495 lines
28 KiB
Python
495 lines
28 KiB
Python
#!/usr/bin/env python
|
||
# -*- coding: UTF-8 -*-
|
||
'''
|
||
@Project ：AutoControlSystem-master
|
||
@File ：camera_coordinate_dete.py
|
||
@IDE ：PyCharm
|
||
@Author ：hjw
|
||
@Date ：2024/8/27 14:24
|
||
'''
|
||
|
||
import numpy as np
|
||
import cv2
|
||
import open3d as o3d
|
||
import time
|
||
from Vision.tool.CameraRVC import camera_rvc
|
||
from Vision.tool.CameraPe import camera_pe
|
||
from Vision.yolo.yolov8_pt_seg import yolov8_segment
|
||
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_mean_value
|
||
from Vision.tool.utils import out_bounds_dete
|
||
from Vision.tool.utils import uv_to_XY
|
||
from Vision.tool.utils import fit_plane_vision
|
||
import os
|
||
|
||
|
||
class Detection_plane_vsion:
|
||
|
||
def __init__(self, use_openvino_model=False, cameraType = 'RVC'):
|
||
self.use_openvino_model = use_openvino_model
|
||
self.cameraType = cameraType
|
||
if self.use_openvino_model == False:
|
||
model_path = ''.join([os.getcwd(), '/Vision/model/pt/one_bag.pt'])
|
||
device = 'cpu'
|
||
self.model = yolov8_segment()
|
||
self.model.load_model(model_path, device)
|
||
else:
|
||
model_path = ''.join([os.getcwd(), '/Vision/model/openvino/one_bag.xml'])
|
||
device = 'CPU'
|
||
self.model = yolov8_segment_openvino(model_path, device, conf_thres=0.6, iou_thres=0.6)
|
||
|
||
img_path = ''.join([os.getcwd(), '/Vision/model/data/2025_01_16_08_47_02.png'])
|
||
point_path = ''.join([os.getcwd(), '/Vision/model/data/2025_01_16_08_47_02.xyz'])
|
||
self.img = cv2.imread(img_path)
|
||
self.img = cv2.resize(self.img,((int(self.img.shape[1]/2),int(self.img.shape[0]/2))))
|
||
self.point = np.loadtxt(point_path).reshape(self.img.shape[0], self.img.shape[1], 3)
|
||
self.seg_distance_threshold = 5
|
||
pass
|
||
|
||
def get_position(self, Point_isVision=False, Box_isPoint=True, First_Depth=True, Iter_Max_Pixel=30,
|
||
save_img_point=0, Height_reduce=60, width_reduce=100, Xmin=0, Xmax=1050, Ymin=0, Ymax=780):
|
||
"""
|
||
检测料袋相关信息
|
||
:param Point_isVision: 点云可视化
|
||
:param Box_isPoint: True 返回点云值； False 返回box相机坐标
|
||
:param First_Depth: True 返回料袋中心点深度最小的点云值； False 返回面积最大的料袋中心点云值
|
||
:param Iter_Max_Pixel: [int] 点云为NAN时，向该点周围寻找替代值，寻找最大区域（Iter_Max_Pixel×Iter_Max_Pixel）
|
||
:param save_img_point: 0不保存 ; 1保存原图 ;2保存处理后的图 ; 3保存点云和原图；4 保存点云和处理后的图; 5 异常数据保存（点云NAN）
|
||
:param Height_reduce: 检测框的高内缩像素
|
||
:param width_reduce: 检测框的宽内缩像素
|
||
:param Xmin: 限定料袋中心点的范围
|
||
:param Xmax: 限定料袋中心点的范围
|
||
:param Ymin: 限定料袋中心点的范围
|
||
:param Ymax: 限定料袋中心点的范围
|
||
|
||
:return ret: bool 相机是否正常工作
|
||
:return img: ndarray 返回img
|
||
:return xyz: list 目标中心点云值形如[x,y,z]
|
||
:return nx_ny_nz: list 拟合平面法向量，形如[a,b,c]
|
||
:return box_list: list 内缩检测框四顶点，形如[[x1,y1],[],[],[]]
|
||
|
||
"""
|
||
#ret, img, pm, _depth_align = self.camera_rvc.get_img_and_point_map() # 拍照，获取图像及
|
||
ret = 1
|
||
img = self.img
|
||
pm = self.point
|
||
# self.camera_rvc.caminit_isok = True
|
||
|
||
if True:
|
||
if ret == 1:
|
||
if save_img_point != 0:
|
||
if get_disk_space(path=os.getcwd()) < 15: # 内存小于15G,停止保存数据
|
||
save_img_point = 0
|
||
print('系统内存不足，无法保存数据')
|
||
else:
|
||
save_path = ''.join([os.getcwd(), '/Vision/model/data/',
|
||
time.strftime('%Y_%m_%d_%H_%M_%S', time.localtime(time.time()))])
