57 lines
2.3 KiB
Python
57 lines
2.3 KiB
Python
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import torch
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import torch.nn as nn
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import torchvision.models as models
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import torchvision.transforms as transforms
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from PIL import Image
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# Load the saved model
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class NNModel:
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#load model
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def __init__(self, path):
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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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self.class_names = ['Bean', 'Bitter_Gourd', 'Bottle_Gourd', 'Brinjal',
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'Broccoli', 'Cabbage', 'Capsicum', 'Carrot', 'Cauliflower',
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'Cucumber', 'Papaya', 'Potato', 'Pumpkin', 'Radish', 'Tomato']
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self.model = models.resnet18(pretrained=False)
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self.num_classes = len(self.class_names)
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self.model.fc = nn.Linear(self.model.fc.in_features, self.num_classes)
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self.model.load_state_dict(torch.load(path)) #"neural_network/save/first_model.pth"
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#self.model.to(self.device)
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self.model.eval()
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print(self.class_names)
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print(self.num_classes)
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def input_image(self, path): #"resources/image.jpg"
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# Define the image transformations
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preprocess = transforms.Compose([
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transforms.Resize(224),
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#transforms.CenterCrop(224),
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transforms.ToTensor(),
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#transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
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])
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# Preprocess the input image
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self.input_image = Image.open(path).convert("RGB")
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self.input_tensor = preprocess(self.input_image)
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print("Input image shape:", self.input_image.size)
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input_batch = self.input_tensor.unsqueeze(0)
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return input_batch
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def predicte(self, input_batch):
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with torch.no_grad():
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self.input_batch = input_batch.to(self.device)
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self.output = self.model(self.input_batch)
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print("Output shape:", self.output.shape)
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print("Number of classes:", self.num_classes)
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# Get the predicted class probabilities and labels
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self.probabilities = torch.nn.functional.softmax(self.output[0], dim=0)
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self.predicted_class_index = torch.argmax(self.probabilities).item()
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self.predicted_class = self.class_names[self.predicted_class_index]
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# Use the predicted class in your game logic
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print(f"The predicted class is: {self.predicted_class}")
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