UPDATE Vision 新增方法：用于寻找拍照点，获取图像目标及中心点云坐标
This commit is contained in:
HJW
@ -25,12 +25,19 @@ import os
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class Detection:
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class Detection:
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def __init__(self, use_openvino_model=True, cameraIsRVC = True):
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def __init__(self, use_openvino_model=True, cameraType = 'RVC'): # cameraType = 'RVC' or cameraType = 'Pe'
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"""
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初始化相机及模型
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:param use_openvino_model: 选择模型，默认使用openvino
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:param cameraType: 选择相机 如本相机 'RVC', 图漾相机 'Pe'
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"""
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self.use_openvino_model = use_openvino_model
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self.use_openvino_model = use_openvino_model
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if self.use_openvino_model==False:
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if self.use_openvino_model == False:
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model_path = ''.join([os.getcwd(), '/Vision/model/pt/best.pt'])
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model_path = ''.join([os.getcwd(), '/Vision/model/pt/best.pt'])
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device = 'cpu'
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device = 'cpu'
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if cameraIsRVC == True:
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self.cameraType = cameraType
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if self.cameraType == 'RVC':
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self.camera_rvc = camera_rvc()
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self.camera_rvc = camera_rvc()
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self.seg_distance_threshold = 0.005
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self.seg_distance_threshold = 0.005
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else:
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else:
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@ -41,7 +48,7 @@ class Detection:
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else:
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else:
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model_path = ''.join([os.getcwd(), '/Vision/model/openvino/best.xml'])
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model_path = ''.join([os.getcwd(), '/Vision/model/openvino/best.xml'])
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device = 'CPU'
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device = 'CPU'
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if cameraIsRVC == True:
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if self.cameraType == 'RVC':
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self.camera_rvc = camera_rvc()
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self.camera_rvc = camera_rvc()
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self.seg_distance_threshold = 0.005
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self.seg_distance_threshold = 0.005
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else:
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else:
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@ -50,13 +57,20 @@ class Detection:
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self.model = yolov8_segment_openvino(model_path, device, conf_thres=0.3, iou_thres=0.3)
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self.model = yolov8_segment_openvino(model_path, device, conf_thres=0.3, iou_thres=0.3)
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def get_position(self, Point_isVision=False, save_img_point=False, Height_reduce = 30, width_reduce = 30):
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def get_position(self, Point_isVision=False, save_img_point=False, Height_reduce = 30, width_reduce = 30):
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""
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"""
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'''
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检测料袋相关信息
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:param api: None
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:param Point_isVision: 点云可视化
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:return: ret , img, (x,y,z), (nx, ny, nz)
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:param save_img_point: 保存点云和图片
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'''
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:param Height_reduce: 检测框的高内缩像素
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:param width_reduce: 检测框的宽内缩像素
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:return ret: bool 相机是否正常工作
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:return img: ndarry 返回img
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:return xyz: list 目标中心点云值形如[x,y,z]
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:return nx_ny_nz: list 拟合平面法向量，形如[a,b,c]
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:return box_list: list 内缩检测框四顶点，形如[[x1,y1],[],[],[]]
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"""
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ret, img, pm = self.camera_rvc.get_img_and_point_map() # 拍照，获取图像及
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ret, img, pm = self.camera_rvc.get_img_and_point_map() # 拍照，获取图像及
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if self.camera_rvc.caminit_isok == True:
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if self.camera_rvc.caminit_isok == True:
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if ret == 1:
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if ret == 1:
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@ -196,7 +210,6 @@ class Detection:
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num_iterations=5000)
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num_iterations=5000)
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[a, b, c, d] = plane_model
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[a, b, c, d] = plane_model
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#print(f"Plane equation: {a:.2f}x + {b:.2f}y + {c:.2f}z + {d:.2f} = 0")
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#print(f"Plane equation: {a:.2f}x + {b:.2f}y + {c:.2f}z + {d:.2f} = 0")
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# inlier_cloud = pcd.select_by_index(inliers) # 点云可视化
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# inlier_cloud = pcd.select_by_index(inliers) # 点云可视化
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# inlier_cloud.paint_uniform_color([1.0, 0, 0])
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# inlier_cloud.paint_uniform_color([1.0, 0, 0])
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# outlier_cloud = pcd.select_by_index(inliers, invert=True)
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# outlier_cloud = pcd.select_by_index(inliers, invert=True)
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@ -227,8 +240,12 @@ class Detection:
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[box_point_x2, box_point_y2, box_point_z2],
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[box_point_x2, box_point_y2, box_point_z2],
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[box_point_x3, box_point_y3, box_point_z3],
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[box_point_x3, box_point_y3, box_point_z3],
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[box_point_x4, box_point_y4, box_point_z4]])
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[box_point_x4, box_point_y4, box_point_z4]])
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xyz.append([point_x*1000, point_y*1000, point_z*1000])
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if self.cameraType=='RVC':
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Depth_Z.append(point_z*1000)
