#!/bin/env python

##
## calculate the concave hull of a set of points
## see: CONCAVE HULL: A K-NEAREST NEIGHBOURS APPROACH
##      FOR THE COMPUTATION OF THE REGION OCCUPIED BY A
##      SET OF POINTS
##      Adriano Moreira and Maribel Yasmina Santos 2007
##

import numpy as np
import scipy.spatial as spt
from matplotlib.path import Path
from test.zonghe import lineintersect as li

def GetFirstPoint(dataset):
    ''' Returns index of first point, which has the lowest y value '''
    # todo: what if there is more than one point with lowest y?
    imin = np.argmin(dataset[:,1])
    return dataset[imin]

def GetNearestNeighbors(dataset, point, k):
    ''' Returns indices of k nearest neighbors of point in dataset'''
    # todo: experiment with leafsize for performance
    mytree = spt.cKDTree(dataset,leafsize=10)
    distances, indices = mytree.query(point,k)
    # todo: something strange here, we get more indices than points in dataset
    #       so have to do this
    return dataset[indices[:dataset.shape[0]]]

def SortByAngle(kNearestPoints, currentPoint, prevPoint):
    ''' Sorts the k nearest points given by angle '''
    angles = np.zeros(kNearestPoints.shape[0])
    i = 0
    for NearestPoint in kNearestPoints:
        # calculate the angle
        angle = np.arctan2(NearestPoint[1]-currentPoint[1],
                NearestPoint[0]-currentPoint[0]) - \
                np.arctan2(prevPoint[1]-currentPoint[1],
                prevPoint[0]-currentPoint[0])
        angle = np.rad2deg(angle)
        # only positive angles
        angle = np.mod(angle+360,360)
        #print NearestPoint[0], NearestPoint[1], angle
        angles[i] = angle
        i=i+1
    return kNearestPoints[np.argsort(angles)]


def removePoint(dataset, point):
    delmask = [np.logical_or(dataset[:,0]!=point[0],dataset[:,1]!=point[1])]
    newdata = dataset[delmask]
    return newdata


def concaveHull(dataset, k):
    assert k >= 3, 'k has to be greater or equal to 3.'
    points = dataset
    # todo: remove duplicate points from dataset
    # todo: check if dataset consists of only 3 or less points
    # todo: make sure that enough points for a given k can be found

    firstpoint = GetFirstPoint(points)
    # init hull as list to easily append stuff
    hull = []
    # add first point to hull
    hull.append(firstpoint)
    # and remove it from dataset
    points = removePoint(points,firstpoint)

    currentPoint = firstpoint
    # set prevPoint to a Point righ of currentpoint (angle=0)

    prevPoint = (currentPoint[0]+10, currentPoint[1])


    step = 2

    while ( (not np.array_equal(firstpoint, currentPoint) or (step==2)) and points.size > 0 ):
        if ( step == 5 ): # we're far enough to close too early
            points = np.append(points, [firstpoint], axis=0)
        kNearestPoints = GetNearestNeighbors(points, currentPoint, k)
        cPoints = SortByAngle(kNearestPoints, currentPoint, prevPoint)
        # avoid intersections: select first candidate that does not intersect any
        # polygon edge
        its = True
        i = 0
        while ( (its==True) and (i<cPoints.shape[0]) ):
                i=i+1
                if ( np.array_equal(cPoints[i-1], firstpoint) ):
                    lastPoint = 1
                else:
                    lastPoint = 0
                j = 2
                its = False
                while ( (its==False) and (j<np.shape(hull)[0]-lastPoint) ):
                    its = li.doLinesIntersect(hull[step-1-1], cPoints[i-1],
                            hull[step-1-j-1],hull[step-j-1])
                    j=j+1
        if ( its==True ):
            # print("all candidates intersect -- restarting with k = ",k+1)
            return concaveHull(dataset,k+1)
        prevPoint = currentPoint
        currentPoint = cPoints[i-1]
        # add current point to hull
        hull.append(currentPoint)
        points = removePoint(points,currentPoint)
        step = step+1
    # check if all points are inside the hull
    p = Path(hull)
    pContained = p.contains_points(dataset, radius=0.0000000001)

    if (not pContained.all()):
        # print("not all points of dataset contained in hull -- restarting with k = ",k+1)
        return concaveHull(dataset, k+1)

    # print("finished with k = ",k)
    return hull



if __name__ == '__main__':



    points_E = np.array([[1,1],[2,1],[3,1],[4,1],[5,1],[6,1],[6,2],[6,3],[5,3],[4,3],
                    [3,3],[3,4],[3,5],[4,5],[5,5],[5,6],[5,7],[4,7],[3,7],[3,8],
                    [3,9],[4,9],[5,9],[6,9],[6,10],[6,11],[5,11],[4,11],[3,11],[2,11],
                    [1,11],[1,10],[1,9],[1,8],[1,7],[1,6],[1,5],[1,4],[1,3],[1,2],
                    [5,2],[4,2],[3,2],[2,2],[2,3],[2,4],[2,5],[2,6],[2,7],[2,8],
                    [2,9],[2,10],[3,10],[4,10],[5,10],[3,6],[4,6],[5,6],[4.5,7],[3,8.5],
                    ])

    print(concaveHull(points_E, 3))