# 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 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 Image, ImageOps
            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.normal(0, 1, (256, 256, 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 import Image
            from PIL.ImageEnhance import Brightness
            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.random.choice([0, 1])
            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 import Image
            from PIL.ImageEnhance import Contrast

            # 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() + np.random.choice([0, 1])
            # 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)

    def test_auto_contrast(self, nb_rand_test=100):

        def _auto_contrast(img, cutoff=0):
            from PIL import Image
            from PIL.ImageOps import autocontrast

            # Image.fromarray defaultly supports RGB, not BGR.
            # convert from BGR to RGB
            img = Image.fromarray(img[..., ::-1], mode='RGB')
            contrasted_img = autocontrast(img, cutoff)
            # 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 without cut-off
        assert_array_equal(mmcv.auto_contrast(img), _auto_contrast(img))
        # test case with cut-off as int
        assert_array_equal(
            mmcv.auto_contrast(img, 10), _auto_contrast(img, 10))
        # test case with cut-off as float
        assert_array_equal(
            mmcv.auto_contrast(img, 12.5), _auto_contrast(img, 12.5))
        # test case with cut-off as tuple
        assert_array_equal(
            mmcv.auto_contrast(img, (10, 10)), _auto_contrast(img, 10))
        # test case with cut-off with sum over 100
        assert_array_equal(
            mmcv.auto_contrast(img, 60), _auto_contrast(img, 60))

        # test auto_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)
            # cut-offs are not set as tuple since in `build.yml`, pillow 6.2.2
            # is installed, which does not support setting low cut-off and high
            #  cut-off differently.
            # With pillow above 8.0.0, cutoff can be set as tuple
            cutoff = np.random.rand() * 100
            assert_array_equal(
                mmcv.auto_contrast(img, cutoff), _auto_contrast(img, cutoff))

    def test_adjust_sharpness(self, nb_rand_test=100):

        def _adjust_sharpness(img, factor):
            # adjust the sharpness of image using
            # PIL.ImageEnhance.Sharpness
            from PIL import Image
            from PIL.ImageEnhance import Sharpness
            img = Image.fromarray(img)
            sharpened_img = Sharpness(img).enhance(factor)
            return np.asarray(sharpened_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 invalid type of kernel
        with pytest.raises(AssertionError):
            mmcv.adjust_sharpness(img, 1., kernel=1.)
        # test case with invalid shape of kernel
        kernel = np.ones((3, 3, 3))
        with pytest.raises(AssertionError):
            mmcv.adjust_sharpness(img, 1., kernel=kernel)
        # test case with all-zero kernel, factor 0.0
        kernel = np.zeros((3, 3))
        assert_array_equal(
            mmcv.adjust_sharpness(img, 0., kernel=kernel), np.zeros_like(img))

        # test case with factor 1.0
        assert_array_equal(mmcv.adjust_sharpness(img, 1.), img)
        # test adjust_sharpness 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()
            # Note the gap between PIL.ImageEnhance.Sharpness and
            # mmcv.adjust_sharpness mainly comes from the difference ways of
            # handling img edges when applying filters
            np.testing.assert_allclose(
                mmcv.adjust_sharpness(img, factor).astype(np.int32)[1:-1,
                                                                    1:-1],
                _adjust_sharpness(img, factor).astype(np.int32)[1:-1, 1:-1],
                rtol=0,
                atol=1)

    def test_adjust_lighting(self):
        img = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]).astype(np.uint8)
        img = np.stack([img, img, img], axis=-1)

        # eigval and eigvec must be np.ndarray
        with pytest.raises(AssertionError):
            mmcv.adjust_lighting(img, 1, np.ones((3, 1)))
        with pytest.raises(AssertionError):
            mmcv.adjust_lighting(img, np.array([1]), (1, 1, 1))
        # we must have the same number of eigval and eigvec
        with pytest.raises(AssertionError):
            mmcv.adjust_lighting(img, np.array([1]), np.eye(2))
        with pytest.raises(AssertionError):
            mmcv.adjust_lighting(img, np.array([1]), np.array([1]))

        img_adjusted = mmcv.adjust_lighting(
            img,
            np.random.normal(0, 1, 2),
            np.random.normal(0, 1, (3, 2)),
            alphastd=0.)
        assert_array_equal(img_adjusted, img)

    def test_lut_transform(self):
        lut_table = np.array(list(range(256)))

