import mmcv
import numpy as np
from numpy import random

from ..builder import PIPELINES


@PIPELINES.register_module()
class Resize(object):
    """Resize images & seg.

    This transform resizes the input image to some scale. If the input dict
    contains the key "scale", then the scale in the input dict is used,
    otherwise the specified scale in the init method is used.

    ``img_scale`` can either be a tuple (single-scale) or a list of tuple
    (multi-scale). There are 3 multiscale modes:

    - ``ratio_range is not None``: randomly sample a ratio from the ratio range
    and multiply it with the image scale.

    - ``ratio_range is None and multiscale_mode == "range"``: randomly sample a
    scale from the a range.

    - ``ratio_range is None and multiscale_mode == "value"``: randomly sample a
    scale from multiple scales.

    Args:
        img_scale (tuple or list[tuple]): Images scales for resizing.
        multiscale_mode (str): Either "range" or "value".
        ratio_range (tuple[float]): (min_ratio, max_ratio)
        keep_ratio (bool): Whether to keep the aspect ratio when resizing the
            image.
    """

    def __init__(self,
                 img_scale=None,
                 multiscale_mode='range',
                 ratio_range=None,
                 keep_ratio=True,
                 crop_size=None,
                 setr_multi_scale=False):

        if img_scale is None:
            self.img_scale = None
        else:
            if isinstance(img_scale, list):
                self.img_scale = img_scale
            else:
                self.img_scale = [img_scale]
            # assert mmcv.is_list_of(self.img_scale, tuple)

        if ratio_range is not None:
            # mode 1: given a scale and a range of image ratio
            assert len(self.img_scale) == 1
        else:
            # mode 2: given multiple scales or a range of scales
            assert multiscale_mode in ['value', 'range']

        self.multiscale_mode = multiscale_mode
        self.ratio_range = ratio_range
        self.keep_ratio = keep_ratio
        self.crop_size = crop_size
        self.setr_multi_scale = setr_multi_scale

    @staticmethod
    def random_select(img_scales):
        """Randomly select an img_scale from given candidates.

        Args:
            img_scales (list[tuple]): Images scales for selection.

        Returns:
            (tuple, int): Returns a tuple ``(img_scale, scale_dix)``,
                where ``img_scale`` is the selected image scale and
                ``scale_idx`` is the selected index in the given candidates.
        """

        assert mmcv.is_list_of(img_scales, tuple)
        scale_idx = np.random.randint(len(img_scales))
        img_scale = img_scales[scale_idx]
        return img_scale, scale_idx

    @staticmethod
    def random_sample(img_scales):
        """Randomly sample an img_scale when ``multiscale_mode=='range'``.

        Args:
            img_scales (list[tuple]): Images scale range for sampling.
                There must be two tuples in img_scales, which specify the lower
                and uper bound of image scales.

        Returns:
            (tuple, None): Returns a tuple ``(img_scale, None)``, where
                ``img_scale`` is sampled scale and None is just a placeholder
                to be consistent with :func:`random_select`.
        """

        assert mmcv.is_list_of(img_scales, tuple) and len(img_scales) == 2
        img_scale_long = [max(s) for s in img_scales]
        img_scale_short = [min(s) for s in img_scales]
        long_edge = np.random.randint(
            min(img_scale_long),
            max(img_scale_long) + 1)
        short_edge = np.random.randint(
            min(img_scale_short),
            max(img_scale_short) + 1)
        img_scale = (long_edge, short_edge)
        return img_scale, None

    @staticmethod
    def random_sample_ratio(img_scale, ratio_range):
        """Randomly sample an img_scale when ``ratio_range`` is specified.

        A ratio will be randomly sampled from the range specified by
        ``ratio_range``. Then it would be multiplied with ``img_scale`` to
        generate sampled scale.

        Args:
            img_scale (tuple): Images scale base to multiply with ratio.
            ratio_range (tuple[float]): The minimum and maximum ratio to scale
                the ``img_scale``.

