mirror of
https://github.com/RapidAI/RapidOCR.git
synced 2026-11-10 21:10:55 +08:00
Mohammad Hijjawi
f65c7da00e
TextDetector.get_preprocess replaced limit_side_len with a hard-coded 960/1500/2000 bucket whenever limit_type was "max", so the configured value was ignored and large images reached the detector unresized. Pass the configured limit_side_len through for both limit types, matching PaddleOCR DetResizeForTest.
97 lines
3.4 KiB
Python
97 lines
3.4 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# -*- encoding: utf-8 -*-
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# @Author: SWHL
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# @Contact: liekkaskono@163.com
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import time
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from typing import Any, Dict
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import numpy as np
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from rapidocr.inference_engine.base import get_engine
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from .utils import DBPostProcess, DetPreProcess, TextDetOutput
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_BOX_SORT_Y_THRESHOLD = 10
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class TextDetector:
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def __init__(self, cfg: Dict[str, Any]):
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self.limit_side_len = cfg.get("limit_side_len")
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self.limit_type = cfg.get("limit_type")
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self.mean = cfg.get("mean")
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self.std = cfg.get("std")
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self.preprocess_op = None
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post_process = {
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"thresh": cfg.get("thresh", 0.3),
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"box_thresh": cfg.get("box_thresh", 0.5),
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"max_candidates": cfg.get("max_candidates", 1000),
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"unclip_ratio": cfg.get("unclip_ratio", 1.6),
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"use_dilation": cfg.get("use_dilation", True),
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"score_mode": cfg.get("score_mode", "fast"),
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}
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self.postprocess_op = DBPostProcess(**post_process)
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self.session = get_engine(cfg.engine_type)(cfg)
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def __call__(self, img: np.ndarray) -> TextDetOutput:
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start_time = time.perf_counter()
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if img is None:
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raise ValueError("img is None")
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ori_img_shape = img.shape[0], img.shape[1]
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self.preprocess_op = self.get_preprocess()
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prepro_img = self.preprocess_op(img)
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if prepro_img is None:
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return TextDetOutput()
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preds = self.session(prepro_img)
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boxes, scores = self.postprocess_op(preds, ori_img_shape)
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if len(boxes) < 1:
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return TextDetOutput()
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boxes = self.sorted_boxes(boxes)
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elapse = time.perf_counter() - start_time
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return TextDetOutput(img, boxes, scores, elapse=elapse)
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def get_preprocess(self) -> DetPreProcess:
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return DetPreProcess(self.limit_side_len, self.limit_type, self.mean, self.std)
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@staticmethod
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def sorted_boxes(dt_boxes: np.ndarray) -> np.ndarray:
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"""
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Equivalent NumPy implementation of the original bubble-adjusted sort.
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"""
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if len(dt_boxes) == 0:
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return dt_boxes
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# Step 1: Stable sort by y (top to bottom)
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y_coords = dt_boxes[:, 0, 1]
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y_order = np.argsort(y_coords, kind="stable")
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boxes_y_sorted = dt_boxes[y_order]
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y_sorted = y_coords[y_order]
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# Step 2: Assign line IDs based on adjacent y differences
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dy = np.diff(y_sorted)
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line_increments = (dy >= _BOX_SORT_Y_THRESHOLD).astype(np.int32)
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line_ids = np.concatenate([[0], np.cumsum(line_increments)])
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# Now, within each line_id group, sort by x (left to right)
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x_coords = boxes_y_sorted[:, 0, 0]
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final_order_in_y_sorted = np.lexsort((x_coords, line_ids))
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return boxes_y_sorted[final_order_in_y_sorted]
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