858 lines
46 KiB
Python
858 lines
46 KiB
Python
#!/usr/bin/env python
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# -*- coding: UTF-8 -*-
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'''
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@Project ：AutoControlSystem-master
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@File ：camera_coordinate_dete.py
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@IDE ：PyCharm
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@Author ：hjw
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@Date ：2024/8/27 14:24
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'''
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import numpy as np
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import cv2
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import open3d as o3d
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import time
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import os
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from Vision.tool.CameraRVC import camera_rvc
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from Vision.tool.CameraPe_color2depth import camera_pe as camera_pe_color2depth
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from Vision.tool.CameraPe_depth2color import camera_pe as camera_pe_depth2color
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from Vision.yolo.yolov8_pt_seg import yolov8_segment
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from Vision.yolo.yolov8_openvino import yolov8_segment_openvino
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from Vision.yolo.yolov8_pt_pose import yolov8_pose
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from Vision.tool.utils import find_position
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from Vision.tool.utils import class_names
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from Vision.tool.utils import get_disk_space
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from Vision.tool.utils import remove_nan_mean_value
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from Vision.tool.utils import out_bounds_dete, find_closest_point_index
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from Vision.tool.utils import uv_to_XY, shrink_quadrilateral
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class Detection:
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def __init__(self, use_openvino_model=False, use_pose_model=True, use_seg_pt_model=True, cameraType = 'Pe', alignmentType = 'color2depth'): # cameraType = 'RVC' or cameraType = 'Pe'
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"""
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初始化相机及模型
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:param use_openvino_model: 加载分割模型
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:param use_pose_model: 加载关键点pt模型
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:param use_seg_pt_model: 加载分割pt模型
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:param use_openvino_model: 选择模型，默认使用openvino
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:param cameraType: 选择相机 如本相机 'RVC', 图漾相机 'Pe'
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:param alignmentType: 相机对齐方式 color2depth：彩色图对齐深度图 ；depth2color：深度图对齐彩色图
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"""
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if use_seg_pt_model: # 优先使用pt模型
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use_openvino_model = False
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elif use_openvino_model:
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use_seg_pt_model = False
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self.use_openvino_model = use_openvino_model
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self.cameraType = cameraType
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self.use_pose_model = use_pose_model
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self.use_seg_pt_model = use_seg_pt_model
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self.alignmentType = alignmentType
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if self.cameraType == 'RVC':
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self.camera_rvc = camera_rvc()
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self.seg_distance_threshold = 10 # 1厘米
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elif self.cameraType == 'Pe':
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if self.alignmentType == 'color2depth':
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self.camera_rvc = camera_pe_color2depth()
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else:
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self.camera_rvc = camera_pe_depth2color()
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self.seg_distance_threshold = 15 # 2厘米
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else:
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print('相机参数错误')
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return
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# 加载openvino-seg
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if self.use_openvino_model:
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model_path = ''.join([os.getcwd(), './Vision/model/openvino/one_bag.xml'])
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device = 'CPU'
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self.model_seg = yolov8_segment_openvino(model_path, device, conf_thres=0.6, iou_thres=0.6)
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# 加载pt-seg
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if self.use_seg_pt_model:
