import sys

sys.path.append("../src")

import os
import math
import random
import logging as log

import cv2 as cv
import numpy as np
import scipy.cluster
import scipy.ndimage
import scipy.signal

from geometry import Line
from annotations import DataFile,computeBoundingBox
from hough import show,prepareEdgeImg,HoughTransform
from analyzer.epoint import EPoint
from analyzer.corners import Corners

random.seed(361)
log.basicConfig(level=log.DEBUG,format="%(message)s")


def kmeans(img):
	arr=np.reshape(img,(-1,3)).astype(np.float)
	wood=[193,165,116]
	(centers,distortion)=scipy.cluster.vq.kmeans(arr,3)
	log.debug("k-means centers: %s",centers)
	(black,empty,white)=sorted(centers,key=sum)
	if np.linalg.norm(black)>np.linalg.norm(black-wood):
		black=None
	if np.linalg.norm(white-[255,255,255])>np.linalg.norm(white-wood):
		white=None
	log.debug("black, white: %s, %s",black,white)
	return (black,white,centers)


def quantize(img,centers):
	origShape=img.shape
	data=np.reshape(img,(-1,3))
	(keys,dists)=scipy.cluster.vq.vq(data,centers)
	pixels=np.array([centers[k] for k in keys],dtype=np.uint8).reshape(origShape)
	return pixels


def filterStones(contours,bwImg,stoneDims):
	contourImg=cv.cvtColor(bwImg,cv.COLOR_GRAY2BGR)
	res=[]
	for (i,c) in enumerate(contours):
		keep=True
		moments=cv.moments(c)
		center=(moments["m10"]/(moments["m00"] or 1), moments["m01"]/(moments["m00"] or 1))
		area=cv.contourArea(c)
		(x,y,w,h)=cv.boundingRect(c)
		if w>stoneDims[0] or h>stoneDims[1]*1.5 or w<2 or h<2:
			cv.drawMarker(contourImg,tuple(map(int,center)),(0,0,255),cv.MARKER_TILTED_CROSS,12)
			keep=False
		coverage1=area/(w*h or 1)
		hull=cv.convexHull(c)
		coverage2=area/(cv.contourArea(hull) or 1)
		# if coverage2<0.8:
		# 	cv.drawMarker(contourImg,tuple(map(int,center)),(0,127,255),cv.MARKER_DIAMOND,12)
		# 	keep=False
		if keep:
			res.append((EPoint(*center),c))
			cv.drawMarker(contourImg,tuple(map(int,center)),(255,0,0),cv.MARKER_CROSS)
	log.debug("accepted: %s",len(res))
	log.debug("rejected: %s",len(contours)-len(res))
	show(contourImg,"accepted and rejected stones")
	return res


class BoardDetector:
	def __init__(self,annotationsPath):
		self._annotations=DataFile(annotationsPath)

		self._rectW=0
		self._rectH=0
		self._rect=None

	def __call__(self,img,filename):
		# approximately detect the board
		(h,w)=img.shape[:2]
		log.debug("image dimensions: %s x %s",w,h)
		show(img,filename)
		(x1,y1,x2,y2)=self._detectRough(img,filename)
		rect=img[y1:y2,x1:x2]
		self._rectW=x2-x1
		self._rectH=y2-y1
		self._rect=rect

		# quantize colors
		(black,white,colors)=self._sampleColors(rect)
		quantized=quantize(rect,colors)
		gray=cv.cvtColor(rect,cv.COLOR_BGR2GRAY)
		edges=cv.Canny(gray,70,130)
		show(edges,"edges")
		quantized=quantized & (255-cv.cvtColor(edges,cv.COLOR_GRAY2BGR))
		show(quantized,"quantized, edges separated")

		# detect black and white stones
		stones=self._detectStones(quantized,black,white)

		# detect lines from edges and stones
		edgeImg=prepareEdgeImg(rect)
		hough=HoughTransform(edgeImg)
		stonesImg=np.zeros((self._rectH,self._rectW),np.uint8)
		for (point,c) in stones:
			cv.circle(stonesImg,(int(point.x),int(point.y)),2,255,-1)
		# cv.drawContours(stonesImg,[c for (point,c) in stones],-1,255,-1)
		show(stonesImg,"detected stones")
		hough.update(stonesImg,10)
		lines=hough.extract()

		linesImg=np.copy(rect)
		for line in lines:
			self._drawLine(linesImg,line)
		show(linesImg,"detected lines")

		# # detect vanishing points of the lines
		# imgCenter=EPoint(w//2-x1, h//2-y1)
		# (a,b,c,d)=(p-EPoint(x1,y1) for p in self._annotations[filename][0])
		# (p,q,r,s)=(Line(a,b),Line(b,c),Line(c,d),Line(d,a))
		# v1=p.intersect(r)
		# v2=q.intersect(s)
		# log.debug("true vanishing points: %s ~ %s, %s ~ %s",v1,v1.toPolar(imgCenter),v2,v2.toPolar(imgCenter))
		# vanish=self._detectVanishingPoints(lines,imgCenter,(v1.toPolar(imgCenter),v2.toPolar(imgCenter)))
		#
		# # rectify the image
		# matrix=self._computeTransformationMatrix(vanish,lines)
		# transformed=cv.warpPerspective(rect,matrix,(self._rectW,self._rectH))
		#
		# # determine precise board edges

	def _detectRough(self,img,filename):
		corners=self._annotations[filename][0]
		(x1,y1,x2,y2)=computeBoundingBox(corners)
		log.debug("bounding box: (%s,%s) - (%s,%s)",x1,y1,x2,y2)
		return (x1,y1,x2,y2)

	def _sampleColors(self,rect):
		(h,w)=rect.shape[:2]
		minirect=rect[h//4:3*h//4, w//4:3*w//4]
		return kmeans(minirect)

	def _detectStones(self,quantized,black,white):
		(h,w)=quantized.shape[:2]
		mask=self._maskStones(quantized,black,white)
		stoneDims=(w/19,h/19)
		log.debug("stone dims: %s - %s",tuple(x/2 for x in stoneDims),stoneDims)

