Source code for mmocr.utils.polygon_utils

# Copyright (c) OpenMMLab. All rights reserved.
import math
import operator
from functools import reduce
from typing import List, Optional, Sequence, Tuple, Union

import numpy as np
import pyclipper
import shapely
from mmengine.utils import is_list_of
from shapely.geometry import MultiPolygon, Polygon

from mmocr.utils import bbox2poly, valid_boundary
from mmocr.utils.check_argument import is_2dlist
from mmocr.utils.typing_utils import ArrayLike


def rescale_polygon(polygon: ArrayLike,
                    scale_factor: Tuple[int, int],
                    mode: str = 'mul') -> np.ndarray:
    """Rescale a polygon according to scale_factor.

    The behavior is different depending on the mode. When mode is 'mul', the
    coordinates will be multiplied by scale_factor, which is usually used in
    preprocessing transforms such as :func:`Resize`.
    The coordinates will be divided by scale_factor if mode is 'div'. It can be
    used in postprocessors to recover the polygon in the original
    image size.

    Args:
        polygon (ArrayLike): A polygon. In any form can be converted
            to an 1-D numpy array. E.g. list[float], np.ndarray,
            or torch.Tensor. Polygon is written in
            [x1, y1, x2, y2, ...].
        scale_factor (tuple(int, int)): (w_scale, h_scale).
        model (str): Rescale mode. Can be 'mul' or 'div'. Defaults to 'mul'.

    Returns:
        np.ndarray: Rescaled polygon.
    """
    assert len(polygon) % 2 == 0
    assert mode in ['mul', 'div']
    polygon = np.array(polygon, dtype=np.float32)
    poly_shape = polygon.shape
    reshape_polygon = polygon.reshape(-1, 2)
    scale_factor = np.array(scale_factor, dtype=float)
    if mode == 'div':
        scale_factor = 1 / scale_factor
    polygon = (reshape_polygon * scale_factor[None]).reshape(poly_shape)
    return polygon


def rescale_polygons(polygons: Union[ArrayLike, Sequence[ArrayLike]],
                     scale_factor: Tuple[int, int],
                     mode: str = 'mul'
                     ) -> Union[ArrayLike, Sequence[np.ndarray]]:
    """Rescale polygons according to scale_factor.

    The behavior is different depending on the mode. When mode is 'mul', the
    coordinates will be multiplied by scale_factor, which is usually used in
    preprocessing transforms such as :func:`Resize`.
    The coordinates will be divided by scale_factor if mode is 'div'. It can be
    used in postprocessors to recover the polygon in the original
    image size.

    Args:
        polygons (list[ArrayLike] or ArrayLike): A list of polygons, each
            written in [x1, y1, x2, y2, ...] and in any form can be converted
            to an 1-D numpy array. E.g. list[list[float]],
            list[np.ndarray], or list[torch.Tensor].
        scale_factor (tuple(int, int)): (w_scale, h_scale).
        model (str): Rescale mode. Can be 'mul' or 'div'. Defaults to 'mul'.

    Returns:
        list[np.ndarray] or np.ndarray: Rescaled polygons. The type of the
        return value depends on the type of the input polygons.
    """
    results = []
    for polygon in polygons:
        results.append(rescale_polygon(polygon, scale_factor, mode))
    if isinstance(polygons, np.ndarray):
        results = np.array(results)
    return results


def poly2bbox(polygon: ArrayLike) -> np.array:
    """Converting a polygon to a bounding box.

    Args:
         polygon (ArrayLike): A polygon. In any form can be converted
             to an 1-D numpy array. E.g. list[float], np.ndarray,
             or torch.Tensor. Polygon is written in
             [x1, y1, x2, y2, ...].

     Returns:
         np.array: The converted bounding box [x1, y1, x2, y2]
    """
    assert len(polygon) % 2 == 0
    polygon = np.array(polygon, dtype=np.float32)
    x = polygon[::2]
    y = polygon[1::2]
    return np.array([min(x), min(y), max(x), max(y)])


def poly2shapely(polygon: ArrayLike) -> Polygon:
    """Convert a polygon to shapely.geometry.Polygon.

