File size: 5,336 Bytes
3bbb319 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import os.path as osp
import mmcv
import numpy as np
import pytest
from mmdet.core.mask import BitmapMasks, PolygonMasks
from mmdet.datasets.pipelines import (FilterAnnotations, LoadImageFromFile,
LoadImageFromWebcam,
LoadMultiChannelImageFromFiles)
class TestLoading:
@classmethod
def setup_class(cls):
cls.data_prefix = osp.join(osp.dirname(__file__), '../../data')
def test_load_img(self):
results = dict(
img_prefix=self.data_prefix, img_info=dict(filename='color.jpg'))
transform = LoadImageFromFile()
results = transform(copy.deepcopy(results))
assert results['filename'] == osp.join(self.data_prefix, 'color.jpg')
assert results['ori_filename'] == 'color.jpg'
assert results['img'].shape == (288, 512, 3)
assert results['img'].dtype == np.uint8
assert results['img_shape'] == (288, 512, 3)
assert results['ori_shape'] == (288, 512, 3)
assert repr(transform) == transform.__class__.__name__ + \
"(to_float32=False, color_type='color', channel_order='bgr', " + \
"file_client_args={'backend': 'disk'})"
# no img_prefix
results = dict(
img_prefix=None, img_info=dict(filename='tests/data/color.jpg'))
transform = LoadImageFromFile()
results = transform(copy.deepcopy(results))
assert results['filename'] == 'tests/data/color.jpg'
assert results['ori_filename'] == 'tests/data/color.jpg'
assert results['img'].shape == (288, 512, 3)
# to_float32
transform = LoadImageFromFile(to_float32=True)
results = transform(copy.deepcopy(results))
assert results['img'].dtype == np.float32
# gray image
results = dict(
img_prefix=self.data_prefix, img_info=dict(filename='gray.jpg'))
transform = LoadImageFromFile()
results = transform(copy.deepcopy(results))
assert results['img'].shape == (288, 512, 3)
assert results['img'].dtype == np.uint8
transform = LoadImageFromFile(color_type='unchanged')
results = transform(copy.deepcopy(results))
assert results['img'].shape == (288, 512)
assert results['img'].dtype == np.uint8
def test_load_multi_channel_img(self):
results = dict(
img_prefix=self.data_prefix,
img_info=dict(filename=['color.jpg', 'color.jpg']))
transform = LoadMultiChannelImageFromFiles()
results = transform(copy.deepcopy(results))
assert results['filename'] == [
osp.join(self.data_prefix, 'color.jpg'),
osp.join(self.data_prefix, 'color.jpg')
]
assert results['ori_filename'] == ['color.jpg', 'color.jpg']
assert results['img'].shape == (288, 512, 3, 2)
assert results['img'].dtype == np.uint8
assert results['img_shape'] == (288, 512, 3, 2)
assert results['ori_shape'] == (288, 512, 3, 2)
assert results['pad_shape'] == (288, 512, 3, 2)
assert results['scale_factor'] == 1.0
assert repr(transform) == transform.__class__.__name__ + \
"(to_float32=False, color_type='unchanged', " + \
"file_client_args={'backend': 'disk'})"
def test_load_webcam_img(self):
img = mmcv.imread(osp.join(self.data_prefix, 'color.jpg'))
results = dict(img=img)
transform = LoadImageFromWebcam()
results = transform(copy.deepcopy(results))
assert results['filename'] is None
assert results['ori_filename'] is None
assert results['img'].shape == (288, 512, 3)
assert results['img'].dtype == np.uint8
assert results['img_shape'] == (288, 512, 3)
assert results['ori_shape'] == (288, 512, 3)
def _build_filter_annotations_args():
kwargs = (dict(min_gt_bbox_wh=(100, 100)),
dict(min_gt_bbox_wh=(100, 100), keep_empty=False),
dict(min_gt_bbox_wh=(1, 1)), dict(min_gt_bbox_wh=(.01, .01)),
dict(min_gt_bbox_wh=(.01, .01),
by_mask=True), dict(by_mask=True),
dict(by_box=False, by_mask=True))
targets = (None, 0, 1, 2, 1, 1, 1)
return list(zip(targets, kwargs))
@pytest.mark.parametrize('target, kwargs', _build_filter_annotations_args())
def test_filter_annotations(target, kwargs):
filter_ann = FilterAnnotations(**kwargs)
bboxes = np.array([[2., 10., 4., 14.], [2., 10., 2.1, 10.1]])
raw_masks = np.zeros((2, 24, 24))
raw_masks[0, 10:14, 2:4] = 1
bitmap_masks = BitmapMasks(raw_masks, 24, 24)
results = dict(gt_bboxes=bboxes, gt_masks=bitmap_masks)
results = filter_ann(results)
if results is not None:
results = results['gt_bboxes'].shape[0]
assert results == target
polygons = [[np.array([2.0, 10.0, 4.0, 10.0, 4.0, 14.0, 2.0, 14.0])],
[np.array([2.0, 10.0, 2.1, 10.0, 2.1, 10.1, 2.0, 10.1])]]
polygon_masks = PolygonMasks(polygons, 24, 24)
results = dict(gt_bboxes=bboxes, gt_masks=polygon_masks)
results = filter_ann(results)
if results is not None:
results = len(results.get('gt_masks').masks)
assert results == target
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