import fastdeploy as fd
import cv2
import os


def parse_arguments():
    import argparse
    import ast
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--model", required=True, help="Path of PaddleClas model.")
    parser.add_argument(
        "--image", type=str, required=True, help="Path of test image file.")
    parser.add_argument(
        "--topk", type=int, default=1, help="Return topk results.")
    parser.add_argument(
        "--device",
        type=str,
        default='cpu',
        help="Type of inference device, support 'cpu' or 'gpu' or 'ipu' or 'kunlunxin' or 'ascend' ."
    )
    parser.add_argument(
        "--device_id",
        type=int,
        default=0,
        help="Define which GPU card used to run model.")
    parser.add_argument(
        "--backend",
        type=str,
        default="default",
        help="Type of inference backend, support ort/trt/paddle/openvino, default 'openvino' for cpu, 'tensorrt' for gpu"
    )
    return parser.parse_args()


def build_option(args):

    option = fd.RuntimeOption()

    if args.device.lower() == "gpu":
        option.use_gpu(args.device_id)

    if args.backend.lower() == "trt":
        assert args.device.lower(
        ) == "gpu", "TensorRT backend require inference on device GPU."
        option.use_trt_backend()

    elif args.backend.lower() == "pptrt":
        assert args.device.lower(
        ) == "gpu", "Paddle-TensorRT backend require inference on device GPU."
        option.use_paddle_infer_backend()
        option.paddle_infer_option.enable_trt = True

    elif args.backend.lower() == "ort":
        option.use_ort_backend()

    elif args.backend.lower() == "paddle":
        option.use_paddle_infer_backend()

    elif args.backend.lower() == "openvino":
        assert args.device.lower(
        ) == "cpu", "OpenVINO backend require inference on device CPU."
        option.use_openvino_backend()

    elif args.backend.lower() == "pplite":
        assert args.device.lower(
        ) == "cpu", "Paddle Lite backend require inference on device CPU."
        option.use_lite_backend()

    return option


args = parse_arguments()

# 配置runtime，加载模型
runtime_option = build_option(args)

model_file = os.path.join(args.model, "inference.pdmodel")
params_file = os.path.join(args.model, "inference.pdiparams")
config_file = os.path.join(args.model, "inference_cls.yaml")
model = fd.vision.classification.PaddleClasModel(
    model_file, params_file, config_file, runtime_option=runtime_option)

# 预测图片分类结果
im = cv2.imread(args.image)
result = model.predict(im, args.topk)
print(result)