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# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import cv2
import numpy as np
import pytest
from numpy.testing import assert_array_equal
import mmcv
class TestGeometric:
@classmethod
def setup_class(cls):
cls.data_dir = osp.join(osp.dirname(__file__), '../data')
# the test img resolution is 400x300
cls.img_path = osp.join(cls.data_dir, 'color.jpg')
cls.img = cv2.imread(cls.img_path)
def test_imresize(self):
resized_img = mmcv.imresize(self.img, (1000, 600))
assert resized_img.shape == (600, 1000, 3)
resized_img, w_scale, h_scale = mmcv.imresize(self.img, (1000, 600),
True)
assert (resized_img.shape == (600, 1000, 3) and w_scale == 2.5
and h_scale == 2.0)
resized_img_dst = np.empty((600, 1000, 3), dtype=self.img.dtype)
resized_img = mmcv.imresize(self.img, (1000, 600), out=resized_img_dst)
assert id(resized_img_dst) == id(resized_img)
assert_array_equal(resized_img_dst,
mmcv.imresize(self.img, (1000, 600)))
for mode in ['nearest', 'bilinear', 'bicubic', 'area', 'lanczos']:
resized_img = mmcv.imresize(
self.img, (1000, 600), interpolation=mode)
assert resized_img.shape == (600, 1000, 3)
# test pillow resize
for mode in [
'nearest', 'bilinear', 'bicubic', 'box', 'lanczos', 'hamming'
]:
resized_img = mmcv.imresize(
self.img, (1000, 600), interpolation=mode, backend='pillow')
assert resized_img.shape == (600, 1000, 3)
# resize backend must be 'cv2' or 'pillow'
with pytest.raises(ValueError):
mmcv.imresize(self.img, (1000, 600), backend='not support')
def test_imresize_to_multiple(self):
# test size and keep_ratio = False
resized_img = mmcv.imresize_to_multiple(
self.img, divisor=16, size=(511, 513), keep_ratio=False)
assert resized_img.shape == (528, 512, 3)
resized_img = mmcv.imresize_to_multiple(
self.img, divisor=(16, 32), size=(511, 513), keep_ratio=False)
assert resized_img.shape == (544, 512, 3)
# test size, keep_ratio = True, and return_scale
resized_img, w_scale, h_scale = mmcv.imresize_to_multiple(
self.img,
divisor=16,
size=(1000, 600),
keep_ratio=True,
return_scale=True)
assert resized_img.shape == (
608, 800, 3) and h_scale == 608 / 300 and w_scale == 800 / 400
resized_img, w_scale, h_scale = mmcv.imresize_to_multiple(
self.img,
divisor=(18, 16),
size=(1000, 600),
keep_ratio=True,
return_scale=True)
assert resized_img.shape == (
608, 810, 3) and h_scale == 608 / 300 and w_scale == 810 / 400
# test scale_factor and return_scale
resized_img, w_scale, h_scale = mmcv.imresize_to_multiple(
self.img, divisor=16, scale_factor=2, return_scale=True)
assert resized_img.shape == (
608, 800, 3) and h_scale == 608 / 300 and w_scale == 800 / 400
resized_img, w_scale, h_scale = mmcv.imresize_to_multiple(
self.img, divisor=16, scale_factor=(2, 3), return_scale=True)
assert resized_img.shape == (
912, 800, 3) and h_scale == 912 / 300 and w_scale == 800 / 400
resized_img, w_scale, h_scale = mmcv.imresize_to_multiple(
self.img, divisor=(18, 16), scale_factor=(2, 3), return_scale=True)
assert resized_img.shape == (
912, 810, 3) and h_scale == 912 / 300 and w_scale == 810 / 400
# one of size and scale_factor should be given
with pytest.raises(ValueError):
mmcv.imresize_to_multiple(
self.img, divisor=16, size=(1000, 600), scale_factor=2)