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model_path = ''.join([os.getcwd(), './Vision/model/pt/one_bag.pt'])
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device = 'cpu'
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self.model_seg = yolov8_segment()
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self.model_seg.load_model(model_path, device)
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# 加载pt-pose
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if self.use_pose_model:
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model_path = ''.join([os.getcwd(), './Vision/model/pt/one_bag_pose.pt'])
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device = 'cpu'
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self.model_pose = yolov8_pose(model_path, device)
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def get_position(self, Use_Pose_Model_Pro=False, Point_isVision=False, Box_isPoint=True, First_Depth =True, Iter_Max_Pixel = 30, save_img_point=0, Height_reduce = 80, width_reduce = 60, Xmin =160, Xmax = 1050, Ymin =290 ,Ymax = 780):
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"""
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检测料袋相关信息
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:param Use_Pose_Model_Pro: True: 选用关键点推理 False : 选用分割模型推理
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:param Point_isVision: 点云可视化
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:param Box_isPoint: True 返回点云值； False 返回box相机坐标
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: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
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: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）
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: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）
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:param Height_reduce: 检测框的高内缩像素
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:param width_reduce: 检测框的宽内缩像素
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:param Xmin: 限定料袋中心点的范围
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:param Xmax: 限定料袋中心点的范围
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:param Ymin: 限定料袋中心点的范围
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:param Ymax: 限定料袋中心点的范围
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:return ret: bool 相机是否正常工作
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:return img: ndarray 返回img
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:return xyz: list 目标中心点云值形如[x,y,z]
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:return nx_ny_nz: list 拟合平面法向量，形如[a,b,c]
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:return box_list: list 内缩检测框四顶点，形如[[x1,y1],[],[],[]]
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"""
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# ret, img, pm, _depth_align = self.camera_rvc.get_img_and_point_map() # 拍照，获取图像及
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ret = 1
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pm1 = np.loadtxt('D:\pychram_rob\AutoControlSystem-git\Vision\model\data\\2024_11_29_10_05_58.xyz', dtype=np.float32)
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img = cv2.imread('D:\pychram_rob\AutoControlSystem-git\Vision\model\data\\2024_11_29_10_05_58.png')
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pm = pm1.reshape((img.shape[0], img.shape[1], 3))
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if self.camera_rvc.caminit_isok == True:
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if ret == 1:
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if save_img_point != 0:
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if get_disk_space(path=os.getcwd()) < 15: # 内存小于15G,停止保存数据
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save_img_point = 0
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print('系统内存不足，无法保存数据')
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else:
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save_path = ''.join([os.getcwd(), '/Vision/model/data/',
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time.strftime('%Y_%m_%d_%H_%M_%S', time.localtime(time.time()))])
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save_img_name = ''.join([save_path, '.png'])
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save_point_name = ''.join([save_path, '.xyz'])
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if save_img_point == 5:
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Abnormal_data_img = img.copy()
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if save_img_point==1 or save_img_point==3:
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cv2.imwrite(save_img_name, img)
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if save_img_point==3 or save_img_point==4 or save_img_point==5:
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row_list = list(range(1, img.shape[0], 2))
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column_list = list(range(1, img.shape[1], 2))
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pm_save = pm.copy()
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pm_save1 = np.delete(pm_save, row_list, axis=0)
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point_new = np.delete(pm_save1, column_list, axis=1)
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point_new = point_new.reshape(-1, 3)
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if save_img_point==5:
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Abnormal_data_point = point_new.copy()
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else:
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np.savetxt(save_point_name, point_new)
