| import tensorflow as tf | |
| from tensorflow.keras.preprocessing import image | |
| import numpy as np | |
| # Load the model | |
| model = tf.keras.models.load_model('nsfw_classifier.h5') | |
| # Load an image file to test, resizing it to 150x150 pixels (as required by this model) | |
| img = image.load_img('', target_size=(512, 512)) | |
| # Convert the image to a numpy array | |
| img_array = image.img_to_array(img) | |
| # Add a fourth dimension to the image (since Keras expects a list of images, not a single image) | |
| img_array = np.expand_dims(img_array, axis=0)/ | |
| # Normalize the image | |
| img_array /= 255. | |
| # Use the model to predict the image's class | |
| pred = model.predict(img_array) | |
| # The model returns a probability between 0 and 1 | |
| # You can convert this to the class label like this: | |
| label = 'NSFW' if pred[0][0] > 0.5 else 'SFW' | |
| print(pred[0][0]) | |
| print("The image is classified as:", label) | |