UPDATE Vision 更新模型

Vision/camera_coordinate_dete.py

@@ -26,7 +26,7 @@ import os
 
 class Detection:
 
-    def __init__(self, use_openvino_model=False, cameraType = 'RVC'):                   # cameraType = 'RVC'  or cameraType = 'Pe'
+    def __init__(self, use_openvino_model=False, cameraType = 'Pe'):                   # cameraType = 'RVC'  or cameraType = 'Pe'
         """
             初始化相机及模型
             :param use_openvino_model:  选择模型，默认使用openvino
@@ -36,14 +36,14 @@ 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'])
+            model_path = ''.join([os.getcwd(), '/Vision/model/pt/one_bag.pt'])
             device = 'cpu'
             if self.cameraType == 'RVC':
                 self.camera_rvc = camera_rvc()
-                self.seg_distance_threshold = 0.01                  # 1厘米
+                self.seg_distance_threshold = 10                  # 1厘米
             elif self.cameraType == 'Pe':
                 self.camera_rvc = camera_pe()
-                self.seg_distance_threshold = 20                    # 2厘米
+                self.seg_distance_threshold = 10                    # 2厘米
             else:
                 print('相机参数错误')
                 return
@@ -54,7 +54,7 @@ class Detection:
             device = 'CPU'
             if self.cameraType == 'RVC':
                 self.camera_rvc = camera_rvc()
-                self.seg_distance_threshold = 0.01
+                self.seg_distance_threshold = 10
             elif self.cameraType == 'Pe':
                 self.camera_rvc = camera_pe()
                 self.seg_distance_threshold = 20
@@ -64,7 +64,7 @@ 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 = 30, width_reduce = 30):
+    def get_position(self, Point_isVision=False, Box_isPoint=True, First_Depth =True, Iter_Max_Pixel = 30, save_img_point=0, Height_reduce = 50, width_reduce = 50):
         """
             检测料袋相关信息
             :param Point_isVision:  点云可视化
@@ -75,7 +75,7 @@ class Detection:
             :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
-            :return img: ndarry   返回img
+            :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],[],[],[]]

Vision/camera_coordinate_dete_img.py

@@ -30,7 +30,7 @@ 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'])
+            model_path = ''.join([os.getcwd(), '/Vision/model/pt/one_bag.pt'])
             device = 'cpu'
             self.model = yolov8_segment()
             self.model.load_model(model_path, device)
@@ -39,14 +39,14 @@ class Detection:
             device = 'CPU'
             self.model = yolov8_segment_openvino(model_path, device, conf_thres=0.3, iou_thres=0.3)
 
-        img_path = ''.join([os.getcwd(), '/Vision/model/data/test0911.png'])
+        img_path = ''.join([os.getcwd(), '/Vision/model/data/2024_11_29_10_05_58.png'])
-        point_path = ''.join([os.getcwd(), '/Vision/model/data/test0911.xyz'])
+        point_path = ''.join([os.getcwd(), '/Vision/model/data/2024_11_29_10_05_58.xyz'])
         self.img = cv2.imread(img_path)
-        self.point = np.loadtxt(point_path).reshape((1080, 1440, 3))
+        self.point = np.loadtxt(point_path).reshape((self.img.shape[0], self.img.shape[1], 3))
         pass
 
 
-    def get_position(self, Point_isVision=False, Box_isPoint=False, First_Depth =True, Iter_Max_Pixel = 30, save_img_point=0, seg_distance_threshold = 0.01, Height_reduce = 30, width_reduce = 30):
+    def get_position(self, Point_isVision=False, Box_isPoint=True, First_Depth =True, Iter_Max_Pixel = 30, save_img_point=0, seg_distance_threshold = 10, Height_reduce = 50, width_reduce = 50):
         """
             检测料袋相关信息
             :param Point_isVision:  点云可视化
@@ -167,7 +167,7 @@ class Detection:
                     '''
 
                     rect = cv2.minAreaRect(max_contour)
-                    if rect[1][0] - width_reduce < 30 or rect[1][1] - Height_reduce < 30:
+                    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:

Vision/detect_person.py

@@ -1,5 +1,6 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
+
 '''
 # @Time    : 2024/9/25 11:47
 # @Author  : hjw
@@ -23,7 +24,7 @@ class DetectionPerson:
     :return: person [bool], img [ndarray]
     """
     def __init__(self) -> None:
-        model_path = ''.join([os.getcwd(), '/Vision/model/pt/person.pt'])
+        model_path = ''.join([os.getcwd(), '/Vision/model/pt/person_detect.pt'])
         ip = "192.168.1.65"
         port = 554
         name = "admin"
@@ -61,10 +62,10 @@ class DetectionPerson:
                 # cv2.imshow('img2', img_src)
                 # cv2.waitKey(1)
                 for result in results:
-                    if self.names[result[5]]=="person":
+                    if self.names[result[5]] == "person":
                         person = True
                         conf = round(result[4], 2)
-                        #print(conf)
+                        # print(conf)
                         self.draw_box(img_src, result[:4], conf, self.names[result[5]], lw, sf, tf)
 
             return person, img_src

Vision/yolo/yolov8_pt_seg.py

@@ -330,7 +330,7 @@ class yolov8_segment():
         preds = result[0]
         proto = result[1][-1]
         # NMS
-        det = non_max_suppression(preds, conf_thres=0.25, iou_thres=0.3, nc=len(self.model.CLASSES))[0]
+        det = non_max_suppression(preds, conf_thres=0.4, iou_thres=0.4, nc=len(self.model.CLASSES))[0]
         if det.shape[0] != 0:
             # bbox还原至原图尺寸
             det[:, :4] = scale_boxes(img.shape[2:], det[:, :4], ori_img.shape)

get_position_test.py

@@ -22,10 +22,11 @@ import os
 """
    测试 Vision
 """
+
 def detectionPosition_test():
     detection = Detection()
     while True:
-        ret, img, xyz, nx_ny_nz, box = detection.get_position()
+        ret, img, xyz, nx_ny_nz, box = detection.get_position(Point_isVision=True)
         if ret==1:
             print('xyz点云坐标：', xyz)
             print('nx_ny_nz法向量：', nx_ny_nz)
@@ -37,7 +38,7 @@ def detectionPosition_test():
                 print("target_position:", target_position)
                 print("noraml_base", noraml_base)
             cv2.imshow('img', img)
-            cv2.waitKey(1)
+            cv2.waitKey(0)
 
 def take_photo_position_test():
     detection = Detection()
@@ -113,4 +114,4 @@ def bag_collection_test():
 
 
 if __name__ == '__main__':
-    bag_collection_test()
+    detectionPosition_test()