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if self.use_pose_model and Use_Pose_Model_Pro:
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real_model_pro_isPose = True
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else:
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real_model_pro_isPose = False
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if real_model_pro_isPose:
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flag, det_cpu, category_names, score_list = self.model_pose.model_inference(img)#用关键点检测模型
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else:
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if self.use_openvino_model == False:
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flag, det_cpu, dst_img, masks, category_names = self.model_seg.model_inference(img, 0) #用分割模型
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else:
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flag, det_cpu, scores, masks, category_names = self.model_seg.segment_objects(img)
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if flag == 1:
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xyz = []
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nx_ny_nz = []
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RegionalArea = []
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Depth_Z = []
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uv = []
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seg_point = []
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box_list = []
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if Point_isVision==True:
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pm2 = pm.copy()
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pm2 = pm2.reshape(-1, 3)
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pm2 = pm2[~np.isnan(pm2).all(axis=-1), :]
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pm2[:, 2] = pm2[:, 2] + 0.25
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pcd2 = o3d.geometry.PointCloud()
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pcd2.points = o3d.utility.Vector3dVector(pm2)
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# o3d.visualization.draw_geometries([pcd2])
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for i, item in enumerate(det_cpu):#提供检测到的框信息
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# 画box
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if real_model_pro_isPose:
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label = category_names[i]
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score = score_list[i]
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box_x1 = item[0][0]
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box_y1 = item[0][1]
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box_x2 = item[3][0]
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box_y2 = item[3][1]
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pass
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else:
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box_x1, box_y1, box_x2, box_y2 = item[0:4].astype(np.int32)#找最近的框的1，3角点坐标
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if self.use_openvino_model == False:
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label = category_names[int(item[5])]
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score = item[4]
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else:
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label = class_names[int(item[4])]
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score = item[4]
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rand_color = (0, 255, 255)
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org = (int((box_x1 + box_x2) / 2), int((box_y1 + box_y2) / 2))
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x_center = int((box_x1 + box_x2) / 2)
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y_center = int((box_y1 + box_y2) / 2)
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text = '{}|{:.2f}'.format(label, score)
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cv2.putText(img, text, org=org, fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=0.8,
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color=rand_color,
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thickness=2)
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# 画mask
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# mask = masks[i].cpu().numpy().astype(int)
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if real_model_pro_isPose:
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# 创建一个与输入数组相同形状的掩码，初始值全为 0
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mask = np.zeros(pm.shape[:2], dtype=np.uint8)
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# 将四点坐标转换为 numpy 数组
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if item[0][0] < item[1][0]:
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arr = [[item[0][0], item[0][1]],
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[item[1][0], item[1][1]],
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[item[3][0], item[3][1]],
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[item[2][0], item[2][1]]]
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# new_points.reshape((-1, 1, 2))
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else:
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arr = [[item[3][0], item[3][1]],
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[item[2][0], item[2][1]],
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[item[0][0], item[0][1]],
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[item[1][0], item[1][1]]]
