mirror of https://github.com/open-mmlab/mmcv.git
269 lines
11 KiB
Python
269 lines
11 KiB
Python
# Copyright (c) Open-MMLab. All rights reserved.
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import os.path as osp
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import cv2
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import numpy as np
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import pytest
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from numpy.testing import assert_array_equal
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import mmcv
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class TestPhotometric:
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@classmethod
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def setup_class(cls):
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# the test img resolution is 400x300
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cls.img_path = osp.join(osp.dirname(__file__), '../data/color.jpg')
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cls.img = cv2.imread(cls.img_path)
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cls.mean = np.array([123.675, 116.28, 103.53], dtype=np.float32)
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cls.std = np.array([58.395, 57.12, 57.375], dtype=np.float32)
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def test_imnormalize(self):
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rgb_img = self.img[:, :, ::-1]
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baseline = (rgb_img - self.mean) / self.std
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img = mmcv.imnormalize(self.img, self.mean, self.std)
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assert np.allclose(img, baseline)
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assert id(img) != id(self.img)
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img = mmcv.imnormalize(rgb_img, self.mean, self.std, to_rgb=False)
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assert np.allclose(img, baseline)
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assert id(img) != id(rgb_img)
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def test_imnormalize_(self):
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img_for_normalize = np.float32(self.img)
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rgb_img_for_normalize = np.float32(self.img[:, :, ::-1])
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baseline = (rgb_img_for_normalize - self.mean) / self.std
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img = mmcv.imnormalize_(img_for_normalize, self.mean, self.std)
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assert np.allclose(img_for_normalize, baseline)
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assert id(img) == id(img_for_normalize)
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img = mmcv.imnormalize_(
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rgb_img_for_normalize, self.mean, self.std, to_rgb=False)
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assert np.allclose(img, baseline)
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assert id(img) == id(rgb_img_for_normalize)
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def test_imdenormalize(self):
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norm_img = (self.img[:, :, ::-1] - self.mean) / self.std
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rgb_baseline = (norm_img * self.std + self.mean)
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bgr_baseline = rgb_baseline[:, :, ::-1]
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img = mmcv.imdenormalize(norm_img, self.mean, self.std)
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assert np.allclose(img, bgr_baseline)
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img = mmcv.imdenormalize(norm_img, self.mean, self.std, to_bgr=False)
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assert np.allclose(img, rgb_baseline)
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def test_iminvert(self):
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img = np.array([[0, 128, 255], [1, 127, 254], [2, 129, 253]],
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dtype=np.uint8)
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img_r = np.array([[255, 127, 0], [254, 128, 1], [253, 126, 2]],
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dtype=np.uint8)
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assert_array_equal(mmcv.iminvert(img), img_r)
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def test_solarize(self):
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img = np.array([[0, 128, 255], [1, 127, 254], [2, 129, 253]],
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dtype=np.uint8)
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img_r = np.array([[0, 127, 0], [1, 127, 1], [2, 126, 2]],
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dtype=np.uint8)
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assert_array_equal(mmcv.solarize(img), img_r)
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img_r = np.array([[0, 127, 0], [1, 128, 1], [2, 126, 2]],
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dtype=np.uint8)
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assert_array_equal(mmcv.solarize(img, 100), img_r)
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def test_posterize(self):
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img = np.array([[0, 128, 255], [1, 127, 254], [2, 129, 253]],
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dtype=np.uint8)
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img_r = np.array([[0, 128, 128], [0, 0, 128], [0, 128, 128]],
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dtype=np.uint8)
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assert_array_equal(mmcv.posterize(img, 1), img_r)
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img_r = np.array([[0, 128, 224], [0, 96, 224], [0, 128, 224]],
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dtype=np.uint8)
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assert_array_equal(mmcv.posterize(img, 3), img_r)
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def test_adjust_color(self):
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img = np.array([[0, 128, 255], [1, 127, 254], [2, 129, 253]],
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dtype=np.uint8)
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img = np.stack([img, img, img], axis=-1)
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assert_array_equal(mmcv.adjust_color(img), img)
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img_gray = mmcv.bgr2gray(img)
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img_r = np.stack([img_gray, img_gray, img_gray], axis=-1)
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assert_array_equal(mmcv.adjust_color(img, 0), img_r)