    Args:
        polygon (ArrayLike): A set of points of 2k shape.

    Returns:
        polygon (Polygon): A polygon object.
    """
    polygon = np.array(polygon, dtype=np.float32)
    assert polygon.size % 2 == 0 and polygon.size >= 6

    polygon = polygon.reshape([-1, 2])
    return Polygon(polygon)


def polys2shapely(polygons: Sequence[ArrayLike]) -> Sequence[Polygon]:
    """Convert a nested list of boundaries to a list of Polygons.

    Args:
        polygons (list): The point coordinates of the instance boundary.

    Returns:
        list: Converted shapely.Polygon.
    """
    return [poly2shapely(polygon) for polygon in polygons]


def shapely2poly(polygon: Polygon) -> np.array:
    """Convert a nested list of boundaries to a list of Polygons.

    Args:
        polygon (Polygon): A polygon represented by shapely.Polygon.

    Returns:
        np.array: Converted numpy array
    """
    return np.array(polygon.exterior.coords).reshape(-1, )


def crop_polygon(polygon: ArrayLike,
                 crop_box: np.ndarray) -> Union[np.ndarray, None]:
    """Crop polygon to be within a box region.

    Args:
        polygon (ndarray): polygon in shape (N, ).
        crop_box (ndarray): target box region in shape (4, ).

    Returns:
        np.array or None: Cropped polygon. If the polygon is not within the
            crop box, return None.
    """
    poly = poly_make_valid(poly2shapely(polygon))
    crop_poly = poly_make_valid(poly2shapely(bbox2poly(crop_box)))
    area, poly_cropped = poly_intersection(poly, crop_poly, return_poly=True)
    if area == 0 or area is None or not isinstance(
            poly_cropped, shapely.geometry.polygon.Polygon):
        return None
    else:
        poly_cropped = poly_make_valid(poly_cropped)
        poly_cropped = np.array(poly_cropped.boundary.xy, dtype=np.float32)
        poly_cropped = poly_cropped.T
        # reverse poly_cropped to have clockwise order
        poly_cropped = poly_cropped[::-1, :].reshape(-1)
        return poly_cropped


def poly_make_valid(poly: Polygon) -> Polygon:
    """Convert a potentially invalid polygon to a valid one by eliminating
    self-crossing or self-touching parts. Note that if the input is a line, the
    returned polygon could be an empty one.

    Args:
        poly (Polygon): A polygon needed to be converted.

    Returns:
        Polygon: A valid polygon, which might be empty.
    """
    assert isinstance(poly, Polygon)
    fixed_poly = poly if poly.is_valid else poly.buffer(0)
    # Sometimes the fixed_poly is still a MultiPolygon,
    # so we need to find the convex hull of the MultiPolygon, which should
    # always be a Polygon (but could be empty).
    if not isinstance(fixed_poly, Polygon):
        fixed_poly = fixed_poly.convex_hull
    return fixed_poly


def poly_intersection(poly_a: Polygon,
                      poly_b: Polygon,
                      invalid_ret: Optional[Union[float, int]] = None,
                      return_poly: bool = False
                      ) -> Tuple[float, Optional[Polygon]]:
    """Calculate the intersection area between two polygons.

    Args:
        poly_a (Polygon): Polygon a.
        poly_b (Polygon): Polygon b.
        invalid_ret (float or int, optional): The return value when the
            invalid polygon exists. If it is not specified, the function
            allows the computation to proceed with invalid polygons by
            cleaning the their self-touching or self-crossing parts.
            Defaults to None.
        return_poly (bool): Whether to return the polygon of the intersection
            Defaults to False.

