# ONNX for image classification model

import onnxruntime as ort
import numpy
from PIL import Image

ort_sess = ort.InferenceSession('model.onnx')
classes = [ "train" ,  "seaplane" ,  "motorbus" ,  "airplane" ,  "stair" ,  "bicycle" ,  "bus" ,  "car" ,  "crosswalk" ,  "hydrant" ,  "motorcycle" ,  "mountain" ,  "stairs" ,  "tow truck" ,  "traffic light" ,  "traffic sign" ,  "truck" , ]

img = Image.open("image.jpg").convert('RGB')
img = img.resize((300, 300 * img.size[1] // img.size[0]), Image.ANTIALIAS)
inp_numpy = numpy.array(img)[None].astype('float32')


class_scores = ort_sess.run(None, {'input': inp_numpy})[0][0]



print("")
print("class_scores", class_scores)
print("Class : ", classes[class_scores.argmax()])