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