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box = arr.copy()
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box_outside = arr.copy()
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box = shrink_quadrilateral(box, Height_reduce)
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pts = np.array(box, np.int32)
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# 将四点构成的四边形区域在掩码上标记为 255
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cv2.fillPoly(mask, [pts], 255)
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# 根据掩码提取对应区域的数据
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pm_seg = pm[mask == 255]
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# box =[[[item[0][0]+width_reduce, item[0][1]+Height_reduce]],
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# [[item[1][0]-width_reduce, item[1][1]+Height_reduce]],
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# [[item[3][0]-width_reduce, item[3][1]-Height_reduce]],
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# [[item[2][0]+width_reduce, item[2][1]-Height_reduce]]]
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box = box.reshape((-1, 1, 2))
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# box = np.array(box)
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# 内缩
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# box_outside = [[[item[0][0], item[0][1]]],
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# [[item[1][0], item[1][1]]],
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# [[item[3][0], item[3][1]]],
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# [[item[2][0], item[2][1]]]]# 外框
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box_outside = np.array(box_outside)
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box_outside = box_outside.reshape((-1, 1, 2))
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# box_outside = np.array(box_outside)
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else:
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if self.use_openvino_model == False:
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mask = masks[i].cpu().data.numpy().astype(int)
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else:
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mask = masks[i].astype(int)
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mask = mask[box_y1:box_y2, box_x1:box_x2]
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# mask = masks[i].numpy().astype(int)
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h, w = box_y2 - box_y1, box_x2 - box_x1
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mask_colored = np.zeros((h, w, 3), dtype=np.uint8)
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mask_colored[np.where(mask)] = rand_color
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##################################
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imgray = cv2.cvtColor(mask_colored, cv2.COLOR_BGR2GRAY)
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# cv2.imshow('mask',imgray)
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# cv2.waitKey(1)
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# 2、二进制图像
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ret, binary = cv2.threshold(imgray, 10, 255, 0)
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# 阈值 二进制图像
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# cv2.imshow('bin',binary)
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# cv2.waitKey(1)
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contours, hierarchy = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)#检测物体轮廓，在灰度化和二值化之后，contours是轮廓信息
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# all_point_list = contours_in(contours)
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# print(len(all_point_list))
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max_contour = None
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max_perimeter = 0
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for contour in contours: # 排除小分割区域或干扰区域
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perimeter = cv2.arcLength(contour, True)#计算周长
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if perimeter > max_perimeter:
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max_perimeter = perimeter
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max_contour = contour
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'''
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拟合最小外接矩形，计算矩形中心
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'''
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rect = cv2.minAreaRect(max_contour)#计算一组点的最小外接矩形
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if rect[1][0]-width_reduce > 30 and rect[1][1]-Height_reduce > 30:
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rect_reduce = (
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(rect[0][0], rect[0][1]), (rect[1][0] - width_reduce, rect[1][1] - Height_reduce), rect[2])
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else:
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rect_reduce = (
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(rect[0][0], rect[0][1]), (rect[1][0], rect[1][1]), rect[2])
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# cv2.boxPoints可以将轮廓点转换为四个角点坐标
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box_outside = cv2.boxPoints(rect)#计算顶点坐标
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# 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
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startidx = box_outside.sum(axis=1).argmin()