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xyz.append([point_x*1000, point_y*1000, point_z*1000])
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Depth_Z.append(point_z*1000)
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else:
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xyz.append([point_x, point_y, point_z])
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Depth_Z.append(point_z)
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nx_ny_nz.append([a, b, c])
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nx_ny_nz.append([a, b, c])
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RegionalArea.append(cv2.contourArea(max_contour))
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RegionalArea.append(cv2.contourArea(max_contour))
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uv.append([x_rotation_center, y_rotation_center])
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uv.append([x_rotation_center, y_rotation_center])
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@ -272,6 +289,141 @@ class Detection:
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print("RVC X Camera is not opened!")
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print("RVC X Camera is not opened!")
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return 0, None, None, None, None
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return 0, None, None, None, None
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def get_take_photo_position(self, Height_reduce = 30, width_reduce = 30):
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"""
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检测当前拍照点能否检测到料袋
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:param Height_reduce:
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:param width_reduce:
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:return ret: bool 相机是否正常工作
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:return img: ndarry 返回img
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:return find_target: bool 是否有目标
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:return xyz: list 目标中心点云值，形如[x,y,z]
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"""
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ret, img, pm = self.camera_rvc.get_img_and_point_map() # 拍照，获取图像及
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find_target = False
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if self.camera_rvc.caminit_isok == True:
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if ret == 1:
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if self.use_openvino_model == False:
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flag, det_cpu, dst_img, masks, category_names = self.model.model_inference(img, 0)
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else:
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flag, det_cpu, scores, masks, category_names = self.model.segment_objects(img)
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if flag == 1:
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xyz = []
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RegionalArea = []
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Depth_Z = []
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uv = []
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for i, item in enumerate(det_cpu):
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find_target = True
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# 画box
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box_x1, box_y1, box_x2, box_y2 = item[0:4].astype(np.int32)
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if self.use_openvino_model == False:
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label = category_names[int(item[5])]
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else:
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label = class_names[int(item[4])]
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rand_color = (0, 255, 255)
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score = item[4]
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org = (int((box_x1 + box_x2) / 2), int((box_y1 + box_y2) / 2))
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x_center = int((box_x1 + box_x2) / 2)
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y_center = int((box_y1 + box_y2) / 2)
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text = '{}|{:.2f}'.format(label, score)
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cv2.putText(img, text, org=org, fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=0.8,
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color=rand_color,
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thickness=2)
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# 画mask
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# mask = masks[i].cpu().numpy().astype(int)
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if self.use_openvino_model == False:
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mask = masks[i].cpu().data.numpy().astype(int)
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else:
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mask = masks[i].astype(int)
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mask = mask[box_y1:box_y2, box_x1:box_x2]
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# mask = masks[i].numpy().astype(int)
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h, w = box_y2 - box_y1, box_x2 - box_x1
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mask_colored = np.zeros((h, w, 3), dtype=np.uint8)
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mask_colored[np.where(mask)] = rand_color
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##################################
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imgray = cv2.cvtColor(mask_colored, cv2.COLOR_BGR2GRAY)
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# cv2.imshow('mask',imgray)
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# cv2.waitKey(1)
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# 2、二进制图像
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ret, binary = cv2.threshold(imgray, 10, 255, 0)
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# 阈值 二进制图像
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# cv2.imshow('bin',binary)
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# cv2.waitKey(1)
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contours, hierarchy = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
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# all_point_list = contours_in(contours)
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# print(len(all_point_list))
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max_contour = None
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max_perimeter = 0
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for contour in contours: # 排除小分割区域或干扰区域
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perimeter = cv2.arcLength(contour, True)
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if perimeter > max_perimeter:
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max_perimeter = perimeter
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max_contour = contour
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'''
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拟合最小外接矩形，计算矩形中心
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'''
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rect = cv2.minAreaRect(max_contour)
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rect_reduce = (
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(rect[0][0], rect[0][1]), (rect[1][0] - width_reduce, rect[1][1] - Height_reduce), rect[2])
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# cv2.boxPoints可以将轮廓点转换为四个角点坐标
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box_outside = cv2.boxPoints(rect)
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# 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
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startidx = box_outside.sum(axis=1).argmin()