with pytest.raises(ValueError):
mmcv.imresize_to_multiple(
self.img, divisor=16, size=None, scale_factor=None)
def test_imresize_like(self):
a = np.zeros((100, 200, 3))
resized_img = mmcv.imresize_like(self.img, a)
assert resized_img.shape == (100, 200, 3)
def test_rescale_size(self):
new_size, scale_factor = mmcv.rescale_size((400, 300), 1.5, True)
assert new_size == (600, 450) and scale_factor == 1.5
new_size, scale_factor = mmcv.rescale_size((400, 300), 0.934, True)
assert new_size == (374, 280) and scale_factor == 0.934
new_size = mmcv.rescale_size((400, 300), 1.5)
assert new_size == (600, 450)
new_size = mmcv.rescale_size((400, 300), 0.934)
assert new_size == (374, 280)
new_size, scale_factor = mmcv.rescale_size((400, 300), (1000, 600),
True)
assert new_size == (800, 600) and scale_factor == 2.0
new_size, scale_factor = mmcv.rescale_size((400, 300), (180, 200),
True)
assert new_size == (200, 150) and scale_factor == 0.5
new_size = mmcv.rescale_size((400, 300), (1000, 600))
assert new_size == (800, 600)
new_size = mmcv.rescale_size((400, 300), (180, 200))
assert new_size == (200, 150)
with pytest.raises(ValueError):
mmcv.rescale_size((400, 300), -0.5)
with pytest.raises(TypeError):
mmcv.rescale_size()((400, 300), [100, 100])
def test_imrescale(self):
# rescale by a certain factor
resized_img = mmcv.imrescale(self.img, 1.5)
assert resized_img.shape == (450, 600, 3)
resized_img = mmcv.imrescale(self.img, 0.934)
assert resized_img.shape == (280, 374, 3)
# rescale by a certain max_size
# resize (400, 300) to (max_1000, max_600)
resized_img = mmcv.imrescale(self.img, (1000, 600))
assert resized_img.shape == (600, 800, 3)
resized_img, scale = mmcv.imrescale(
self.img, (1000, 600), return_scale=True)
assert resized_img.shape == (600, 800, 3) and scale == 2.0
# resize (400, 300) to (max_200, max_180)
resized_img = mmcv.imrescale(self.img, (180, 200))
assert resized_img.shape == (150, 200, 3)
resized_img, scale = mmcv.imrescale(
self.img, (180, 200), return_scale=True)
assert resized_img.shape == (150, 200, 3) and scale == 0.5
# test exceptions
with pytest.raises(ValueError):
mmcv.imrescale(self.img, -0.5)
with pytest.raises(TypeError):
mmcv.imrescale(self.img, [100, 100])
def test_imflip(self):
# direction must be "horizontal" or "vertical" or "diagonal"
with pytest.raises(AssertionError):
mmcv.imflip(np.random.rand(80, 60, 3), direction='random')
# test horizontal flip (color image)
img = np.random.rand(80, 60, 3)
h, w, c = img.shape
flipped_img = mmcv.imflip(img)
assert flipped_img.shape == img.shape
for i in range(h):
for j in range(w):
for k in range(c):
assert flipped_img[i, j, k] == img[i, w - 1 - j, k]
# test vertical flip (color image)
flipped_img = mmcv.imflip(img, direction='vertical')
assert flipped_img.shape == img.shape
for i in range(h):
for j in range(w):
for k in range(c):
assert flipped_img[i, j, k] == img[h - 1 - i, j, k]
# test diagonal flip (color image)
flipped_img = mmcv.imflip(img, direction='diagonal')
assert flipped_img.shape == img.shape
for i in range(h):
for j in range(w):
for k in range(c):
assert flipped_img[i, j, k] == img[h - 1 - i, w - 1 - j, k]
# test horizontal flip (grayscale image)
img = np.random.rand(80, 60)
h, w = img.shape
flipped_img = mmcv.imflip(img)