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box_outside = np.roll(box_outside, 4 - startidx, 0)#外框
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box_outside = np.intp(box_outside)
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box_outside = box_outside.reshape((-1, 1, 2)).astype(np.int32)
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# cv2.boxPoints可以将轮廓点转换为四个角点坐标
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box_reduce = cv2.boxPoints(rect_reduce)
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startidx = box_reduce.sum(axis=1).argmin()
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box_reduce = np.roll(box_reduce, 4 - startidx, 0)#内框
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box_reduce = np.intp(box_reduce)
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box_reduce = box_reduce.reshape((-1, 1, 2)).astype(np.int32)
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box_outside = box_outside + [[[box_x1, box_y1]], [[box_x1, box_y1]], [[box_x1, box_y1]],
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[[box_x1, box_y1]]]
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box = box_reduce + [[[box_x1, box_y1]], [[box_x1, box_y1]], [[box_x1, box_y1]],
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[[box_x1, box_y1]]]#我也当他是锚点
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'''
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提取区域范围内的（x, y）
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'''
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mask_inside = np.zeros(binary.shape, np.uint8)
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cv2.fillPoly(mask_inside, [box_reduce], (255))
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pixel_point2 = cv2.findNonZero(mask_inside)
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# result = np.zeros_like(color_image)
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select_point = []
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for i in range(pixel_point2.shape[0]):
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select_point.append(pm[pixel_point2[i][0][1]+box_y1, pixel_point2[i][0][0]+box_x1])#我为什么要加这个box_y1和box_x1呢？是因为mask取出来不是原图的坐标了，box_y1和box_x1相当于mask在原图的锚点，用来帮助剪切后的形状回到原图的位置
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select_point = np.array(select_point)
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pm_seg = select_point.reshape(-1, 3)#小框里面对应的点云
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pm_seg = pm_seg[~np.isnan(pm_seg).all(axis=-1), :] # 剔除 nan
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if pm_seg.size < 100:
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print("分割点云数量较少，无法拟合平面")
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continue
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# cv2.imshow('result', point_result)
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'''
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拟合平面，计算法向量
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'''
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pcd = o3d.geometry.PointCloud()#创建点云对象
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pcd.points = o3d.utility.Vector3dVector(pm_seg)#转换格式
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plane_model, inliers = pcd.segment_plane(distance_threshold=self.seg_distance_threshold,
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ransac_n=5,
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num_iterations=5000)#平面分割，平面拟合，plane_model拟合平面的系数
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[a, b, c, d] = plane_model#ax+by+cz+d=0，a,b,c就是法向量
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# print(f"Plane equation: {a:.2f}x + {b:.2f}y + {c:.2f}z + {d:.2f} = 0")
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# inlier_cloud = pcd.select_by_index(inliers) # 点云可视化
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# inlier_cloud.paint_uniform_color([1.0, 0, 0])
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# outlier_cloud = pcd.select_by_index(inliers, invert=True)
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# outlier_cloud.paint_uniform_color([0, 1, 0])
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# o3d.visualization.draw_geometries([inlier_cloud, outlier_cloud])
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print(box)
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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])#判断box有没有超过点云范围，pm直接是整个图片的点云，box只是分割模型识别的框
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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])
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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])
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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])
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if Box_isPoint == True:#保证box的坐标能被传回来，如果这个点上的没有，就用旁边的均值
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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)
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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)
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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)#求中心点位置
|
||
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), 2, (255, 255, 255), 3) # 标出中心点，只是标出来
|
||
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]])#四个顶点加入到列表中
|
||
else:
|
||
box_list.append([[x1, y1, z1],
|
||
[x2, y2, z2],
|
||
[x3, y3, z3],
|
||
[x4, y4, z4],
|
||
])
|
||
if self.cameraType=='RVC':#换单位？
|
||
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)
|
||
if real_model_pro_isPose:
|
||
RegionalArea.append(0)
|
||
else:
|
||
