This: -enchanced image dataset -fixed a bug where images where not loaded -fixed a bug where tractor wasnt moving -fixed a bug where training on gpu caused a crash

.gitignore

@@ -1,3 +1,5 @@
 __pycache__/
 .idea/
 tree.png
+dataset/
+dataset.zip

Image.py

@@ -66,6 +66,9 @@ def getRandomImageFromDataBase():
     imgPath = os.path.join(folderPath, random_image)
 
     while imgPath in imagePathList:
+        for event in pygame.event.get():
+            if event.type == pygame.QUIT:
+                quit()
         label = random.choice(neuralnetwork.labels)
         folderPath = f"dataset/test/{label}"
         files = os.listdir(folderPath)

Tractor.py

@@ -197,6 +197,9 @@ class Tractor:
         count = 0
         for i in range(initPos[1], dCon.NUM_Y):
             for j in range(initPos[0], dCon.NUM_X):
+                for event in pygame.event.get():
+                    if event.type == pygame.QUIT:
+                        quit()
                 if self.slot.imagePath != None:
                     predictedLabel = nn.predictLabel(self.slot.imagePath, model)
                     print(str("Coords: ({:02d}, {:02d})").format(self.slot.x_axis, self.slot.y_axis), "real:", self.slot.label, "predicted:", predictedLabel, "correct" if (self.slot.label == predictedLabel) else "incorrect", 'nawożę za pomocą:', nn.fertilizer[predictedLabel])

neuralnetwork.py

@@ -9,12 +9,12 @@ from PIL import Image
 import random
 
 imageSize = (128, 128)
-labels = ['beetroot', 'carrot', 'potato'] # musi być w kolejności alfabetycznej
+labels = ['carrot', 'potato', 'tomato'] # musi być w kolejności alfabetycznej
 fertilizer = {labels[0]: 'kompost', labels[1]: 'saletra amonowa', labels[2]: 'superfosfat'}
 
 torch.manual_seed(42)
 
-device = torch.device('cuda') if torch.cuda.is_available () else torch.device('cpu')
+device = torch.device('cuda')
 
 def getTransformation():
     transform=transforms.Compose([
@@ -67,9 +67,11 @@ def getModel():
     return model
     
 def saveModel(model, path):
+    print("Saving model")
     torch.save(model.state_dict(), path)
 
 def loadModel(path):
+    print("Loading model")
     model = getModel()
     model.load_state_dict(torch.load(path))
     return model
@@ -83,6 +85,8 @@ def trainNewModel(n_iter=100, batch_size=256):
 def predictLabel(imagePath, model):
     image = Image.open(imagePath).convert("RGB")
     image = preprocess_image(image)
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model.to(device)
     with torch.no_grad():
         model.eval()  # Ustawienie modelu w tryb ewaluacji
         output = model(image)
@@ -97,9 +101,10 @@ def predictLabel(imagePath, model):
 
 
 def preprocess_image(image):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     transform = getTransformation()
-    image = transform(image).unsqueeze(0)  # Dodanie wymiaru batch_size
-
+    image = transform(image).unsqueeze(0)  # Add batch dimension
+    image = image.to(device)  # Move the image tensor to the same device as the model
     return image