#!/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 from Vision.tool.CameraRVC import camera from Vision.yolo.yolov8_pt_seg import yolov8_segment class Detection: def __init__(self): model_path = './pt_model/best.pt' device = 'cpu' self.camera_rvc = camera() self.model = yolov8_segment() self.model.load_model(model_path, device) if self.camera_rvc.caminit_isok == True: print("RVC X Camera is opened!") else: print("RVC X Camera is not opened!") def get_position(self): "" ''' :param api: None :return: ret , img, (x,y,z), (nx, ny, nz) ''' 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: flag, det_cpu, dst_img, masks, category_names = self.model.model_inference(img, 0) if flag == 1: xyz = [] nx_ny_nz = [] RegionalArea = [] Depth_Z = [] uv = [] for i, item in enumerate(det_cpu): # 画box box_x1, box_y1, box_x2, box_y2 = item[0:4].astype(np.int32) label = category_names[int(item[5])] 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) mask = masks[i].cpu().data.numpy().astype(int) # mask = masks[i].numpy().astype(int) bbox_image = img[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)] = 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_piont_list = contours_in(contours) # print(len(all_piont_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 ''' 提取区域范围内的（x, y） ''' mask_inside = np.zeros(binary.shape, np.uint8) cv2.drawContours(mask_inside, [max_contour], 0, 255, -1) 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 # cv2.imshow('result', piont_result) ''' 拟合平面，计算法向量 ''' pcd = o3d.geometry.PointCloud() pcd.points = o3d.utility.Vector3dVector(pm_seg) # pcd = o3d.io.read_point_cloud("./Data/seg_point.xyz") plane_model, inliers = pcd.segment_plane(distance_threshold=0.01, ransac_n=5, num_iterations=1000) [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]) ''' 拟合最小外接矩形，计算矩形中心 ''' rect = cv2.minAreaRect(max_contour) # cv2.boxPoints可以将轮廓点转换为四个角点坐标 box = cv2.boxPoints(rect) # 这一步不影响后面的画图，但是可以保证四个角点坐标为顺时针 startidx = box.sum(axis=1).argmin() box = np.roll(box, 4 - startidx, 0) # 在原图上画出预测的外接矩形 box = box.reshape((-1, 1, 2)).astype(np.int32) box = box + [[[box_x1, box_y1]], [[box_x1, box_y1]], [[box_x1, box_y1]], [[box_x1, box_y1]]] x_rotation_center = int((box[0][0][0] + box[1][0][0] + box[2][0][0] + box[3][0][0]) / 4) y_rotation_center = int((box[0][0][1] + box[1][0][1] + box[2][0][1] + box[3][0][1]) / 4) point_x, point_y, point_z = pm[y_rotation_center, x_rotation_center] 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: xyz.append([point_x*1000, point_y*1000, point_z*1000]) Depth_Z.append(point_z*1000) nx_ny_nz.append([a, b, c]) RegionalArea.append(cv2.contourArea(max_contour)) uv.append([x_rotation_center,y_rotation_center]) cv2.polylines(img, [box], True, (0, 255, 0), 2) min_value = min(Depth_Z) # 求深度最大值 min_idx = Depth_Z.index(min_value) # 求最大值对应索引 cv2.circle(img, (uv[min_idx][0], uv[min_idx][1]), 20, (255, 0, 0), 5) # 标出中心点 return 1, img, xyz[min_idx], nx_ny_nz[min_idx] else: return 1, img, None, None else: print("RVC X Camera capture failed!") return 0, None, None, None else: print("RVC X Camera is not opened!") return 0, None, None, None