    Returns:
        float or tuple(float, Polygon): Returns the intersection area or
        a tuple ``(area, Optional[poly_obj])``, where the `area` is the
        intersection area between two polygons and `poly_obj` is The Polygon
        object of the intersection area, which will be `None` if the input is
        invalid. `poly_obj` will be returned only if `return_poly` is `True`.
    """
    assert isinstance(poly_a, Polygon)
    assert isinstance(poly_b, Polygon)
    assert invalid_ret is None or isinstance(invalid_ret, (float, int))

    if invalid_ret is None:
        poly_a = poly_make_valid(poly_a)
        poly_b = poly_make_valid(poly_b)

    poly_obj = None
    area = invalid_ret
    if poly_a.is_valid and poly_b.is_valid:
        if poly_a.intersects(poly_b):
            poly_obj = poly_a.intersection(poly_b)
            area = poly_obj.area
        else:
            poly_obj = Polygon()
            area = 0.0
    return (area, poly_obj) if return_poly else area


def poly_union(
    poly_a: Polygon,
    poly_b: Polygon,
    invalid_ret: Optional[Union[float, int]] = None,
    return_poly: bool = False
) -> Tuple[float, Optional[Union[Polygon, MultiPolygon]]]:
    """Calculate the union area between two polygons.

    Args:
        poly_a (Polygon): Polygon a.
        poly_b (Polygon): Polygon b.
        invalid_ret (float or int, optional): The return value when the
            invalid polygon exists. If it is not specified, the function
            allows the computation to proceed with invalid polygons by
            cleaning the their self-touching or self-crossing parts.
            Defaults to False.
        return_poly (bool): Whether to return the polygon of the union.
            Defaults to False.

    Returns:
        tuple: Returns a tuple ``(area, Optional[poly_obj])``, where
        the `area` is the union between two polygons and `poly_obj` is the
        Polygon or MultiPolygon object of the union of the inputs. The type
        of object depends on whether they intersect or not. Set as `None`
        if the input is invalid. `poly_obj` will be returned only if
        `return_poly` is `True`.
    """
    assert isinstance(poly_a, Polygon)
    assert isinstance(poly_b, Polygon)
    assert invalid_ret is None or isinstance(invalid_ret, (float, int))

    if invalid_ret is None:
        poly_a = poly_make_valid(poly_a)
        poly_b = poly_make_valid(poly_b)

    poly_obj = None
    area = invalid_ret
    if poly_a.is_valid and poly_b.is_valid:
        poly_obj = poly_a.union(poly_b)
        area = poly_obj.area
    return (area, poly_obj) if return_poly else area


def poly_iou(poly_a: Polygon,
             poly_b: Polygon,
             zero_division: float = 0.) -> float:
    """Calculate the IOU between two polygons.

    Args:
        poly_a (Polygon): Polygon a.
        poly_b (Polygon): Polygon b.
        zero_division (float): The return value when invalid polygon exists.

    Returns:
        float: The IoU between two polygons.
    """
    assert isinstance(poly_a, Polygon)
    assert isinstance(poly_b, Polygon)
    area_inters = poly_intersection(poly_a, poly_b)
    area_union = poly_union(poly_a, poly_b)
    return area_inters / area_union if area_union != 0 else zero_division


def is_poly_inside_rect(poly: ArrayLike, rect: np.ndarray) -> bool:
    """Check if the polygon is inside the target region.
        Args:
            poly (ArrayLike): Polygon in shape (N, ).
            rect (ndarray): Target region [x1, y1, x2, y2].

        Returns:
            bool: Whether the polygon is inside the cropping region.
        """

    poly = poly2shapely(poly)
    rect = poly2shapely(bbox2poly(rect))
    return rect.contains(poly)


def offset_polygon(poly: ArrayLike, distance: float) -> ArrayLike:
    """Offset (expand/shrink) the polygon by the target distance. It's a
    wrapper around pyclipper based on Vatti clipping algorithm.

    Warning:
        Polygon coordinates will be casted to int type in PyClipper. Mind the
        potential precision loss caused by the casting.

    Args:
        poly (ArrayLike): A polygon. In any form can be converted
            to an 1-D numpy array. E.g. list[float], np.ndarray,
            or torch.Tensor. Polygon is written in
            [x1, y1, x2, y2, ...].
        distance (float): The offset distance. Positive value means expanding,
            negative value means shrinking.

