From 92f7030306752946d7545e41aad5811d919418ca Mon Sep 17 00:00:00 2001 From: hjw <1576345902@qq.com> Date: Thu, 5 Sep 2024 09:25:20 +0000 Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20Vision/camera=5Fcoordinate?= =?UTF-8?q?=5Fdete.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Vision/camera_coordinate_dete.py | 328 +++++++++++++++---------------- 1 file changed, 164 insertions(+), 164 deletions(-) diff --git a/Vision/camera_coordinate_dete.py b/Vision/camera_coordinate_dete.py index 7c0c0f7..1be2d14 100644 --- a/Vision/camera_coordinate_dete.py +++ b/Vision/camera_coordinate_dete.py @@ -1,164 +1,164 @@ -#!/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 .tool.CameraRVC import camera -from .yolo.yolov8_pt_seg import yolov8_segment - - -class Detection: - - def __init__(self, model_path, device): - 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 = [] - 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]): - # k = pixel_point2[i] - select_point.append(pm[pixel_point2[i][0][1], pixel_point2[i][0][0]]) - 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=0.1, - ransac_n=3, - num_iterations=100) - [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[x_rotation_center, y_rotation_center] - cv2.circle(img, (x_rotation_center, y_rotation_center), 4, (255, 255, 255), 5) # 标出中心点 - x1, y1, z1, = pm[x_rotation_center+1, y_rotation_center+1] - print('x1 y1 z1 :', x1, y1, z1) - #print('像素坐标（x, y）：', x_rotation_center,y_rotation_center) - print('x y z :', point_x, point_y, point_z) - # point_x=point_x*1000 - # point_y=point_y*1000 - # point_z=point_z*1000 - print('nx ny nz :', a, b, c) - # getPosition(x, y, z, a, b, c) - if(point_x == np.nan): - continue - else: - xyz.append([x1*1000, y1*1000, z1*1000]) - nx_ny_nz.append([a, b, c]) - cv2.polylines(img, [box], True, (0, 255, 0), 2) - - return 1, img, xyz, nx_ny_nz - 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 - - - +#!/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, device): + 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 + + +