import numpy as np
import pycocotools.mask as mask_util
import cv2

class GenericMask:
    """
    Attribute:
        polygons (list[ndarray]): list[ndarray]: polygons for this mask.
            Each ndarray has format [x, y, x, y, ...]
        mask (ndarray): a binary mask
    """

    def __init__(self, mask_or_polygons, height, width):
        self._mask = self._polygons = self._has_holes = None
        self.height = height
        self.width = width

        m = mask_or_polygons
        if isinstance(m, dict):
            # RLEs
            assert "counts" in m and "size" in m
            if isinstance(m["counts"], list):  # uncompressed RLEs
                h, w = m["size"]
                assert h == height and w == width
                m = mask_util.frPyObjects(m, h, w)
            self._mask = mask_util.decode(m)[:, :]
            return

        if isinstance(m, list):  # list[ndarray]
            self._polygons = [np.asarray(x).reshape(-1) for x in m]
            return

        if isinstance(m, np.ndarray):  # assumed to be a binary mask
            assert m.shape[1] != 2, m.shape
            assert m.shape == (height, width), m.shape
            self._mask = m.astype("uint8")
            return

        raise ValueError("GenericMask cannot handle object {} of type '{}'".format(m, type(m)))

    @property
    def mask(self):
        if self._mask is None:
            self._mask = self.polygons_to_mask(self._polygons)
        return self._mask

    @property
    def polygons(self):
        if self._polygons is None:
            self._polygons, self._has_holes = self.mask_to_polygons(self._mask)
        return self._polygons

    @property
    def has_holes(self):
        if self._has_holes is None:
            if self._mask is not None:
                self._polygons, self._has_holes = self.mask_to_polygons(self._mask)
            else:
                self._has_holes = False  # if original format is polygon, does not have holes
        return self._has_holes

    def mask_to_polygons(self, mask):
        # cv2.RETR_CCOMP flag retrieves all the contours and arranges them to a 2-level
        # hierarchy. External contours (boundary) of the object are placed in hierarchy-1.
        # Internal contours (holes) are placed in hierarchy-2.
        # cv2.CHAIN_APPROX_NONE flag gets vertices of polygons from contours.
        mask = np.ascontiguousarray(mask)  # some versions of cv2 does not support incontiguous arr
        #res = cv2.findContours(mask.astype("uint8"), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)
        res = cv2.findContours(mask.astype("uint8"), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
        hierarchy = res[-1]
        if hierarchy is None:  # empty mask
            return [], False
        has_holes = (hierarchy.reshape(-1, 4)[:, 3] >= 0).sum() > 0
        res = res[-2]
        res = [x.flatten() for x in res]
        # These coordinates from OpenCV are integers in range [0, W-1 or H-1].
        # We add 0.5 to turn them into real-value coordinate space. A better solution
        # would be to first +0.5 and then dilate the returned polygon by 0.5.
        res = [x + 0.5 for x in res if len(x) >= 6]
        return res, has_holes

    def polygons_to_mask(self, polygons):
        rle = mask_util.frPyObjects(polygons, self.height, self.width)
        rle = mask_util.merge(rle)
        return mask_util.decode(rle)[:, :]

    def area(self):
        return self.mask.sum()

    def bbox(self):
        p = mask_util.frPyObjects(self.polygons, self.height, self.width)
        p = mask_util.merge(p)
        bbox = mask_util.toBbox(p)
        bbox[2] += bbox[0]
        bbox[3] += bbox[1]
        return bbox