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box_outside = np.roll(box_outside, 4 - startidx, 0)
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box_outside = np.intp(box_outside)
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box_outside = box_outside.reshape((-1, 1, 2)).astype(np.int32)
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# cv2.boxPoints可以将轮廓点转换为四个角点坐标
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box_reduce = cv2.boxPoints(rect_reduce)
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startidx = box_reduce.sum(axis=1).argmin()
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box_reduce = np.roll(box_reduce, 4 - startidx, 0)
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box_reduce = np.intp(box_reduce)
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box_reduce = box_reduce.reshape((-1, 1, 2)).astype(np.int32)
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box_outside = box_outside + [[[box_x1, box_y1]], [[box_x1, box_y1]], [[box_x1, box_y1]],[[box_x1, box_y1]]]
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box = box_reduce + [[[box_x1, box_y1]], [[box_x1, box_y1]], [[box_x1, box_y1]], [[box_x1, box_y1]]]
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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])
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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])
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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])
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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])
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x_rotation_center = int((box[0][0][0] + box[1][0][0] + box[2][0][0] + box[3][0][0]) / 4)
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y_rotation_center = int((box[0][0][1] + box[1][0][1] + box[2][0][1] + box[3][0][1]) / 4)
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point_x, point_y, point_z = remove_nan_mean_value(pm, y_rotation_center, x_rotation_center)
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cv2.circle(img, (x_rotation_center, y_rotation_center), 4, (255, 255, 255), 5) # 标出中心点
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if np.isnan(point_x): # 点云值为无效值
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continue
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else:
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if self.cameraType == 'RVC':
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xyz.append([point_x * 1000, point_y * 1000, point_z * 1000])
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Depth_Z.append(point_z * 1000)
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else:
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xyz.append([point_x, point_y, point_z])
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Depth_Z.append(point_z)
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RegionalArea.append(cv2.contourArea(max_contour))
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uv.append([x_rotation_center, y_rotation_center])
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cv2.polylines(img, [box], True, (0, 255, 0), 2)
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cv2.polylines(img, [box_outside], True, (226, 12, 89), 2)
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_idx = find_position(Depth_Z, RegionalArea, 100,True)
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if _idx == None:
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return 1, img, find_target, None
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else:
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cv2.circle(img, (uv[_idx][0], uv[_idx][1]), 30, (0, 0, 255), 20) # 标出中心点
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return 1, img, find_target, xyz[_idx]
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else:
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return 0, None, None
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else:
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return 0, None, None
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pass
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def get_center_position(self):
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def get_center_position(self):
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""
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""
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@ -25,8 +25,9 @@ import os
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class Detection:
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class Detection:
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def __init__(self, use_openvino_model = True, cameraIsRVC = True):
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def __init__(self, use_openvino_model=True, cameraType = 'RVC'):
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self.use_openvino_model = use_openvino_model
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self.use_openvino_model = use_openvino_model
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self.cameraType = cameraType
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if self.use_openvino_model == False:
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if self.use_openvino_model == False:
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model_path = ''.join([os.getcwd(), '/Vision/model/pt/best.pt'])
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model_path = ''.join([os.getcwd(), '/Vision/model/pt/best.pt'])
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device = 'cpu'
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device = 'cpu'
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@ -44,14 +45,20 @@ class Detection:
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pass
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pass
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def get_position(self, Point_isVision=False, save_img_point=True, seg_distance_threshold = 0.01, Height_reduce = 30, width_reduce = 30):
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"""
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检测料袋相关信息
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:param Point_isVision: 点云可视化
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:param save_img_point: 保存点云和图片
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:param Height_reduce: 检测框的高内缩像素
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:param width_reduce: 检测框的宽内缩像素
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:return ret: bool 相机是否正常工作
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:return img: ndarry 返回img
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:return xyz: list 目标中心点云值形如[x,y,z]
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:return nx_ny_nz: list 拟合平面法向量，形如[a,b,c]
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:return box_list: list 内缩检测框四顶点，形如[[x1,y1],[],[],[]]
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def get_position(self, Point_isVision=True, save_img_point=True, seg_distance_threshold = 0.01, Height_reduce = 30, width_reduce = 30):
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"""
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""
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'''
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:param api: None
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:return: ret , img, (x,y,z), (nx, ny, nz)
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'''
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#ret, img, pm = self.camera_rvc.get_img_and_point_map() # 拍照，获取图像及
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ret = 1
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ret = 1
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img = self.img
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img = self.img
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pm = self.point
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pm = self.point
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@ -267,6 +274,141 @@ class Detection:
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print("RVC X Camera capture failed!")