        Returns:
            (tuple, None): Returns a tuple ``(scale, None)``, where
                ``scale`` is sampled ratio multiplied with ``img_scale`` and
                None is just a placeholder to be consistent with
                :func:`random_select`.
        """

        assert isinstance(img_scale, tuple) and len(img_scale) == 2
        min_ratio, max_ratio = ratio_range
        assert min_ratio <= max_ratio
        ratio = np.random.random_sample() * (max_ratio - min_ratio) + min_ratio
        scale = int(img_scale[0] * ratio), int(img_scale[1] * ratio)
        return scale, None

    def _random_scale(self, results):
        """Randomly sample an img_scale according to ``ratio_range`` and
        ``multiscale_mode``.

        If ``ratio_range`` is specified, a ratio will be sampled and be
        multiplied with ``img_scale``.
        If multiple scales are specified by ``img_scale``, a scale will be
        sampled according to ``multiscale_mode``.
        Otherwise, single scale will be used.

        Args:
            results (dict): Result dict from :obj:`dataset`.

        Returns:
            dict: Two new keys 'scale` and 'scale_idx` are added into
                ``results``, which would be used by subsequent pipelines.
        """

        if self.ratio_range is not None:
            scale, scale_idx = self.random_sample_ratio(
                self.img_scale[0], self.ratio_range)
        elif len(self.img_scale) == 1:
            scale, scale_idx = self.img_scale[0], 0
        elif self.multiscale_mode == 'range':
            scale, scale_idx = self.random_sample(self.img_scale)
        elif self.multiscale_mode == 'value':
            scale, scale_idx = self.random_select(self.img_scale)
        else:
            raise NotImplementedError

        results['scale'] = scale
        results['scale_idx'] = scale_idx

    def _resize_img(self, results):
        """Resize images with ``results['scale']``."""

        if self.keep_ratio:
            if self.setr_multi_scale:
                if min(results['scale']) < self.crop_size[0]:
                    new_short = self.crop_size[0]
                else:
                    new_short = min(results['scale'])
                    
                h, w = results['img'].shape[:2]
                if h > w:
                    new_h, new_w = new_short * h / w, new_short
                else:
                    new_h, new_w = new_short, new_short * w / h
                results['scale'] = (new_h, new_w)

            img, scale_factor = mmcv.imrescale(
                results['img'], results['scale'], return_scale=True)
            # the w_scale and h_scale has minor difference
            # a real fix should be done in the mmcv.imrescale in the future
            new_h, new_w = img.shape[:2]
            h, w = results['img'].shape[:2]
            w_scale = new_w / w
            h_scale = new_h / h
        else:
            img, w_scale, h_scale = mmcv.imresize(
                results['img'], results['scale'], return_scale=True)
        scale_factor = np.array([w_scale, h_scale, w_scale, h_scale],
                                dtype=np.float32)
        results['img'] = img
        results['img_shape'] = img.shape
        results['pad_shape'] = img.shape  # in case that there is no padding
        results['scale_factor'] = scale_factor
        results['keep_ratio'] = self.keep_ratio

    def _resize_seg(self, results):
        """Resize semantic segmentation map with ``results['scale']``."""
        for key in results.get('seg_fields', []):
            if self.keep_ratio:
                gt_seg = mmcv.imrescale(
                    results[key], results['scale'], interpolation='nearest')
            else:
                gt_seg = mmcv.imresize(
                    results[key], results['scale'], interpolation='nearest')
            results['gt_semantic_seg'] = gt_seg

    def __call__(self, results):
        """Call function to resize images, bounding boxes, masks, semantic
        segmentation map.

        Args:
            results (dict): Result dict from loading pipeline.

        Returns:
            dict: Resized results, 'img_shape', 'pad_shape', 'scale_factor',
                'keep_ratio' keys are added into result dict.
        """

        if 'scale' not in results:
            self._random_scale(results)
        self._resize_img(results)
        self._resize_seg(results)
        return results

    def __repr__(self):
        repr_str = self.__class__.__name__
        repr_str += (f'(img_scale={self.img_scale}, '
                     f'multiscale_mode={self.multiscale_mode}, '
                     f'ratio_range={self.ratio_range}, '
                     f'keep_ratio={self.keep_ratio})')
        return repr_str


@PIPELINES.register_module()
class RandomFlip(object):
    """Flip the image & seg.

