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import numpy as np
import tensorflow as tf
import tensorflow_hub as hub
from PIL import Image
def pre_process(image: Image.Image):
# load the image and convert into
# numpy array
img = image.resize((180,120))
# asarray() class is used to convert
# PIL images into NumPy arrays
numpydata = np.array(img)
image = np.expand_dims(numpydata, axis=0)
#image = image//255.0
return image
def predict(image: Image.Image):
#save_option = tf.saved_model.LoadOptions(experimental_io_device='/job:localhost', )
model = tf.keras.models.load_model('leaf_classify.h5',custom_objects={'KerasLayer':hub.KerasLayer})#, options=save_option)
pre = model.predict(image,batch_size = None)
#result = np.argmax(pre)
pred = tf.nn.sigmoid(pre)
classes = ['Alstonia Scholaris','Arjun','Bael','Basil','Chinar','Gauva','Jamun','Jatropa','Lemon','Mango','Pomegranate',
'Pongamia Pinnata']
return {classes[i]: float(pred[0][i]) for i in range(len(classes))} |