mirror of
https://github.com/RapidAI/RapidOCR.git
synced 2026-11-11 05:20:49 +08:00
Mohammad Hijjawi
cee004f994
When the text classifier turns a crop by 180 degrees, recognition runs on the rotated crop, but CalRecBoxes mapped the word boxes back as if the crop had never been rotated. Every word/char box on an upside-down line therefore landed on its mirror position: the first character got the box of the last. Pass the classifier's rotation decision (same "180" label and cls_thresh rule TextClassifier uses) to CalRecBoxes, and rotate the word boxes back by 180 degrees within the crop before mapping them to the original image.
111 lines
3.6 KiB
Python
111 lines
3.6 KiB
Python
# -*- encoding: utf-8 -*-
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# @Author: Mohammad Hijjawi
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from typing import List, Optional, Tuple
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import numpy as np
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import pytest
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from rapidocr import RapidOCR
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from rapidocr.ch_ppocr_cls import TextClsOutput
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from rapidocr.ch_ppocr_det import TextDetOutput
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from rapidocr.ch_ppocr_rec import TextRecOutput
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from rapidocr.ch_ppocr_rec.typings import WordInfo, WordType
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LINE_TXT = "我是人"
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# A 200x20 horizontal line and a 20x200 vertical line in a 400x400 image.
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HORIZONTAL_BOX = [[100, 50], [300, 50], [300, 70], [100, 70]]
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VERTICAL_BOX = [[100, 100], [120, 100], [120, 300], [100, 300]]
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def make_word_info() -> WordInfo:
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return WordInfo(
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words=[list(LINE_TXT)],
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word_cols=[[2, 15, 28]],
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word_types=[WordType.CN],
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line_txt_len=30,
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confs=[0.9, 0.9, 0.9],
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)
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def run_word_boxes(
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boxes: List[List[List[int]]],
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txts: Tuple[str, ...],
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cls_res: Optional[List[Tuple[str, float]]],
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):
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engine = RapidOCR(params={"Global.return_word_box": True})
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# The crop handed to recognition is always laid out horizontally.
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crops = [np.zeros((20, 200, 3), dtype=np.uint8) for _ in boxes]
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det_res = TextDetOutput(
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boxes=np.array(boxes, dtype=np.float32), scores=[0.9] * len(boxes)
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)
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cls_out = TextClsOutput(img_list=crops, cls_res=cls_res)
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rec_res = TextRecOutput(
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imgs=crops,
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txts=txts,
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scores=tuple(0.9 for _ in txts),
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word_results=tuple(make_word_info() for _ in txts),
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)
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return engine.build_final_output(
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np.zeros((400, 400, 3), dtype=np.uint8),
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det_res,
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cls_out,
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rec_res,
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crops,
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{"preprocess": {"ratio_h": 1.0, "ratio_w": 1.0}},
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)
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def word_centers(word_line) -> List[Tuple[float, float]]:
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return [tuple(np.array(points).mean(axis=0)) for _, _, points in word_line]
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@pytest.mark.parametrize(
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"box,cls_res,axis,reading_direction",
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[
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(HORIZONTAL_BOX, None, 0, 1),
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(HORIZONTAL_BOX, [("0", 0.99)], 0, 1),
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# below cls_thresh the crop is not rotated, so neither are the boxes
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(HORIZONTAL_BOX, [("180", 0.5)], 0, 1),
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# issue #328: upside-down text is read right to left in the image
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(HORIZONTAL_BOX, [("180", 0.99)], 0, -1),
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(VERTICAL_BOX, [("0", 0.99)], 1, 1),
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(VERTICAL_BOX, [("180", 0.99)], 1, -1),
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],
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)
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def test_word_boxes_follow_cls_rotation(box, cls_res, axis, reading_direction):
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result = run_word_boxes([box], (LINE_TXT,), cls_res)
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word_line = result.word_results[0]
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assert "".join(txt for txt, _, _ in word_line) == LINE_TXT
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centers = [center[axis] for center in word_centers(word_line)]
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assert np.all(reading_direction * np.diff(centers) > 0)
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def test_rotated_word_boxes_are_mirrored_within_line():
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upright = run_word_boxes([HORIZONTAL_BOX], (LINE_TXT,), [("0", 0.99)])
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flipped = run_word_boxes([HORIZONTAL_BOX], (LINE_TXT,), [("180", 0.99)])
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upright_xs = [
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sorted({p[0] for p in points}) for _, _, points in upright.word_results[0]
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]
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flipped_xs = [
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sorted({p[0] for p in points}) for _, _, points in flipped.word_results[0]
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]
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# Each character's box is mirrored about the centre of the line.
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for (x0, x1), (fx0, fx1) in zip(upright_xs, flipped_xs):
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assert (fx0, fx1) == (400 - x1, 400 - x0)
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def test_rotation_flags_stay_aligned_after_empty_lines_are_dropped():
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result = run_word_boxes(
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[HORIZONTAL_BOX, VERTICAL_BOX],
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("", LINE_TXT),
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[("0", 0.99), ("180", 0.99)],
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)
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assert result.txts == (LINE_TXT,)
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ys = [center[1] for center in word_centers(result.word_results[0])]
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assert np.all(np.diff(ys) < 0)
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