mmdetection-2.26.0/tests/test_data/test_pipelines/test_loading.py

# 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