fixes

source/NN/neural_network.py

@@ -4,10 +4,10 @@ from torch.utils.data import DataLoader
 from torchvision import datasets, transforms, utils
 from torchvision.transforms import Compose, Lambda, ToTensor
 import matplotlib.pyplot as plt
-from model import *
+from NN.model import *
 from PIL import Image
 
-device = torch.device('cuda')
+device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
 
 #data transform to tensors:
 data_transformer = transforms.Compose([
@@ -56,12 +56,12 @@ def accuracy(model, dataset):
     return correct.float() / len(dataset)
 
 
-model = Conv_Neural_Network_Model()
+# model = Conv_Neural_Network_Model()
-model.to(device)
+# model.to(device)
 
 #loading the already saved model:
-model.load_state_dict(torch.load('CNN_model.pth'))
+# model.load_state_dict(torch.load('CNN_model.pth'))
-model.eval()
+# model.eval()
 
 # #training the model:
 # train(model, train_set)
@@ -72,13 +72,13 @@ model.eval()
 
 def load_model():
     model = Conv_Neural_Network_Model()
-    model.load_state_dict(torch.load('CNN_model.pth'))
+    model.load_state_dict(torch.load('CNN_model.pth', map_location=torch.device('cpu')))
     model.eval()
     return model
 
 
 def load_image(image_path):
-    testImage = Image.open(image_path)
+    testImage = Image.open(image_path).convert('RGB')
     testImage = data_transformer(testImage)
     testImage = testImage.unsqueeze(0)
     return testImage
@@ -98,17 +98,17 @@ def guess_image(model, image_tensor):
 # print(f"The predicted image is: {prediction}")
 
 #TEST - loading the image and getting results:
-testImage_path = 'resources/images/plant_photos/1c76aa4d-11f4-47d1-8bdd-2cb78deeeccf.jpg'
+# testImage_path = 'resources/images/plant_photos/1c76aa4d-11f4-47d1-8bdd-2cb78deeeccf.jpg'
-testImage = Image.open(testImage_path)
+# testImage = Image.open(testImage_path)
-testImage = data_transformer(testImage)
+# testImage = data_transformer(testImage)
-testImage = testImage.unsqueeze(0)
+# testImage = testImage.unsqueeze(0)
-testImage = testImage.to(device)
+# testImage = testImage.to(device)
 
-model.load_state_dict(torch.load('CNN_model.pth'))
+# model.load_state_dict(torch.load('CNN_model.pth'))
-model.to(device)
+# model.to(device)
-model.eval()
+# model.eval()
 
-testOutput = model(testImage)
+# testOutput = model(testImage)
-_, predicted = torch.max(testOutput, 1)
+# _, predicted = torch.max(testOutput, 1)
-predicted_class = train_set.classes[predicted.item()]
+# predicted_class = train_set.classes[predicted.item()]
-print(f'The predicted class is: {predicted_class}')
+# print(f'The predicted class is: {predicted_class}')

source/main.py

@@ -13,6 +13,7 @@ from plant import Plant
 from bfs import graphsearch, Istate, succ
 from astar import a_star
 from NN.neural_network import load_model, load_image, guess_image
+from PIL import  Image
 
 WIN = pygame.display.set_mode((WIDTH, HEIGHT))
 pygame.display.set_caption('Intelligent tractor')

source/plant.py

@@ -21,17 +21,17 @@ class Plant:
 
     def update_name(self, predicted_class):
         if predicted_class == "Apple":
-            self.name = "Apple"
+            self.name = "apple"
-            self.plant_type = "fruit"
+            self.plant_type = 'fruit'
 
         elif predicted_class == "Radish":
-            self.name = "Radish"
+            self.name = "radish"
-            self.plant_type = "vegetable"
+            self.plant_type = 'vegetable'
 
         elif predicted_class == "Cauliflower":
-            self.name = "Cauliflower"
+            self.name = "cauliflower"
-            self.plant_type = "vegetable"
+            self.plant_type = 'vegetable'
 
         elif predicted_class == "Wheat":
-            self.name = "Wheat"
+            self.name = "wheat"
-            self.plant_type = "cereal"
+            self.plant_type = 'cereal'

source/tile.py

@@ -1,4 +1,5 @@
 import random
+import time
 import os
 import numpy as np
 import matplotlib.pyplot as plt
@@ -54,6 +55,9 @@ class Tile:
     def display_photo(self):
         image_path = self.photo
         img = Image.open(image_path)
+        plt.ion()
         plt.imshow(img)
         plt.axis('off')
         plt.show()
+        time.sleep(5)
+        plt.close()