wozek and regal sprite objects

.vscode/settings.json

@@ -1,5 +0,0 @@
-{
-    "python.analysis.extraPaths": [
-        "./DecisionTree"
-    ]
-}

DecisionTree/drzewo_decyzyjne.py

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+import graphviz
+import pandas as pd
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.tree import export_graphviz
+
+plikZPrzecinkami = open("training_data.txt", 'w')
+
+with open('200permutations_table.txt', 'r') as plik:
+    for linia in plik:
+        liczby = linia.strip()
+        wiersz = ""
+        licznik = 0
+        for liczba in liczby:
+            wiersz += liczba
+            wiersz += ";"
+        wiersz = wiersz[:-1]
+        wiersz += '\n'
+        plikZPrzecinkami.write(wiersz)
+
+plikZPrzecinkami.close()
+
+x = pd.read_csv('training_data.txt', delimiter=';',
+                names=['wielkosc', 'waga,', 'priorytet', 'ksztalt', 'kruchosc', 'dolna', 'gorna', 'g > d'])
+y = pd.read_csv('decisions.txt', names=['polka'])
+# X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=1)  # 70% treningowe and 30% testowe
+
+# Tworzenie instancji klasyfikatora ID3
+clf = DecisionTreeClassifier(criterion='entropy')
+
+# Trenowanie klasyfikatora
+clf.fit(x.values, y.values)
+# clf.fit(X_train, y_train)
+
+
+# Predykcja na nowych danych
+new_data = [[2, 2, 1, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]]
+predictions = clf.predict(new_data)
+# y_pred = clf.predict(X_test)
+
+
+print(predictions)
+# print("Accuracy:", clf.score(new_data, predictions))
+# print("Accuracy:", metrics.accuracy_score(y_test, y_pred))
+
+
+# Wygenerowanie pliku .dot reprezentującego drzewo
+dot_data = export_graphviz(clf, out_file=None, feature_names=list(x.columns), class_names=['0', '1'], filled=True,
+                           rounded=True)
+
+# Tworzenie obiektu graphviz z pliku .dot
+graph = graphviz.Source(dot_data)
+
+# Wyświetlanie drzewa
+graph.view()
+
+z = pd.concat([x, y], axis=1)
+z.to_csv('dane.csv', index=False)

NeuralNetwork/learning_results.txt

@@ -1,31 +0,0 @@
-Epoch:  1 Train Loss: 65 Train Accuracy: 0.5754245754245755
-Epoch:  2 Train Loss: 25 Train Accuracy: 0.7457542457542458
-Epoch:  3 Train Loss: 8 Train Accuracy: 0.8431568431568431
-Epoch:  4 Train Loss: 2 Train Accuracy: 0.9010989010989011
-Epoch:  5 Train Loss: 1 Train Accuracy: 0.9335664335664335
-Epoch:  6 Train Loss: 0 Train Accuracy: 0.9545454545454546
-Epoch:  7 Train Loss: 0 Train Accuracy: 0.972027972027972
-Epoch:  8 Train Loss: 0 Train Accuracy: 0.9820179820179821
-Epoch:  9 Train Loss: 0 Train Accuracy: 0.994005994005994
-Epoch:  10 Train Loss: 0 Train Accuracy: 0.9945054945054945
-
-Epoch:  1 Train Loss: 42 Train Accuracy: 0.6428571428571429
-Epoch:  2 Train Loss: 11 Train Accuracy: 0.8306693306693307
-Epoch:  3 Train Loss: 3 Train Accuracy: 0.8921078921078921
-Epoch:  4 Train Loss: 2 Train Accuracy: 0.8891108891108891
-Epoch:  5 Train Loss: 1 Train Accuracy: 0.9335664335664335
-Epoch:  6 Train Loss: 0 Train Accuracy: 0.952047952047952
-Epoch:  7 Train Loss: 0 Train Accuracy: 0.9545454545454546
-Epoch:  8 Train Loss: 0 Train Accuracy: 0.9655344655344655
-Epoch:  9 Train Loss: 0 Train Accuracy: 0.9815184815184815
-Epoch:  10 Train Loss: 0 Train Accuracy: 0.9805194805194806
-Epoch:  11 Train Loss: 0 Train Accuracy: 0.9855144855144855
-Epoch:  12 Train Loss: 0 Train Accuracy: 0.989010989010989
-Epoch:  13 Train Loss: 0 Train Accuracy: 0.9925074925074925
-Epoch:  14 Train Loss: 0 Train Accuracy: 0.9915084915084915
-Epoch:  15 Train Loss: 0 Train Accuracy: 0.9885114885114885
-Epoch:  16 Train Loss: 0 Train Accuracy: 0.994005994005994
-Epoch:  17 Train Loss: 0 Train Accuracy: 0.997002997002997
-Epoch:  18 Train Loss: 0 Train Accuracy: 0.9965034965034965
-Epoch:  19 Train Loss: 0 Train Accuracy: 0.999000999000999
-Epoch:  20 Train Loss: 0 Train Accuracy: 1.0

