diff --git a/Vision/camera_coordinate_dete_img.py b/Vision/camera_coordinate_dete_img.py new file mode 100644 index 0000000..ba446fb --- /dev/null +++ b/Vision/camera_coordinate_dete_img.py @@ -0,0 +1,188 @@ +#!/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' + img_path = './pt_model/test0824.png' + point_path = './pt_model/test0824.xyz' + self.model = yolov8_segment() + self.model.load_model(model_path, device) + self.img = cv2.imread(img_path) + self.point = np.loadtxt(point_path).reshape((1080, 1440, 3)) + + + + def get_position(self, Point_isVision=True): + "" + ''' + :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 + ret = 1 + img = self.img + pm = self.point + 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 = [] + seg_point = [] + 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) + 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(0) + 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 t in range(pixel_point2.shape[0]): + select_point.append(pm[pixel_point2[t][0][1] + box_y1, pixel_point2[t][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) + plane_model, inliers = pcd.segment_plane(distance_threshold=0.01, + 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]) + + ''' + 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]) + seg_point.append(pm_seg) + cv2.polylines(img, [box], True, (0, 255, 0), 2) + + min_value = min(Depth_Z) # 求深度最小值 + min_idx = Depth_Z.index(min_value) # 求最小值对应索引 + cv2.circle(img, (uv[min_idx][0], uv[min_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[min_idx]) + plane_model, inliers = pcd.segment_plane(distance_threshold=0.01, + 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, 1, 0]) + o3d.visualization.draw_geometries([inlier_cloud, outlier_cloud, pcd2]) + + return 1, img, xyz[min_idx], nx_ny_nz[min_idx] + else: + return 1, img, None, None + + def release(self): + self.model.clear() + + +