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https://github.com/open-mmlab/mmocr.git
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116 lines
3.6 KiB
Python
116 lines
3.6 KiB
Python
import numpy as np
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import pytest
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import torch
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from mmocr.models.textdet.postprocess.wrapper import (comps2boundaries,
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db_decode, fcenet_decode,
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poly_nms,
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textsnake_decode)
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def test_db_boxes_from_bitmaps():
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"""Test the boxes_from_bitmaps function in db_decoder."""
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pred = np.array([[[0.8, 0.8, 0.8, 0.8, 0], [0.8, 0.8, 0.8, 0.8, 0],
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[0.8, 0.8, 0.8, 0.8, 0], [0.8, 0.8, 0.8, 0.8, 0],
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[0.8, 0.8, 0.8, 0.8, 0]]])
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preds = torch.FloatTensor(pred).requires_grad_(True)
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boxes = db_decode(preds, text_repr_type='quad', min_text_width=0)
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assert len(boxes) == 1
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def test_fcenet_decode():
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k = 1
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preds = []
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preds.append(torch.ones(1, 4, 10, 10))
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preds.append(torch.ones(1, 4 * k + 2, 10, 10))
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boundaries = fcenet_decode(
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preds=preds,
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fourier_degree=k,
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num_reconstr_points=50,
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scale=1,
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nms_thr=0.01)
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assert isinstance(boundaries, list)
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def test_poly_nms():
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threshold = 0
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polygons = []
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polygons.append([10, 10, 10, 30, 30, 30, 30, 10, 0.95])
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polygons.append([15, 15, 15, 25, 25, 25, 25, 15, 0.9])
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polygons.append([40, 40, 40, 50, 50, 50, 50, 40, 0.85])
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polygons.append([5, 5, 5, 15, 15, 15, 15, 5, 0.7])
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keep_poly = poly_nms(polygons, threshold)
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assert isinstance(keep_poly, list)
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def test_comps2boundaries():
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# test comps2boundaries
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x1 = np.arange(2, 18, 2)
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x2 = x1 + 2
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y1 = np.ones(8) * 2
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y2 = y1 + 2
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comp_scores = np.ones(8, dtype=np.float32) * 0.9
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text_comps = np.stack([x1, y1, x2, y1, x2, y2, x1, y2,
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comp_scores]).transpose()
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comp_labels = np.array([1, 1, 1, 1, 1, 3, 5, 5])
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shuffle = [3, 2, 5, 7, 6, 0, 4, 1]
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boundaries = comps2boundaries(text_comps[shuffle], comp_labels[shuffle])
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assert len(boundaries) == 3
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# test comps2boundaries with blank inputs
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boundaries = comps2boundaries(text_comps[[]], comp_labels[[]])
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assert len(boundaries) == 0
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def test_textsnake_decode():
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maps = torch.zeros((1, 6, 224, 224), dtype=torch.float)
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maps[:, 0:2, :, :] = -10.
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maps[:, 0, 60:100, 50:170] = 10.
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maps[:, 1, 75:85, 60:160] = 10.
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maps[:, 2, 75:85, 60:160] = 0.
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maps[:, 3, 75:85, 60:160] = 1.
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maps[:, 4, 75:85, 60:160] = 10.
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# test decoding with text center region of small area
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maps[:, 0:2, 150:152, 5:7] = 10.
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results = textsnake_decode(torch.squeeze(maps))
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assert len(results) == 1
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# test decoding with small radius
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maps.fill_(0.)
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maps[:, 0:2, :, :] = -10.
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maps[:, 0, 120:140, 20:40] = 10.
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maps[:, 1, 120:140, 20:40] = 10.
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maps[:, 2, 120:140, 20:40] = 0.
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maps[:, 3, 120:140, 20:40] = 1.
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maps[:, 4, 120:140, 20:40] = 0.5
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results = textsnake_decode(torch.squeeze(maps))
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assert len(results) == 0
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def test_db_decode():
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pred = torch.zeros((1, 8, 8))
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pred[0, 2:7, 2:7] = 0.8
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expect_result_quad = [[
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1.0, 8.0, 1.0, 1.0, 8.0, 1.0, 8.0, 8.0, 0.800000011920929
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]]
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expect_result_poly = [[
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8, 2, 8, 6, 6, 8, 2, 8, 1, 6, 1, 2, 2, 1, 6, 1, 0.800000011920929
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]]
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with pytest.raises(ValueError):
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db_decode(preds=pred, text_repr_type='dummpy')
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result_quad = db_decode(
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preds=pred, text_repr_type='quad', min_text_width=1)
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result_poly = db_decode(
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preds=pred, text_repr_type='poly', min_text_width=1)
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assert result_quad == expect_result_quad
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assert result_poly == expect_result_poly
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