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print("RVC X Camera capture failed!")
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return 0, None, None, None, None
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return 0, None, None, None, None
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def get_take_photo_position(self, Point_isVision=False, save_img_point=False, Height_reduce = 30, width_reduce = 30):
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"""
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专用于拍照位置点查找，检测当前拍照点能否检测到料袋
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:param Height_reduce:
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:param width_reduce:
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:return ret: bool 相机是否正常工作
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:return img: ndarry 返回img
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:return find_target: bool 是否有目标
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:return xyz: list 目标中心点云值，形如[x,y,z]
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"""
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ret = 1
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img = self.img
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pm = self.point
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find_target = False
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||||||
|
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)
|
||||||
|
rect_reduce = (
|
||||||
|
(rect[0][0], rect[0][1]), (rect[1][0] - width_reduce, rect[1][1] - Height_reduce), 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)
|
||||||
|
else:
|
||||||
|
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):
|
def release(self):
|
||||||
self.model.clear()
|
self.model.clear()
|
||||||
|
|
||||||
|
|||||||
@ -19,7 +19,6 @@ import os
|
|||||||
"""
|
"""
|
||||||
def detectionPosition_test():
|
def detectionPosition_test():
|
||||||
detection = Detection()
|
detection = Detection()
|
||||||
|
|
||||||
while True:
|
while True:
|
||||||
ret, img, xyz, nx_ny_nz, box = detection.get_position()
|
ret, img, xyz, nx_ny_nz, box = detection.get_position()
|
||||||
if ret==1:
|
if ret==1:
|
||||||
@ -35,6 +34,18 @@ def detectionPosition_test():
|
|||||||
cv2.imshow('img', img)
|
cv2.imshow('img', img)
|
||||||
cv2.waitKey(1)
|
cv2.waitKey(1)
|
||||||
|
|
||||||
|
def take_photo_position_test():
|
||||||
|
detection = Detection()
|
||||||
|
while True:
|
||||||
|
ret, img, find_target, xyz = detection.get_take_photo_position()
|
||||||
|
if ret==1:
|
||||||
|
print('是否检测到目标：', find_target)
|
||||||
|
if xyz!=None:
|
||||||
|
print('xyz点云坐标：', xyz)
|
||||||
|
else:
|
||||||
|
print('目标点云无效')
|
||||||
|
cv2.imshow('img', img)
|
||||||
|
cv2.waitKey(1)
|
||||||
|
|
||||||
def detectionPerson_test():
|
def detectionPerson_test():
|
||||||
detectionPerson = DetectionPerson()
|
detectionPerson = DetectionPerson()
|
||||||
@ -50,5 +61,6 @@ def detectionPerson_test():
|
|||||||
else:
|
else:
|
||||||
break
|
break
|
||||||
|
|
||||||
|
|
||||||
if __name__ == '__main__':
|
if __name__ == '__main__':
|
||||||
detectionPosition_test()
|
detectionPosition_test()
|
||||||
Reference in New Issue
Block a user