mirror of
https://github.com/open-mmlab/mmcv.git
synced 2025-06-03 21:54:52 +08:00
a0cc5a8450
* add brightness and contrast augmentation * remove unnecessary * reformat * relax the precision constrain for adjust_brightness aug * fix percision assertion error in unit test * remove toy * rename alpha as factor * use np.testing.assert_allclose in place of np.less_equal
203 lines
8.6 KiB
Python
203 lines
8.6 KiB
Python
# Copyright (c) Open-MMLab. All rights reserved.
|
|
import os.path as osp
|
|
|
|
import cv2
|
|
import numpy as np
|
|
from numpy.testing import assert_array_equal
|
|
|
|
import mmcv
|
|
|
|
|
|
class TestPhotometric:
|
|
|
|
@classmethod
|
|
def setup_class(cls):
|
|
# the test img resolution is 400x300
|
|
cls.img_path = osp.join(osp.dirname(__file__), '../data/color.jpg')
|
|
cls.img = cv2.imread(cls.img_path)
|
|
cls.mean = np.array([123.675, 116.28, 103.53], dtype=np.float32)
|
|
cls.std = np.array([58.395, 57.12, 57.375], dtype=np.float32)
|
|
|
|
def test_imnormalize(self):
|
|
rgb_img = self.img[:, :, ::-1]
|
|
baseline = (rgb_img - self.mean) / self.std
|
|
img = mmcv.imnormalize(self.img, self.mean, self.std)
|
|
assert np.allclose(img, baseline)
|
|
assert id(img) != id(self.img)
|
|
img = mmcv.imnormalize(rgb_img, self.mean, self.std, to_rgb=False)
|
|
assert np.allclose(img, baseline)
|
|
assert id(img) != id(rgb_img)
|
|
|
|
def test_imnormalize_(self):
|
|
img_for_normalize = np.float32(self.img)
|
|
rgb_img_for_normalize = np.float32(self.img[:, :, ::-1])
|
|
baseline = (rgb_img_for_normalize - self.mean) / self.std
|
|
img = mmcv.imnormalize_(img_for_normalize, self.mean, self.std)
|
|
assert np.allclose(img_for_normalize, baseline)
|
|
assert id(img) == id(img_for_normalize)
|
|
img = mmcv.imnormalize_(
|
|
rgb_img_for_normalize, self.mean, self.std, to_rgb=False)
|
|
assert np.allclose(img, baseline)
|
|
assert id(img) == id(rgb_img_for_normalize)
|
|
|
|
def test_imdenormalize(self):
|
|
norm_img = (self.img[:, :, ::-1] - self.mean) / self.std
|
|
rgb_baseline = (norm_img * self.std + self.mean)
|
|
bgr_baseline = rgb_baseline[:, :, ::-1]
|
|
img = mmcv.imdenormalize(norm_img, self.mean, self.std)
|
|
assert np.allclose(img, bgr_baseline)
|
|
img = mmcv.imdenormalize(norm_img, self.mean, self.std, to_bgr=False)
|
|
assert np.allclose(img, rgb_baseline)
|
|
|
|
def test_iminvert(self):
|
|
img = np.array([[0, 128, 255], [1, 127, 254], [2, 129, 253]],
|
|
dtype=np.uint8)
|
|
img_r = np.array([[255, 127, 0], [254, 128, 1], [253, 126, 2]],
|
|
dtype=np.uint8)
|
|
assert_array_equal(mmcv.iminvert(img), img_r)
|
|
|
|
def test_solarize(self):
|
|
img = np.array([[0, 128, 255], [1, 127, 254], [2, 129, 253]],
|
|
dtype=np.uint8)
|
|
img_r = np.array([[0, 127, 0], [1, 127, 1], [2, 126, 2]],
|
|
dtype=np.uint8)
|
|
assert_array_equal(mmcv.solarize(img), img_r)
|
|
img_r = np.array([[0, 127, 0], [1, 128, 1], [2, 126, 2]],
|
|
dtype=np.uint8)
|
|
assert_array_equal(mmcv.solarize(img, 100), img_r)
|
|
|
|
def test_posterize(self):
|
|
img = np.array([[0, 128, 255], [1, 127, 254], [2, 129, 253]],
|
|
dtype=np.uint8)
|
|
img_r = np.array([[0, 128, 128], [0, 0, 128], [0, 128, 128]],
|
|
dtype=np.uint8)
|
|
assert_array_equal(mmcv.posterize(img, 1), img_r)
|
|
img_r = np.array([[0, 128, 224], [0, 96, 224], [0, 128, 224]],
|
|
dtype=np.uint8)
|
|
assert_array_equal(mmcv.posterize(img, 3), img_r)
|
|
|
|
def test_adjust_color(self):
|
|
img = np.array([[0, 128, 255], [1, 127, 254], [2, 129, 253]],
|
|
dtype=np.uint8)
|
|
img = np.stack([img, img, img], axis=-1)
|
|
assert_array_equal(mmcv.adjust_color(img), img)
|
|
img_gray = mmcv.bgr2gray(img)
|
|
img_r = np.stack([img_gray, img_gray, img_gray], axis=-1)
|
|
assert_array_equal(mmcv.adjust_color(img, 0), img_r)
|
|
assert_array_equal(mmcv.adjust_color(img, 0, 1), img_r)
|
|
assert_array_equal(
|
|
mmcv.adjust_color(img, 0.5, 0.5),
|
|
np.round(np.clip((img * 0.5 + img_r * 0.5), 0,
|
|
255)).astype(img.dtype))
|
|
assert_array_equal(
|
|
mmcv.adjust_color(img, 1, 1.5),
|
|
np.round(np.clip(img * 1 + img_r * 1.5, 0, 255)).astype(img.dtype))