assert flipped_img.shape == img.shape
for i in range(h):
for j in range(w):
assert flipped_img[i, j] == img[i, w - 1 - j]
# test vertical flip (grayscale image)
flipped_img = mmcv.imflip(img, direction='vertical')
assert flipped_img.shape == img.shape
for i in range(h):
for j in range(w):
assert flipped_img[i, j] == img[h - 1 - i, j]
# test diagonal flip (grayscale image)
flipped_img = mmcv.imflip(img, direction='diagonal')
assert flipped_img.shape == img.shape
for i in range(h):
for j in range(w):
assert flipped_img[i, j] == img[h - 1 - i, w - 1 - j]
def test_imflip_(self):
# direction must be "horizontal" or "vertical" or "diagonal"
with pytest.raises(AssertionError):
mmcv.imflip_(np.random.rand(80, 60, 3), direction='random')
# test horizontal flip (color image)
img = np.random.rand(80, 60, 3)
h, w, c = img.shape
img_for_flip = img.copy()
flipped_img = mmcv.imflip_(img_for_flip)
assert flipped_img.shape == img.shape
assert flipped_img.shape == img_for_flip.shape
assert id(flipped_img) == id(img_for_flip)
for i in range(h):
for j in range(w):
for k in range(c):
assert flipped_img[i, j, k] == img[i, w - 1 - j, k]
assert flipped_img[i, j, k] == img_for_flip[i, j, k]
# test vertical flip (color image)
img_for_flip = img.copy()
flipped_img = mmcv.imflip_(img_for_flip, direction='vertical')
assert flipped_img.shape == img.shape
assert flipped_img.shape == img_for_flip.shape
assert id(flipped_img) == id(img_for_flip)
for i in range(h):
for j in range(w):
for k in range(c):
assert flipped_img[i, j, k] == img[h - 1 - i, j, k]
assert flipped_img[i, j, k] == img_for_flip[i, j, k]
# test diagonal flip (color image)
img_for_flip = img.copy()
flipped_img = mmcv.imflip_(img_for_flip, direction='diagonal')
assert flipped_img.shape == img.shape
assert flipped_img.shape == img_for_flip.shape
assert id(flipped_img) == id(img_for_flip)
for i in range(h):
for j in range(w):
for k in range(c):
assert flipped_img[i, j, k] == img[h - 1 - i, w - 1 - j, k]
assert flipped_img[i, j, k] == img_for_flip[i, j, k]
# test horizontal flip (grayscale image)
img = np.random.rand(80, 60)
h, w = img.shape
img_for_flip = img.copy()
flipped_img = mmcv.imflip_(img_for_flip)
assert flipped_img.shape == img.shape
assert flipped_img.shape == img_for_flip.shape
assert id(flipped_img) == id(img_for_flip)
for i in range(h):
for j in range(w):
assert flipped_img[i, j] == img[i, w - 1 - j]
assert flipped_img[i, j] == img_for_flip[i, j]
# test vertical flip (grayscale image)
img_for_flip = img.copy()
flipped_img = mmcv.imflip_(img_for_flip, direction='vertical')
assert flipped_img.shape == img.shape
assert flipped_img.shape == img_for_flip.shape
assert id(flipped_img) == id(img_for_flip)
for i in range(h):
for j in range(w):
assert flipped_img[i, j] == img[h - 1 - i, j]
assert flipped_img[i, j] == img_for_flip[i, j]
# test diagonal flip (grayscale image)
img_for_flip = img.copy()
flipped_img = mmcv.imflip_(img_for_flip, direction='diagonal')
assert flipped_img.shape == img.shape
assert flipped_img.shape == img_for_flip.shape
assert id(flipped_img) == id(img_for_flip)
for i in range(h):
for j in range(w):
assert flipped_img[i, j] == img[h - 1 - i, w - 1 - j]
assert flipped_img[i, j] == img_for_flip[i, j]
def test_imcrop(self):
