import fastdeploy as fd
import cv2
import os


def parse_arguments():
    import argparse
    import ast
    parser = argparse.ArgumentParser()
    parser.add_argument("--model", default=None, help="Path of yolov5 model.")
    parser.add_argument(
        "--image", default=None, help="Path of test image file.")
    parser.add_argument(
        "--device",
        type=str,
        default='cpu',
        help="Type of inference device, support 'cpu' or 'gpu' or 'kunlunxin'.")
    parser.add_argument(
        "--use_trt",
        type=ast.literal_eval,
        default=False,
        help="Wether to use tensorrt.")
    return parser.parse_args()


def build_option(args):
    option = fd.RuntimeOption()
    if args.device.lower() == "kunlunxin":
        option.use_kunlunxin()

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

    if args.use_trt:
        option.use_trt_backend()
        option.set_trt_input_shape("images", [1, 3, 640, 640])
    return option


args = parse_arguments()

# 配置runtime，加载模型
runtime_option = build_option(args)
model_file = os.path.join(args.model, "model.pdmodel")
params_file = os.path.join(args.model, "model.pdiparams")
model = fd.vision.detection.YOLOv5(
    model_file,
    params_file,
    runtime_option=runtime_option,
    model_format=fd.ModelFormat.PADDLE)

# 预测图片检测结果
if args.image is None:
    image = fd.utils.get_detection_test_image()
else:
    image = args.image
im = cv2.imread(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")