|
||
save_img_name = ''.join([save_path, '.png'])
|
||
save_point_name = ''.join([save_path, '.xyz'])
|
||
if save_img_point == 5:
|
||
Abnormal_data_img = img.copy()
|
||
if save_img_point == 1 or save_img_point == 3:
|
||
cv2.imwrite(save_img_name, img)
|
||
if save_img_point == 3 or save_img_point == 4 or save_img_point == 5:
|
||
row_list = list(range(1, img.shape[0], 2))
|
||
column_list = list(range(1, img.shape[1], 2))
|
||
pm_save = pm.copy()
|
||
pm_save1 = np.delete(pm_save, row_list, axis=0)
|
||
point_new = np.delete(pm_save1, column_list, axis=1)
|
||
point_new = point_new.reshape(-1, 3)
|
||
if save_img_point == 5:
|
||
Abnormal_data_point = point_new.copy()
|
||
else:
|
||
np.savetxt(save_point_name, point_new)
|
||
if self.use_openvino_model == False:
|
||
flag, det_cpu, dst_img, masks, category_names = self.model.model_inference(img, 0)
|
||
else:
|
||
flag, det_cpu, scores, masks, category_names = self.model.segment_objects(img)
|
||
if flag == 1:
|
||
xyz = []
|
||
nx_ny_nz = []
|
||
RegionalArea = []
|
||
Depth_Z = []
|
||
uv = []
|
||
seg_point = []
|
||
box_list = []
|
||
if Point_isVision == True:
|
||
pm2 = pm.copy()
|
||
pm2 = pm2.reshape(-1, 3)
|
||
pm2 = pm2[~np.isnan(pm2).all(axis=-1), :]
|
||
pm2[:, 2] = pm2[:, 2] + 0.25
|
||
pcd2 = o3d.geometry.PointCloud()
|
||
pcd2.points = o3d.utility.Vector3dVector(pm2)
|
||
# o3d.visualization.draw_geometries([pcd2])
|
||
|
||
for i, item in enumerate(det_cpu):
|
||
|
||
# 画box
|
||
box_x1, box_y1, box_x2, box_y2 = item[0:4].astype(np.int32)
|
||
if self.use_openvino_model == False:
|
||
label = category_names[int(item[5])]
|
||
else:
|
||
label = class_names[int(item[4])]
|
||
rand_color = (0, 255, 255)
|
||
score = item[4]
|
||
org = (int((box_x1 + box_x2) / 2), int((box_y1 + box_y2) / 2))
|
||
x_center = int((box_x1 + box_x2) / 2)
|
||
y_center = int((box_y1 + box_y2) / 2)
|
||
text = '{}|{:.2f}'.format(label, score)
|
||
cv2.putText(img, text, org=org, fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=0.8,
|
||
color=rand_color,
|
||
thickness=2)
|
||
# 画mask
|
||
# mask = masks[i].cpu().numpy().astype(int)
|
||
if self.use_openvino_model == False:
|
||
mask = masks[i].cpu().data.numpy().astype(int)
|
||
else:
|
||
mask = masks[i].astype(int)
|
||
mask = mask[box_y1:box_y2, box_x1:box_x2]
|
||
|
||
# mask = masks[i].numpy().astype(int)
|
||
h, w = box_y2 - box_y1, box_x2 - box_x1
|
||
mask_colored = np.zeros((h, w, 3), dtype=np.uint8)
|
||
mask_colored[np.where(mask)] = rand_color
|
||
##################################
|
||
imgray = cv2.cvtColor(mask_colored, cv2.COLOR_BGR2GRAY)
|
||
# cv2.imshow('mask',imgray)
|
||
# cv2.waitKey(1)
|
||
# 2、二进制图像
|
||
ret, binary = cv2.threshold(imgray, 10, 255, 0)
|
||
# 阈值 二进制图像
|
||
# cv2.imshow('bin',binary)
|
||
# cv2.waitKey(1)
|
||
contours, hierarchy = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
|
||
# all_point_list = contours_in(contours)
|
||
# print(len(all_point_list))
|
||
max_contour = None
|
||
max_perimeter = 0
|
||
for contour in contours: # 排除小分割区域或干扰区域
|
||
perimeter = cv2.arcLength(contour, True)
|
||
if perimeter > max_perimeter:
|
||
max_perimeter = perimeter
|
||
max_contour = contour
|
||
|
||
'''
|
||
拟合最小外接矩形，计算矩形中心
|
||
'''
|
||
|
||
rect = cv2.minAreaRect(max_contour)
