diff --git a/Auto_Ctrl/.gitignore b/Auto_Ctrl/.gitignore new file mode 100644 index 0000000..7b63bc8 --- /dev/null +++ b/Auto_Ctrl/.gitignore @@ -0,0 +1,4 @@ +Input +Output +Imgs +*.jpg \ No newline at end of file diff --git a/Auto_Ctrl/model-old.pkl b/Auto_Ctrl/model-old.pkl new file mode 100644 index 0000000..436e5ec --- /dev/null +++ b/Auto_Ctrl/model-old.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1ca6d267a1b151f02a2096b1e7fbcea8abc298b6feec224c200a8b9a81fc2fc8 +size 107513 diff --git a/Auto_Ctrl/model.pkl b/Auto_Ctrl/model.pkl new file mode 100644 index 0000000..436e5ec --- /dev/null +++ b/Auto_Ctrl/model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1ca6d267a1b151f02a2096b1e7fbcea8abc298b6feec224c200a8b9a81fc2fc8 +size 107513 diff --git a/Auto_Ctrl/predictor_Syringe_Pump.py b/Auto_Ctrl/predictor_Syringe_Pump.py index d567f10..fc8a1dc 100644 --- a/Auto_Ctrl/predictor_Syringe_Pump.py +++ b/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 + ) diff --git a/Picture_Train/.gitignore b/Picture_Train/.gitignore new file mode 100644 index 0000000..6320cd2 --- /dev/null +++ b/Picture_Train/.gitignore @@ -0,0 +1 @@ +data \ No newline at end of file