preprocess_to_tf.py

# Prepare images for TensorFlow requirements

#Convert to expected type: float
img_laptop_tf = np.array(img_laptop).astype(np.float32) 
# Setup expected input shape: [1,224,224,3]
img_laptop_tf = np.expand_dims(img_laptop_tf, axis = 0) 
# Convert to expected values ranges: [0, 1]
img_laptop_tf = (1.0/255.0) * img_laptop_tf 

print( 'Image shape:', img_laptop_tf.shape)
print( 'First few values: ', img_laptop_tf.flatten()[0:4], 'max value: ', np.amax(img_laptop_tf))

# >> Image shape: (1, 224, 224, 3)
# >> First few values:  [0.8078432  0.78823537 0.7686275  0.7960785 ] max value:  1.0

# The same for golden retriever img
img_golden_tf = np.array(img_golden).astype(np.float32) 
img_golden_tf = np.expand_dims(img_golden_tf, axis = 0) 
img_golden_tf = (1.0/255.0) * img_golden_tf