|
||
if rect[1][0] - width_reduce > 30 and rect[1][1] - Height_reduce > 30:
|
||
rect_reduce = (
|
||
(rect[0][0], rect[0][1]), (rect[1][0] - width_reduce, rect[1][1] - Height_reduce),
|
||
rect[2])
|
||
else:
|
||
rect_reduce = (
|
||
(rect[0][0], rect[0][1]), (rect[1][0], rect[1][1]), rect[2])
|
||
|
||
# cv2.boxPoints可以将轮廓点转换为四个角点坐标
|
||
box_outside = 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_outside.sum(axis=1).argmin()
|
||
box_outside = np.roll(box_outside, 4 - startidx, 0)
|
||
box_outside = np.intp(box_outside)
|
||
box_outside = box_outside.reshape((-1, 1, 2)).astype(np.int32)
|
||
|
||
# cv2.boxPoints可以将轮廓点转换为四个角点坐标
|
||
box_reduce = cv2.boxPoints(rect_reduce)
|
||
startidx = box_reduce.sum(axis=1).argmin()
|
||
box_reduce = np.roll(box_reduce, 4 - startidx, 0)
|
||
box_reduce = np.intp(box_reduce)
|
||
box_reduce = box_reduce.reshape((-1, 1, 2)).astype(np.int32)
|
||
|
||
'''
|
||
提取区域范围内的（x, y）
|
||
'''
|
||
mask_inside = np.zeros(binary.shape, np.uint8)
|
||
cv2.fillPoly(mask_inside, [box_reduce], (255))
|
||
pixel_point2 = cv2.findNonZero(mask_inside)
|
||
# result = np.zeros_like(color_image)
|
||
select_point = []
|
||
for i in range(pixel_point2.shape[0]):
|
||
select_point.append(pm[pixel_point2[i][0][1] + box_y1, pixel_point2[i][0][0] + box_x1])
|
||
select_point = np.array(select_point)
|
||
pm_seg = select_point.reshape(-1, 3)
|
||
pm_seg = pm_seg[~np.isnan(pm_seg).all(axis=-1), :] # 剔除 nan
|
||
if pm_seg.size < 100:
|
||
print("分割点云数量较少，无法拟合平面")
|
||
continue
|
||
|
||
# cv2.imshow('result', point_result)
|
||
'''
|
||
拟合平面，计算法向量
|
||
'''
|
||
pcd = o3d.geometry.PointCloud()
|
||
pcd.points = o3d.utility.Vector3dVector(pm_seg)
|
||
plane_model, inliers = pcd.segment_plane(distance_threshold=self.seg_distance_threshold,
|
||
ransac_n=5,
|
||
num_iterations=5000)
|
||
[a, b, c, d] = plane_model
|
||
print('[a, b, c, d]',[a, b, c, d] )
|
||
# print(f"Plane equation: {a:.2f}x + {b:.2f}y + {c:.2f}z + {d:.2f} = 0")
|
||
# inlier_cloud = pcd.select_by_index(inliers) # 点云可视化
|
||
# inlier_cloud.paint_uniform_color([1.0, 0, 0])
|
||
# outlier_cloud = pcd.select_by_index(inliers, invert=True)
|
||
# outlier_cloud.paint_uniform_color([0, 1, 0])
|
||
# o3d.visualization.draw_geometries([inlier_cloud, outlier_cloud])
|
||
|
||
box_outside = box_outside + [[[box_x1, box_y1]], [[box_x1, box_y1]], [[box_x1, box_y1]],
|
||
[[box_x1, box_y1]]]
|
||
box = box_reduce + [[[box_x1, box_y1]], [[box_x1, box_y1]], [[box_x1, box_y1]],
|
||
[[box_x1, box_y1]]]
|
||
|
||
box[0][0][1], box[0][0][0] = out_bounds_dete(pm.shape[0], pm.shape[1], box[0][0][1],
|
||
box[0][0][0])
|
||
box[1][0][1], box[1][0][0] = out_bounds_dete(pm.shape[0], pm.shape[1], box[1][0][1],
|
||
box[1][0][0])
|
||
box[2][0][1], box[2][0][0] = out_bounds_dete(pm.shape[0], pm.shape[1], box[2][0][1],
|
||
box[2][0][0])
|
||
box[3][0][1], box[3][0][0] = out_bounds_dete(pm.shape[0], pm.shape[1], box[3][0][1],
|
||
box[3][0][0])
|
||
if Box_isPoint == True:
|
||
box_point_x1, box_point_y1, box_point_z1 = remove_nan_mean_value(pm, box[0][0][1],
|
||
box[0][0][0],
|
||
iter_max=Iter_Max_Pixel)
|
||
box_point_x2, box_point_y2, box_point_z2 = remove_nan_mean_value(pm, box[1][0][1],
|
||
box[1][0][0],
|
||
iter_max=Iter_Max_Pixel)
|
||