    If the input dict contains the key "flip", then the flag will be used,
    otherwise it will be randomly decided by a ratio specified in the init
    method.

    Args:
        flip_ratio (float, optional): The flipping probability. Default: None.
        direction(str, optional): The flipping direction. Options are
            'horizontal' and 'vertical'. Default: 'horizontal'.
    """

    def __init__(self, flip_ratio=None, direction='horizontal'):
        self.flip_ratio = flip_ratio
        self.direction = direction
        if flip_ratio is not None:
            assert flip_ratio >= 0 and flip_ratio <= 1
        assert direction in ['horizontal', 'vertical']

    def __call__(self, results):
        """Call function to flip bounding boxes, masks, semantic segmentation
        maps.

        Args:
            results (dict): Result dict from loading pipeline.

        Returns:
            dict: Flipped results, 'flip', 'flip_direction' keys are added into
                result dict.
        """

        if 'flip' not in results:
            flip = True if np.random.rand() < self.flip_ratio else False
            results['flip'] = flip
        if 'flip_direction' not in results:
            results['flip_direction'] = self.direction
        if results['flip']:
            # flip image
            results['img'] = mmcv.imflip(
                results['img'], direction=results['flip_direction'])

            # flip segs
            for key in results.get('seg_fields', []):
                # use copy() to make numpy stride positive
                results[key] = mmcv.imflip(
                    results[key], direction=results['flip_direction']).copy()
        return results

    def __repr__(self):
        return self.__class__.__name__ + f'(flip_ratio={self.flip_ratio})'


@PIPELINES.register_module()
class Pad(object):
    """Pad the image & mask.

    There are two padding modes: (1) pad to a fixed size and (2) pad to the
    minimum size that is divisible by some number.
    Added keys are "pad_shape", "pad_fixed_size", "pad_size_divisor",

    Args:
        size (tuple, optional): Fixed padding size.
        size_divisor (int, optional): The divisor of padded size.
        pad_val (float, optional): Padding value. Default: 0.
        seg_pad_val (float, optional): Padding value of segmentation map.
            Default: 255.
    """

    def __init__(self,
                 size=None,
                 size_divisor=None,
                 pad_val=0,
                 seg_pad_val=255):
        self.size = size
        self.size_divisor = size_divisor
        self.pad_val = pad_val
        self.seg_pad_val = seg_pad_val
        # only one of size and size_divisor should be valid
        assert size is not None or size_divisor is not None
        assert size is None or size_divisor is None

    def _pad_img(self, results):
        """Pad images according to ``self.size``."""
        if self.size is not None:
            padded_img = mmcv.impad(
                results['img'], shape=self.size, pad_val=self.pad_val)
        elif self.size_divisor is not None:
            padded_img = mmcv.impad_to_multiple(
                results['img'], self.size_divisor, pad_val=self.pad_val)
        results['img'] = padded_img
        results['pad_shape'] = padded_img.shape
        results['pad_fixed_size'] = self.size
        results['pad_size_divisor'] = self.size_divisor

    def _pad_seg(self, results):
        """Pad masks according to ``results['pad_shape']``."""
        for key in results.get('seg_fields', []):
            results[key] = mmcv.impad(
                results[key],
                shape=results['pad_shape'][:2],
                pad_val=self.seg_pad_val)

    def __call__(self, results):
        """Call function to pad images, masks, semantic segmentation maps.

        Args:
            results (dict): Result dict from loading pipeline.

        Returns:
            dict: Updated result dict.
        """

        self._pad_img(results)
        self._pad_seg(results)
        return results

    def __repr__(self):
        repr_str = self.__class__.__name__
        repr_str += f'(size={self.size}, size_divisor={self.size_divisor}, ' \
                    f'pad_val={self.pad_val})'
        return repr_str


@PIPELINES.register_module()
class Normalize(object):
    """Normalize the image.

