feat: add --check-crop flag to verify image cropping

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

train.py

@@ -5,11 +5,41 @@ from torch.utils.data import DataLoader, random_split, WeightedRandomSampler
 from torchvision import datasets, transforms
 from PIL import Image, ImageDraw
 import os
+import argparse
 
 from model import (CropLowerRightTriangle, GarageDoorCNN, TRIANGLE_CROP_WIDTH,
                    TRIANGLE_CROP_HEIGHT, RESIZE_DIM)
 
 
+def check_crop():
+    """Saves a sample cropped image for debugging purposes and exits."""
+    # Find a sample image from your dataset
+    SAMPLE_IMAGE_DIR = 'data/labelled/open'
+    if not os.path.isdir(SAMPLE_IMAGE_DIR) or not os.listdir(SAMPLE_IMAGE_DIR):
+        print(f"Error: Cannot find sample image in '{SAMPLE_IMAGE_DIR}'.")
+        print("Please ensure the directory exists and contains images.")
+        return
+
+    sample_image_name = os.listdir(SAMPLE_IMAGE_DIR)[0]
+    sample_image_path = os.path.join(SAMPLE_IMAGE_DIR, sample_image_name)
+
+    print(f"Creating debug crop from image: {sample_image_path}")
+
+    # Load the image
+    image = Image.open(sample_image_path)
+
+    # Create the transform
+    cropper = CropLowerRightTriangle(triangle_width=TRIANGLE_CROP_WIDTH, triangle_height=TRIANGLE_CROP_HEIGHT)
+
+    # Apply the transform
+    cropped_image = cropper(image)
+
+    # Save the result
+    output_path = "cropped_debug_output.png"
+    cropped_image.save(output_path)
+    print(f"Debug image saved to '{output_path}'.")
+
+
 def train_model():
     # --- Hyperparameters and Configuration ---
     DATA_DIR = 'data/labelled'
@@ -104,9 +134,16 @@ def train_model():
     print(f"Model saved to {MODEL_SAVE_PATH}")
 
 if __name__ == '__main__':
-    # Check if data directory exists
-    if not os.path.isdir('data/labelled/open') or not os.path.isdir('data/labelled/closed'):
-        print("Error: Data directories 'data/open' and 'data/closed' not found.")
-        print("Please create them and place your image snapshots inside.")
+    parser = argparse.ArgumentParser(description="Train a CNN for garage door detection or check the image crop.")
+    parser.add_argument('--check-crop', action='store_true', help='Save a sample cropped image and exit.')
+    args = parser.parse_args()
+
+    if args.check_crop:
+        check_crop()
     else:
-        train_model()
+        # Check if data directory exists
+        if not os.path.isdir('data/labelled/open') or not os.path.isdir('data/labelled/closed'):
+            print("Error: Data directories 'data/open' and 'data/closed' not found.")
+            print("Please create them and place your image snapshots inside.")
+        else:
+            train_model()