box_point_x3, box_point_y3, box_point_z3 = remove_nan_mean_value(pm, box[2][0][1],
|
||
box[2][0][0],
|
||
iter_max=Iter_Max_Pixel)
|
||
box_point_x4, box_point_y4, box_point_z4 = remove_nan_mean_value(pm, box[3][0][1],
|
||
box[3][0][0],
|
||
iter_max=Iter_Max_Pixel)
|
||
else:
|
||
x1, y1, z1 = uv_to_XY(box[0][0][0], box[0][0][1])
|
||
x2, y2, z2 = uv_to_XY(box[1][0][0], box[1][0][1])
|
||
x3, y3, z3 = uv_to_XY(box[2][0][0], box[2][0][1])
|
||
x4, y4, z4 = uv_to_XY(box[3][0][0], box[3][0][1])
|
||
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 = remove_nan_mean_value(pm, y_rotation_center, x_rotation_center,
|
||
iter_max=Iter_Max_Pixel)
|
||
if x_rotation_center < Xmin or x_rotation_center > Xmax or y_rotation_center < Ymin or y_rotation_center > Ymax:
|
||
continue
|
||
cv2.circle(img, (x_rotation_center, y_rotation_center), 4, (255, 255, 255), 5) # 标出中心点
|
||
if np.isnan(point_x): # 点云值为无效值
|
||
continue
|
||
else:
|
||
if Box_isPoint == True:
|
||
box_list.append(
|
||
[[box_point_x1, box_point_y1, box_point_z1],
|
||
[box_point_x2, box_point_y2, box_point_z2],
|
||
[box_point_x3, box_point_y3, box_point_z3],
|
||
[box_point_x4, box_point_y4, box_point_z4]])
|
||
print(box_list)
|
||
else:
|
||
box_list.append([[x1, y1, z1],
|
||
[x2, y2, z2],
|
||
[x3, y3, z3],
|
||
[x4, y4, z4],
|
||
])
|
||
if self.cameraType == 'RVC':
|
||
xyz.append([point_x * 1000, point_y * 1000, point_z * 1000])
|
||
Depth_Z.append(point_z * 1000)
|
||
elif self.cameraType == 'Pe':
|
||
xyz.append([point_x, point_y, point_z])
|
||
Depth_Z.append(point_z)
|
||
nx_ny_nz.append([a, b, c])
|
||
RegionalArea.append(cv2.contourArea(max_contour))
|
||
uv.append([x_rotation_center, y_rotation_center])
|
||
seg_point.append(pm_seg)
|
||
cv2.polylines(img, [box], True, (0, 255, 0), 2)
|
||
cv2.polylines(img, [box_outside], True, (226, 12, 89), 2)
|
||
|
||
_idx = find_position(Depth_Z, RegionalArea, 100, First_Depth)
|
||
|
||
if _idx == None:
|
||
if save_img_point == 5:
|
||
cv2.imwrite(save_img_name, Abnormal_data_img)
|
||
np.savetxt(save_point_name, Abnormal_data_point)
|
||
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
|
||
|
||
if Point_isVision == True:
|
||
# 拟合平面展示
|
||
plane_vision = fit_plane_vision(box_list, [a, b, c, d])
|
||
pcd = o3d.geometry.PointCloud()
|
||
pcd.points = o3d.utility.Vector3dVector(seg_point[_idx])
|
||
plane_model, inliers = pcd.segment_plane(distance_threshold=self.seg_distance_threshold,
|
||
ransac_n=5,
|
||
num_iterations=5000)
|
||
inlier_cloud = pcd.select_by_index(inliers) # 点云可视化
|
||
inlier_cloud.paint_uniform_color([1.0, 0, 0])
|
||
outlier_cloud = pcd.select_by_index(inliers, invert=True)
|
||
outlier_cloud.paint_uniform_color([0, 0, 1])
|
||
o3d.visualization.draw_geometries([inlier_cloud, outlier_cloud, pcd2, plane_vision])
|
||
if save_img_point == 2 or save_img_point == 4:
|
||
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]
|
||
else:
|
||
if save_img_point == 2 or save_img_point == 4:
|
||
save_img = cv2.resize(img, (720, 540))
|
||
cv2.imwrite(save_img_name, save_img)
|
||
if save_img_point == 5:
|
||
cv2.imwrite(save_img_name, Abnormal_data_img)
|
||
np.savetxt(save_point_name, Abnormal_data_point)
|
||
return 1, img, None, None, None
|
||
|
||
else:
|
||
print("RVC X Camera capture failed!")
