@ -217,20 +231,20 @@ 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]
return 1, img, xyz[_idx], nx_ny_nz[_idx], box_list[_idx], pca_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
return 1, img, None, None, None, None
else:
print("RVC X Camera capture failed!")
return 0, None, None, None
return 0, None, None, None, None, None
else:
print("RVC X Camera is not opened!")
return 0, None, None, None
return 0, None, None, None, None, None
def release(self):
self.camera_rvc.release()

31
Vision/camera_coordinate_dete_img.py
View File

@ -18,6 +18,10 @@ from Vision.yolo.yolov8_openvino import yolov8_segment_openvino
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
class Detection:
@ -34,8 +38,8 @@ class Detection:
device = 'CPU'
self.model = yolov8_segment_openvino(model_path, device, conf_thres=0.3, iou_thres=0.3)
img_path = ''.join([os.getcwd(), '/Vision/model/data/test0910.png'])
point_path = ''.join([os.getcwd(), '/Vision/model/data/test0910.xyz'])
img_path = ''.join([os.getcwd(), '/Vision/model/data/test0911.png'])
point_path = ''.join([os.getcwd(), '/Vision/model/data/test0911.xyz'])
self.img = cv2.imread(img_path)
self.point = np.loadtxt(point_path).reshape((1080, 1440, 3))
@ -81,6 +85,9 @@ class Detection:
Depth_Z = []
uv = []
seg_point = []
box_list = []
pca_list = []
pca_nx_ny_nz = []
if Point_isVision == True:
pm2 = pm.copy()
@ -156,7 +163,8 @@ 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
'''
@ -181,12 +189,17 @@ class Detection:
rect = cv2.minAreaRect(max_contour)
# cv2.boxPoints可以将轮廓点转换为四个角点坐标
box = cv2.boxPoints(rect)
# 这一步不影响后面的画图，但是可以保证四个角点坐标为顺时针
startidx = box.sum(axis=1).argmin()
box = np.roll(box, 4 - startidx, 0)
box1 = np.roll(box, 4 - startidx, 0)
# 在原图上画出预测的外接矩形
box = box.reshape((-1, 1, 2)).astype(np.int32)
box = box + [[[box_x1, box_y1]], [[box_x1, box_y1]], [[box_x1, box_y1]], [[box_x1, box_y1]]]
box_piont_x1, box_piont_y1, box_piont_z1 = remove_nan(pm, box[0][0][1], box[0][0][0])
box_piont_x2, box_piont_y2, box_piont_z2 = remove_nan(pm, box[1][0][1], box[1][0][0])
box_piont_x3, box_piont_y3, box_piont_z3 = remove_nan(pm, box[2][0][1], box[2][0][0])
box_piont_x4, box_piont_y4, box_piont_z4 = remove_nan(pm, box[3][0][1], box[3][0][0])
x_rotation_center = int((box[0][0][0] + box[1][0][0] + box[2][0][0] + box[3][0][0]) / 4)
y_rotation_center = int((box[0][0][1] + box[1][0][1] + box[2][0][1] + box[3][0][1]) / 4)
@ -195,6 +208,8 @@ class Detection:
if np.isnan(point_x): # 点云值为无效值
continue
else:
box_list.append([[box_piont_x1, box_piont_y1, box_piont_z1], [box_piont_x2, box_piont_y2, box_piont_z2],
[box_piont_x3, box_piont_y3, box_piont_z3], [box_piont_x4, box_piont_y4, box_piont_z4]])
xyz.append([point_x * 1000, point_y * 1000, point_z * 1000])
Depth_Z.append(point_z * 1000)
nx_ny_nz.append([a, b, c])
@ -206,7 +221,7 @@ class Detection:
_idx = find_position(Depth_Z, RegionalArea, 100, True)
if _idx == None:
return 1, img, None, None
return 1, img, None, None, None
else:
cv2.circle(img, (uv[_idx][0], uv[_idx][1]), 30, (0, 0, 255), 20) # 标出中心点
@ -225,17 +240,17 @@ 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]
return 1, img, xyz[_idx], nx_ny_nz[_idx], box_list[_idx], pca_list[_idx], pca_nx_ny_nz[_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
return 1, img, None, None, None, None, None
else:
print("RVC X Camera capture failed!")
return 0, None, None, None
return 0, None, None, None, None, None, None
def release(self):
self.model.clear()

41
Vision/tool/utils.py
View File

@ -12,6 +12,47 @@ 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]
if np.isnan(piont_x):
for i in range(10):
piont_x, piont_y, piont_z = pm[y+i, x]
if np.isnan(piont_x)==False:
break
return piont_x, piont_y, piont_z
def get_disk_space(path='C:'):
usage = psutil.disk_usage(path)