0517

Former-commit-id: 3aa4827fc8d838566a43ce36572a3cc975199242

Auto_Ctrl/.gitignore

@@ -0,0 +1,4 @@
+Input
+Output
+Imgs
+*.jpg

Auto_Ctrl/model-old.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1ca6d267a1b151f02a2096b1e7fbcea8abc298b6feec224c200a8b9a81fc2fc8
+size 107513

Auto_Ctrl/model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1ca6d267a1b151f02a2096b1e7fbcea8abc298b6feec224c200a8b9a81fc2fc8
+size 107513

Auto_Ctrl/predictor_Syringe_Pump.py

@@ -12,10 +12,10 @@ import serial
 from datetime import datetime
 from scipy.optimize import curve_fit
 import numpy as np
-import re
+import joblib
 import json
 import Find_COM
-import builtins
+from threading import Thread
 
 
 class MAT:
@@ -27,9 +27,9 @@ class MAT:
         self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
         self.port = Find_COM.list_ch340_ports()[0]  # 串口名
         self.pump_ser = serial.Serial(self.port, 9600) # 初始化串口
-        self.usb_port = Find_COM.list_USB_ports()  # 串口名
-        if self.usb_port:
-            self.usb_ser = serial.Serial(self.usb_port, 115200) # 初始化串口
+        # self.usb_port = Find_COM.list_USB_ports()  # 串口名
+        # if self.usb_port:
+        #     self.usb_ser = serial.Serial(self.usb_port, 115200) # 初始化串口
         self.classes = classes
         self.total_volume = 0 # 记录总体积
         self.now_volume = 0 # 记录当前注射泵内体积
@@ -42,6 +42,7 @@ class MAT:
         # 将开始时间转化为年月日时分秒的格式，后续文件命名都已此命名
         self.formatted_time = datetime.fromtimestamp(self.start_time).strftime('%Y%m%d_%H%M%S')
 
+        self.model = joblib.load("model.pkl")
         print("实验开始于", self.formatted_time)
 
     def get_picture(self, frame, typ=0, date=''): # 拍摄照片并保存
@@ -166,6 +167,60 @@ class MAT:
         plt.pause(1)
         plt.close()
 
+    
+    def preproc(self, im):
+        try:
+            hsv = cv2.cvtColor(im,cv2.COLOR_BGR2HSV)
+            mask = hsv[:,:,1] > 150
+            mask = mask[:,:,np.newaxis]
+            cnt = np.count_nonzero(mask)
+            hsv*=mask
+            h = round(np.sum(hsv[:,:,0])/cnt)
+            s = round(np.sum(hsv[:,:,1])/cnt)
+            v = round(np.sum(hsv[:,:,2])/cnt)
+            return h,s,v
+        except Exception as e :
+            print(e)
+            return None
+        # name = f"{cl}_{h}_{s}_{v}.jpg"
+
+    def _pred(self):
+        suc,im = self.cap.read()
+        if not suc:
+            print("Failed to capture frame from camera.")
+            return None
+        
+        ret = self.my_predictor(im)
+        if ret is None:
+            cv2.imwrite("tmp.jpg",im)
+            return self.predictor("tmp.jpg")
+        else:
+            if ret == self.end_kind:
+                print("Stop at ",self.total_volume)
+                self.running = False
+                self.start_move_3()
+            else:
+                self.thr = Thread(target=self._pred).start()
+            return ret,0.9
+
+    def my_predictor(self,im):
+        model = self.model
+        ret = self.preproc(im)
+        if ret is None:
+            return None
+        arr = np.array(ret)
+        if len(arr.shape) == 1:
+            arr = arr.reshape(1, -1)
+        
+        # 进行预测并转换为类别名
+        pred_labels = model.predict(arr)
+        # pred_classes = [label_map[label] for label in pred_labels]
+        mp = ["orange", "yellow"]
+        if len(pred_labels) == 1:
+            return mp[pred_labels[0]]
+        else:
+            return None
+
     def predictor(self, im_file): # 预测分类
         image = Image.open(im_file)
         data_transform = transforms.Compose([
@@ -207,11 +262,24 @@ class MAT:
         cv2.destroyAllWindows()
         print("Experiment finished.")
 
