255 lines
8.9 KiB
Python
255 lines
8.9 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import copy
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import os.path as osp
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import mmcv
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import numpy as np
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import pytest
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from PIL import Image
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from mmseg.datasets.transforms import PhotoMetricDistortion, RandomCrop
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from mmseg.registry import TRANSFORMS
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from mmseg.utils import register_all_modules
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register_all_modules()
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def test_resize():
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# Test `Resize`, `RandomResize` and `RandomChoiceResize` from
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# MMCV transform. Noted: `RandomResize` has args `scales` but
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# `Resize` and `RandomResize` has args `scale`.
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transform = dict(type='Resize', scale=(1333, 800), keep_ratio=True)
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resize_module = TRANSFORMS.build(transform)
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results = dict()
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# (288, 512, 3)
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img = mmcv.imread(
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osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
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results['img'] = img
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results['img_shape'] = img.shape
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results['ori_shape'] = img.shape
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# Set initial values for default meta_keys
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results['pad_shape'] = img.shape
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results['scale_factor'] = 1.0
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resized_results = resize_module(results.copy())
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# img_shape = results['img'].shape[:2] in ``MMCV resize`` function
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# so right now it is (750, 1333) rather than (750, 1333, 3)
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assert resized_results['img_shape'] == (750, 1333)
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# test keep_ratio=False
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transform = dict(
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type='RandomResize',
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scale=(1280, 800),
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ratio_range=(1.0, 1.0),
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resize_type='Resize',
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keep_ratio=False)
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resize_module = TRANSFORMS.build(transform)
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resized_results = resize_module(results.copy())
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assert resized_results['img_shape'] == (800, 1280)
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# test `RandomChoiceResize`, which in older mmsegmentation
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# `Resize` is multiscale_mode='range'
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transform = dict(type='RandomResize', scale=[(1333, 400), (1333, 1200)])
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resize_module = TRANSFORMS.build(transform)
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resized_results = resize_module(results.copy())
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assert max(resized_results['img_shape'][:2]) <= 1333
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assert min(resized_results['img_shape'][:2]) >= 400
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assert min(resized_results['img_shape'][:2]) <= 1200
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# test RandomChoiceResize, which in older mmsegmentation
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# `Resize` is multiscale_mode='value'
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transform = dict(
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type='RandomChoiceResize',
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scales=[(1333, 800), (1333, 400)],
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resize_type='Resize',
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keep_ratio=False)
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resize_module = TRANSFORMS.build(transform)
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resized_results = resize_module(results.copy())
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assert resized_results['img_shape'] in [(800, 1333), (400, 1333)]
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transform = dict(type='Resize', scale_factor=(0.9, 1.1), keep_ratio=True)
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resize_module = TRANSFORMS.build(transform)
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resized_results = resize_module(results.copy())
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assert max(resized_results['img_shape'][:2]) <= 1333 * 1.1
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# test scale=None and scale_factor is tuple.
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# img shape: (288, 512, 3)
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transform = dict(
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type='Resize', scale=None, scale_factor=(0.5, 2.0), keep_ratio=True)
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resize_module = TRANSFORMS.build(transform)
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resized_results = resize_module(results.copy())
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assert int(288 * 0.5) <= resized_results['img_shape'][0] <= 288 * 2.0
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assert int(512 * 0.5) <= resized_results['img_shape'][1] <= 512 * 2.0
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# test minimum resized image shape is 640
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transform = dict(type='Resize', scale=(2560, 640), keep_ratio=True)
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resize_module = TRANSFORMS.build(transform)
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resized_results = resize_module(results.copy())
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assert resized_results['img_shape'] == (640, 1138)
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# test minimum resized image shape is 640 when img_scale=(512, 640)
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# where should define `scale_factor` in MMCV new ``Resize`` function.