        # test assertion image values should between 0 and 255.
        with pytest.raises(AssertionError):
            mmcv.lut_transform(np.array([256]), lut_table)
        with pytest.raises(AssertionError):
            mmcv.lut_transform(np.array([-1]), lut_table)

        # test assertion lut_table should be ndarray with shape (256, )
        with pytest.raises(AssertionError):
            mmcv.lut_transform(np.array([0]), list(range(256)))
        with pytest.raises(AssertionError):
            mmcv.lut_transform(np.array([1]), np.array(list(range(257))))

        img = mmcv.lut_transform(self.img, lut_table)
        baseline = cv2.LUT(self.img, lut_table)
        assert np.allclose(img, baseline)

        input_img = np.array(
            [[[0, 128, 255], [255, 128, 0]], [[0, 128, 255], [255, 128, 0]]],
            dtype=float)
        img = mmcv.lut_transform(input_img, lut_table)
        baseline = cv2.LUT(np.array(input_img, dtype=np.uint8), lut_table)
        assert np.allclose(img, baseline)

        input_img = np.random.randint(0, 256, size=(7, 8, 9, 10, 11))
        img = mmcv.lut_transform(input_img, lut_table)
        baseline = cv2.LUT(np.array(input_img, dtype=np.uint8), lut_table)
        assert np.allclose(img, baseline)

    def test_clahe(self):

        def _clahe(img, clip_limit=40.0, tile_grid_size=(8, 8)):
            clahe = cv2.createCLAHE(clip_limit, tile_grid_size)
            return clahe.apply(np.array(img, dtype=np.uint8))

        # test assertion image should have the right shape
        with pytest.raises(AssertionError):
            mmcv.clahe(self.img)

        # test assertion tile_grid_size should be a tuple with 2 integers
        with pytest.raises(AssertionError):
            mmcv.clahe(self.img[:, :, 0], tile_grid_size=(8.0, 8.0))
        with pytest.raises(AssertionError):
            mmcv.clahe(self.img[:, :, 0], tile_grid_size=(8, 8, 8))
        with pytest.raises(AssertionError):
            mmcv.clahe(self.img[:, :, 0], tile_grid_size=[8, 8])

        # test with different channels
        for i in range(self.img.shape[-1]):
            img = mmcv.clahe(self.img[:, :, i])
            img_std = _clahe(self.img[:, :, i])
            assert np.allclose(img, img_std)
            assert id(img) != id(self.img[:, :, i])
            assert id(img_std) != id(self.img[:, :, i])

        # test case with clip_limit=1.2
        for i in range(self.img.shape[-1]):
            img = mmcv.clahe(self.img[:, :, i], 1.2)
            img_std = _clahe(self.img[:, :, i], 1.2)
            assert np.allclose(img, img_std)
            assert id(img) != id(self.img[:, :, i])
            assert id(img_std) != id(self.img[:, :, i])

    def test_adjust_hue(self):
        from PIL import Image

        def _adjust_hue(img, hue_factor):
            input_mode = img.mode
            if input_mode in {'L', '1', 'I', 'F'}:
                return img
            h, s, v = img.convert('HSV').split()
            np_h = np.array(h, dtype=np.uint8)
            # uint8 addition take cares of rotation across boundaries
            with np.errstate(over='ignore'):
                np_h += np.uint8(hue_factor * 255)
            h = Image.fromarray(np_h, 'L')
            img = Image.merge('HSV', (h, s, v)).convert(input_mode)
            return img

        pil_img = Image.fromarray(self.img)

        # test case with img is not ndarray
        with pytest.raises(TypeError):
            mmcv.adjust_hue(pil_img, hue_factor=0.0)

        # test case with hue_factor > 0.5 or hue_factor < -0.5
        with pytest.raises(ValueError):
            mmcv.adjust_hue(self.img, hue_factor=-0.6)
        with pytest.raises(ValueError):
            mmcv.adjust_hue(self.img, hue_factor=0.6)

        for i in np.arange(-0.5, 0.5, 0.2):
            pil_res = _adjust_hue(pil_img, hue_factor=i)
            pil_res = np.array(pil_res)
            cv2_res = mmcv.adjust_hue(self.img, hue_factor=i)
            assert np.allclose(pil_res, cv2_res, atol=10.0)