import fastdeploy as fd
import cv2
import os
from fastdeploy import ModelFormat


def parse_arguments():
    import argparse
    import ast
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--model", required=True, help="Path of yolov7 paddle model.")
    parser.add_argument(
        "--image", required=True, help="Path of test image file.")
    parser.add_argument(
        "--device",
        type=str,
        default='cpu',
        help="Type of inference device, support 'cpu', 'kunlunxin' or 'gpu'.")
    return parser.parse_args()


def build_option(args):
    option = fd.RuntimeOption()
    if args.device.lower() == "gpu":
        option.use_gpu(0)

    if args.device.lower() == "kunlunxin":
        option.use_kunlunxin()

    if args.device.lower() == "ascend":
        option.use_ascend()

    return option


args = parse_arguments()

model_file = os.path.join(args.model, "model.pdmodel")
params_file = os.path.join(args.model, "model.pdiparams")
# 配置runtime，加载模型
runtime_option = build_option(args)
model = fd.vision.detection.YOLOv7(
    model_file,
    params_file,
    runtime_option=runtime_option,
    model_format=ModelFormat.PADDLE)

# 预测图片检测结果
im = cv2.imread(args.image)
result = model.predict(im)
print(result)

# 预测结果可视化
vis_im = fd.vision.vis_detection(im, result)
cv2.imwrite("visualized_result.jpg", vis_im)
print("Visualized result save in ./visualized_result.jpg")