|
||
return 0, None, None, None, None
|
||
|
||
else:
|
||
print("RVC X Camera is not opened!")
|
||
return 0, None, None, None, None
|
||
|
||
def get_take_photo_position(self, Height_reduce=30, width_reduce=30):
|
||
"""
|
||
专用于拍照位置点查找，检测当前拍照点能否检测到料袋
|
||
:param Height_reduce:
|
||
:param width_reduce:
|
||
:return ret: bool 相机是否正常工作
|
||
:return img: ndarry 返回img
|
||
:return find_target: bool 是否有目标
|
||
:return xyz: list 目标中心点云值，形如[x,y,z]
|
||
|
||
"""
|
||
ret = 1
|
||
img = self.img
|
||
pm = self.point
|
||
find_target = False
|
||
|
||
if ret == 1:
|
||
if self.use_openvino_model == False:
|
||
flag, det_cpu, dst_img, masks, category_names = self.model.model_inference(img, 0)
|
||
else:
|
||
flag, det_cpu, scores, masks, category_names = self.model.segment_objects(img)
|
||
if flag == 1:
|
||
xyz = []
|
||
RegionalArea = []
|
||
Depth_Z = []
|
||
uv = []
|
||
for i, item in enumerate(det_cpu):
|
||
find_target = True
|
||
# 画box
|
||
box_x1, box_y1, box_x2, box_y2 = item[0:4].astype(np.int32)
|
||
if self.use_openvino_model == False:
|
||
label = category_names[int(item[5])]
|
||
else:
|
||
label = class_names[int(item[4])]
|
||
rand_color = (0, 255, 255)
|
||
score = item[4]
|
||
org = (int((box_x1 + box_x2) / 2), int((box_y1 + box_y2) / 2))
|
||
x_center = int((box_x1 + box_x2) / 2)
|
||
y_center = int((box_y1 + box_y2) / 2)
|
||
text = '{}|{:.2f}'.format(label, score)
|
||
cv2.putText(img, text, org=org, fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=0.8,
|
||
color=rand_color,
|
||
thickness=2)
|
||
# 画mask
|
||
# mask = masks[i].cpu().numpy().astype(int)
|
||
if self.use_openvino_model == False:
|
||
mask = masks[i].cpu().data.numpy().astype(int)
|
||
else:
|
||
mask = masks[i].astype(int)
|
||
mask = mask[box_y1:box_y2, box_x1:box_x2]
|
||
|
||
# mask = masks[i].numpy().astype(int)
|
||
h, w = box_y2 - box_y1, box_x2 - box_x1
|
||
mask_colored = np.zeros((h, w, 3), dtype=np.uint8)
|
||
mask_colored[np.where(mask)] = rand_color
|
||
##################################
|
||
imgray = cv2.cvtColor(mask_colored, cv2.COLOR_BGR2GRAY)
|
||
# cv2.imshow('mask',imgray)
|
||
# cv2.waitKey(1)
|
||
# 2、二进制图像
|
||
ret, binary = cv2.threshold(imgray, 10, 255, 0)
|
||
# 阈值 二进制图像
|
||
# cv2.imshow('bin',binary)
|
||
# cv2.waitKey(1)
|
||
contours, hierarchy = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
|
||
# all_point_list = contours_in(contours)
|
||
# print(len(all_point_list))
|
||
max_contour = None
|
||
max_perimeter = 0
|
||
for contour in contours: # 排除小分割区域或干扰区域
|
||
perimeter = cv2.arcLength(contour, True)
|
||
if perimeter > max_perimeter:
|
||
max_perimeter = perimeter
|
||
max_contour = contour
|
||
|
||
'''
|
||
