From 0b34eaca539c2820ae21bcf035647e51ac2637a3 Mon Sep 17 00:00:00 2001 From: HJW <1576345902@qq.com> Date: Wed, 9 Oct 2024 10:44:59 +0800 Subject: [PATCH] =?UTF-8?q?UPDATE=20Vision=20box=EF=BC=9A=E7=94=B1?= =?UTF-8?q?=E7=82=B9=E4=BA=91=E5=9D=90=E6=A0=87=E6=94=B9=E4=B8=BA=E5=83=8F?= =?UTF-8?q?=E7=B4=A0=E5=9D=90=E6=A0=87?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Vision/camera_coordinate_dete.py | 75 ++++++++++++++++------------ Vision/camera_coordinate_dete_img.py | 58 +++++++++++---------- 2 files changed, 74 insertions(+), 59 deletions(-) diff --git a/Vision/camera_coordinate_dete.py b/Vision/camera_coordinate_dete.py index 73576c7..7150977 100644 --- a/Vision/camera_coordinate_dete.py +++ b/Vision/camera_coordinate_dete.py @@ -33,16 +33,19 @@ class Detection: """ self.use_openvino_model = use_openvino_model + self.cameraType = cameraType if self.use_openvino_model == False: model_path = ''.join([os.getcwd(), '/Vision/model/pt/best.pt']) device = 'cpu' - self.cameraType = cameraType if self.cameraType == 'RVC': self.camera_rvc = camera_rvc() self.seg_distance_threshold = 0.005 - else: + elif self.cameraType == 'Pe': self.camera_rvc = camera_pe() self.seg_distance_threshold = 10 + else: + print('相机参数错误') + return self.model = yolov8_segment() self.model.load_model(model_path, device) else: @@ -51,17 +54,20 @@ class Detection: if self.cameraType == 'RVC': self.camera_rvc = camera_rvc() self.seg_distance_threshold = 0.005 - else: + elif self.cameraType == 'Pe': self.camera_rvc = camera_pe() self.seg_distance_threshold = 10 + 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, save_img_point=False, Height_reduce = 30, width_reduce = 30): + def get_position(self, Point_isVision=False, save_img_point=0, Height_reduce = 30, width_reduce = 30): """ 检测料袋相关信息 :param Point_isVision: 点云可视化 - :param save_img_point: 保存点云和图片 + :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 :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 @@ -74,22 +80,25 @@ class Detection: 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 == True: + 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 = False + 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']) - 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) - np.savetxt(save_point_name, point_new) + 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: + 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) + 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: @@ -165,8 +174,13 @@ class Detection: ''' rect = cv2.minAreaRect(max_contour) - rect_reduce = ( - (rect[0][0], rect[0][1]), (rect[1][0] - width_reduce, rect[1][1] - Height_reduce), rect[2]) + 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 @@ -209,7 +223,7 @@ class Detection: 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") + # 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) @@ -224,10 +238,6 @@ class Detection: 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]) - box_point_x1, box_point_y1, box_point_z1 = remove_nan_mean_value(pm, box[0][0][1], box[0][0][0]) - box_point_x2, box_point_y2, box_point_z2 = remove_nan_mean_value(pm, box[1][0][1], box[1][0][0]) - box_point_x3, box_point_y3, box_point_z3 = remove_nan_mean_value(pm, box[2][0][1], box[2][0][0]) - box_point_x4, box_point_y4, box_point_z4 = remove_nan_mean_value(pm, 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) @@ -235,15 +245,11 @@ class Detection: if np.isnan(point_x): # 点云值为无效值 continue else: - 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]]) + box_list.append(box) if self.cameraType=='RVC': xyz.append([point_x*1000, point_y*1000, point_z*1000]) Depth_Z.append(point_z*1000) - else: + elif self.cameraType=='Pe': xyz.append([point_x, point_y, point_z]) Depth_Z.append(point_z) nx_ny_nz.append([a, b, c]) @@ -271,12 +277,12 @@ class Detection: 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 == True: + 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 == True: + 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, None, None, None @@ -367,8 +373,13 @@ class Detection: ''' rect = cv2.minAreaRect(max_contour) - rect_reduce = ( - (rect[0][0], rect[0][1]), (rect[1][0] - width_reduce, rect[1][1] - Height_reduce), rect[2]) + 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 @@ -402,7 +413,7 @@ class Detection: if self.cameraType == 'RVC': xyz.append([point_x * 1000, point_y * 1000, point_z * 1000]) Depth_Z.append(point_z * 1000) - else: + elif self.cameraType == 'Pe': xyz.append([point_x, point_y, point_z]) Depth_Z.append(point_z) RegionalArea.append(cv2.contourArea(max_contour)) diff --git a/Vision/camera_coordinate_dete_img.py b/Vision/camera_coordinate_dete_img.py index 639c2db..3a6c6d8 100644 --- a/Vision/camera_coordinate_dete_img.py +++ b/Vision/camera_coordinate_dete_img.py @@ -45,11 +45,11 @@ class Detection: pass - def get_position(self, Point_isVision=False, save_img_point=True, seg_distance_threshold = 0.01, Height_reduce = 30, width_reduce = 30): + def get_position(self, Point_isVision=False, save_img_point=0, seg_distance_threshold = 0.01, Height_reduce = 30, width_reduce = 30): """ 检测料袋相关信息 :param Point_isVision: 点云可视化 - :param save_img_point: 保存点云和图片 + :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 :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 @@ -57,28 +57,30 @@ class Detection: :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 = 1 img = self.img pm = self.point if ret == 1: - if save_img_point == True: + 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 = False + 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']) - 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) - np.savetxt(save_point_name, point_new) + 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: + 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) + np.savetxt(save_point_name, point_new) if self.use_openvino_model == False: @@ -156,7 +158,12 @@ class Detection: ''' rect = cv2.minAreaRect(max_contour) - rect_reduce = ((rect[0][0], rect[0][1]), (rect[1][0] - width_reduce, rect[1][1] - Height_reduce), rect[2]) + 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 @@ -217,11 +224,6 @@ class Detection: 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]) - box_point_x1, box_point_y1, box_point_z1 = remove_nan_mean_value(pm, box[0][0][1], box[0][0][0]) - box_point_x2, box_point_y2, box_point_z2 = remove_nan_mean_value(pm, box[1][0][1], box[1][0][0]) - box_point_x3, box_point_y3, box_point_z3 = remove_nan_mean_value(pm, box[2][0][1], box[2][0][0]) - box_point_x4, box_point_y4, box_point_z4 = remove_nan_mean_value(pm, 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) @@ -229,9 +231,7 @@ class Detection: if np.isnan(point_x): # 点云值为无效值 continue else: - 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]]) - xyz.append([point_x * 1000, point_y * 1000, point_z * 1000]) + box_list.append(box) Depth_Z.append(point_z * 1000) nx_ny_nz.append([a, b, c]) RegionalArea.append(cv2.contourArea(max_contour)) @@ -258,13 +258,13 @@ class Detection: 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]) - if save_img_point == True: + 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 == True: + if save_img_point == 2 or save_img_point == 4: save_img = cv2.resize(img, (720, 540)) cv2.imwrite(save_img_name, save_img) @@ -274,7 +274,7 @@ class Detection: print("RVC X Camera capture failed!") return 0, None, None, None, None - def get_take_photo_position(self, Point_isVision=False, save_img_point=False, Height_reduce = 30, width_reduce = 30): + 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: @@ -354,8 +354,12 @@ class Detection: ''' rect = cv2.minAreaRect(max_contour) - rect_reduce = ( - (rect[0][0], rect[0][1]), (rect[1][0] - width_reduce, rect[1][1] - Height_reduce), rect[2]) + 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 @@ -389,7 +393,7 @@ class Detection: if self.cameraType == 'RVC': xyz.append([point_x * 1000, point_y * 1000, point_z * 1000]) Depth_Z.append(point_z * 1000) - else: + elif self.cameraType == 'Pe': xyz.append([point_x, point_y, point_z]) Depth_Z.append(point_z) RegionalArea.append(cv2.contourArea(max_contour))