RegionalArea.append(cv2.contourArea(max_contour))#计算面积
|
||
nx_ny_nz.append([a, b, c])#法向量
|
||
uv.append([x_rotation_center, y_rotation_center])#中心点x,y
|
||
seg_point.append(pm_seg)#区域点云
|
||
cv2.polylines(img, [box], True, (0, 255, 0), 2)#把框可视化
|
||
cv2.polylines(img, [box_outside], True, (226, 12, 89), 2)#外框可视化
|
||
|
||
_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), 10) # 标出中心点
|
||
|
||
if Point_isVision==True:
|
||
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])
|
||
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 read_data(self, xyz_path, img_path):
|
||
pm1 = np.loadtxt(xyz_path, dtype=np.float32)
|
||
img = cv2.imread(img_path)
|
||
pm = pm1.reshape((img.shape[0], img.shape[1], 3))
|
||
return img, pm
|
||
|
||
def save_data(self, img, pm, save_img_point, save_path):
|
||
if save_img_point == 0:
|
||
return
|
||
if not os.path.exists(os.path.dirname(save_path)):
|
||
os.makedirs(os.path.dirname(save_path))
|
||
save_img_name = save_path + '.png'
|
||
save_point_name = save_path + '.xyz'
|
||
|
||
if save_img_point in (1, 3):
|
||
cv2.imwrite(save_img_name, img)
|
||
if save_img_point in (3, 4):
|
||
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)
|
||
np.savetxt(save_point_name, point_new)
|
||
|
||
def model_inference(self, img, Use_Pose_Model_Pro):
|
||
real_model_pro_isPose = self.use_pose_model and Use_Pose_Model_Pro
|
||
if real_model_pro_isPose:
|
||
flag, det_cpu, category_names, score_list = self.model_pose.model_inference(img)
|
||
return flag, det_cpu, category_names, score_list, real_model_pro_isPose
|
||
else:
|
||
if self.use_openvino_model:
|
||
flag, det_cpu, scores, masks, category_names = self.model_seg.segment_objects(img)
|
||
else:
|
||
flag, det_cpu, dst_img, masks, category_names = self.model_seg.model_inference(img, 0)
|
||
return flag, det_cpu, category_names, masks, real_model_pro_isPose
|
||
|
||
def get_box_3d_points(self, pm, box, Box_isPoint=True, Iter_Max_Pixel=30):
|
||
"""
|
||
输入: box 为 (4, 2) 像素坐标 [[x1,y1], ..., [x4,y4]]
|
||
输出: 4个点的3D坐标 [x, y, z]
|
||
"""
|
||
box = np.array(box).reshape(-1, 2) # 强制为 (4, 2)
|
||
pts_3d = []
|
||
for pt in box:
|
||
# 确保 pt 是 [x, y] 结构
|
||
x_img, y_img = int(pt[0]), int(pt[1])
|
||
if Box_isPoint:
|
||
x3d, y3d, z3d = remove_nan_mean_value(pm, y_img, x_img, iter_max=Iter_Max_Pixel)
|
||
else:
|
||
x3d, y3d, z3d = uv_to_XY(x_img, y_img)
|
||
pts_3d.append([x3d, y3d, z3d])
|
||
return pts_3d
|
||
|
||
def process_mask_and_get_box(self, i,item, masks, pm, box_coords, Height_reduce, width_reduce, real_model_pro_isPose, use_openvino_model):
|
||
"""
|
||
处理mask，提取区域点云和box（内缩和外框）
|
||
返回 box (内缩), box_outside(外框), pm_seg(区域点云)
|
||
"""
|
||
if real_model_pro_isPose:
|
||
# 关键点模型的box四点坐标已经给出
|
||
mask = np.zeros(pm.shape[:2], dtype=np.uint8)
|
||
if item[0][0] < item[1][0]:
|
||
arr = [[item[0][0], item[0][1]], [item[1][0], item[1][1]], [item[3][0], item[3][1]], [item[2][0], item[2][1]]]
|
||
else:
|
||
arr = [[item[3][0], item[3][1]], [item[2][0], item[2][1]], [item[0][0], item[0][1]], [item[1][0], item[1][1]]]
|
||
box = shrink_quadrilateral(arr, Height_reduce)
|
||
pts = np.array(box, np.int32)
|
||
cv2.fillPoly(mask, [pts], 255)
|
||
pm_seg = pm[mask == 255]
|
||
box = np.array(box).reshape((-1, 1, 2)).astype(np.int32)
|
||
box_outside = np.array(arr).reshape((-1, 1, 2)).astype(np.int32)
|
||
else:
|
||
# 分割模型
|
||
box_x1, box_y1, box_x2, box_y2 = box_coords
|
||
if not use_openvino_model:
|
||
mask = masks[i].cpu().data.numpy().astype(int)
|
||
else:
|
||
mask = masks[i].astype(int)
|
||
mask = mask[box_y1:box_y2, box_x1:box_x2]
|
||
|
||
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)] = (0, 255, 255)
|
||
imgray = cv2.cvtColor(mask_colored, cv2.COLOR_BGR2GRAY)
|
||
ret, binary = cv2.threshold(imgray, 10, 255, 0)
|
||
contours, hierarchy = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
|
||
|
||
max_contour = None
|
||
max_perimeter = 0
|
||
for contour in contours:
|
||
perimeter = cv2.arcLength(contour, True)
|
||
if perimeter > max_perimeter:
|
||
max_perimeter = perimeter
|
||
max_contour = contour
|
||
rect = cv2.minAreaRect(max_contour)
|
||
if rect[1][0] - width_reduce > 30 and rect[1][1] - Height_reduce > 30:
|
||
rect_reduce = (rect[0], (rect[1][0] - width_reduce, rect[1][1] - Height_reduce), rect[2])
|
||
else:
|
||
rect_reduce = rect
|
||
|
||
box_outside = cv2.boxPoints(rect)
|
||
startidx = box_outside.sum(axis=1).argmin()
|
||
box_outside = np.roll(box_outside, 4 - startidx, 0).astype(np.int32).reshape((-1, 1, 2))
|
||
|
||
box_reduce = cv2.boxPoints(rect_reduce)
|
||
startidx = box_reduce.sum(axis=1).argmin()
|
||
box_reduce = np.roll(box_reduce, 4 - startidx, 0).astype(np.int32).reshape((-1, 1, 2))
|
||
|
||
box_outside += np.array([[[box_x1, box_y1]]] * 4)
|
||
box = box_reduce + np.array([[[box_x1, box_y1]]] * 4)
|
||
|
||
mask_inside = np.zeros(binary.shape, np.uint8)
|
||
cv2.fillPoly(mask_inside, [box_reduce], (255))
|
||
pixel_point2 = cv2.findNonZero(mask_inside)
|
||
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])
|
||
pm_seg = np.array(select_point).reshape(-1, 3)
|
||
|
||
pm_seg = pm_seg[~np.isnan(pm_seg).all(axis=1), :]
|
||
return box, box_outside, pm_seg,max_contour
|
||
|
||
def fit_plane_and_get_normal(self, pm_seg):
|
||
if pm_seg.shape[0] < 100:
|
||
print("分割点云数量较少，无法拟合平面")
|
||
return None
|
||
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
|
||
return [a, b, c]
|
||
|
||
def get_position_test(self, Use_Pose_Model_Pro=False, Point_isVision=False, Box_isPoint=True,
|
||
First_Depth=True, Iter_Max_Pixel=30, save_img_point=0,
|
||
Height_reduce=80, width_reduce=60,
|
||
Xmin=160, Xmax=1050, Ymin=290, Ymax=780):
|
||
|
||
if self.camera_rvc.caminit_isok:
|
||
print("RVC X Camera is not opened!")
