PaddleX/paddlex/pipelines/PPOCR/pipeline.py

# copyright (c) 2024 PaddlePaddle Authors. All Rights Reserve.
# 
# 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 os
import cv2

from ..base import BasePipeline
from ...modules import create_model, PaddleInferenceOption
from ...modules.text_detection import transforms as text_det_T
from .utils import draw_ocr_box_txt


class OCRPipeline(BasePipeline):
    """OCR Pipeline
    """
    support_models = "ocr"

    def __init__(self,
                 text_det_model_name=None,
                 text_rec_model_name=None,
                 text_det_model_dir=None,
                 text_rec_model_dir=None,
                 text_det_kernel_option=None,
                 text_rec_kernel_option=None,
                 output_dir=None,
                 **kwargs):
        self.text_det_model_name = text_det_model_name
        self.text_rec_model_name = text_rec_model_name
        self.text_det_model_dir = text_det_model_dir
        self.text_rec_model_dir = text_rec_model_dir
        self.output_dir = output_dir
        self.text_det_kernel_option = self.get_kernel_option(
        ) if text_det_kernel_option is None else text_det_kernel_option
        self.text_rec_kernel_option = self.get_kernel_option(
        ) if text_rec_kernel_option is None else text_rec_kernel_option

        self.text_det_post_transforms = [
            text_det_T.DBPostProcess(
                thresh=0.3,
                box_thresh=0.6,
                max_candidates=1000,
                unclip_ratio=1.5,
                use_dilation=False,
                score_mode='fast',
                box_type='quad'),
            # TODO
            text_det_T.CropByPolys(det_box_type="foo")
        ]
        if self.text_det_model_name is not None and self.text_rec_model_name is not None:
            self.load_model()

    def load_model(self):
        """load model predictor
        """
        assert self.text_det_model_name is not None and self.text_rec_model_name is not None
        self.text_det_model = create_model(
            self.text_det_model_name,
            self.text_det_model_dir,
            kernel_option=self.text_det_kernel_option,
            post_transforms=self.text_det_post_transforms)
        self.text_rec_model = create_model(
            self.text_rec_model_name,
            self.text_rec_model_dir,
            kernel_option=self.text_rec_kernel_option)

    def predict(self, input_path):
        """predict
        """
        result = self.text_det_model.predict({"input_path": input_path})
        all_rec_result = []
        for i, img in enumerate(result["sub_imgs"]):
            rec_result = self.text_rec_model.predict({"image": img})
            all_rec_result.append(rec_result["rec_text"][0])
        result["rec_text"] = all_rec_result

        if self.output_dir is not None:
            draw_img = draw_ocr_box_txt(result['original_image'],
                                        result['dt_polys'], result["rec_text"])
            cv2.imwrite(
                os.path.join(self.output_dir, "ocr_result.jpg"),
                draw_img[:, :, ::-1], )

        return result

    def update_model_name(self, model_name_list):
        """update model name and re

        Args:
            model_list (list): list of model name.
        """
        assert len(model_name_list) == 2
        self.text_det_model_name = model_name_list[0]
        self.text_rec_model_name = model_name_list[1]

    def get_kernel_option(self):
        """get kernel option
        """
        kernel_option = PaddleInferenceOption()
        kernel_option.set_device("gpu")
        return kernel_option