# yapf: disable
bboxes = np.array([[100, 100, 199, 199], # center
[0, 0, 150, 100], # left-top corner
[250, 200, 399, 299], # right-bottom corner
[0, 100, 399, 199], # wide
[150, 0, 299, 299]]) # tall
# yapf: enable
# crop one bbox
patch = mmcv.imcrop(self.img, bboxes[0, :])
patches = mmcv.imcrop(self.img, bboxes[[0], :])
assert patch.shape == (100, 100, 3)
patch_path = osp.join(self.data_dir, 'patches')
ref_patch = np.load(patch_path + '/0.npy')
assert_array_equal(patch, ref_patch)
assert isinstance(patches, list) and len(patches) == 1
assert_array_equal(patches[0], ref_patch)
# crop with no scaling and padding
patches = mmcv.imcrop(self.img, bboxes)
assert len(patches) == bboxes.shape[0]
for i in range(len(patches)):
ref_patch = np.load(patch_path + f'/{i}.npy')
assert_array_equal(patches[i], ref_patch)
# crop with scaling and no padding
patches = mmcv.imcrop(self.img, bboxes, 1.2)
for i in range(len(patches)):
ref_patch = np.load(patch_path + f'/scale_{i}.npy')
assert_array_equal(patches[i], ref_patch)
# crop with scaling and padding
patches = mmcv.imcrop(self.img, bboxes, 1.2, pad_fill=[255, 255, 0])
for i in range(len(patches)):
ref_patch = np.load(patch_path + f'/pad_{i}.npy')
assert_array_equal(patches[i], ref_patch)
patches = mmcv.imcrop(self.img, bboxes, 1.2, pad_fill=0)
for i in range(len(patches)):
ref_patch = np.load(patch_path + f'/pad0_{i}.npy')
assert_array_equal(patches[i], ref_patch)
def test_impad(self):
# grayscale image
img = np.random.rand(10, 10).astype(np.float32)
padded_img = mmcv.impad(img, padding=(0, 0, 2, 5), pad_val=0)
assert_array_equal(img, padded_img[:10, :10])
assert_array_equal(
np.zeros((5, 12), dtype='float32'), padded_img[10:, :])
assert_array_equal(
np.zeros((15, 2), dtype='float32'), padded_img[:, 10:])
# RGB image
img = np.random.rand(10, 10, 3).astype(np.float32)
padded_img = mmcv.impad(img, padding=(0, 0, 2, 5), pad_val=0)
assert_array_equal(img, padded_img[:10, :10, :])
assert_array_equal(
np.zeros((5, 12, 3), dtype='float32'), padded_img[10:, :, :])
assert_array_equal(
np.zeros((15, 2, 3), dtype='float32'), padded_img[:, 10:, :])
# RGB image with different values for three channels.
img = np.random.randint(256, size=(10, 10, 3)).astype('uint8')
padded_img = mmcv.impad(
img, padding=(0, 0, 2, 5), pad_val=(100, 110, 120))
assert_array_equal(img, padded_img[:10, :10, :])
assert_array_equal(
np.array([100, 110, 120], dtype='uint8') * np.ones(
(5, 12, 3), dtype='uint8'), padded_img[10:, :, :])
assert_array_equal(
np.array([100, 110, 120], dtype='uint8') * np.ones(
(15, 2, 3), dtype='uint8'), padded_img[:, 10:, :])
# Pad the grayscale image to shape (15, 12)
img = np.random.rand(10, 10).astype(np.float32)
padded_img = mmcv.impad(img, shape=(15, 12))
assert_array_equal(img, padded_img[:10, :10])
assert_array_equal(
np.zeros((5, 12), dtype='float32'), padded_img[10:, :])
assert_array_equal(
np.zeros((15, 2), dtype='float32'), padded_img[:, 10:])
# Pad the RGB image to shape (15, 12)
img = np.random.rand(10, 10, 3).astype(np.float32)
padded_img = mmcv.impad(img, shape=(15, 12))
assert_array_equal(img, padded_img[:10, :10, :])
assert_array_equal(
np.zeros((5, 12, 3), dtype='float32'), padded_img[10:, :, :])
assert_array_equal(
np.zeros((15, 2, 3), dtype='float32'), padded_img[:, 10:, :])