|
||
return 0, None, None, None, None
|
||
|
||
# 这里示例用固定路径，建议修改为参数输入
|
||
xyz_path = 'D:/pychram_rob/AutoControlSystem-git/Vision/model/data/2024_11_29_10_05_58.xyz'
|
||
img_path = 'D:/pychram_rob/AutoControlSystem-git/Vision/model/data/2024_11_29_10_05_58.png'
|
||
img, pm = self.read_data(xyz_path, img_path)
|
||
|
||
if save_img_point != 0:
|
||
free_space = get_disk_space(path=os.getcwd())
|
||
if free_space < 15:
|
||
print('系统内存不足，无法保存数据')
|
||
save_img_point = 0
|
||
else:
|
||
save_path = os.path.join(os.getcwd(), 'Vision/model/data/',
|
||
time.strftime('%Y_%m_%d_%H_%M_%S', time.localtime()))
|
||
self.save_data(img, pm, save_img_point, save_path)
|
||
|
||
flag, det_cpu, category_names, extra, real_model_pro_isPose = self.model_inference(img, Use_Pose_Model_Pro)
|
||
if flag != 1:
|
||
print("模型推理失败")
|
||
return 1, img, None, None, None
|
||
|
||
xyz_list = []
|
||
normal_list = []
|
||
area_list = []
|
||
depth_list = []
|
||
uv_list = []
|
||
seg_point_list = []
|
||
box_list = []
|
||
|
||
for i, item in enumerate(det_cpu):
|
||
if real_model_pro_isPose:
|
||
box_coords = None
|
||
else:
|
||
box_coords = item[0:4].astype(np.int32)
|
||
|
||
masks = extra if not real_model_pro_isPose else None
|
||
box, box_outside, pm_seg,max_contour = self.process_mask_and_get_box(i,
|
||
item, masks, pm, box_coords, Height_reduce, width_reduce,
|
||
real_model_pro_isPose, self.use_openvino_model)
|
||
|
||
if pm_seg.shape[0] < 100:
|
||
continue
|
||
|
||
normal = self.fit_plane_and_get_normal(pm_seg)
|
||
if normal is None:
|
||
continue
|
||
|
||
# 计算中心点坐标
|
||
if real_model_pro_isPose:
|
||
x_center = int((item[0][0] + item[1][0] + item[2][0] + item[3][0]) / 4)
|
||
y_center = int((item[0][1] + item[1][1] + item[2][1] + item[3][1]) / 4)
|
||
else:
|
||
x_center = int(np.mean(box[:, 0, 0]))
|
||
y_center = int(np.mean(box[:, 0, 1]))
|
||
|
||
# 确保中心点坐标在范围内
|
||
if x_center < Xmin or x_center > Xmax or y_center < Ymin or y_center > Ymax:
|
||
continue
|
||
|
||
# 获取中心点点云坐标
|
||
point_x, point_y, point_z = remove_nan_mean_value(pm, y_center, x_center, iter_max=Iter_Max_Pixel)
|
||
if np.isnan(point_x):
|
||
continue
|
||
|
||
# 计算面积（如果有轮廓）
|
||
if real_model_pro_isPose:
|
||
area = 0
|
||
else:
|
||
area = cv2.contourArea(max_contour) if 'max_contour' in locals() else 0
|
||
|
||
xyz = [point_x, point_y, point_z]
|
||
if self.cameraType == 'RVC':
|
||
xyz = [v * 1000 for v in xyz] # 换单位为mm
|
||
depth_list.append(point_z * 1000)
|
||
else:
|
||
depth_list.append(point_z)
|
||
|
||
xyz_list.append(xyz)
