nie potrzeba filderu all

algorithms/neural_network/neural_network_interface.py

@@ -12,7 +12,7 @@ CNN = NeuralNetwork().to(device)
 
 def train(model):
     model.train()
-    trainset = WaterSandTreeGrass('./data/train_csv_file.csv', './data/train/all', transform=setup_photos)
+    trainset = WaterSandTreeGrass('./data/train_csv_file.csv', transform=setup_photos)
     train_loader = DataLoader(trainset, batch_size=batch_size, shuffle=True)
 
     criterion = nn.CrossEntropyLoss()
@@ -46,7 +46,7 @@ def check_accuracy(loader):
     num_samples = 0
     model = NeuralNetwork()
 
-    model.load_state_dict(torch.load("./learnedNetwork.pt"))
+    model.load_state_dict(torch.load("./learnedNetwork.pt", map_location=device))
     model = model.to(device)
 
     with torch.no_grad():
@@ -64,18 +64,12 @@ def check_accuracy(loader):
         print(f"Got {num_correct}/{num_samples} with accuracy {float(num_correct)/float(num_samples)*100:.2f}")
 
 
-testset_loader = DataLoader(
-    WaterSandTreeGrass('./data/test_csv_file.csv', './data/test/all', transform=setup_photos),
-    batch_size=batch_size
-)
-
-
 def what_is_it(img_path):
     image = read_image(img_path, mode=ImageReadMode.RGB)
     image = setup_photos(image).unsqueeze(0)
     model = NeuralNetwork()
 
-    model.load_state_dict(torch.load("./learnedNetwork.pt"))
+    model.load_state_dict(torch.load("./learnedNetwork.pt", map_location=device))
     model = model.to(device)
     image = image.to(device)
 
@@ -85,7 +79,4 @@ def what_is_it(img_path):
         return id_to_class[idx]
 
 
-
-check_accuracy(testset_loader)
-
 print(what_is_it('./data/test/water/water.png'))

algorithms/neural_network/watersandtreegrass.py

@@ -3,22 +3,19 @@ from torch.utils.data import Dataset
 import pandas as pd
 from torchvision.io import read_image, ImageReadMode
 from common.helpers import createCSV
-import os
 
 
 class WaterSandTreeGrass(Dataset):
-    def __init__(self, annotations_file, img_dir, transform=None):
+    def __init__(self, annotations_file, transform=None):
         createCSV()
         self.img_labels = pd.read_csv(annotations_file)
-        self.img_dir = img_dir
         self.transform = transform
 
     def __len__(self):
         return len(self.img_labels)
 
     def __getitem__(self, idx):
-        img_path = os.path.join(self.img_dir, self.img_labels.iloc[idx, 0])
+        image = read_image(self.img_labels.iloc[idx, 0], mode=ImageReadMode.RGB)
-        image = read_image(img_path, mode=ImageReadMode.RGB)
         label = torch.tensor(int(self.img_labels.iloc[idx, 1]))
 
         if self.transform:

common/constants.py

@@ -70,12 +70,13 @@ BAR_ANIMATION_SPEED = 1
 BAR_WIDTH_MULTIPLIER = 0.9  # (0;1>
 BAR_HEIGHT_MULTIPLIER = 0.1
 
+
 #NEURAL_NETWORK
 learning_rate = 0.001
 batch_size = 7
 num_epochs = 10
 
-device = torch.device('cuda')
+device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
 classes = ['grass', 'sand', 'tree', 'water']
 
 setup_photos = transforms.Compose([

common/helpers.py

@@ -14,34 +14,43 @@ def draw_text(text, color, surface, x, y, text_size=30, is_bold=False):
     textrect.topleft = (x, y)
     surface.blit(textobj, textrect)
 
-def createCSV():
-    train_csvfile = open('./data/train_csv_file.csv', 'w', newline="")
-    writer = csv.writer(train_csvfile)
-    writer.writerow(["filename", "type"])
 
+def createCSV():
     train_data_path = './data/train'
     test_data_path = './data/test'
 
-    for class_name in classes:
+    if os.path.exists(train_data_path):
-        class_dir = train_data_path + "/" + class_name
+        train_csvfile = open('./data/train_csv_file.csv', 'w', newline="")
-        for filename in os.listdir(class_dir):
+        writer = csv.writer(train_csvfile)
-            f = os.path.join(class_dir, filename)
+        writer.writerow(["filepath", "type"])
-            if os.path.isfile(f):
-                writer.writerow([filename, class_to_id[class_name]])
 
-    test_csvfile = open('./data/test_csv_file.csv', 'w', newline="")
+        for class_name in classes:
-    writer = csv.writer(test_csvfile)
+            class_dir = train_data_path + "/" + class_name
-    writer.writerow(["filename", "type"])
+            for filename in os.listdir(class_dir):
+                f = os.path.join(class_dir, filename)
+                if os.path.isfile(f):
+                    writer.writerow([f, class_to_id[class_name]])
 
-    for class_name in classes:
+        train_csvfile.close()
-        class_dir = test_data_path + "/" + class_name
-        for filename in os.listdir(class_dir):
-            f = os.path.join(class_dir, filename)
-            if os.path.isfile(f):
-                writer.writerow([filename, class_to_id[class_name]])
 
-    test_csvfile.close()
+    else:
-    train_csvfile.close()
+        print("Brak plików do uczenia")
+
+    if os.path.exists(train_data_path):
+        test_csvfile = open('./data/test_csv_file.csv', 'w', newline="")
+        writer = csv.writer(test_csvfile)
+        writer.writerow(["filepath", "type"])
+
+        for class_name in classes:
+            class_dir = test_data_path + "/" + class_name
+            for filename in os.listdir(class_dir):
+                f = os.path.join(class_dir, filename)
+                if os.path.isfile(f):
+                    writer.writerow([f, class_to_id[class_name]])
+
+        test_csvfile.close()
+    else:
+        print("Brak plików do testowania")
 
 
 def print_numbers():

logic/level.py

@@ -155,4 +155,3 @@ class Level:
         # update and draw the game
         self.sprites.draw(self.screen)
         self.sprites.update()
-