diff --git a/.idea/.gitignore b/.idea/.gitignore new file mode 100644 index 0000000..359bb53 --- /dev/null +++ b/.idea/.gitignore @@ -0,0 +1,3 @@ +# 默认忽略的文件 +/shelf/ +/workspace.xml diff --git a/.idea/AutoControlSystem.iml b/.idea/AutoControlSystem.iml new file mode 100644 index 0000000..042213b --- /dev/null +++ b/.idea/AutoControlSystem.iml @@ -0,0 +1,12 @@ + + + + + + + + + + \ No newline at end of file diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml new file mode 100644 index 0000000..105ce2d --- /dev/null +++ b/.idea/inspectionProfiles/profiles_settings.xml @@ -0,0 +1,6 @@ + + + + \ No newline at end of file diff --git a/.idea/misc.xml b/.idea/misc.xml new file mode 100644 index 0000000..0b962ea --- /dev/null +++ b/.idea/misc.xml @@ -0,0 +1,7 @@ + + + + + + \ No newline at end of file diff --git a/.idea/modules.xml b/.idea/modules.xml new file mode 100644 index 0000000..ab07734 --- /dev/null +++ b/.idea/modules.xml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/.idea/vcs.xml b/.idea/vcs.xml new file mode 100644 index 0000000..35eb1dd --- /dev/null +++ b/.idea/vcs.xml @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/EMV.py b/EMV.py new file mode 100644 index 0000000..d9713a2 --- /dev/null +++ b/EMV.py @@ -0,0 +1,62 @@ +import socket +import binascii + +# 网络继电器的 IP 和端口 +HOST = '192.168.20.18' +PORT = 50000 + + +valve_commands = { + 1: { + 'open': '00000000000601050000FF00', + 'close': '000000000006010500000000', + }, + 2: { + 'open': '00000000000601050001FF00', + 'close': '000000000006010500010000', + }, + 3: { + 'open': '00000000000601050002FF00', + 'close': '000000000006010500020000', + } +} + + +# 将十六进制字符串转换为字节数据并发送 +def send_command(command): + byte_data = binascii.unhexlify(command) + + # 创建套接字并连接到继电器 + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock: + sock.connect((HOST, PORT)) + sock.send(byte_data) + response = sock.recv(1024) + return response + + +# 控制电磁阀打开 +def open(grasp, throw, shake): + if grasp: + print("打开电磁阀 1")#抓取 + send_command(valve_commands[1]['open']) + if throw: + print("打开电磁阀 2")#扔袋 + send_command(valve_commands[2]['open']) + if shake: + print("打开电磁阀 3")#震动 + send_command(valve_commands[3]['open']) + + +# 控制电磁阀关闭 +def close(grasp, throw, shake ): + if grasp: + print("关闭电磁阀 1") + send_command(valve_commands[1]['close']) + if throw: + print("关闭电磁阀 2") + send_command(valve_commands[2]['close']) + if shake: + print("关闭电磁阀 3") + send_command(valve_commands[3]['close']) + +close(True,True,True)#参数传True和False \ No newline at end of file diff --git a/Trace/handeye_calibration.py b/Trace/handeye_calibration.py index 1c5c2fd..de2128e 100644 --- a/Trace/handeye_calibration.py +++ b/Trace/handeye_calibration.py @@ -59,7 +59,7 @@ def getPosition(x,y,z,a,b,c,rotation,points): camera2robot = np.loadtxt('./Trace/com_pose2.txt', delimiter=' ') #相对目录且分隔符采用os.sep # robot2base = rotation # camera2base = robot2base @ camera2robot - target_position = np.dot(camera2robot, target) + target_position_raw = np.dot(camera2robot, target) corner_points_camera = np.asarray(points) corner_points_base = np.dot(camera2robot[:3, :3], corner_points_camera.T).T + camera2robot[:3, 3] @@ -76,8 +76,14 @@ def getPosition(x,y,z,a,b,c,rotation,points): # 单位化方向向量 short_edge_direction = edge_vector / np.linalg.norm(edge_vector) + delta = -30#沿法向量方向抬高和压低，-指表示抬高，+值表示压低 angle = np.asarray([a,b,c]) noraml = camera2robot[:3, :3]@angle + normal_vector = noraml / np.linalg.norm(noraml) + target_position = target_position_raw[:3] + delta * normal_vector # target_position 沿法向量移动 + noraml_base = vec2rpy(noraml,short_edge_direction) return target_position,noraml_base + + diff --git a/Vision/camera_coordinate_dete.py b/Vision/camera_coordinate_dete.py index 15ce6cd..e18f189 100644 --- a/Vision/camera_coordinate_dete.py +++ b/Vision/camera_coordinate_dete.py @@ -75,7 +75,7 @@ class Detection: 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 = 60, width_reduce = 100, Xmin =330, Xmax = 1050, Ymin =290 ,Ymax = 690 ): + 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 @@ -618,6 +618,7 @@ class Detection: 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 diff --git a/Vision/camera_coordinate_dete_planevison.py b/Vision/camera_coordinate_dete_planevison.py new file mode 100644 index 0000000..932870b --- /dev/null +++ b/Vision/camera_coordinate_dete_planevison.py @@ -0,0 +1,494 @@ +#!/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 +from Vision.tool.CameraRVC import camera_rvc +from Vision.tool.CameraPe import camera_pe +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 +from Vision.tool.utils import fit_plane_vision +import os + + +class Detection_plane_vsion: + + def __init__(self, use_openvino_model=False, cameraType = 'RVC'): + self.use_openvino_model = use_openvino_model + self.cameraType = cameraType + if self.use_openvino_model == False: + model_path = ''.join([os.getcwd(), '/Vision/model/pt/one_bag.pt']) + device = 'cpu' + 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' + self.model = yolov8_segment_openvino(model_path, device, conf_thres=0.6, iou_thres=0.6) + + img_path = ''.join([os.getcwd(), '/Vision/model/data/2025_01_16_08_47_02.png']) + point_path = ''.join([os.getcwd(), '/Vision/model/data/2025_01_16_08_47_02.xyz']) + self.img = cv2.imread(img_path) + self.img = cv2.resize(self.img,((int(self.img.shape[1]/2),int(self.img.shape[0]/2)))) + self.point = np.loadtxt(point_path).reshape(self.img.shape[0], self.img.shape[1], 3) + self.seg_distance_threshold = 5 + pass + + def get_position(self, Point_isVision=False, Box_isPoint=True, First_Depth=True, Iter_Max_Pixel=30, + save_img_point=0, Height_reduce=60, width_reduce=100, Xmin=0, Xmax=1050, Ymin=0, 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 + ret = 1 + img = self.img + pm = self.point + # self.camera_rvc.caminit_isok = True + + if 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('[a, b, c, d]',[a, b, c, d] ) + # 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_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), 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]]) + print(box_list) + 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: + # 拟合平面展示 + plane_vision = fit_plane_vision(box_list, [a, b, c, d]) + 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, plane_vision]) + 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): + """ + 专用于拍照位置点查找，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 = 1 + img = self.img + pm = self.point + find_target = False + + 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 + + + def release(self): + self.model.clear() + + + diff --git a/Vision/tool/utils.py b/Vision/tool/utils.py index 81248b8..27ff772 100644 --- a/Vision/tool/utils.py +++ b/Vision/tool/utils.py @@ -151,6 +151,7 @@ def remove_nan(pm, y, x): break return point_x, point_y, point_z + def get_disk_space(path='C:'): usage = psutil.disk_usage(path) @@ -164,19 +165,27 @@ def get_disk_space(path='C:'): def find_position(Depth_Z, RegionalArea, RegionalArea_Threshold, first_depth=True): if first_depth == True: sorted_id = sorted(range(len(Depth_Z)), key=lambda k: Depth_Z[k], reverse=False) - Depth_Z1 = [Depth_Z[i] for i in sorted_id] - RegionalArea1 = [RegionalArea[i] for i in sorted_id] - for i in range(len(Depth_Z1)): - if RegionalArea1[i] > RegionalArea_Threshold: - return sorted_id[i] + # Depth_Z1 = [Depth_Z[i] for i in sorted_id] + # RegionalArea1 = [RegionalArea[i] for i in sorted_id] + # for i in range(len(Depth_Z1)): + # if RegionalArea1[i] > RegionalArea_Threshold: + # return sorted_id[i] + if len(sorted_id)>0: + return sorted_id[0] + else: + return else: sorted_id = sorted(range(len(RegionalArea)), key=lambda k: RegionalArea[k], reverse=True) - Depth_Z1 = [Depth_Z[i] for i in sorted_id] - RegionalArea1 = [RegionalArea[i] for i in sorted_id] - for i in range(len(Depth_Z1)): - if RegionalArea1[i] > RegionalArea_Threshold: - return sorted_id[i] + # Depth_Z1 = [Depth_Z[i] for i in sorted_id] + # RegionalArea1 = [RegionalArea[i] for i in sorted_id] + # for i in range(len(Depth_Z1)): + # if RegionalArea1[i] > RegionalArea_Threshold: + # return sorted_id[i] + if len(sorted_id)>0: + return sorted_id[0] + else: + return class_names = ['box', 'other'] @@ -328,6 +337,52 @@ def draw_comparison(img1, img2, name1, name2, fontsize=2.6, text_thickness=3): +def fit_plane_vision(box_list, normal_vector): + plane_x = [] + plane_y = [] + plane_z = [] + print(box_list) + plane_x.append(box_list[0][0][0]) + plane_x.append(box_list[0][1][0]) + plane_x.append(box_list[0][2][0]) + plane_x.append(box_list[0][3][0]) + plane_y.append(box_list[0][0][1]) + plane_y.append(box_list[0][1][1]) + plane_y.append(box_list[0][2][1]) + plane_y.append(box_list[0][3][1]) + plane_z.append(box_list[0][0][2]) + plane_z.append(box_list[0][1][2]) + plane_z.append(box_list[0][2][2]) + plane_z.append(box_list[0][3][2]) + # 定义平面方程的参数 + a = normal_vector[0] + b = normal_vector[1] + c = normal_vector[2] + d = normal_vector[3] + + # 定义平面的范围 + x_range = (int(min(plane_x)), int(max(plane_x))) + y_range = (int(min(plane_y)), int(max(plane_y))) + z_range = (int(min(plane_z)), int(max(plane_z))) + + # 生成平面网格 + x = np.linspace(x_range[0], x_range[1], 10) + y = np.linspace(y_range[0], y_range[1], 20) + X, Y = np.meshgrid(x, y) + Z = -(a * X + b * Y + d) / c # 根据平面方程计算 Z 坐标 + + # 确保 Z 坐标在指定范围内 + Z = np.clip(Z, z_range[0], z_range[1]) + + # 创建 TriangleMesh 对象 + import open3d as o3d + plane_mesh = o3d.geometry.TriangleMesh() + plane_mesh.vertices = o3d.utility.Vector3dVector(np.vstack((X.ravel(), Y.ravel(), Z.ravel())).T) + plane_mesh.triangles = o3d.utility.Vector3iVector( + np.array([[i, i + 1, i + 100] for i in range(99)] + [[i + 1, i + 101, i + 100] for i in range(99)])) + plane_mesh.paint_uniform_color([1, 0.5, 0.5]) # 设置平面颜色 + + return plane_mesh diff --git a/get_position_test.py b/get_position_test.py index b1947dd..5cec46f 100644 --- a/get_position_test.py +++ b/get_position_test.py @@ -11,6 +11,7 @@ import cv2 import os from Vision.camera_coordinate_dete import Detection +from Vision.camera_coordinate_dete_planevison import Detection_plane_vsion from Trace.handeye_calibration import * from Vision.tool.utils import get_disk_space from Vision.tool.utils import remove_nan_mean_value @@ -41,6 +42,23 @@ def detectionPosition_test(): cv2.imshow('img', img) cv2.waitKey(0) +def Detection_plane_vsion_test(): + detection = Detection_plane_vsion() + while True: + ret, img, xyz, nx_ny_nz, box = detection.get_position(Point_isVision=True, save_img_point=1) + if ret==1: + print('xyz点云坐标：', xyz) + print('nx_ny_nz法向量：', nx_ny_nz) + print('矩形框四顶点：', box) + # img = cv2.resize(img,(720, 540)) + if xyz!=None: + transformation_matrix = R_matrix(521.534, 0.705, 850.03, 0.0, 90.0, 0.0) # (x, y, z, u, v, w) + target_position, noraml_base = getPosition(xyz[0], xyz[1], xyz[2], nx_ny_nz[0], nx_ny_nz[1], nx_ny_nz[2], transformation_matrix, box) + print("target_position:", target_position) + print("noraml_base", noraml_base) + cv2.imshow('img', img) + cv2.waitKey(0) + def take_photo_position_test(): detection = Detection() while True: @@ -65,6 +83,7 @@ def detectionPerson_test(): else: cv2.destroyAllWindows() + print("person", person) # video_path = ''.join([os.getcwd(), '/Vision/model/data/1.mp4']) # cam = cv2.VideoCapture(video_path) @@ -77,7 +96,6 @@ def detectionPerson_test(): # cv2.waitKey(1) # else: # break - def HIK_Camera_test(): MY_CAMERA = camera_HIK("192.168.1.121", 554, "admin", "zlzk.123") while True: @@ -86,6 +104,7 @@ def HIK_Camera_test(): img = cv2.resize(frame, (800, 600)) cv2.imshow('img', img) cv2.waitKey(1) + def detectionBagNum_test(): detectionBagNum = DetectionBagNum() video_path = ''.join([os.getcwd(), '/Vision/model/data/2.mp4']) @@ -115,4 +134,4 @@ def bag_collection_test(): if __name__ == '__main__': - detectionPosition_test() \ No newline at end of file + Detection_plane_vsion_test() \ No newline at end of file diff --git a/log/log.log b/log/log.log index 9138c0a..feedae0 100644 --- a/log/log.log +++ b/log/log.log @@ -1,1564 +1,33 @@ -2025-01-11 16:55:33 - INFO - ϵͳ -2025-01-11 16:56:07 - INFO - ϵͳ -2025-01-11 16:56:39 - ERROR - ޻ظ -2025-01-11 16:56:39 - INFO - -2025-01-11 16:56:45 - ERROR - ޻ظ -2025-01-11 16:56:45 - INFO - -2025-01-11 16:56:50 - ERROR - ޻ظ -2025-01-11 16:56:50 - INFO - -2025-01-11 16:56:55 - ERROR - ޻ظ -2025-01-11 16:56:55 - INFO - -2025-01-11 16:57:01 - ERROR - ޻ظ -2025-01-11 16:57:01 - INFO - -2025-01-11 16:57:06 - ERROR - ޻ظ -2025-01-11 16:57:06 - INFO - -2025-01-11 16:57:12 - ERROR - ޻ظ -2025-01-11 16:57:12 - INFO - -2025-01-11 16:57:14 - INFO - ƶλ:ֱߣm0:7.0-m2:50.0-m3:1.0-m4:7.0-m5:50.0-m6:1.0 -2025-01-11 16:57:14 - INFO - {"dsID":"www.hc-system.com.HCRemoteCommand","reqType":"AddRCC","emptyList":"1","instructions":[{"oneshot":"1","action":"10","m0":"7.0","m1":"50.0","m2":"1.0","m3":"7.0","m4":"50.0","m5":"1.0","ckStatus":"0x3F","speed":"50","delay":"0","smooth":"0","tool":"2"}]} -2025-01-11 16:57:17 - ERROR - ޻ظ -2025-01-11 16:57:17 - INFO - -2025-01-11 16:57:22 - ERROR - ޻ظ -2025-01-11 16:57:23 - INFO - -2025-01-11 16:57:28 - ERROR - ޻ظ -2025-01-11 16:57:28 - INFO - -2025-01-11 16:57:33 - ERROR - ޻ظ -2025-01-11 16:57:33 - INFO - -2025-01-11 16:57:39 - ERROR - ޻ظ -2025-01-11 16:57:39 - INFO - -2025-01-11 16:57:44 - ERROR - ޻ظ -2025-01-11 16:57:44 - INFO - -2025-01-11 16:57:50 - ERROR - ޻ظ -2025-01-11 16:57:50 - INFO - -2025-01-11 16:57:52 - INFO - ƶλ:ֱߣm0:7.0-m2:51.0-m3:1.0-m4:7.0-m5:51.0-m6:1.0 -2025-01-11 16:57:52 - INFO - 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