dodanie podstaw sieci nuronowej

App.py

@@ -9,6 +9,7 @@ import Osprzet
 import Ui
 import BFS
 import AStar
+import neuralnetwork
 
 
 bfs1_flag=False
@@ -18,7 +19,9 @@ Astar = False
 Astar2 = False
 if bfs3_flag or Astar or Astar2:
    Pole.stoneFlag = True
-TreeFlag=True
+TreeFlag=False
+nnFlag=True
+newModel=False
 
 pygame.init()
 show_console=True
@@ -29,7 +32,7 @@ image_loader=Image.Image()
 image_loader.load_images()
 goalTreasure = AStar.getRandomGoalTreasure() # nie wiem czy to najlepsze miejsce, obecnie pole zawiera pole gasStation, które służy do renderowania odpowiedniego zdjęcia
 pole=Pole.Pole(screen,image_loader, goalTreasure)
-pole.draw_grid() #musi byc tutaj wywołane ponieważ inicjalizuje sloty do slownika
+pole.draw_grid(nnFlag) #musi byc tutaj wywołane ponieważ inicjalizuje sloty do slownika
 ui=Ui.Ui(screen)
 #Tractor creation
 traktor_slot = pole.get_slot_from_cord((0, 0))
@@ -40,7 +43,7 @@ def init_demo(): #Demo purpose
     old_info=""
     traktor.draw_tractor()
     time.sleep(2)
-    pole.randomize_colors()
+    pole.randomize_colors(nnFlag)
     traktor.draw_tractor()
     start_flag=True
     while True:
@@ -116,6 +119,18 @@ def init_demo(): #Demo purpose
             if(TreeFlag):
                 traktor.move_forward(pole)
                 traktor.tree_move(pole)
+            if(nnFlag):
+                global model
+                if (newModel):
+                    print_to_console("uczenie sieci neuronowej")
+                    model = neuralnetwork.trainNewModel()
+                    neuralnetwork.saveModel(model)
+                    print('model został wygenerowany')
+                else:
+                    model = neuralnetwork.loadModel('model.pth')
+                    print_to_console("model został załądowny")
+                testset = neuralnetwork.getDataset(False)
+                print(neuralnetwork.accuracy(model, testset))
             start_flag=False
         # demo_move()
         old_info=get_info(old_info)

Image.py

@@ -1,6 +1,8 @@
 import pygame
 import displayControler as dCon
 import random
+import neuralnetwork
+import os
 
 class Image:
     def __init__(self):
@@ -53,3 +55,13 @@ class Image:
     
     def return_gasStation(self):
         return self.gasStation_image
+    
+def getRandomImageFromDataBase():
+    label = random.choice(neuralnetwork.labels)
+
+    folderPath = f"dataset/test/{label}"
+    files = os.listdir(folderPath)
+    random_image = random.choice(files)
+    imgPath = os.path.join(folderPath, random_image)
+
+    return pygame.image.load(imgPath), label, imgPath

Pole.py

@@ -6,6 +6,8 @@ import time
 import Ui
 import math
 import random
+import neuralnetwork
+import Image
 
 stoneList = [(3,3), (3,4), (3,5), (3,6), (4,6), (5,6), (6,6), (7,6), (8,6), (9,6), (10,6), (11,6), (12,6), (13,6), (14,6), (15,6), (16,6), (16,7), (16,8), (16,9)]
 stoneFlag = False
@@ -30,7 +32,7 @@ class Pole:
         return self.slot_dict
 
     #Draw grid and tractor (new one)
-    def draw_grid(self):
+    def draw_grid(self, nn=False):
         for x in range(0,dCon.NUM_X): #Draw all cubes in X axis
             for y in range(0,dCon.NUM_Y): #Draw all cubes in Y axis
                 new_slot=Slot.Slot(x,y,Colors.BROWN,self.screen,self.image_loader) #Creation of empty slot 
@@ -48,7 +50,7 @@ class Pole:
                 st=self.slot_dict[self.gasStation]
                 st.set_gasStation_image()
     
-    def randomize_colors(self):
+    def randomize_colors(self, nn = False):
         pygame.display.update()
         time.sleep(3)
         #self.ui.render_text("Randomizing Crops")
@@ -59,7 +61,7 @@ class Pole:
             if(coordinates==(0,0)):
                 continue
             else:
-                self.slot_dict[coordinates].set_random_plant()
+                self.slot_dict[coordinates].set_random_plant(nn)
 
     def change_color_of_slot(self,coordinates,color): #Coordinates must be tuple (x,y) (left top slot has cord (0,0) ), color has to be from defined in Colors.py or custom in RGB value (R,G,B)
         self.get_slot_from_cord(coordinates).color_change(color)