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assert_array_equal(mmcv.adjust_color(img, 0, 1), img_r)
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assert_array_equal(
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mmcv.adjust_color(img, 0.5, 0.5),
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np.round(np.clip((img * 0.5 + img_r * 0.5), 0,
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255)).astype(img.dtype))
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assert_array_equal(
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mmcv.adjust_color(img, 1, 1.5),
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np.round(np.clip(img * 1 + img_r * 1.5, 0, 255)).astype(img.dtype))
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assert_array_equal(
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mmcv.adjust_color(img, 0.8, -0.6, gamma=2),
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np.round(np.clip(img * 0.8 - 0.6 * img_r + 2, 0,
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255)).astype(img.dtype))
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assert_array_equal(
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mmcv.adjust_color(img, 0.8, -0.6, gamma=-0.6),
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np.round(np.clip(img * 0.8 - 0.6 * img_r - 0.6, 0,
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255)).astype(img.dtype))
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# test float type of image
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img = img.astype(np.float32)
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assert_array_equal(
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np.round(mmcv.adjust_color(img, 0.8, -0.6, gamma=-0.6)),
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np.round(np.clip(img * 0.8 - 0.6 * img_r - 0.6, 0, 255)))
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def test_imequalize(self, nb_rand_test=100):
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def _imequalize(img):
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# equalize the image using PIL.ImageOps.equalize
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from PIL import ImageOps, Image
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img = Image.fromarray(img)
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equalized_img = np.asarray(ImageOps.equalize(img))
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return equalized_img
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img = np.array([[0, 128, 255], [1, 127, 254], [2, 129, 253]],
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dtype=np.uint8)
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img = np.stack([img, img, img], axis=-1)
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equalized_img = mmcv.imequalize(img)
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assert_array_equal(equalized_img, _imequalize(img))
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# test equalize with case step=0
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img = np.array([[0, 0, 0], [120, 120, 120], [255, 255, 255]],
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dtype=np.uint8)
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img = np.stack([img, img, img], axis=-1)
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assert_array_equal(mmcv.imequalize(img), img)
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# test equalize with randomly sampled image.
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for _ in range(nb_rand_test):
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img = np.clip(
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np.random.uniform(0, 1, (1000, 1200, 3)) * 260, 0,
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255).astype(np.uint8)
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equalized_img = mmcv.imequalize(img)
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assert_array_equal(equalized_img, _imequalize(img))
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def test_adjust_brightness(self, nb_rand_test=100):
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def _adjust_brightness(img, factor):
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# adjust the brightness of image using
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# PIL.ImageEnhance.Brightness
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from PIL.ImageEnhance import Brightness
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from PIL import Image
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img = Image.fromarray(img)
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brightened_img = Brightness(img).enhance(factor)
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return np.asarray(brightened_img)
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img = np.array([[0, 128, 255], [1, 127, 254], [2, 129, 253]],
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dtype=np.uint8)
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img = np.stack([img, img, img], axis=-1)
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# test case with factor 1.0
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assert_array_equal(mmcv.adjust_brightness(img, 1.), img)
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# test case with factor 0.0
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assert_array_equal(mmcv.adjust_brightness(img, 0.), np.zeros_like(img))
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# test adjust_brightness with randomly sampled images and factors.
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for _ in range(nb_rand_test):
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img = np.clip(
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np.random.uniform(0, 1, (1000, 1200, 3)) * 260, 0,
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255).astype(np.uint8)
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factor = np.random.uniform()
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np.testing.assert_allclose(
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mmcv.adjust_brightness(img, factor).astype(np.int32),
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_adjust_brightness(img, factor).astype(np.int32),
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rtol=0,
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atol=1)
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def test_adjust_contrast(self, nb_rand_test=100):
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def _adjust_contrast(img, factor):
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from PIL.ImageEnhance import Contrast
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from PIL import Image
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# Image.fromarray defaultly supports RGB, not BGR.