# Pad the RGB image to shape (15, 12) with different values for
# three channels.
img = np.random.randint(256, size=(10, 10, 3)).astype('uint8')
padded_img = mmcv.impad(img, shape=(15, 12), pad_val=(100, 110, 120))
assert_array_equal(img, padded_img[:10, :10, :])
assert_array_equal(
np.array([100, 110, 120], dtype='uint8') * np.ones(
(5, 12, 3), dtype='uint8'), padded_img[10:, :, :])
assert_array_equal(
np.array([100, 110, 120], dtype='uint8') * np.ones(
(15, 2, 3), dtype='uint8'), padded_img[:, 10:, :])
# RGB image with padding=[5, 2]
img = np.random.rand(10, 10, 3).astype(np.float32)
padded_img = mmcv.impad(img, padding=(5, 2), pad_val=0)
assert padded_img.shape == (14, 20, 3)
assert_array_equal(img, padded_img[2:12, 5:15, :])
assert_array_equal(
np.zeros((2, 5, 3), dtype='float32'), padded_img[:2, :5, :])
assert_array_equal(
np.zeros((2, 5, 3), dtype='float32'), padded_img[12:, :5, :])
assert_array_equal(
np.zeros((2, 5, 3), dtype='float32'), padded_img[:2, 15:, :])
assert_array_equal(
np.zeros((2, 5, 3), dtype='float32'), padded_img[12:, 15:, :])
# RGB image with type(pad_val) = tuple
pad_val = (0, 1, 2)
img = np.random.rand(10, 10, 3).astype(np.float32)
padded_img = mmcv.impad(img, padding=(0, 0, 5, 2), pad_val=pad_val)
assert padded_img.shape == (12, 15, 3)
assert_array_equal(img, padded_img[:10, :10, :])
assert_array_equal(pad_val[0] * np.ones((2, 15, 1), dtype='float32'),
padded_img[10:, :, 0:1])
assert_array_equal(pad_val[1] * np.ones((2, 15, 1), dtype='float32'),
padded_img[10:, :, 1:2])
assert_array_equal(pad_val[2] * np.ones((2, 15, 1), dtype='float32'),
padded_img[10:, :, 2:3])
assert_array_equal(pad_val[0] * np.ones((12, 5, 1), dtype='float32'),
padded_img[:, 10:, 0:1])
assert_array_equal(pad_val[1] * np.ones((12, 5, 1), dtype='float32'),
padded_img[:, 10:, 1:2])
assert_array_equal(pad_val[2] * np.ones((12, 5, 1), dtype='float32'),
padded_img[:, 10:, 2:3])
# test different padding mode with channel number = 3
for mode in ['constant', 'edge', 'reflect', 'symmetric']:
img = np.random.rand(10, 10, 3).astype(np.float32)
padded_img = mmcv.impad(
img, padding=(0, 0, 5, 2), pad_val=pad_val, padding_mode=mode)
assert padded_img.shape == (12, 15, 3)
# test different padding mode with channel number = 1
for mode in ['constant', 'edge', 'reflect', 'symmetric']:
img = np.random.rand(10, 10).astype(np.float32)
padded_img = mmcv.impad(
img, padding=(0, 0, 5, 2), pad_val=0, padding_mode=mode)
assert padded_img.shape == (12, 15)
