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| import os | |
| import tensorflow as tf | |
| class_labels = ['The Agra Fort', 'Ajanta Caves', 'Alai Darwaza', 'Amarnath Temple', 'The Amber Fort', 'Basilica of Bom Jesus', 'Brihadisvara Temple', 'Charar-e-Sharief shrine', 'Charminar', 'Chhatrapati Shivaji Terminus', 'Chota Imambara', 'Dal Lake', 'The Elephanta Caves', 'Ellora Caves', 'Fatehpur Sikri', 'Gateway of India', 'Ghats in Varanasi', 'Gol Gumbaz', 'Golden Temple', 'Group of Monuments at Mahabalipuram', 'Hampi', 'Hawa Mahal', "Humayun's Tomb", 'The India gate', 'Iron Pillar', 'Jagannath Temple, Puri', 'Jageshwar', 'Jama Masjid', 'Jamali Kamali Tomb', 'Jantar Mantar, Jaipur', 'Jantar Mantar, New Delhi', 'Kedarnath Temple', 'Khajuraho Temple', 'Konark Sun Temple', 'Mahabodhi Temple', 'Meenakshi Temple', 'Nalanda mahavihara', 'Parliament House, New Delhi', 'Qutb Minar', 'Qutb Minar Complex', 'Ram Mandir', 'Rani ki Vav', 'Rashtrapati Bhavan', 'The Red Fort', 'Sanchi', 'Supreme Court of India', 'Swaminarayan Akshardham (Delhi)', 'Taj Hotels', 'The Lotus Temple', 'The Mysore Palace', 'The Statue of Unity', 'The Taj Mahal', 'Vaishno Devi Temple', 'Venkateswara Temple, Tirumala', 'Victoria Memorial, Kolkata', 'Vivekananda Rock Memorial'] | |
| import gdown | |
| import os | |
| import tensorflow as tf | |
| # URL with the file ID | |
| #v3 - https://drive.google.com/file/d/1psuUF80mDO3EZkx2Rl9F5EkqbVHSxFqI/view?usp=sharing | |
| #v4 - https://drive.google.com/file/d/10weM6bZ9R8xVl0KxKig9WRzss4OpIa7f/view?usp=sharing | |
| #v5 - https://drive.google.com/file/d/1PXixJsrUaVcHEEC-jDlv4tHT2qrCrf5c/view?usp=sharing | |
| file_id = '1PXixJsrUaVcHEEC-jDlv4tHT2qrCrf5c' # Replace with your file ID | |
| url = f'https://drive.google.com/uc?id={file_id}' | |
| # Output path to save the model | |
| output = 'LandmarkClassifierV5.h5' # Replace with your model file name | |
| # Download the file if it doesn't exist | |
| if not os.path.exists(output): | |
| gdown.download(url, output, quiet=False) | |
| # Load your model | |
| def load_model(): | |
| # Replace with code to load your actual model | |
| loaded_model = tf.keras.models.load_model(output) | |
| return loaded_model | |
| model = load_model() | |
| # Print model summary to verify | |
| model.summary() | |
| # Predict on a new image | |
| from tensorflow.keras.preprocessing import image | |
| import numpy as np | |
| import requests | |
| import numpy as np | |
| from PIL import Image | |
| from io import BytesIO | |
| import matplotlib.pyplot as plt | |
| def identify_landmark(img): | |
| # Preprocess the image | |
| img = img.resize((224, 224)) | |
| img_array = image.img_to_array(img) | |
| img_array = np.expand_dims(img_array, axis=0) | |
| img_array /= 255.0 | |
| # Get predictions | |
| predictions = model.predict(img_array) | |
| # Get the index of the class with the highest probability | |
| predicted_class_index = np.argmax(predictions[0]) | |
| # Get the probability of the predicted class | |
| predicted_probability = predictions[0][predicted_class_index] | |
| # Map the predicted class index to the class label | |
| predicted_class_label = class_labels[predicted_class_index] | |
| return predicted_class_label,predicted_probability | |
| # def identify_landmark(img): | |
| # img1 = img.copy() | |
| # # Preprocess the image | |
| # img = img.resize((224, 224)) | |
| # img_array = image.img_to_array(img) | |
| # img_array = np.expand_dims(img_array, axis=0) | |
| # img_array /= 255.0 | |
| # # Get predictions | |
| # predictions = model.predict(img_array) | |
| # # Get the index of the class with the highest probability | |
| # predicted_class_index = np.argmax(predictions[0]) | |
| # # Map the predicted class index to the class label | |
| # return class_labels[predicted_class_index] | |
| # def generate_landmark_path(img_path): | |
| # img = image.load_img(img_path, target_size=(224, 224)) | |
| # img1 = img.copy() | |
| # # Preprocess the image | |
| # img = img.resize((224, 224)) | |
| # img_array = image.img_to_array(img) | |
| # img_array = np.expand_dims(img_array, axis=0) | |
| # img_array /= 255.0 | |
| # # Get predictions | |
| # predictions = model.predict(img_array) | |
| # # Get the index of the class with the highest probability | |
| # predicted_class_index = np.argmax(predictions[0]) | |
| # # Map the predicted class index to the class label | |
| # plt.imshow(img1) | |
| # plt.axis("off") | |
| # plt.title(class_labels[predicted_class_index]) | |
| # plt.show() | |
| # return wikipedia.summary(class_labels[predicted_class_index]) | |
| # def generate_landmark_url(img_url): | |
| # try: | |
| # # Download the image from the URL | |
| # response = requests.get(img_url) | |
| # response.raise_for_status() # Check if the request was successful | |
| # # Open the image | |
| # img = Image.open(BytesIO(response.content)) | |
| # img1 = img.copy() | |
| # # Preprocess the image | |
| # img = img.resize((224, 224)) | |
| # img_array = image.img_to_array(img) | |
| # img_array = np.expand_dims(img_array, axis=0) | |
| # img_array /= 255.0 | |
| # # Get predictions | |
| # predictions = model.predict(img_array) | |
| # # Get the index of the class with the highest probability | |
| # predicted_class_index = np.argmax(predictions[0]) | |
| # # Map the predicted class index to the class label | |
| # plt.imshow(img1) | |
| # plt.axis("off") | |
| # plt.title(class_labels[predicted_class_index]) | |
| # plt.show() | |
| # return wikipedia.summary(class_labels[predicted_class_index]) | |
| # except requests.exceptions.RequestException as e: | |
| # print(f"Error downloading the image: {e}") | |
| # return "Invalid image URL." | |
| # except IOError as e: | |
| # print(f"Error opening the image: {e}") | |
| # return "Invalid image file." | |
| # except Exception as e: | |
| # print(f"An error occurred: {e}") | |
| # return "An error occurred while processing the image." |