    Returns:
        np.array: 1-D Offsetted polygon ndarray in float32 type. If the
        result polygon is invalid or has been split into several parts,
        return an empty array.
    """
    poly = np.array(poly).reshape(-1, 2)
    pco = pyclipper.PyclipperOffset()
    pco.AddPath(poly, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON)
    # Returned result will be in type of int32, convert it back to float32
    # following MMOCR's convention
    result = np.array(pco.Execute(distance), dtype=object)
    if len(result) > 0 and isinstance(result[0], list):
        # The processed polygon has been split into several parts
        result = np.array([])
    result = result.astype(np.float32)
    # Always use the first polygon since only one polygon is expected
    # But when the resulting polygon is invalid, return the empty array
    # as it is
    return result if len(result) == 0 else result[0].flatten()


def boundary_iou(src: List,
                 target: List,
                 zero_division: Union[int, float] = 0) -> float:
    """Calculate the IOU between two boundaries.

    Args:
       src (list): Source boundary.
       target (list): Target boundary.
       zero_division (int or float): The return value when invalid
                                    boundary exists.

    Returns:
       float: The iou between two boundaries.
    """
    assert valid_boundary(src, False)
    assert valid_boundary(target, False)
    src_poly = poly2shapely(src)
    target_poly = poly2shapely(target)

    return poly_iou(src_poly, target_poly, zero_division=zero_division)


def sort_points(points):
    # TODO Add typehints & test & docstring
    """Sort arbitrary points in clockwise order in Cartesian coordinate, you
    may need to reverse the output sequence if you are using OpenCV's image
    coordinate.

    Reference:
    https://github.com/novioleo/Savior/blob/master/Utils/GeometryUtils.py.

    Warning: This function can only sort convex polygons.

    Args:
        points (list[ndarray] or ndarray or list[list]): A list of unsorted
            boundary points.

    Returns:
        list[ndarray]: A list of points sorted in clockwise order.
    """
    assert is_list_of(points, np.ndarray) or isinstance(points, np.ndarray) \
        or is_2dlist(points)
    center_point = tuple(
        map(operator.truediv,
            reduce(lambda x, y: map(operator.add, x, y), points),
            [len(points)] * 2))
    return sorted(
        points,
        key=lambda coord: (180 + math.degrees(
            math.atan2(*tuple(map(operator.sub, coord, center_point))))) % 360)


def sort_vertex(points_x, points_y):
    # TODO Add typehints & test
    """Sort box vertices in clockwise order from left-top first.

    Args:
        points_x (list[float]): x of four vertices.
        points_y (list[float]): y of four vertices.

    Returns:
        tuple[list[float], list[float]]: Sorted x and y of four vertices.

        - sorted_points_x (list[float]): x of sorted four vertices.
        - sorted_points_y (list[float]): y of sorted four vertices.
    """
    assert is_list_of(points_x, (float, int))
    assert is_list_of(points_y, (float, int))
    assert len(points_x) == 4
    assert len(points_y) == 4
    vertices = np.stack((points_x, points_y), axis=-1).astype(np.float32)
    vertices = _sort_vertex(vertices)
    sorted_points_x = list(vertices[:, 0])
    sorted_points_y = list(vertices[:, 1])
    return sorted_points_x, sorted_points_y


def _sort_vertex(vertices):
    # TODO Add typehints & docstring & test
    assert vertices.ndim == 2
    assert vertices.shape[-1] == 2
    N = vertices.shape[0]
    if N == 0:
        return vertices

    center = np.mean(vertices, axis=0)
    directions = vertices - center
    angles = np.arctan2(directions[:, 1], directions[:, 0])
    sort_idx = np.argsort(angles)
    vertices = vertices[sort_idx]

    left_top = np.min(vertices, axis=0)
    dists = np.linalg.norm(left_top - vertices, axis=-1, ord=2)
    lefttop_idx = np.argmin(dists)
    indexes = (np.arange(N, dtype=np.int_) + lefttop_idx) % N
    return vertices[indexes]


def sort_vertex8(points):
    # TODO Add typehints & docstring & test
    """Sort vertex with 8 points [x1 y1 x2 y2 x3 y3 x4 y4]"""
    assert len(points) == 8
    vertices = _sort_vertex(np.array(points, dtype=np.float32).reshape(-1, 2))
    sorted_box = list(vertices.flatten())
    return sorted_box