keras-applications

import numpy as np from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input, decode_predictions from tensorflow.keras.preprocessing import image import os # Create a dummy image for demonstration dummy_image_path = 'dummy_elephant.jpg' if not os.path.exists(dummy_image_path): try: from PIL import Image img_data = np.random.randint(0, 255, size=(224, 224, 3), dtype=np.uint8) dummy_img = Image.fromarray(img_data) dummy_img.save(dummy_image_path) print(f"Created dummy image: {dummy_image_path}") except ImportError: print("Pillow not installed. Skipping dummy image creation.") print("Please provide a real image path to run the example.") dummy_image_path = None if dummy_image_path: # Load the pre-trained ResNet50 model # weights='imagenet' downloads weights if not already present model = ResNet50(weights='imagenet') print("ResNet50 model loaded successfully.") # Load an image and resize it to the target size expected by ResNet50 img = image.load_img(dummy_image_path, target_size=(224, 224)) x = image.img_to_array(img) x = np.expand_dims(x, axis=0) # Add batch dimension # Preprocess the image for the model (e.g., channel reordering, mean subtraction) x = preprocess_input(x) # Make predictions preds = model.predict(x) # Decode the top 3 predictions decoded_preds = decode_predictions(preds, top=3)[0] print('Predicted:', decoded_preds) # Clean up dummy image os.remove(dummy_image_path) else: print("Quickstart example requires Pillow to create a dummy image.")