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# convert from BGR to RGB
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img = Image.fromarray(img[..., ::-1], mode='RGB')
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contrasted_img = Contrast(img).enhance(factor)
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# convert from RGB to BGR
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return np.asarray(contrasted_img)[..., ::-1]
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img = np.array([[0, 128, 255], [1, 127, 254], [2, 129, 253]],
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dtype=np.uint8)
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img = np.stack([img, img, img], axis=-1)
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# test case with factor 1.0
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assert_array_equal(mmcv.adjust_contrast(img, 1.), img)
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# test case with factor 0.0
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assert_array_equal(
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mmcv.adjust_contrast(img, 0.), _adjust_contrast(img, 0.))
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# test adjust_contrast with randomly sampled images and factors.
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for _ in range(nb_rand_test):
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img = np.clip(
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np.random.uniform(0, 1, (1200, 1000, 3)) * 260, 0,
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255).astype(np.uint8)
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factor = np.random.uniform()
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# Note the gap (less_equal 1) between PIL.ImageEnhance.Contrast
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# and mmcv.adjust_contrast comes from the gap that converts from
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# a color image to gray image using mmcv or PIL.
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np.testing.assert_allclose(
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mmcv.adjust_contrast(img, factor).astype(np.int32),
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_adjust_contrast(img, factor).astype(np.int32),
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rtol=0,
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atol=1)
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def test_lut_transform(self):
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lut_table = np.array(list(range(256)))
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# test assertion image values should between 0 and 255.
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with pytest.raises(AssertionError):
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mmcv.lut_transform(np.array([256]), lut_table)
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with pytest.raises(AssertionError):
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mmcv.lut_transform(np.array([-1]), lut_table)
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# test assertion lut_table should be ndarray with shape (256, )
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with pytest.raises(AssertionError):
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mmcv.lut_transform(np.array([0]), list(range(256)))
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with pytest.raises(AssertionError):
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mmcv.lut_transform(np.array([1]), np.array(list(range(257))))
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img = mmcv.lut_transform(self.img, lut_table)
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baseline = cv2.LUT(self.img, lut_table)
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assert np.allclose(img, baseline)
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input_img = np.array(
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[[[0, 128, 255], [255, 128, 0]], [[0, 128, 255], [255, 128, 0]]],
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dtype=np.float)
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img = mmcv.lut_transform(input_img, lut_table)
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baseline = cv2.LUT(np.array(input_img, dtype=np.uint8), lut_table)
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assert np.allclose(img, baseline)
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input_img = np.random.randint(0, 256, size=(7, 8, 9, 10, 11))
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img = mmcv.lut_transform(input_img, lut_table)
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baseline = cv2.LUT(np.array(input_img, dtype=np.uint8), lut_table)
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assert np.allclose(img, baseline)
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def test_clahe(self):
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def _clahe(img, clip_limit=40.0, tile_grid_size=(8, 8)):
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clahe = cv2.createCLAHE(clip_limit, tile_grid_size)
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return clahe.apply(np.array(img, dtype=np.uint8))
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# test assertion image should have the right shape
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with pytest.raises(AssertionError):
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mmcv.clahe(self.img)
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# test assertion tile_grid_size should be a tuple with 2 integers
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with pytest.raises(AssertionError):
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mmcv.clahe(self.img[:, :, 0], tile_grid_size=(8.0, 8.0))
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with pytest.raises(AssertionError):
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mmcv.clahe(self.img[:, :, 0], tile_grid_size=(8, 8, 8))
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with pytest.raises(AssertionError):
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mmcv.clahe(self.img[:, :, 0], tile_grid_size=[8, 8])
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# test with different channels
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for i in range(self.img.shape[-1]):
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img = mmcv.clahe(self.img[:, :, i])
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img_std = _clahe(self.img[:, :, i])
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assert np.allclose(img, img_std)
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assert id(img) != id(self.img[:, :, i])
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assert id(img_std) != id(self.img[:, :, i])
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# test case with clip_limit=1.2
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for i in range(self.img.shape[-1]):
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img = mmcv.clahe(self.img[:, :, i], 1.2)
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img_std = _clahe(self.img[:, :, i], 1.2)
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assert np.allclose(img, img_std)
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assert id(img) != id(self.img[:, :, i])
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assert id(img_std) != id(self.img[:, :, i])
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