import cv2
import torch
import numpy as np
import pycocotools.mask as mask_utils

# transpose
FLIP_LEFT_RIGHT = 0
FLIP_TOP_BOTTOM = 1


class MaskList(object):
    """
    This class is unfinished and not meant for use yet
    It is supposed to contain the binary masks for all instances in a list of 2D tensors (H, W)
    """

    def __init__(self, masks, size, mode):
        assert(isinstance(masks, list))
        assert(mode in ['mask', 'rle'])
        self.masks = masks
        self.size = size # (image_width, image_height)
        self.mode = mode

    def transpose(self, method):
        assert (self.mode == "mask"), "RLE masks cannot be transposed. Please convert them to binary first."
        if method not in (FLIP_LEFT_RIGHT, FLIP_TOP_BOTTOM):
            raise NotImplementedError(
                "Only FLIP_LEFT_RIGHT and FLIP_TOP_BOTTOM implemented"
            )

        # width, height = self.size
        masks = np.array(self.masks)
        if masks.ndim == 2:
            masks = np.expand_dims(masks, axis=0)
        if method == FLIP_LEFT_RIGHT:
            masks = np.flip(masks, axis=2)
        elif method == FLIP_TOP_BOTTOM:
            masks = np.flip(masks, axis=1)
        flipped_masks = np.split(masks, masks.shape[0])
        flipped_masks = [mask.squeeze(0) for mask in flipped_masks]
        return MaskList(flipped_masks, self.size, self.mode)

    def resize(self, size, *args, **kwargs):
        """
        Resize the binary mask.
        :param size: tuple, (image_width, image_height)
        :param args:
        :param kwargs:
        :return:
        """
        assert(self.mode == "mask"), "RLE masks cannot be resized. Please convert them to binary first."
        cat_mask = np.array(self.masks)

        cat_mask = cat_mask.transpose(1, 2, 0)
        cat_mask *= 255
        cat_mask = cat_mask.astype(np.uint8)
        resized_mask = cv2.resize(cat_mask, size)
        if resized_mask.ndim == 2:
            resized_mask = np.expand_dims(resized_mask, axis=2)
        try:
            resized_mask = resized_mask.transpose(2, 0, 1)
        except ValueError:
            print("?")
        resized_mask = resized_mask.astype(int)
        resized_mask = resized_mask // 255
        # # visualize to check mask correctness
        # from matplotlib import pyplot as plt
        # plt.figure()
        # plt.imshow(resized_mask[0]*255, cmap='gray')
        # plt.show()
        mask_list = np.split(resized_mask, resized_mask.shape[0])
        mask_list = [mask.squeeze(0) for mask in mask_list]
        return MaskList(mask_list, size, "mask")

    def pad(self, size):
        """
        pad the binary masks according to the new size. New size must be larger than original size in all dimensions
        :param size: New image size, (image_width, image_height)
        :return:
        """
        assert(size[0] >= self.size[0] and size[1] >= self.size[1]), "New size must be larger than original size in all dimensions"
        cat_mask = np.array(self.masks)
        if cat_mask.ndim == 2:
            cat_mask = np.expand_dims(cat_mask, axis=0)
        padded_mask = np.zeros([len(self.masks), size[1], size[0]])
        padded_mask[:, :cat_mask.shape[1], :cat_mask.shape[2]] = cat_mask
        # # visualize to check mask correctness
        # from matplotlib import pyplot as plt
        # plt.figure()
        # plt.imshow(padded_mask[1]*255, cmap='gray')
        # plt.show()
        mask_list = np.split(padded_mask, padded_mask.shape[0])
        mask_list = [mask.squeeze(0) for mask in mask_list]
        return MaskList(mask_list, size, "mask")

    def convert(self, mode):
        """
        Convert mask from between mode "mask" and mode "rle"
        :param mode:
        :return:
        """
        if mode == self.mode:
            return self
        elif mode == "rle" and self.mode == "mask":
            # use pycocotools to encode binary masks to rle
            rle_mask_list = mask_utils.encode(np.asfortranarray(np.array(self.masks).transpose(1, 2, 0).astype(np.uint8)))
            return MaskList(rle_mask_list, self.size, "rle")
        elif mode == "mask" and self.mode == "rle":
            # use pycocotools to decode rle to binary masks
            bimasks = mask_utils.decode(self.masks)
            mask_list = np.split(bimasks.transpose(2, 0, 1), bimasks.shape[2])
            mask_list = [mask.squeeze(0) for mask in mask_list]
            return MaskList(mask_list, self.size, "mask")

    def bbox(self, bbox_mode="xyxy"):
        """
        Generate a bounding box according to the binary mask
        :param bbox_mode:
        :return:
        """
        pass

    def __len__(self):
        return len(self.masks)

    def __repr__(self):
        s = self.__class__.__name__ + "("
        s += "num_masks={}, ".format(len(self))
        s += "image_width={}, ".format(self.size[0])
        s += "image_height={}, ".format(self.size[1])
        s += "mode={})".format(self.mode)
        return s


class Polygons(object):
    """
    This class holds a set of polygons that represents a single instance
    of an object mask. The object can be represented as a set of
    polygons
    """