Slot.py

@@ -16,6 +16,8 @@ class Slot:
         self.field=pygame.Rect(self.x_axis*dCon.CUBE_SIZE,self.y_axis*dCon.CUBE_SIZE,dCon.CUBE_SIZE,dCon.CUBE_SIZE)
         self.image_loader=image_loader
         self.garage_image=None
+        self.label = None
+        self.imagePath = None
 
     def draw(self):
         pygame.draw.rect(self.screen,Colors.BROWN,self.field,0) #Draw field
@@ -38,9 +40,13 @@ class Slot:
         self.plant=color
         self.draw()
 
-    def set_random_plant(self):
+    def set_random_plant(self, nn=False):
+        if not nn:
             (plant_name,self.plant_image)=self.random_plant()
             self.plant=Roslina.Roslina(plant_name)
+        else:
+            self.plant_image, self.label, self.imagePath = self.random_plant_dataset()
+            self.plant=Roslina.Roslina(self.label)
         self.set_image()
 
     def set_image(self):
@@ -66,6 +72,8 @@ class Slot:
 
     def random_plant(self): #Probably will not be used later only for demo purpouse
         return self.image_loader.return_random_plant()
+    def random_plant_dataset(self):
+        return Image.getRandomImageFromDataBase()
     
     def return_plant(self):
         return self.plant

neuralnetwork.py

@@ -0,0 +1,111 @@
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader
+from torchvision import datasets
+from torchvision.transforms import Compose, Lambda, ToTensor
+import torchvision.transforms as transforms
+import matplotlib.pyplot as plt
+from PIL import Image
+import random
+
+imageSize = (128, 128)
+labels = ['beetroot', 'potato', 'carrot']
+labels.sort()
+
+torch.manual_seed(42)
+
+device = torch.device('cuda') if torch.cuda.is_available () else torch.device('cpu')
+
+def getTransformation():
+    transform=transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+        transforms.Resize(imageSize),
+        Lambda(lambda x: x.flatten())])
+    return transform
+
+def getDataset(train=True):
+    transform = getTransformation()
+    if (train):
+        trainset = datasets.ImageFolder(root='dataset/train', transform=transform)
+        return trainset
+    else:
+        testset = datasets.ImageFolder(root='dataset/test', transform=transform)
+        return testset
+
+
+def train(model, dataset, n_iter=100, batch_size=256):
+    optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
+    criterion = nn.NLLLoss()
+    dl = DataLoader(dataset, batch_size=batch_size)
+    model.train()
+    for epoch in range(n_iter):
+        for images, targets in dl:
+            optimizer.zero_grad()
+            out = model(images.to(device))
+            loss = criterion(out, targets.to(device))
+            loss.backward()
+            optimizer.step()
+        if epoch % 10 == 0:
+            print('epoch: %3d loss: %.4f' % (epoch, loss))
+    return model
+
+def accuracy(model, dataset):
+    model.eval()
+    correct = sum([(model(images.to(device)).argmax(dim=1) == targets.to(device)).sum()
+    for images, targets in DataLoader(dataset, batch_size=256)])
+    return correct.float() / len(dataset)
+
+def getModel():
+    hidden_size = 300
+    model = nn.Sequential(
+        nn.Linear(imageSize[0] * imageSize[1] * 3, hidden_size),
+        nn.ReLU(),
+        nn.Linear(hidden_size, len(labels)),
+        nn.LogSoftmax(dim=-1)
+        ).to(device)
+    return model
+    
+def saveModel(model):
+    torch.save(model.state_dict(), 'model.pth')
+
+def loadModel(path):
+    model = getModel()
+    model.load_state_dict(torch.load(path))
+    return model
+
+def trainNewModel(n_iter=100, batch_size=256):
+    trainset = getDataset(True)
+    model = getModel()
+    model = train(model, trainset)
+    return model
+
+def predictLabel(imagePath, model):
+    image = Image.open(imagePath).convert("RGB")
+    image = preprocess_image(image)
+    with torch.no_grad():
+        model.eval()  # Ustawienie modelu w tryb ewaluacji
+        output = model(image)
+
+    # Znalezienie indeksu klasy o największej wartości prawdopodobieństwa
+    predicted_class = torch.argmax(output).item()
+    return labels[predicted_class]
+
+def predictLabel(image, model):
+    image = preprocess_image(image)
+    with torch.no_grad():
+        model.eval()  # Ustawienie modelu w tryb ewaluacji
+        output = model(image)
+
+    # Znalezienie indeksu klasy o największej wartości prawdopodobieństwa
+    predicted_class = torch.argmax(output).item()
+    return labels[predicted_class]
+
+
+def preprocess_image(image):
+    transform = getTransformation()
+    image = transform(image).unsqueeze(0)  # Dodanie wymiaru batch_size
+
+    return image
+
+