clean 3

Former-commit-id: 0d5b15c8877419ac69f03d538f8d1bf9c4e063b9

predictor_Syringe_Pump.py

@@ -1,15 +1,11 @@
 import torch
-from PIL import Image
-import torchvision.transforms as transforms
 import cv2
 import time
 import os
-from model import resnet34
 import serial
 from datetime import datetime
 import numpy as np
 import joblib
-import json
 import Find_COM
 from threading import Thread
 import atexit
@@ -198,37 +194,6 @@ class MAT:
         else:
             return "colored"
 
-    def predictor(self, im_file): # 预测分类
-        image = Image.open(im_file)
-        data_transform = transforms.Compose([
-            transforms.Resize(256),
-            transforms.CenterCrop(224),
-            transforms.ToTensor(),
-            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
-        ])
-        img = data_transform(image)
-        img = torch.unsqueeze(img, dim=0)
-        with open(self.json_path, "r") as f:
-            class_indict = json.load(f)
-
-        model = resnet34(num_classes=self.classes).to(self.device)
-
-        assert os.path.exists(self.weights_path), "file: '{}' dose not exist.".format(self.weights_path)
-        model.load_state_dict(torch.load(self.weights_path, map_location=self.device))
-
-        model.eval()
-        with torch.no_grad():
-            output = torch.squeeze(model(img.to(self.device))).cpu()
-            predict = torch.softmax(output, dim=0)
-            predict_cla = torch.argmax(predict).numpy()
-
-        class_a = "{}".format(class_indict[str(predict_cla)])
-        prob_a = "{:.3}".format(predict[predict_cla].numpy())
-        prob_b = float(prob_a)
-        print('class_:',class_a)
-        print('prob_:',prob_b)
-        return class_a, prob_b
-
     def __del__(self):
         self.pump_ser.close()
         self.cap.release()