import sys

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

import math

from datetime import datetime

import logging as log

import numpy as np

import scipy.optimize

import scipy.signal

import cv2 as cv

from geometry import EPoint,Line

DEBUG=True

class LineBag:

	def __init__(self):

		self._lines=[]

	def put(self,score,alpha,beta,peaks):

		self._lines.append((score,alpha,beta,peaks))

	def pull(self,count):

		self._lines.sort(reverse=True)

		res=[]

		for (score,alpha,beta,peaks) in self._lines:

			if any(abs(alpha-gamma)<10 and abs(beta-delta)<10 for (_,gamma,delta,_) in res): continue

			# avoid intersecting lines

			if any((beta-delta)!=0 and (alpha-gamma)/(beta-delta)<0 for (_,gamma,delta,_) in res): continue

			res.append((score,alpha,beta,peaks))

			if len(res)>=count: break

		return res

class HoughTransform:

	"""Find line sequences with Hough transform.

	Uses usual image coordinates on input and output, with [0,0] in the upper left corner and [height-1,width-1] in the lower right.

	However, internally it uses the usual cartesian coordinates, centered at the image center. [-w/2,-h/2] in the upper left and [w/2,h/2] in the lower right."""

	def __init__(self,img):

		self._angleBandwidth=30 # degrees

		(h,w)=img.shape[:2]

		self._diagLen=int(np.sqrt(h**2+w**2))+1

		self._center=(w//2,h//2)

		self._acc=np.zeros((180,self._diagLen),dtype=np.int32)

		self.update(img)

	def extract(self):

		img=self._createImg()

		self.show(img)

		lines=self._detectLines()

		res=[]

		i=0

		for (score,alpha,beta,peaks) in lines:

			log.debug("score: %s",score)

			log.debug("alpha, beta: %s, %s",alpha,beta)

			self._drawLine(img,alpha,beta,peaks,i)

			res.append([])

			keys=self._readLineKeys(alpha,beta)

			for k in peaks:

				(alphaDeg,d)=keys[k]

				line=Line(alphaDeg*math.pi/180,d-self._diagLen//2)

				res[-1].append(self._transformOutput(line))

			res[-1].sort(key=lambda line: line.d)

			i+=1

		self.show(img)

		return res

	def update(self,img,weight=1):

		start=datetime.now().timestamp()

		for (r,row) in enumerate(img):

			for (c,pix) in enumerate(row):

				if pix==0: continue

				for alphaDeg in range(0,180):

					d=self._computeDist(c,r,alphaDeg)+self._diagLen//2

					self._acc[(alphaDeg,d)]+=weight

		log.debug("Hough updated in %s s",round(datetime.now().timestamp()-start,3))

	def scoreLine(self,line):

		transformed=self._transformInput(line)

		alphaDeg=round(transformed.alpha*180/math.pi)%180

		d=round(transformed.d+self._diagLen//2)

		if not 0<=d<self._diagLen: return 0

		return self._acc[(alphaDeg,d)]

	def show(self,img=None):

		if img is None: img=self._createImg()

		show(img,"Hough transform accumulator")

	def _computeDist(self,x,y,alphaDeg):

		alphaRad=alphaDeg*math.pi/180

		(x0,y0)=self._center

		(dx,dy)=(x-x0,y0-y)

		d=dx*math.cos(alphaRad)+dy*math.sin(alphaRad)

		return int(d)

	def _detectLines(self):

		bag=LineBag()

		for alpha in range(0,180+60,2):

			for beta in range(max(alpha-60,0),min(alpha+60,180+60),2):

				accLine=[self._acc[key] for key in self._readLineKeys(alpha,beta)]

				(peaks,props)=scipy.signal.find_peaks(accLine,prominence=0)

				(prominences,peaks)=zip(*sorted(zip(props["prominences"],peaks),reverse=True)[:19])

				bag.put(sum(prominences),alpha,beta,peaks)

		return bag.pull(2)

	def _readLineKeys(self,alpha,beta):

		n=self._diagLen-1

		res=[]

		for i in range(n+1):

			k=round((alpha*(n-i)+beta*i)/n)

			if k>=180:

				k=k%180

				i=n-i

			res.append((k,i))

		return res

	def _transformInput(self,line):

		reflectedLine=Line(math.pi*2-line.alpha,line.d)

		(x,y)=self._center

		basis=EPoint(x,-y)

		shiftedLine=reflectedLine.shiftBasis(basis)

		if shiftedLine.alpha>=math.pi:

			shiftedLine=Line(shiftedLine.alpha-math.pi,-shiftedLine.d)

		return shiftedLine

	def _transformOutput(self,line):

		(x,y)=self._center

		basis=EPoint(-x,y)

		shiftedLine=line.shiftBasis(basis)

		reflectedLine=Line(math.pi*2-shiftedLine.alpha,shiftedLine.d)

		log.debug("%s -> %s",line,reflectedLine)

		return reflectedLine

	def _createImg(self):

		maxVal=self._acc.max()

		arr=np.expand_dims(np.uint8(255*self._acc//maxVal),axis=2)

		img=np.concatenate((arr,arr,arr),axis=2)

		(h,w)=img.shape[:2]

		for x in range(0,w,4): # y axis

			img[h//2,x]=[255,255,255]

		for y in range(0,h,4):

			img[y,w//2]=[255,255,255]

		return img

	def _drawLine(self,img,alpha,beta,peaks,colorKey):

		colors=[[0,255,255],[255,0,255],[255,255,0]]

		color=colors[colorKey]

		(h,w)=img.shape[:2]

		keys=self._readLineKeys(alpha,beta)

		for (y,x) in keys:

			if x%3!=0: continue

			if y<0 or y>=h: continue

			img[y,x]=color

		for k in peaks:

			(y,x)=keys[k]

			cv.drawMarker(img,(x,y),color,cv.MARKER_TILTED_CROSS,8)

def show(img,filename="x"):

	cv.imshow(filename,img)

	cv.waitKey(0)

	cv.destroyAllWindows()

def filterVert(edges):

	kernel = np.array([[1,0,1],[1,0,1],[1,0,1]],np.uint8)

	edges = cv.erode(edges,kernel)

	kernel=np.array([[0,1,0],[0,1,0],[0,1,0]],np.uint8)

	edges=cv.dilate(edges,kernel)

	return edges

def filterHor(edges):

	kernel = np.array([[1,1,1],[0,0,0],[1,1,1]],np.uint8)

	edges = cv.erode(edges,kernel)

	kernel=np.array([[0,0,0],[1,1,1],[0,0,0]],np.uint8)

	edges=cv.dilate(edges,kernel)

	return edges

def filterDiag(edges):

	kernel = np.array([[0,0,1],[1,0,0],[0,1,0]],np.uint8)

	edges1 = cv.erode(edges,kernel)

	kernel=np.array([[1,0,0],[0,1,0],[0,0,1]],np.uint8)

	edges1=cv.dilate(edges1,kernel)

	kernel = np.array([[0,1,0],[1,0,0],[0,0,1]],np.uint8)

	edges2 = cv.erode(edges,kernel)

	kernel=np.array([[0,0,1],[0,1,0],[1,0,0]],np.uint8)

	edges2=cv.dilate(edges2,kernel)

	return edges1+edges2

def prepareEdgeImg(img):

	gray=cv.cvtColor(img,cv.COLOR_BGR2GRAY)

	show(gray,"greyscale image")

	edges=cv.Canny(gray,70,130)

	show(edges,"Canny edge detector")

	edges=filterHor(edges)+filterVert(edges)+filterDiag(edges)

	show(edges,"kernel filtered edges")

	return edges