# 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
    import ast

    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--det_model", required=True, help="Path of Detection model of PPOCR."
    )
    parser.add_argument(
        "--cls_model", required=True, help="Path of Classification model of PPOCR."
    )
    parser.add_argument(
        "--rec_model", required=True, help="Path of Recognization model of PPOCR."
    )
    parser.add_argument(
        "--rec_label_file", required=True, help="Path of Recognization model of PPOCR."
    )
    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', 'kunlunxin' or 'gpu'.",
    )
    parser.add_argument(
        "--cpu_thread_num",
        type=int,
        default=9,
        help="Number of threads while inference on CPU.",
    )
    return parser.parse_args()


def build_option(args):
    det_option = fd.RuntimeOption()
    cls_option = fd.RuntimeOption()
    rec_option = fd.RuntimeOption()
    if args.device == "npu":
        det_option.use_rknpu2()
        cls_option.use_rknpu2()
        rec_option.use_rknpu2()

    return det_option, cls_option, rec_option


def build_format(args):
    det_format = fd.ModelFormat.ONNX
    cls_format = fd.ModelFormat.ONNX
    rec_format = fd.ModelFormat.ONNX
    if args.device == "npu":
        det_format = fd.ModelFormat.RKNN
        cls_format = fd.ModelFormat.RKNN
        rec_format = fd.ModelFormat.RKNN

    return det_format, cls_format, rec_format


args = parse_arguments()

# Detection模型, 检测文字框
det_model_file = args.det_model
det_params_file = ""
# Classification模型，方向分类，可选
cls_model_file = args.cls_model
cls_params_file = ""
# Recognition模型，文字识别模型
rec_model_file = args.rec_model
rec_params_file = ""
rec_label_file = args.rec_label_file

det_option, cls_option, rec_option = build_option(args)
det_format, cls_format, rec_format = build_format(args)

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

cls_model = fd.vision.ocr.Classifier(
    cls_model_file, cls_params_file, runtime_option=cls_option, model_format=cls_format
)

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

# Det,Rec模型启用静态shape推理
det_model.preprocessor.static_shape_infer = True
rec_model.preprocessor.static_shape_infer = True

if args.device == "npu":
    det_model.preprocessor.disable_normalize()
    det_model.preprocessor.disable_permute()
    cls_model.preprocessor.disable_normalize()
    cls_model.preprocessor.disable_permute()
    rec_model.preprocessor.disable_normalize()
    rec_model.preprocessor.disable_permute()

# 创建PP-OCR，串联3个模型，其中cls_model可选，如无需求，可设置为None
ppocr_v3 = fd.vision.ocr.PPOCRv3(
    det_model=det_model, cls_model=cls_model, rec_model=rec_model
)

# Cls模型和Rec模型的batch size 必须设置为1, 开启静态shape推理
ppocr_v3.cls_batch_size = 1
ppocr_v3.rec_batch_size = 1

# 预测图片准备
im = cv2.imread(args.image)

# 预测并打印结果
result = ppocr_v3.predict(im)

print(result)

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