#!/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 import os from Vision.tool.CameraRVC import camera_rvc from Vision.tool.CameraPe_color2depth import camera_pe as camera_pe_color2depth from Vision.tool.CameraPe_depth2color import camera_pe as camera_pe_depth2color 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 class Detection: def __init__(self, use_openvino_model=False, cameraType = 'Pe', alignmentType = 'color2depth'): # cameraType = 'RVC' or cameraType = 'Pe' """ 初始化相机及模型 :param use_openvino_model: 选择模型，默认使用openvino :param cameraType: 选择相机 如本相机 'RVC', 图漾相机 'Pe' :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 """ self.use_openvino_model = use_openvino_model self.cameraType = cameraType self.alignmentType= alignmentType if self.use_openvino_model == False: model_path = ''.join([os.getcwd(), '/Vision/model/pt/one_bag.pt']) device = 'cpu' if self.cameraType == 'RVC': self.camera_rvc = camera_rvc() self.seg_distance_threshold = 10 # 1厘米 elif self.cameraType == 'Pe': if self.alignmentType=='color2depth': self.camera_rvc = camera_pe_color2depth() else: self.camera_rvc = camera_pe_depth2color() self.seg_distance_threshold = 15 # 2厘米 else: print('相机参数错误') return 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' if self.cameraType == 'RVC': self.camera_rvc = camera_rvc() self.seg_distance_threshold = 10 elif self.cameraType == 'Pe': if self.alignmentType == 'color2depth': self.camera_rvc = camera_pe_color2depth() else: self.camera_rvc = camera_pe_depth2color() self.seg_distance_threshold = 20 else: print('相机参数错误') return self.model = yolov8_segment_openvino(model_path, device, conf_thres=0.3, iou_thres=0.3) def get_position(self, 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): """ 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 if self.camera_rvc.caminit_isok == 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(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_centerXmax or y_rotation_centerYmax: 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]]) 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: 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 get_position_and_depth(self, Point_isVision=False, Box_isPoint=True, First_Depth =True, Target_pixel_threshold = 200, Iter_Max_Pixel = 30, save_img_point=0, Height_reduce = 30, width_reduce = 30): """ 眼在手上，用于料袋顶层抓取，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 Target_pixel_threshold: [int] 设定像素阈值，判断是否可以抓取 :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 :return ret: bool 相机是否正常工作 :return img: ndarry 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 = self.camera_rvc.get_img_and_point_map() # 拍照，获取图像及 if self.camera_rvc.caminit_isok == 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 = [] target_box_area = 0 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): target_box_area = 0 # 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]) target_box_area = rect[1][0] * rect[1][1] # 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(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) 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]]) else: box_list.append([[x1, y1, z1], [x2, y2, z2], [x3, y3, z3], [x4, y4, z4], ]) if target_box_area > img.shape[0]*img.shape[1]*(2/3): # Target_pixel_threshold 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: 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 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()