    Added key is "img_norm_cfg".

    Args:
        mean (sequence): Mean values of 3 channels.
        std (sequence): Std values of 3 channels.
        to_rgb (bool): Whether to convert the image from BGR to RGB,
            default is true.
    """

    def __init__(self, mean, std, to_rgb=True):
        self.mean = np.array(mean, dtype=np.float32)
        self.std = np.array(std, dtype=np.float32)
        self.to_rgb = to_rgb

    def __call__(self, results):
        """Call function to normalize images.

        Args:
            results (dict): Result dict from loading pipeline.

        Returns:
            dict: Normalized results, 'img_norm_cfg' key is added into
                result dict.
        """

        results['img'] = mmcv.imnormalize(results['img'], self.mean, self.std,
                                          self.to_rgb)
        results['img_norm_cfg'] = dict(
            mean=self.mean, std=self.std, to_rgb=self.to_rgb)
        return results

    def __repr__(self):
        repr_str = self.__class__.__name__
        repr_str += f'(mean={self.mean}, std={self.std}, to_rgb=' \
                    f'{self.to_rgb})'
        return repr_str


@PIPELINES.register_module()
class RandomCrop(object):
    """Random crop the image & seg.

    Args:
        crop_size (tuple): Expected size after cropping, (h, w).
        cat_max_ratio (float): The maximum ratio that single category could
            occupy.
    """

    def __init__(self, crop_size, cat_max_ratio=1., ignore_index=255):
        assert crop_size[0] > 0 and crop_size[1] > 0
        self.crop_size = crop_size
        self.cat_max_ratio = cat_max_ratio
        self.ignore_index = ignore_index

    def get_crop_bbox(self, img):
        """Randomly get a crop bounding box."""
        margin_h = max(img.shape[0] - self.crop_size[0], 0)
        margin_w = max(img.shape[1] - self.crop_size[1], 0)
        offset_h = np.random.randint(0, margin_h + 1)
        offset_w = np.random.randint(0, margin_w + 1)
        crop_y1, crop_y2 = offset_h, offset_h + self.crop_size[0]
        crop_x1, crop_x2 = offset_w, offset_w + self.crop_size[1]

        return crop_y1, crop_y2, crop_x1, crop_x2

    def crop(self, img, crop_bbox):
        """Crop from ``img``"""
        crop_y1, crop_y2, crop_x1, crop_x2 = crop_bbox
        img = img[crop_y1:crop_y2, crop_x1:crop_x2, ...]
        return img

    def __call__(self, results):
        """Call function to randomly crop images, semantic segmentation maps.

        Args:
            results (dict): Result dict from loading pipeline.

        Returns:
            dict: Randomly cropped results, 'img_shape' key in result dict is
                updated according to crop size.
        """

        img = results['img']
        crop_bbox = self.get_crop_bbox(img)
        if self.cat_max_ratio < 1.:
            # Repeat 10 times
            for _ in range(10):
                seg_temp = self.crop(results['gt_semantic_seg'], crop_bbox)
                labels, cnt = np.unique(seg_temp, return_counts=True)
                cnt = cnt[labels != self.ignore_index]
                if len(cnt) > 1 and np.max(cnt) / np.sum(
                        cnt) < self.cat_max_ratio:
                    break
                crop_bbox = self.get_crop_bbox(img)

        # crop the image
        img = self.crop(img, crop_bbox)
        img_shape = img.shape
        results['img'] = img
        results['img_shape'] = img_shape

        # crop semantic seg
        for key in results.get('seg_fields', []):
            results[key] = self.crop(results[key], crop_bbox)

        return results

    def __repr__(self):
        return self.__class__.__name__ + f'(crop_size={self.crop_size})'


@PIPELINES.register_module()
class SegRescale(object):
    """Rescale semantic segmentation maps.