|
||
normal_list.append(normal)
|
||
area_list.append(area)
|
||
uv_list.append([x_center, y_center])
|
||
seg_point_list.append(pm_seg)
|
||
box = box.reshape(-1,2)
|
||
print("box.shape:", box.shape)
|
||
print("box example:", box)
|
||
box_3d_points = self.get_box_3d_points(pm, box, Box_isPoint)
|
||
box_list.append(box_3d_points)
|
||
|
||
|
||
# 画图示例
|
||
cv2.polylines(img, [box], True, (0, 255, 0), 2)
|
||
cv2.polylines(img, [box_outside], True, (226, 12, 89), 2)
|
||
cv2.circle(img, (x_center, y_center), 2, (255, 255, 255), 3)
|
||
|
||
# 选取最终结果索引
|
||
idx = find_position(depth_list, area_list, 100, First_Depth)
|
||
if idx is None:
|
||
return 1, img, None, None, None
|
||
|
||
# 标记最终中心点
|
||
cv2.circle(img, (uv_list[idx][0], uv_list[idx][1]), 30, (0, 0, 255), 10)
|
||
|
||
# 点云可视化示例
|
||
if Point_isVision:
|
||
pcd = o3d.geometry.PointCloud()
|
||
pcd.points = o3d.utility.Vector3dVector(seg_point_list[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)
|
||
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])
|
||
|
||
# 保存图像
|
||
if save_img_point in (2, 4):
|
||
save_img = cv2.resize(img, (720, 540))
|
||
save_path = os.path.join(os.getcwd(), 'Vision/model/data/',
|
||
time.strftime('%Y_%m_%d_%H_%M_%S', time.localtime()))
|
||
cv2.imwrite(save_path + '.png', save_img)
|
||
|
||
return 1, img, xyz_list[idx], normal_list[idx], box_list[idx]
|
||
|
||
def get_take_photo_position(self, Height_reduce = 30, width_reduce = 30):
|
||
"""
|
||
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, img, pm = self.camera_rvc.get_img_and_point_map() # 拍照，获取图像及
|
||
find_target = False
|
||
if self.camera_rvc.caminit_isok == True:
|
||
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
|
||
else:
|
||
return 0, None, None
|
||
|
||
pass
|
||
|
||
def get_center_position(self):
|
||
""
|
||
'''
|
||
:param api: None
|
||
:return: ret , img, (x,y,z) 图像中心点位置对应的点云数据
|
||
'''
|
||
ret, img, pm = self.camera_rvc.get_img_and_point_map() # 拍照，获取图像及
|
||
if self.camera_rvc.caminit_isok == True:
|
||
if ret:
|
||
if pm != 'None':
|
||
pm_shape_y = pm.shape[0]
|
||
pm_shape_x = pm.shape[1]
|
||
center_point = [int(pm_shape_y/2), int(pm_shape_x/2)]
|
||
point_x, point_y, point_z = remove_nan_mean_value(pm, center_point[0], center_point[1])
|
||
return img, [point_x, point_y, point_z]
|
||
else:
|
||
print('点云值为NAN')
|
||
return None, None
|
||
else:
|
||
return None, None
|
||
else:
|
||
return None, None
|
||
|
||
def release(self):
|
||
self.camera_rvc.release()
|
||
self.model.clear()
|
||
|
||
|
||
|