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min_size_ratio = max(640 / img.shape[0], 640 / img.shape[1])
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transform = dict(
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type='Resize', scale_factor=min_size_ratio, keep_ratio=True)
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resize_module = TRANSFORMS.build(transform)
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resized_results = resize_module(results.copy())
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assert resized_results['img_shape'] == (640, 1138)
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# test h > w
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img = np.random.randn(512, 288, 3)
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results['img'] = img
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results['img_shape'] = img.shape
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results['ori_shape'] = img.shape
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# Set initial values for default meta_keys
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results['pad_shape'] = img.shape
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results['scale_factor'] = 1.0
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min_size_ratio = max(640 / img.shape[0], 640 / img.shape[1])
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transform = dict(
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type='Resize',
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scale=(2560, 640),
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scale_factor=min_size_ratio,
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keep_ratio=True)
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resize_module = TRANSFORMS.build(transform)
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resized_results = resize_module(results.copy())
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assert resized_results['img_shape'] == (1138, 640)
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def test_flip():
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# test assertion for invalid prob
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with pytest.raises(AssertionError):
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transform = dict(type='RandomFlip', prob=1.5)
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TRANSFORMS.build(transform)
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# test assertion for invalid direction
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with pytest.raises(AssertionError):
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transform = dict(type='RandomFlip', prob=1.0, direction='horizonta')
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TRANSFORMS.build(transform)
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transform = dict(type='RandomFlip', prob=1.0)
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flip_module = TRANSFORMS.build(transform)
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results = dict()
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img = mmcv.imread(
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osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
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original_img = copy.deepcopy(img)
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seg = np.array(
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Image.open(osp.join(osp.dirname(__file__), '../data/seg.png')))
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original_seg = copy.deepcopy(seg)
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results['img'] = img
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results['gt_semantic_seg'] = seg
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results['seg_fields'] = ['gt_semantic_seg']
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results['img_shape'] = img.shape
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results['ori_shape'] = img.shape
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# Set initial values for default meta_keys
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results['pad_shape'] = img.shape
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results['scale_factor'] = 1.0
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results = flip_module(results)
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flip_module = TRANSFORMS.build(transform)
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results = flip_module(results)
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assert np.equal(original_img, results['img']).all()
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assert np.equal(original_seg, results['gt_semantic_seg']).all()
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def test_pad():
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# test assertion if both size_divisor and size is None
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with pytest.raises(AssertionError):
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transform = dict(type='Pad')
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TRANSFORMS.build(transform)
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transform = dict(type='Pad', size_divisor=32)
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transform = TRANSFORMS.build(transform)
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results = dict()
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img = mmcv.imread(
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osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
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original_img = copy.deepcopy(img)
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results['img'] = img
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results['img_shape'] = img.shape
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results['ori_shape'] = img.shape
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# Set initial values for default meta_keys
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results['pad_shape'] = img.shape
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results['scale_factor'] = 1.0
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results = transform(results)
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# original img already divisible by 32
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assert np.equal(results['img'], original_img).all()
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img_shape = results['img'].shape
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assert img_shape[0] % 32 == 0
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assert img_shape[1] % 32 == 0
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def test_normalize():
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img_norm_cfg = dict(
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375],
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to_rgb=True)
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transform = dict(type='Normalize', **img_norm_cfg)
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transform = TRANSFORMS.build(transform)
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results = dict()
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img = mmcv.imread(
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osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
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original_img = copy.deepcopy(img)
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results['img'] = img
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results['img_shape'] = img.shape
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results['ori_shape'] = img.shape
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# Set initial values for default meta_keys
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results['pad_shape'] = img.shape
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results['scale_factor'] = 1.0
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results = transform(results)
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mean = np.array(img_norm_cfg['mean'])
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std = np.array(img_norm_cfg['std'])
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converted_img = (original_img[..., ::-1] - mean) / std
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assert np.allclose(results['img'], converted_img)
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def test_random_crop():
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# test assertion for invalid random crop
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with pytest.raises(AssertionError):
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RandomCrop(crop_size=(-1, 0))
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results = dict()
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img = mmcv.imread(osp.join('tests/data/color.jpg'), 'color')
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seg = np.array(Image.open(osp.join('tests/data/seg.png')))
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results['img'] = img
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results['gt_semantic_seg'] = seg
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results['seg_fields'] = ['gt_semantic_seg']
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results['img_shape'] = img.shape
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results['ori_shape'] = img.shape
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# Set initial values for default meta_keys
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results['pad_shape'] = img.shape
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results['scale_factor'] = 1.0
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h, w, _ = img.shape
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pipeline = RandomCrop(crop_size=(h - 20, w - 20))
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results = pipeline(results)
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assert results['img'].shape[:2] == (h - 20, w - 20)
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assert results['img_shape'][:2] == (h - 20, w - 20)
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assert results['gt_semantic_seg'].shape[:2] == (h - 20, w - 20)
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def test_photo_metric_distortion():
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results = dict()
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img = mmcv.imread(osp.join('tests/data/color.jpg'), 'color')
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seg = np.array(Image.open(osp.join('tests/data/seg.png')))
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results['img'] = img
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results['gt_semantic_seg'] = seg
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results['seg_fields'] = ['gt_semantic_seg']
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results['img_shape'] = img.shape
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results['ori_shape'] = img.shape
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# Set initial values for default meta_keys
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results['pad_shape'] = img.shape
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results['scale_factor'] = 1.0
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pipeline = PhotoMetricDistortion(saturation_range=(1., 1.))
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results = pipeline(results)
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assert (results['gt_semantic_seg'] == seg).all()
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assert results['img_shape'] == img.shape
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