# Padding must be a int or a 2, or 4 element tuple.
with pytest.raises(ValueError):
mmcv.impad(img, padding=(1, 1, 1))
# pad_val must be a int or a tuple
with pytest.raises(TypeError):
mmcv.impad(img, padding=(1, 1, 1, 1), pad_val='wrong')
# When pad_val is a tuple,
# len(pad_val) should be equal to img.shape[-1]
img = np.random.rand(10, 10, 3).astype(np.float32)
with pytest.raises(AssertionError):
mmcv.impad(img, padding=3, pad_val=(100, 200))
with pytest.raises(AssertionError):
mmcv.impad(img, padding=2, pad_val=0, padding_mode='unknown')
with pytest.raises(AssertionError):
mmcv.impad(img, shape=(12, 15), padding=(0, 0, 5, 2))
# Pad shape smaller than image shape
padded_img = mmcv.impad(img, shape=(8, 8))
assert padded_img.shape == (10, 10, 3)
def test_impad_to_multiple(self):
img = np.random.rand(11, 14, 3).astype(np.float32)
padded_img = mmcv.impad_to_multiple(img, 4)
assert padded_img.shape == (12, 16, 3)
img = np.random.rand(20, 12).astype(np.float32)
padded_img = mmcv.impad_to_multiple(img, 5)
assert padded_img.shape == (20, 15)
img = np.random.rand(20, 12).astype(np.float32)
padded_img = mmcv.impad_to_multiple(img, 2)
assert padded_img.shape == (20, 12)
def test_cutout(self):
img = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]).astype(np.uint8)
# shape must be int or tuple
with pytest.raises(AssertionError):
mmcv.cutout(img, 2.5)
# pad_val must be int or float or tuple with the same length
# of img channels
with pytest.raises(AssertionError):
mmcv.cutout(img, 1, (1, 2, 3))
with pytest.raises(TypeError):
mmcv.cutout(img, 1, None)
# test cutout the whole img
assert_array_equal(mmcv.cutout(img, 6), np.zeros_like(img))
# test not cutout
assert_array_equal(mmcv.cutout(img, 0), img)
# test cutout when shape is int
np.random.seed(0)
img_cutout = np.array([[1, 2, 3], [4, 0, 6], [7, 8,
9]]).astype(np.uint8)
assert_array_equal(mmcv.cutout(img, 1), img_cutout)
img_cutout = np.array([[1, 2, 3], [4, 10, 6], [7, 8,
9]]).astype(np.uint8)
assert_array_equal(mmcv.cutout(img, 1, pad_val=10), img_cutout)
# test cutout when shape is tuple
np.random.seed(0)
img_cutout = np.array([[1, 2, 3], [0, 0, 6], [7, 8,
9]]).astype(np.uint8)
assert_array_equal(mmcv.cutout(img, (1, 2)), img_cutout)
img_cutout = np.array([[1, 2, 3], [10, 10, 6], [7, 8,
9]]).astype(np.uint8)
assert_array_equal(mmcv.cutout(img, (1, 2), pad_val=10), img_cutout)
def test_imrotate(self):
img = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]).astype(np.uint8)
assert_array_equal(mmcv.imrotate(img, 0), img)
img_r = np.array([[7, 4, 1], [8, 5, 2], [9, 6, 3]])
assert_array_equal(mmcv.imrotate(img, 90), img_r)
img_r = np.array([[3, 6, 9], [2, 5, 8], [1, 4, 7]])
assert_array_equal(mmcv.imrotate(img, -90), img_r)
img = np.array([[1, 2, 3, 4], [5, 6, 7, 8]]).astype(np.uint8)
img_r = np.array([[0, 6, 2, 0], [0, 7, 3, 0]])
assert_array_equal(mmcv.imrotate(img, 90), img_r)
img_r = np.array([[1, 0, 0, 0], [2, 0, 0, 0]])
assert_array_equal(mmcv.imrotate(img, 90, center=(0, 0)), img_r)
img_r = np.array([[255, 6, 2, 255], [255, 7, 3, 255]])
assert_array_equal(mmcv.imrotate(img, 90, border_value=255), img_r)
img_r = np.array([[5, 1], [6, 2], [7, 3], [8, 4]])