    def __init__(self, polygons, size, mode):
        # assert isinstance(polygons, list), '{}'.format(polygons)
        if isinstance(polygons, list):
            polygons = [torch.as_tensor(p, dtype=torch.float32) for p in polygons]
        elif isinstance(polygons, Polygons):
            polygons = polygons.polygons

        self.polygons = polygons
        self.size = size
        self.mode = mode

    def transpose(self, method):
        if method not in (FLIP_LEFT_RIGHT, FLIP_TOP_BOTTOM):
            raise NotImplementedError(
                "Only FLIP_LEFT_RIGHT and FLIP_TOP_BOTTOM implemented"
            )

        flipped_polygons = []
        width, height = self.size
        if method == FLIP_LEFT_RIGHT:
            dim = width
            idx = 0
        elif method == FLIP_TOP_BOTTOM:
            dim = height
            idx = 1

        for poly in self.polygons:
            p = poly.clone()
            TO_REMOVE = 1
            p[idx::2] = dim - poly[idx::2] - TO_REMOVE
            flipped_polygons.append(p)

        return Polygons(flipped_polygons, size=self.size, mode=self.mode)

    def crop(self, box):
        w, h = box[2] - box[0], box[3] - box[1]

        # TODO chck if necessary
        w = max(w, 1)
        h = max(h, 1)

        cropped_polygons = []
        for poly in self.polygons:
            p = poly.clone()
            p[0::2] = p[0::2] - box[0]  # .clamp(min=0, max=w)
            p[1::2] = p[1::2] - box[1]  # .clamp(min=0, max=h)
            cropped_polygons.append(p)

        return Polygons(cropped_polygons, size=(w, h), mode=self.mode)

    def resize(self, size, *args, **kwargs):
        ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(size, self.size))
        if ratios[0] == ratios[1]:
            ratio = ratios[0]
            scaled_polys = [p * ratio for p in self.polygons]
            return Polygons(scaled_polys, size, mode=self.mode)

        ratio_w, ratio_h = ratios
        scaled_polygons = []
        for poly in self.polygons:
            p = poly.clone()
            p[0::2] *= ratio_w
            p[1::2] *= ratio_h
            scaled_polygons.append(p)

        return Polygons(scaled_polygons, size=size, mode=self.mode)

    def convert(self, mode):
        width, height = self.size
        if mode == "mask":
            rles = mask_utils.frPyObjects(
                [p.detach().numpy() for p in self.polygons], height, width
            )
            rle = mask_utils.merge(rles)
            mask = mask_utils.decode(rle)
            mask = torch.from_numpy(mask)
            # TODO add squeeze?
            return mask

    def __repr__(self):
        s = self.__class__.__name__ + "("
        s += "num_polygons={}, ".format(len(self.polygons))
        s += "image_width={}, ".format(self.size[0])
        s += "image_height={}, ".format(self.size[1])
        s += "mode={})".format(self.mode)
        return s


class SegmentationMask(object):
    """
    This class stores the segmentations for all objects in the image
    """

    def __init__(self, polygons, size, mode=None):
        """
        Arguments:
            polygons: a list of list of lists of numbers. The first
                level of the list correspond to individual instances,
                the second level to all the polygons that compose the
                object, and the third level to the polygon coordinates.
        """
        assert isinstance(polygons, list)

        self.polygons = [Polygons(p, size, mode) for p in polygons]
        self.size = size
        self.mode = mode

    def transpose(self, method):
        if method not in (FLIP_LEFT_RIGHT, FLIP_TOP_BOTTOM):
            raise NotImplementedError(
                "Only FLIP_LEFT_RIGHT and FLIP_TOP_BOTTOM implemented"
            )

        flipped = []
        for polygon in self.polygons:
            flipped.append(polygon.transpose(method))
        return SegmentationMask(flipped, size=self.size, mode=self.mode)

    def crop(self, box):
        w, h = box[2] - box[0], box[3] - box[1]
        cropped = []
        for polygon in self.polygons:
            cropped.append(polygon.crop(box))
        return SegmentationMask(cropped, size=(w, h), mode=self.mode)

    def resize(self, size, *args, **kwargs):
        scaled = []
        for polygon in self.polygons:
            scaled.append(polygon.resize(size, *args, **kwargs))
        return SegmentationMask(scaled, size=size, mode=self.mode)

    def to(self, *args, **kwargs):
        return self

    def __getitem__(self, item):
        if isinstance(item, (int, slice)):
            selected_polygons = [self.polygons[item]]
        else:
            # advanced indexing on a single dimension
            selected_polygons = []
            if isinstance(item, torch.Tensor) and item.dtype == torch.bool:
                item = item.nonzero()
                item = item.squeeze(1) if item.numel() > 0 else item
                item = item.tolist()
            for i in item:
                selected_polygons.append(self.polygons[i])
        return SegmentationMask(selected_polygons, size=self.size, mode=self.mode)

    def __iter__(self):
        return iter(self.polygons)

    def __repr__(self):
        s = self.__class__.__name__ + "("
        s += "num_instances={}, ".format(len(self.polygons))
        s += "image_width={}, ".format(self.size[0])
        s += "image_height={})".format(self.size[1])
        return s