|
|
assert_array_equal(
|
|
mmcv.adjust_color(img, 0.8, -0.6, gamma=2),
|
|
np.round(np.clip(img * 0.8 - 0.6 * img_r + 2, 0,
|
|
255)).astype(img.dtype))
|
|
assert_array_equal(
|
|
mmcv.adjust_color(img, 0.8, -0.6, gamma=-0.6),
|
|
np.round(np.clip(img * 0.8 - 0.6 * img_r - 0.6, 0,
|
|
255)).astype(img.dtype))
|
|
|
|
# test float type of image
|
|
img = img.astype(np.float32)
|
|
assert_array_equal(
|
|
np.round(mmcv.adjust_color(img, 0.8, -0.6, gamma=-0.6)),
|
|
np.round(np.clip(img * 0.8 - 0.6 * img_r - 0.6, 0, 255)))
|
|
|
|
def test_imequalize(self, nb_rand_test=100):
|
|
|
|
def _imequalize(img):
|
|
# equalize the image using PIL.ImageOps.equalize
|
|
from PIL import ImageOps, Image
|
|
img = Image.fromarray(img)
|
|
equalized_img = np.asarray(ImageOps.equalize(img))
|
|
return equalized_img
|
|
|
|
img = np.array([[0, 128, 255], [1, 127, 254], [2, 129, 253]],
|
|
dtype=np.uint8)
|
|
img = np.stack([img, img, img], axis=-1)
|
|
equalized_img = mmcv.imequalize(img)
|
|
assert_array_equal(equalized_img, _imequalize(img))
|
|
|
|
# test equalize with case step=0
|
|
img = np.array([[0, 0, 0], [120, 120, 120], [255, 255, 255]],
|
|
dtype=np.uint8)
|
|
img = np.stack([img, img, img], axis=-1)
|
|
assert_array_equal(mmcv.imequalize(img), img)
|
|
|
|
# test equalize with randomly sampled image.
|
|
for _ in range(nb_rand_test):
|
|
img = np.clip(
|
|
np.random.uniform(0, 1, (1000, 1200, 3)) * 260, 0,
|
|
255).astype(np.uint8)
|
|
equalized_img = mmcv.imequalize(img)
|
|
assert_array_equal(equalized_img, _imequalize(img))
|
|
|
|
def test_adjust_brightness(self, nb_rand_test=100):
|
|
|
|
def _adjust_brightness(img, factor):
|
|
# adjust the brightness of image using
|
|
# PIL.ImageEnhance.Brightness
|
|
from PIL.ImageEnhance import Brightness
|
|
from PIL import Image
|
|
img = Image.fromarray(img)
|
|
brightened_img = Brightness(img).enhance(factor)
|
|
return np.asarray(brightened_img)
|
|
|
|
img = np.array([[0, 128, 255], [1, 127, 254], [2, 129, 253]],
|
|
dtype=np.uint8)
|
|
img = np.stack([img, img, img], axis=-1)
|
|
# test case with factor 1.0
|
|
assert_array_equal(mmcv.adjust_brightness(img, 1.), img)
|
|
# test case with factor 0.0
|
|
assert_array_equal(mmcv.adjust_brightness(img, 0.), np.zeros_like(img))
|
|
# test adjust_brightness with randomly sampled images and factors.
|
|
for _ in range(nb_rand_test):
|
|
img = np.clip(
|
|
np.random.uniform(0, 1, (1000, 1200, 3)) * 260, 0,
|
|
255).astype(np.uint8)
|
|
factor = np.random.uniform()
|
|
np.testing.assert_allclose(
|
|
mmcv.adjust_brightness(img, factor).astype(np.int32),
|
|
_adjust_brightness(img, factor).astype(np.int32),
|
|
rtol=0,
|
|
atol=1)
|
|
|
|
def test_adjust_contrast(self, nb_rand_test=100):
|
|
|
|
def _adjust_contrast(img, factor):
|
|
from PIL.ImageEnhance import Contrast
|
|
from PIL import Image
|
|
# Image.fromarray defaultly supports RGB, not BGR.
|
|
# convert from BGR to RGB
|
|
img = Image.fromarray(img[..., ::-1], mode='RGB')
|
|
contrasted_img = Contrast(img).enhance(factor)
|
|
# convert from RGB to BGR
|
|
return np.asarray(contrasted_img)[..., ::-1]
|
|
|
|
img = np.array([[0, 128, 255], [1, 127, 254], [2, 129, 253]],
|
|
dtype=np.uint8)
|
|
img = np.stack([img, img, img], axis=-1)
|
|
# test case with factor 1.0
|
|
assert_array_equal(mmcv.adjust_contrast(img, 1.), img)
|
|
# test case with factor 0.0
|
|
assert_array_equal(
|
|
mmcv.adjust_contrast(img, 0.), _adjust_contrast(img, 0.))
|
|
# test adjust_contrast with randomly sampled images and factors.
|
|
for _ in range(nb_rand_test):
|
|
img = np.clip(
|
|
np.random.uniform(0, 1, (1200, 1000, 3)) * 260, 0,
|
|
255).astype(np.uint8)
|
|
factor = np.random.uniform()
|
|
# Note the gap (less_equal 1) between PIL.ImageEnhance.Contrast
|
|
# and mmcv.adjust_contrast comes from the gap that converts from
|
|
# a color image to gray image using mmcv or PIL.
|
|
np.testing.assert_allclose(
|
|
mmcv.adjust_contrast(img, factor).astype(np.int32),
|
|
_adjust_contrast(img, factor).astype(np.int32),
|
|
rtol=0,
|
|
atol=1)
|