		(contours,hierarchy)=cv.findContours(mask,cv.RETR_LIST,cv.CHAIN_APPROX_SIMPLE)
		stoneLocs=filterStones(contours,mask,stoneDims)

		return stoneLocs

	def _maskStones(self,quantized,black,white):
		unit=np.array([1,1,1],dtype=np.uint8)
		if black is not None:
			maskB=cv.inRange(quantized,black-unit,black+unit)

			distTransform=cv.distanceTransform(maskB,cv.DIST_L2,5)
			maskB=cv.inRange(distTransform,6,20)
			show(maskB,"black areas")
		else: maskB=np.zeros(quantized.shape[:2],dtype=np.uint8)

		if white is not None:
			maskW=cv.inRange(quantized,white-unit,white+unit)
			distTransform=cv.distanceTransform(maskW,cv.DIST_L2,5)
			maskW=cv.inRange(distTransform,6,20)
			show(maskW,"white areas")
		else: maskW=np.zeros(quantized.shape[:2],dtype=np.uint8)

		stones=cv.bitwise_or(maskB,maskW)
		show(stones,"black and white areas")
		return stones

	def _printLines(self,lines,allPoints,img):
		for (i,line) in enumerate(lines):
			img_=np.copy(img)
			points=list(line.getSortedPoints())
			(a,b)=max(((a,b) for a in points for b in points if a<b),key=lambda ab: ab[0].dist(ab[1]))
			(xa,ya)=a
			(xb,yb)=b
			points.sort(key=lambda p: a.dist(p))
			cv.line(img_,(int(xa),int(ya)),(int(xb),int(yb)),(255,255,0),1)
			cv.imwrite("/tmp/{0}.png".format(i),img_)
			pointDists=",".join(str(round(p1.dist(p2),3)) for (p1,p2) in zip(points[:-1],points[1:]))
			log.debug("\t".join(map(str,[i,line,line.score(allPoints),pointDists])))

	def _detectVanishingPoints(self,lines,imgCenter,trueVs):
		polarHough=PolarHough(math.pi/180,10)
		for (i,ab) in enumerate(lines):
			for cd in lines[i+1:]:
				point=ab.intersect(cd)
				if 0<=point.x<=self._rectW and 0<=point.y<=self._rectH: continue
				# log.debug("%s -> %s",point,point.toPolar(imgCenter))
				polarHough.put(point.toPolar(imgCenter))
		vanish=[EPoint.fromPolar(p,imgCenter) for p in polarHough.extract(2,trueVs)]
		log.debug(vanish)
		return vanish

	def _computeTransformationMatrix(self,vanish,lines):
		(v1,v2)=vanish
		(p,r)=sorted(lines,key=lambda p: point2lineDistance(p.a,p.b,v1))[:2]
		(q,s)=sorted(lines,key=lambda p: point2lineDistance(p.a,p.b,v2))[:2]
		(a,b,c,d)=Corners([p.intersect(q),q.intersect(r),r.intersect(s),s.intersect(p)]) # canonize the abcd order
		a_=EPoint(b.x,min(a.y,d.y))
		b_=EPoint(b.x,max(b.y,c.y))
		c_=EPoint(c.x,max(b.y,c.y))
		d_=EPoint(c.x,min(a.y,d.y))
		abcd=[list(point) for point in (a,b,c,d)]
		abcd_=[list(point) for point in (a_,b_,c_,d_)]
		log.debug("abcd: %s ->",(a,b,c,d))
		log.debug("-> abcd_: %s",(a_,b_,c_,d_))
		matrix=cv.getPerspectiveTransform(np.float32(abcd),np.float32(abcd_))
		log.debug("transformation matrix: %s",matrix)

		rect=np.copy(self._rect)
		for point in (a,b,c,d):
			cv.drawMarker(rect,(int(point.x),int(point.y)),(0,255,255),cv.MARKER_TILTED_CROSS)
		show(rect)
		transformed=cv.warpPerspective(rect,matrix,(self._rectW,self._rectH))
		show(transformed)

		return matrix

	def _drawLine(self,img,line):
		(h,w)=img.shape[:2]
		corners=[EPoint(0,0),EPoint(w,0),EPoint(0,h),EPoint(w,h)] # NW NE SW SE
		borders=[
			[Line.fromPoints(corners[0],corners[1]), Line.fromPoints(corners[2],corners[3])], # N S
			[Line.fromPoints(corners[0],corners[2]), Line.fromPoints(corners[1],corners[3])] # W E
		]

		(a,b)=(line.intersect(borders[0][0]), line.intersect(borders[0][1]))
		log.debug("%s %s",line,(a,b))
		if not a or not b:
			(a,b)=(line.intersect(borders[1][0]), line.intersect(borders[1][1]))
			log.debug("* %s %s",line,(a,b))
		if any(abs(x)>10**5 for x in [*a,*b]):
			log.debug("ignored")
			return
		cv.line(img,(int(a.x),int(a.y)),(int(b.x),int(b.y)),[0,255,0])


if __name__=="__main__":
	detector=BoardDetector(sys.argv[2])
	filepath=sys.argv[1]
	filename=os.path.basename(filepath)
	img=cv.imread(filepath)
	detector(img,filename)