def recognize_tflite(i,r):
    import tflite_runtime.interpreter as tflite
    import cv2
    import PIL.Image
    import PIL.ImageOps
    import PIL.ImageFilter
    invoke_time = time.time()

    i = PIL.Image.open(io.BytesIO(i))
    pilimg=i
    i=i.filter(PIL.ImageFilter.GaussianBlur(1))
    i=PIL.ImageOps.autocontrast(i, cutoff=(0.1,0,25))

    if not objectDetector[0]:
        objectDetector[0]=tflite.Interpreter(num_threads=4, model_path=os.path.join(path,"efficientdet/efficientdet-lite0-f32.tflite"))
        objectDetector[0].allocate_tensors()

        objectDetector[1]=numpy.loadtxt(os.path.join(path,"labelmap.txt"),dtype = str, delimiter="/n") 

    interpreter = objectDetector[0]
    labels = objectDetector[1]

    original_image = toImgOpenCV(i)
    # Get input and output tensors.
    input_details = interpreter.get_input_details()
    output_details = interpreter.get_output_details()

    tensor_w = input_details[0]['shape'][1]
    tensor_h= input_details[0]['shape'][2]

    image = letterbox_image(original_image,(tensor_w,tensor_h))
    image = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)


    input_image = numpy.expand_dims(image,0)