import tensorflow as tf from tensorflow import keras from PIL import Image import numpy as np
# Convert to numpy array img_array = np.array(img)
# Load a pre-trained model (example: VGG16) model = keras.applications.VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
# Expand dimensions for batch feeding img_array = np.expand_dims(img_array, axis=0)
# Normalize img_array = img_array / 255.0
# Resize the image img = img.resize((224, 224)) # Assuming a 224x224 input for a model like VGG16
# Extract features features = model.predict(img_array)
import tensorflow as tf from tensorflow import keras from PIL import Image import numpy as np
# Convert to numpy array img_array = np.array(img)
# Load a pre-trained model (example: VGG16) model = keras.applications.VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
# Expand dimensions for batch feeding img_array = np.expand_dims(img_array, axis=0)
# Normalize img_array = img_array / 255.0
# Resize the image img = img.resize((224, 224)) # Assuming a 224x224 input for a model like VGG16
# Extract features features = model.predict(img_array)