NeuralNetwork/neural_network_learning.py

@@ -1,60 +0,0 @@
-import glob
-from src.torchvision_resize_dataset import combined_dataset, images_path, classes
-import src.data_model
-from torch.optim import Adam
-import torch 
-import torch.nn as nn 
-from torch.utils.data import DataLoader 
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-train_loader = DataLoader(
-    combined_dataset,   #dataset of images
-    batch_size=256,     # accuracy
-    shuffle=True    # rand order
-)
-
-model = src.data_model.DataModel(num_objects=2).to(device)
-
-#optimizer 
-optimizer = Adam(model.parameters(), lr=0.001, weight_decay=0.0001)
-#loss function
-criterion = nn.CrossEntropyLoss()
-
-num_epochs = 20
-# train_size = len(glob.glob(images_path+'*.jpg'))
-train_size = 2002
-
-go_to_accuracy = 0.0
-for epoch in range(num_epochs):
-    #training on dataset
-    model.train()
-    train_accuracy = 0.0
-    train_loss = 0.0
-    for i, (images, labels) in enumerate(train_loader):
-        if torch.cuda.is_available():
-            images = torch.Variable(images.cuda())
-            labels = torch.Variable(labels.cuda())
-        # clearing the optimizer gradients
-        optimizer.zero_grad()
-
-        outputs = model(images) # predoction
-        loss = criterion(outputs, labels)   #loss calculation
-        loss.backward()
-        optimizer.step()
-
-        train_loss += loss.cpu().data*images.size(0)
-        _, prediction = torch.max(outputs.data, 1)
-
-        train_accuracy += int(torch.sum(prediction == labels.data))
-
-    train_accuracy = train_accuracy/train_size
-    train_loss = train_loss/train_size
-
-    model.eval()
-
-    print('Epoch:  '+ str(epoch+1) +' Train Loss: '+ str(int(train_loss)) +' Train Accuracy: '+ str(train_accuracy))
-
-    if train_accuracy > go_to_accuracy:
-        go_to_accuracy= train_accuracy
-        torch.save(model.state_dict(), "best_model.pth") 

