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import os
import torch
import torchvision.transforms as transforms
from torchvision import models
from PIL import Image
# Load class names dynamically
dataset_path = "categorized_images"
class_names = sorted(os.listdir(dataset_path)) # Get categories from folder names
num_classes = len(class_names)
# Load trained model
model = models.mobilenet_v2(weights=models.MobileNet_V2_Weights.IMAGENET1K_V1)
model.classifier[1] = torch.nn.Linear(1280, num_classes)
model.load_state_dict(torch.load("custom_image_model.pth", map_location=torch.device("cpu")))
model.eval()
# Define image transformations
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
def predict_category(image_path):
"""Predicts the category of a single image."""
image = Image.open(image_path).convert("RGB")
image = transform(image).unsqueeze(0)
with torch.no_grad():
output = model(image)
probabilities = torch.nn.functional.softmax(output, dim=1)
predicted_index = torch.argmax(probabilities, dim=1).item()
return class_names[predicted_index]
def categorize_images(image_folder="uncategorized_images", output_folder="categorized_images"):
"""Categorizes all images in a folder."""
if not os.path.exists(image_folder):
print("❌ Image folder not found!")
return
for img_name in os.listdir(image_folder):
img_path = os.path.join(image_folder, img_name)
if not os.path.isfile(img_path):
continue
category = predict_category(img_path)
category_folder = os.path.join(output_folder, category)
os.makedirs(category_folder, exist_ok=True)
new_path = os.path.join(category_folder, img_name)
os.rename(img_path, new_path)
print(f"✅ Moved {img_name} to {category}/")
if __name__ == "__main__":
categorize_images()
print("✅ Categorization complete!")
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