拟合最小外接矩形，计算矩形中心
|
||
'''
|
||
|
||
rect = cv2.minAreaRect(max_contour)
|
||
if rect[1][0] - width_reduce < 30 or rect[1][1] - Height_reduce < 30:
|
||
rect_reduce = (
|
||
(rect[0][0], rect[0][1]), (rect[1][0] - width_reduce, rect[1][1] - Height_reduce), rect[2])
|
||
else:
|
||
rect_reduce = (
|
||
(rect[0][0], rect[0][1]), (rect[1][0], rect[1][1]), rect[2])
|
||
# cv2.boxPoints可以将轮廓点转换为四个角点坐标
|
||
box_outside = 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_outside.sum(axis=1).argmin()
|
||
box_outside = np.roll(box_outside, 4 - startidx, 0)
|
||
box_outside = np.intp(box_outside)
|
||
box_outside = box_outside.reshape((-1, 1, 2)).astype(np.int32)
|
||
|
||
# cv2.boxPoints可以将轮廓点转换为四个角点坐标
|
||
box_reduce = cv2.boxPoints(rect_reduce)
|
||
startidx = box_reduce.sum(axis=1).argmin()
|
||
box_reduce = np.roll(box_reduce, 4 - startidx, 0)
|
||
box_reduce = np.intp(box_reduce)
|
||
box_reduce = box_reduce.reshape((-1, 1, 2)).astype(np.int32)
|
||
|
||
box_outside = box_outside + [[[box_x1, box_y1]], [[box_x1, box_y1]], [[box_x1, box_y1]],[[box_x1, box_y1]]]
|
||
box = box_reduce + [[[box_x1, box_y1]], [[box_x1, box_y1]], [[box_x1, box_y1]], [[box_x1, box_y1]]]
|
||
|
||
box[0][0][1], box[0][0][0] = out_bounds_dete(pm.shape[0], pm.shape[1], box[0][0][1], box[0][0][0])
|
||
box[1][0][1], box[1][0][0] = out_bounds_dete(pm.shape[0], pm.shape[1], box[1][0][1], box[1][0][0])
|
||
box[2][0][1], box[2][0][0] = out_bounds_dete(pm.shape[0], pm.shape[1], box[2][0][1], box[2][0][0])
|
||
box[3][0][1], box[3][0][0] = out_bounds_dete(pm.shape[0], pm.shape[1], 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 = remove_nan_mean_value(pm, y_rotation_center, x_rotation_center)
|
||
cv2.circle(img, (x_rotation_center, y_rotation_center), 4, (255, 255, 255), 5) # 标出中心点
|
||
if np.isnan(point_x): # 点云值为无效值
|
||
continue
|
||
else:
|
||
if self.cameraType == 'RVC':
|
||
xyz.append([point_x * 1000, point_y * 1000, point_z * 1000])
|
||
Depth_Z.append(point_z * 1000)
|
||
elif self.cameraType == 'Pe':
|
||
xyz.append([point_x, point_y, point_z])
|
||
Depth_Z.append(point_z)
|
||
RegionalArea.append(cv2.contourArea(max_contour))
|
||
uv.append([x_rotation_center, y_rotation_center])
|
||
|
||
cv2.polylines(img, [box], True, (0, 255, 0), 2)
|
||
cv2.polylines(img, [box_outside], True, (226, 12, 89), 2)
|
||
|
||
_idx = find_position(Depth_Z, RegionalArea, 100,True)
|
||
|
||
if _idx == None:
|
||
return 1, img, find_target, None
|
||
else:
|
||
cv2.circle(img, (uv[_idx][0], uv[_idx][1]), 30, (0, 0, 255), 20) # 标出中心点
|
||
return 1, img, find_target, xyz[_idx]
|
||
else:
|
||
return 0, None, None
|
||
|
||
|
||
def release(self):
|
||
self.model.clear()
|
||
|
||
|
||
|