NeuralNetwork/prediction.py

@@ -1,147 +0,0 @@
-import torch
-import torch.nn as nn
-from torchvision.transforms import transforms
-import numpy as np
-from torch.autograd import Variable
-from torchvision.models import squeezenet1_1
-import torch.functional as F
-from io import open
-import os
-from PIL import Image
-import pathlib
-import glob
-from tkinter import Tk, Label
-from PIL import Image, ImageTk
-
-absolute_path = os.path.abspath('NeuralNetwork/src/train_images')
-train_path = absolute_path
-absolute_path = os.path.abspath('Images/Items_test')
-pred_path = absolute_path
-
-root=pathlib.Path(train_path)
-classes=sorted([j.name.split('/')[-1] for j in root.iterdir()])
-
-
-class DataModel(nn.Module):
-    def __init__(self, num_classes):
-        super(DataModel, self).__init__()
-        #input (batch=256, nr of channels rgb=3 , size=244x244)
-
-        # convolution
-        self.conv1 = nn.Conv2d(in_channels=3, out_channels=12, kernel_size=3, stride=1, padding=1)
-        #shape (256, 12, 224x224)
-
-        # batch normalization
-        self.bn1 = nn.BatchNorm2d(num_features=12)
-        #shape (256, 12, 224x224)
-        self.reul1 = nn.ReLU()
-
-        self.pool=nn.MaxPool2d(kernel_size=2, stride=2)
-        # reduce image size by factor 2
-        # pooling window moves by 2 pixels at a time instead of 1
-        # shape (256, 12, 112x112)
-
-
-
-        self.conv2 = nn.Conv2d(in_channels=12, out_channels=24, kernel_size=3, stride=1, padding=1)
-        self.bn2 = nn.BatchNorm2d(num_features=24)
-        self.reul2 = nn.ReLU()
-        # shape (256, 24, 112x112)
-
-        self.conv3 = nn.Conv2d(in_channels=24, out_channels=48, kernel_size=3, stride=1, padding=1)
-        #shape (256, 48, 112x112)
-        self.bn3 = nn.BatchNorm2d(num_features=48)
-        #shape (256, 48, 112x112)
-        self.reul3 = nn.ReLU()
-
-        # connected layer
-        self.fc = nn.Linear(in_features=48*112*112, out_features=num_classes)
-
-    def forward(self, input):
-            output = self.conv1(input)
-            output = self.bn1(output)
-            output = self.reul1(output)
-
-            output = self.pool(output)
-            output = self.conv2(output)
-            output  = self.bn2(output)
-            output = self.reul2(output)
-            
-            output = self.conv3(output)
-            output = self.bn3(output)
-            output = self.reul3(output)
-
-            # output shape matrix (256, 48, 112x112)
-            #print(output.shape)
-            #print(self.fc.weight.shape)
-
-            output = output.view(-1, 48*112*112)
-            output = self.fc(output)
-            
-            return output
-
-script_dir = os.path.dirname(os.path.abspath(__file__))
-file_path = os.path.join(script_dir, 'best_model.pth')
-checkpoint=torch.load(file_path)
-model = DataModel(num_classes=2)
-model.load_state_dict(checkpoint)
-model.eval()
-
-transformer = transforms.Compose([
-    transforms.Resize((224, 224)),  # Resize images to (224, 224)
-    transforms.ToTensor(),  # Convert images to tensors, 0-255 to 0-1
-    # transforms.RandomHorizontalFlip(),  # 0.5 chance to flip the image
-    transforms.Normalize([0.5,0.5,0.5], [0.5,0.5,0.5]) 
-])
-
-def prediction(img_path,transformer):
-    
-    image=Image.open(img_path)
-    
-    image_tensor=transformer(image).float()
-    
-    image_tensor=image_tensor.unsqueeze_(0)
-    
-    if torch.cuda.is_available():
-        image_tensor.cuda()
-        
-    input=Variable(image_tensor)
-    
-    output=model(input)
-    
-    index=output.data.numpy().argmax()
-    
-    pred=classes[index]
-    
-    return pred
-
-def prediction_keys():
-
-    #funkcja zwracajaca sciezki do kazdego pliku w folderze w postaci listy
-
-    images_path=glob.glob(pred_path+'/*.jpg')
-
-    pred_list=[]
-
-    for i in images_path:
-        pred_list.append(i)
-
-    return pred_list
-
-def predict_one(path):
-
-    #wyswietlanie obrazka po kazdym podniesieniu itemu
-    root = Tk()
-    root.title("Okno z obrazkiem")
-
-    image = Image.open(path)
-    photo = ImageTk.PhotoImage(image)
-    label = Label(root, image=photo)
-    label.pack()
-
-    root.mainloop()
-
-    #uruchamia sie funkcja spr czy obrazek to paczka czy list
-    pred_print = prediction(path,transformer)
-    print('Zdjecie jest: '+pred_print)
-    return pred_print

