import Image, ImageDraw, ImageFilter

from manual import lines as g_grid, l2ad, intersection, line as g_line
from intrsc import intersections_from_angl_dist
from linef import line_from_angl_dist

class GridFittingFailedError(Exception):
    pass

class MyGaussianBlur(ImageFilter.Filter):
    name = "GaussianBlur"

    def __init__(self, radius=2):
        self.radius = radius
    def filter(self, image):
        return image.gaussian_blur(self.radius)

class V():
    def __init__(self, x, y):
        self.x = x
        self.y = y
    
    def __add__(self, other):
        return V(self.x + other.x, self.y + other.y)

    def __sub__(self, other):
        return V(self.x - other.x, self.y - other.y)

    def __rmul__(self, other):
        return V(other * self.x, other * self.y)

    def t(self):
        return (self.x, self.y)

    def normal(self):
        return V(-self.y, self.x)

def projection(point, line, vector):
    n = vector.normal()
    l2 = g_line(point.t(), (point + n).t())
    return V(*intersection(l2, g_line(*line)))
    

def find(lines, size, l1, l2, bounds, hough, do_something):
    a, b, c, d = [V(*a) for a in bounds]
    l1 = line_from_angl_dist(l1, size)
    l2 = line_from_angl_dist(l2, size)
    v1 = V(*l1[0]) - V(*l1[1])
    v2 = V(*l2[0]) - V(*l2[1])
    a = projection(a, l1, v1) 
    b = projection(b, l1, v1) 
    c = projection(c, l2, v2) 
    d = projection(d, l2, v2) 
    grid = get_grid(a, b, c, d, hough, size)
    dist = distance(lines, grid, size)
    print dist
    
    s = 0.02
    while True:
        ts1 = [(s, 0), (-s, 0), (s, s), (-s, -s), (-s, s), (s, -s), (0, s),  (0, -s)]
        grids = [(get_grid(a + t[0] * v1, b + t[1] * v1, 
                           c, d, hough, size), t) for t in ts1]
        distances = [(distance(lines, grid, size), 
                      grid, t) for grid, t in grids]
        distances.sort(reverse=True)
        if distances[0][0] > dist:
            dist = distances[0][0]
            grid = distances[0][1]
            t = distances[0][2]
            a, b = a + t[0] * v1, b + t[1] * v1
            print dist
            s *= 0.75
        else: 
           break

    print "---"

    s = 0.02
    while True:
        ts1 = [(s, 0), (-s, 0), (s, s), (-s, -s), (-s, s), (s, -s), (0, s),  (0, -s)]
        grids = [(get_grid(a, b, 
                           c + t[0] * v2, d + t[1] * v2, hough, size), t) for t in ts1]
        distances = [(distance(lines, grid, size), 
                      grid, t) for grid, t in grids]
        distances.sort(reverse=True)
        if distances[0][0] > dist:
            dist = distances[0][0]
            grid = distances[0][1]
            t = distances[0][2]
            c, d = c + t[0] * v2, d + t[1] * v2
            print dist
            s *= 0.75
        else:
            break

    grid_lines = [[l2ad(l, size) for l in grid[0]], [l2ad(l, size) for l in grid[1]]]
    return grid, grid_lines

def get_grid(a, b, c, d, hough, size):
    l1 = hough.lines_from_list([a.t(), b.t()])
    l2 = hough.lines_from_list([c.t(), d.t()])
    c = intersections_from_angl_dist([l1, l2], size, get_all=True)
    corners = (c[0] + c[1])
    if len(corners) < 4:
        print l1, l2, c
        raise GridFittingFailedError
    grid = g_grid(corners)
    return grid

def distance(lines, grid, size):
    im_l = Image.new('L', size)
    dr_l = ImageDraw.Draw(im_l)
    for line in sum(lines, []):
        dr_l.line(line_from_angl_dist(line, size), width=1, fill=255)
    im_l = im_l.filter(MyGaussianBlur(radius=3))
    # GaussianBlur is undocumented class, may not work in future versions of PIL
    im_g = Image.new('L', size)
    dr_g = ImageDraw.Draw(im_g)
    for line in grid[0] + grid[1]:
        dr_g.line(line, width=1, fill=255)
    #im_g = im_g.filter(MyGaussianBlur(radius=3))
    im_d, distance = combine(im_l, im_g)
    return distance

def combine(bg, fg):
    bg_l = bg.load()
    fg_l = fg.load()
    res = Image.new('L', fg.size)
    res_l = res.load()

    score = 0
    area = 0

    for x in xrange(fg.size[0]):
        for y in xrange(fg.size[1]):
            if fg_l[x, y]:
                res_l[x, y] = bg_l[x, y] * fg_l[x, y]
                score +=  bg_l[x, y]
                area += 1

    return res, float(score)/area