mirror of https://github.com/open-mmlab/mmocr.git
108 lines
3.6 KiB
Python
108 lines
3.6 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import os.path as osp
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import tempfile
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import unittest
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import cv2
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import numpy as np
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import torch
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from mmengine.structures import InstanceData
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from mmocr.structures import TextDetDataSample
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from mmocr.utils import bbox2poly
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from mmocr.visualization import TextDetLocalVisualizer
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class TestTextDetLocalVisualizer(unittest.TestCase):
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def setUp(self):
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h, w = 12, 10
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self.image = np.random.randint(0, 256, size=(h, w, 3)).astype('uint8')
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# gt_instances
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data_sample = TextDetDataSample()
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gt_instances_data = dict(
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bboxes=self._rand_bboxes(5, h, w),
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polygons=self._rand_polys(5, h, w),
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labels=torch.zeros(5, ))
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gt_instances = InstanceData(**gt_instances_data)
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data_sample.gt_instances = gt_instances
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pred_instances_data = dict(
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bboxes=self._rand_bboxes(5, h, w),
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polygons=self._rand_polys(5, h, w),
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labels=torch.zeros(5, ),
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scores=torch.rand((5, )))
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pred_instances = InstanceData(**pred_instances_data)
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data_sample.pred_instances = pred_instances
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self.data_sample = data_sample
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def test_text_det_local_visualizer(self):
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for with_poly in [True, False]:
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for with_bbox in [True, False]:
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vis_cfg = dict(with_poly=with_poly, with_bbox=with_bbox)
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self._test_add_datasample(vis_cfg=vis_cfg)
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@staticmethod
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def _rand_bboxes(num_boxes, h, w):
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cx, cy, bw, bh = torch.rand(num_boxes, 4).T
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tl_x = ((cx * w) - (w * bw / 2)).clamp(0, w).unsqueeze(0)
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tl_y = ((cy * h) - (h * bh / 2)).clamp(0, h).unsqueeze(0)
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br_x = ((cx * w) + (w * bw / 2)).clamp(0, w).unsqueeze(0)
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br_y = ((cy * h) + (h * bh / 2)).clamp(0, h).unsqueeze(0)
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bboxes = torch.cat([tl_x, tl_y, br_x, br_y], dim=0).T
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return bboxes
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def _rand_polys(self, num_bboxes, h, w):
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bboxes = self._rand_bboxes(num_bboxes, h, w)
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bboxes = bboxes.tolist()
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polys = [bbox2poly(bbox) for bbox in bboxes]
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return polys
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def _test_add_datasample(self, vis_cfg):
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image = self.image
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h, w, c = image.shape
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det_local_visualizer = TextDetLocalVisualizer(**vis_cfg)
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det_local_visualizer.add_datasample('image', image, self.data_sample)
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with tempfile.TemporaryDirectory() as tmp_dir:
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# test out
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out_file = osp.join(tmp_dir, 'out_file.jpg')
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det_local_visualizer.add_datasample(
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'image',
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image,
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self.data_sample,
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out_file=out_file,
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draw_gt=False,
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draw_pred=False)
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self._assert_image_and_shape(out_file, (h, w, c))
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det_local_visualizer.add_datasample(
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'image', image, self.data_sample, out_file=out_file)
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self._assert_image_and_shape(out_file, (h, w * 2, c))
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det_local_visualizer.add_datasample(
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'image',
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image,
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self.data_sample,
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draw_gt=False,
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out_file=out_file)
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self._assert_image_and_shape(out_file, (h, w, c))
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det_local_visualizer.add_datasample(
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'image',
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image,
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self.data_sample,
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draw_pred=False,
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out_file=out_file)
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self._assert_image_and_shape(out_file, (h, w, c))
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def _assert_image_and_shape(self, out_file, out_shape):
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self.assertTrue(osp.exists(out_file))
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drawn_img = cv2.imread(out_file)
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self.assertTrue(drawn_img.shape == out_shape)
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