refactor: extract model to module and add image sorting script

Co-authored-by: aider (gemini/gemini-2.5-pro-preview-05-06) <aider@aider.chat>

sort.py

@@ -0,0 +1,85 @@
+import torch
+import torch.nn.functional as F
+from torchvision import transforms
+from PIL import Image
+import os
+import shutil
+
+from model import CropLowerRightTriangle, GarageDoorCNN
+
+def sort_images():
+    # --- Configuration ---
+    MODEL_PATH = 'garage_door_cnn.pth'
+    SOURCE_DIR = 'data/hourly_photos/'
+    DEST_DIR = 'data/sorted/open/'
+    
+    # These must match the parameters used during training
+    TRIANGLE_CROP_WIDTH = 556
+    TRIANGLE_CROP_HEIGHT = 1184
+    RESIZE_DIM = 64
+
+    # The classes are sorted alphabetically by ImageFolder: ['closed', 'open']
+    CLASS_NAMES = ['closed', 'open']
+    TARGET_CLASS = 'open'
+    TARGET_CLASS_IDX = CLASS_NAMES.index(TARGET_CLASS)
+
+    # --- Setup ---
+    # Create destination directory if it doesn't exist
+    os.makedirs(DEST_DIR, exist_ok=True)
+
+    # Set up device
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print(f"Using device: {device}")
+
+    # Load model
+    model = GarageDoorCNN(resize_dim=RESIZE_DIM)
+    model.load_state_dict(torch.load(MODEL_PATH, map_location=device))
+    model.to(device)
+    model.eval()
+
+    # Define image transforms
+    data_transform = transforms.Compose([
+        CropLowerRightTriangle(triangle_width=TRIANGLE_CROP_WIDTH, triangle_height=TRIANGLE_CROP_HEIGHT),
+        transforms.Resize((RESIZE_DIM, RESIZE_DIM)),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+    ])
+
+    # --- Process Images ---
+    print(f"Scanning images in {SOURCE_DIR}...")
+    with torch.no_grad():
+        for filename in os.listdir(SOURCE_DIR):
+            file_path = os.path.join(SOURCE_DIR, filename)
+            if os.path.isfile(file_path):
+                try:
+                    image = Image.open(file_path).convert('RGB')
+                    
+                    # Apply transformations
+                    input_tensor = data_transform(image)
+                    input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model
+                    input_batch = input_batch.to(device)
+
+                    # Get model output
+                    output = model(input_batch)
+                    
+                    # Get probabilities and prediction
+                    probabilities = F.softmax(output, dim=1)
+                    confidence, pred_idx = torch.max(probabilities, 1)
+                    
+                    if pred_idx.item() == TARGET_CLASS_IDX:
+                        print(f"Found 'open' image: {file_path} with confidence: {confidence.item():.4f}")
+                        # Copy file
+                        shutil.copy(file_path, os.path.join(DEST_DIR, filename))
+
+                except Exception as e:
+                    print(f"Could not process file {file_path}: {e}")
+    
+    print("Sorting complete.")
+
+if __name__ == '__main__':
+    if not os.path.exists('garage_door_cnn.pth'):
+        print("Error: Model file 'garage_door_cnn.pth' not found. Please run train.py first.")
+    elif not os.path.isdir('data/hourly_photos'):
+        print("Error: Source directory 'data/hourly_photos' not found.")
+    else:
+        sort_images()