+    def save_img(self):
+        suc,im = self.cap.read()
+        cv2.imshow("new",im)
+        name  = f"Imgs/{self.formatted_time}_{self.total_volume}.jpg"
+        if not cv2.imwrite(name,im):
+            print("Failed to save image",name)
 
-    def run(self,quick_speed = 0.2, slow_speed = 0.05,switching_point = 5, end_kind = 'orange', end_prob =0.5):
+    def run(self,quick_speed = 0.2, mid_speed=0.1,slow_speed = 0.05,expect = 5, end_kind = 'orange', end_prob =0.5):
         n = 1
         total_n = n
-        while True:
+        # self.wait = False
+        self.running = True
+        self.end_kind = end_kind
+        self.cnt = 0
+        self.thr = Thread(target=self._pred)
+        self.thr.start()
+        switching_point = expect * 0.9
+        while self.running:
             if self.now_volume <= 0:
                 self.start_move_1()  # 抽取12ml
                 self.now_volume += 12
@@ -229,44 +297,37 @@ class MAT:
 
             self.total_volume = round(self.total_volume, 3)
 
-            # 读取图片
-            ret, frame = self.cap.read()
-            if not ret:
-                print("Failed to capture frame from camera.")
-                break
+            # suc,im = self.cap.read()
+            # cv2.imshow('Color', im)
+            # cv2.waitKey(1)
 
-            name = self.get_picture(frame, 0, self.formatted_time)
-            im_file = 'Input/' + name
-
-            cv2.imshow('Color', frame)
-            cv2.waitKey(1)
-
-            class_a, prob_b = self.predictor(im_file)
+            # class_a, prob_b = self.my_predictor(im_file)
+            # class_a, prob_b = self.predictor(im_file)
+            
             self.volume_list.append(self.total_volume)
-
+            self.save_img()
+            cv2.waitKey(1)
+            
             # 如果有电压测量设备，可以在这里读取电压
             # self.voltage_list.append(self.voltage())
 
-            if class_a == end_kind and prob_b > end_prob:  # 判断终点
-                print('----->>Visual Endpoint<<-----')
-                print(f"Total Volume: {self.total_volume} ml")
-                print(f"Image File: {im_file}")
-                self.color_list.append(1)
-                break
-            else:
-                self.color_list.append(0)
-
+            # if class_a == end_kind and prob_b > end_prob:  # 判断终点
+            #     print('----->>Visual Endpoint<<-----')
+            #     print(f"Total Volume: {self.total_volume} ml")
+            #     print(f"Image File: {im_file}")
+            #     self.color_list.append(1)
+            #     break
+            # else:
+            #     self.color_list.append(0)
             print(f"Current Total Volume: {self.total_volume} ml")
+        self.save_img()
+        print('----->>Visual Endpoint<<-----')
+        print(f"Total Volume: {self.total_volume} ml")
+        # print(f"Image File: {im_file}")
         print("Volume List:", self.volume_list)
         print("Voltage List:", self.voltage_list)
         print("Color List:", self.color_list)
 
-        # 保存实验数据到JSON文件
-        with builtins.open(f'Output/{self.formatted_time}.json', 'w') as f:
-            json.dump(
-                {"volume_list": self.volume_list, 'voltage_list': self.voltage_list, 'color_list': self.color_list},
-                f)
-
 
 if __name__ == "__main__":
     import warnings
@@ -275,6 +336,12 @@ if __name__ == "__main__":
 
     # 创建MAT类的实例并运行
     mat = MAT(videoSourceIndex = 0, weights_path = "resnet34-1Net.pth", json_path = 'class_indices.json', classes = 2)
-    mat.run(quick_speed = 0.2, slow_speed = 0.05, switching_point = 5, end_kind = 'orange', end_prob = 0.5)
+    mat.run(
+        quick_speed = 0.3, 
+        slow_speed = 0.2, 
+        expect = 11.2, 
+        end_kind = 'orange', 
+        end_prob = 0.5
+    )
 
 

Picture_Train/.gitignore

@@ -0,0 +1 @@
+data