assert_array_equal(mmcv.imrotate(img, 90, auto_bound=True), img_r)
with pytest.raises(ValueError):
mmcv.imrotate(img, 90, center=(0, 0), auto_bound=True)
def test_imshear(self):
img = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]).astype(np.uint8)
assert_array_equal(mmcv.imshear(img, 0), img)
# magnitude=1, horizontal
img_sheared = np.array([[1, 2, 3], [0, 4, 5], [0, 0, 7]],
dtype=np.uint8)
assert_array_equal(mmcv.imshear(img, 1), img_sheared)
# magnitude=-1, vertical
img_sheared = np.array([[1, 5, 9], [4, 8, 0], [7, 0, 0]],
dtype=np.uint8)
assert_array_equal(mmcv.imshear(img, -1, 'vertical'), img_sheared)
# magnitude=1, vertical, borderValue=100
borderValue = 100
img_sheared = np.array(
[[1, borderValue, borderValue], [4, 2, borderValue], [7, 5, 3]],
dtype=np.uint8)
assert_array_equal(
mmcv.imshear(img, 1, 'vertical', borderValue), img_sheared)
# magnitude=1, vertical, borderValue=100, img shape (h,w,3)
img = np.stack([img, img, img], axis=-1)
img_sheared = np.stack([img_sheared, img_sheared, img_sheared],
axis=-1)
assert_array_equal(
mmcv.imshear(img, 1, 'vertical', borderValue), img_sheared)
# test tuple format of borderValue
assert_array_equal(
mmcv.imshear(img, 1, 'vertical',
(borderValue, borderValue, borderValue)), img_sheared)
# test invalid length of borderValue
with pytest.raises(AssertionError):
mmcv.imshear(img, 0.5, 'horizontal', (borderValue, ))
# test invalid type of borderValue
with pytest.raises(ValueError):
mmcv.imshear(img, 0.5, 'horizontal', [borderValue])
# test invalid value of direction
with pytest.raises(AssertionError):
mmcv.imshear(img, 0.5, 'diagonal')
def test_imtranslate(self):
img = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.uint8)
assert_array_equal(mmcv.imtranslate(img, 0), img)
# offset=1, horizontal
img_translated = np.array([[128, 1, 2], [128, 4, 5], [128, 7, 8]],
dtype=np.uint8)
assert_array_equal(
mmcv.imtranslate(img, 1, border_value=128), img_translated)
# offset=-1, vertical
img_translated = np.array([[4, 5, 6], [7, 8, 9], [0, 0, 0]],
dtype=np.uint8)
assert_array_equal(
mmcv.imtranslate(img, -1, 'vertical'), img_translated)
# offset=-2, horizontal
img = np.array([[1, 2, 3, 4], [5, 6, 7, 8]], dtype=np.uint8)
img = np.stack([img, img, img], axis=-1)
img_translated = [[3, 4, 128, 128], [7, 8, 128, 128]]
img_translated = np.stack(
[img_translated, img_translated, img_translated], axis=-1)
assert_array_equal(
mmcv.imtranslate(img, -2, border_value=128), img_translated)
# offset=2, vertical
border_value = (110, 120, 130)
img_translated = np.stack([
np.ones((2, 4)) * border_value[0],
np.ones((2, 4)) * border_value[1],
np.ones((2, 4)) * border_value[2]
],
axis=-1).astype(np.uint8)
assert_array_equal(
mmcv.imtranslate(img, 2, 'vertical', border_value), img_translated)
# test invalid number elements in border_value
with pytest.raises(AssertionError):
mmcv.imtranslate(img, 1, border_value=(1, ))
# test invalid type of border_value
with pytest.raises(ValueError):
mmcv.imtranslate(img, 1, border_value=[1, 2, 3])
# test invalid value of direction
with pytest.raises(AssertionError):
mmcv.imtranslate(img, 1, 'diagonal')
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