# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import fastdeploy as fd
import cv2
import os


def parse_arguments():
    import argparse
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--det_model", required=True, help="Path of Detection model of PPOCR.")
    parser.add_argument(
        "--rec_model",
        required=True,
        help="Path of Recognization model of PPOCR.")
    parser.add_argument(
        "--table_model",
        required=True,
        help="Path of Table recognition model of PPOCR.")
    parser.add_argument(
        "--rec_label_file",
        required=True,
        help="Path of Recognization model of PPOCR.")
    parser.add_argument(
        "--table_char_dict_path",
        type=str,
        required=True,
        help="tabel recognition dict path.")
    parser.add_argument(
        "--rec_bs",
        type=int,
        default=6,
        help="Recognition model inference batch size")
    parser.add_argument(
        "--image", type=str, required=True, help="Path of test image file.")
    parser.add_argument(
        "--device",
        type=str,
        default='cpu',
        help="Type of inference device, support 'cpu' or 'gpu'.")
    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):
    det_option = fd.RuntimeOption()
    rec_option = fd.RuntimeOption()
    table_option = fd.RuntimeOption()

    if args.device.lower() == "gpu":
        det_option.use_gpu(args.device_id)
        rec_option.use_gpu(args.device_id)
        table_option.use_gpu(args.device_id)

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

        # If use TRT backend, the dynamic shape will be set as follow.
        # We recommend that users set the length and height of the detection model to a multiple of 32.
        # We also recommend that users set the Trt input shape as follow.
        det_option.set_trt_input_shape("x", [1, 3, 64, 64], [1, 3, 640, 640],
                                       [1, 3, 960, 960])

        rec_option.set_trt_input_shape("x", [1, 3, 48, 10],
                                       [args.rec_bs, 3, 48, 320],
                                       [args.rec_bs, 3, 48, 2304])

        table_option.set_trt_input_shape("x", [1, 3, 488, 488])

        # Users could save TRT cache file to disk as follow.
        det_option.set_trt_cache_file(args.det_model + "/det_trt_cache.trt")
        rec_option.set_trt_cache_file(args.rec_model + "/rec_trt_cache.trt")
        table_option.set_trt_cache_file(args.table_model +
                                        "/table_trt_cache.trt")

    elif args.backend.lower() == "ort":
        det_option.use_ort_backend()
        rec_option.use_ort_backend()
        table_option.use_ort_backend()

    elif args.backend.lower() == "paddle":
        det_option.use_paddle_infer_backend()
        rec_option.use_paddle_infer_backend()
        table_option.use_paddle_infer_backend()

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

    return det_option, rec_option, table_option


args = parse_arguments()

det_model_file = os.path.join(args.det_model, "inference.pdmodel")
det_params_file = os.path.join(args.det_model, "inference.pdiparams")

rec_model_file = os.path.join(args.rec_model, "inference.pdmodel")
rec_params_file = os.path.join(args.rec_model, "inference.pdiparams")
rec_label_file = args.rec_label_file

table_model_file = os.path.join(args.table_model, "inference.pdmodel")
table_params_file = os.path.join(args.table_model, "inference.pdiparams")
table_char_dict_path = args.table_char_dict_path

# Set the runtime option
det_option, rec_option, table_option = build_option(args)

det_model = fd.vision.ocr.DBDetector(
    det_model_file, det_params_file, runtime_option=det_option)

rec_model = fd.vision.ocr.Recognizer(
    rec_model_file, rec_params_file, rec_label_file, runtime_option=rec_option)

table_model = fd.vision.ocr.StructureV2Table(
    table_model_file,
    table_params_file,
    table_char_dict_path,
    runtime_option=table_option)

det_model.preprocessor.max_side_len = 960
det_model.postprocessor.det_db_thresh = 0.3
det_model.postprocessor.det_db_box_thresh = 0.6
det_model.postprocessor.det_db_unclip_ratio = 1.5
det_model.postprocessor.det_db_score_mode = "slow"
det_model.postprocessor.use_dilation = False

ppstructurev2_table = fd.vision.ocr.PPStructureV2Table(
    det_model=det_model, rec_model=rec_model, table_model=table_model)

ppstructurev2_table.rec_batch_size = args.rec_bs

# Read the input image
im = cv2.imread(args.image)

# Predict and reutrn the results
result = ppstructurev2_table.predict(im)

print(result)

# Visuliaze the results.
vis_im = fd.vision.vis_ppocr(im, result)
cv2.imwrite("visualized_result.jpg", vis_im)
print("Visualized result save in ./visualized_result.jpg")