    Args:
        scale_factor (float): The scale factor of the final output.
    """

    def __init__(self, scale_factor=1):
        self.scale_factor = scale_factor

    def __call__(self, results):
        """Call function to scale the semantic segmentation map.

        Args:
            results (dict): Result dict from loading pipeline.

        Returns:
            dict: Result dict with semantic segmentation map scaled.
        """
        for key in results.get('seg_fields', []):
            if self.scale_factor != 1:
                results[key] = mmcv.imrescale(
                    results[key], self.scale_factor, interpolation='nearest')
        return results

    def __repr__(self):
        return self.__class__.__name__ + f'(scale_factor={self.scale_factor})'


@PIPELINES.register_module()
class PhotoMetricDistortion(object):
    """Apply photometric distortion to image sequentially, every transformation
    is applied with a probability of 0.5. The position of random contrast is in
    second or second to last.

    1. random brightness
    2. random contrast (mode 0)
    3. convert color from BGR to HSV
    4. random saturation
    5. random hue
    6. convert color from HSV to BGR
    7. random contrast (mode 1)
    8. randomly swap channels

    Args:
        brightness_delta (int): delta of brightness.
        contrast_range (tuple): range of contrast.
        saturation_range (tuple): range of saturation.
        hue_delta (int): delta of hue.
    """

    def __init__(self,
                 brightness_delta=32,
                 contrast_range=(0.5, 1.5),
                 saturation_range=(0.5, 1.5),
                 hue_delta=18):
        self.brightness_delta = brightness_delta
        self.contrast_lower, self.contrast_upper = contrast_range
        self.saturation_lower, self.saturation_upper = saturation_range
        self.hue_delta = hue_delta

    def convert(self, img, alpha=1, beta=0):
        """Multiple with alpha and add beat with clip."""
        img = img.astype(np.float32) * alpha + beta
        img = np.clip(img, 0, 255)
        return img.astype(np.uint8)

    def brightness(self, img):
        """Brightness distortion."""
        if random.randint(2):
            return self.convert(
                img,
                beta=random.uniform(-self.brightness_delta,
                                    self.brightness_delta))
        return img

    def contrast(self, img):
        """Contrast distortion."""
        if random.randint(2):
            return self.convert(
                img,
                alpha=random.uniform(self.contrast_lower, self.contrast_upper))
        return img

    def saturation(self, img):
        """Saturation distortion."""
        if random.randint(2):
            img = mmcv.bgr2hsv(img)
            img[:, :, 1] = self.convert(
                img[:, :, 1],
                alpha=random.uniform(self.saturation_lower,
                                     self.saturation_upper))
            img = mmcv.hsv2bgr(img)
        return img

    def hue(self, img):
        """Hue distortion."""
        if random.randint(2):
            img = mmcv.bgr2hsv(img)
            img[:, :,
                0] = (img[:, :, 0].astype(int) +
                      random.randint(-self.hue_delta, self.hue_delta)) % 180
            img = mmcv.hsv2bgr(img)
        return img

    def __call__(self, results):
        """Call function to perform photometric distortion on images.

        Args:
            results (dict): Result dict from loading pipeline.

        Returns:
            dict: Result dict with images distorted.
        """

        img = results['img']
        # random brightness
        img = self.brightness(img)

        # mode == 0 --> do random contrast first
        # mode == 1 --> do random contrast last
        mode = random.randint(2)
        if mode == 1:
            img = self.contrast(img)

        # random saturation
        img = self.saturation(img)

        # random hue
        img = self.hue(img)

        # random contrast
        if mode == 0:
            img = self.contrast(img)

        results['img'] = img
        return results

    def __repr__(self):
        repr_str = self.__class__.__name__
        repr_str += (f'(brightness_delta={self.brightness_delta}, '
                     f'contrast_range=({self.contrast_lower}, '
                     f'{self.contrast_upper}), '
                     f'saturation_range=({self.saturation_lower}, '
                     f'{self.saturation_upper}), '
                     f'hue_delta={self.hue_delta})')
        return repr_str