NeuralNetwork/src/data_model.py

@@ -1,61 +0,0 @@
-import torch.nn as nn 
-import torch
-
-
-class DataModel(nn.Module):
-    def __init__(self, num_objects):
-        super(DataModel, self).__init__()
-        #input (batch=256, nr of channels rgb=3 , size=244x244)
-
-        # convolution
-        self.conv1 = nn.Conv2d(in_channels=3, out_channels=12, kernel_size=3, stride=1, padding=1)
-        #shape (256, 12, 224x224)
-
-        # batch normalization
-        self.bn1 = nn.BatchNorm2d(num_features=12)
-        #shape (256, 12, 224x224)
-        self.reul1 = nn.ReLU()
-
-        self.pool=nn.MaxPool2d(kernel_size=2, stride=2)
-        # reduce image size by factor 2
-        # pooling window moves by 2 pixels at a time instead of 1
-        # shape (256, 12, 112x112)
-
-
-
-        self.conv2 = nn.Conv2d(in_channels=12, out_channels=24, kernel_size=3, stride=1, padding=1)
-        self.bn2 = nn.BatchNorm2d(num_features=24)
-        self.reul2 = nn.ReLU()
-        # shape (256, 24, 112x112)
-
-        self.conv3 = nn.Conv2d(in_channels=24, out_channels=48, kernel_size=3, stride=1, padding=1)
-        #shape (256, 48, 112x112)
-        self.bn3 = nn.BatchNorm2d(num_features=48)
-        #shape (256, 48, 112x112)
-        self.reul3 = nn.ReLU()
-
-        # connected layer
-        self.fc = nn.Linear(in_features=48*112*112, out_features=num_objects)
-
-    def forward(self, input):
-            output = self.conv1(input)
-            output = self.bn1(output)
-            output = self.reul1(output)
-
-            output = self.pool(output)
-            output = self.conv2(output)
-            output  = self.bn2(output)
-            output = self.reul2(output)
-            
-            output = self.conv3(output)
-            output = self.bn3(output)
-            output = self.reul3(output)
-
-            # output shape matrix (256, 48, 112x112)
-            #print(output.shape)
-            #print(self.fc.weight.shape)
-
-            output = output.view(-1, 48*112*112)
-            output = self.fc(output)
-            
-            return output

NeuralNetwork/src/torchvision_resize_dataset.py

@@ -1,31 +0,0 @@
-import glob
-import pathlib
-import torchvision.transforms as transforms
-from torchvision.datasets import ImageFolder
-from torch.utils.data import ConcatDataset
-
-# images have to be the same size for the algorithm to work
-transform = transforms.Compose([
-    transforms.Resize((224, 224)),  # Resize images to (224, 224)
-    transforms.ToTensor(),  # Convert images to tensors, 0-255 to 0-1
-    # transforms.RandomHorizontalFlip(),  # 0.5 chance to flip the image
-    transforms.Normalize([0.5,0.5,0.5], [0.5,0.5,0.5]) 
-])
-
-letters_path = 'C:/Users/wojmed/Documents/VS repositories/Inteligentny_Wozek/NeuralNetwork/src/train_images/letters'
-package_path = 'C:/Users/wojmed/Documents/VS repositories/Inteligentny_Wozek/NeuralNetwork/src/train_images/package'
-images_path = 'C:/Users/wojmed/Documents/VS repositories/Inteligentny_Wozek/NeuralNetwork/src/train_images'
-
-# # Load images from folders
-# letter_folder = ImageFolder(letters_path, transform=transform)
-# package_folder = ImageFolder(package_path, transform=transform)
-
-# Combine the both datasets into a single dataset
-#combined_dataset = ConcatDataset([letter_folder, package_folder])
-combined_dataset = ImageFolder(images_path, transform=transform)
-
-#image classes
-path=pathlib.Path(images_path)
-classes = sorted([i.name.split("/")[-1] for i in path.iterdir()])
-
-# print(classes)