import cv2
import math
import numpy as np

from Constants import Constants
from EnumParkingStatus import EnumParkingStatus


class StatusInferEngine():
    def __init__(self, jsonEditor):
        self.jsonEditor = jsonEditor

    def inferUnknownStatus(self):
        self._loadUnknownAnnotations()
        self._computeStatus()

    def _computeStatus(self):
        for imgId, listSpaces in self.dictSpaces.items():
            for space in listSpaces:
                status = int(space[Constants.JSON_PARKING_STATUS_ID_KEY])
                if (status == EnumParkingStatus.UNKNOWN.value):
                    spacePosition =  space[Constants.JSON_SEGMENTATION_KEY]
                    npSpace = np.array([spacePosition])
                    npSpace.shape = (4,2)
                    centroid = self._computeCentroidSpace(npSpace)
                    if (imgId in self.dictSegs):
                        list = self.dictSegs[imgId]
                        idx, mindDist = self._getMinimumDistance(centroid, list)
                        if (cv2.pointPolygonTest(np.array([npSpace]), list[idx], measureDist = False) > 0):
                            space[Constants.JSON_PARKING_STATUS_ID_KEY] = EnumParkingStatus.OCCUPIED_NEED_VAL.value
                        else:
                            space[Constants.JSON_PARKING_STATUS_ID_KEY] = EnumParkingStatus.EMPTY_NEED_VAL.value
                    else:
                        space[Constants.JSON_PARKING_STATUS_ID_KEY] = EnumParkingStatus.EMPTY_NEED_VAL.value

    def _loadUnknownAnnotations(self):
        self.dictSpaces = {}
        self.dictSegs = {}
        for annot in self.jsonEditor.json[Constants.JSON_ANNOTATIONS_KEY]:
            tipo = int(annot[Constants.JSON_LINK_CATEG_KEY])
            imgId = annot[Constants.JSON_LINK_IMAGE_ID_KEY]
            if(tipo == Constants.JSON_PARKING_SPACE_KEY):
                if imgId not in self.dictSpaces:
                    self.dictSpaces[imgId] = []
                self.dictSpaces[imgId].append(annot)
            elif(tipo == Constants.JSON_VEHICLE_KEY):
                if imgId not in self.dictSegs:
                    self.dictSegs[imgId] = []
                self.dictSegs[imgId].append(self._computeCentroidVehicle(annot))

    def _getMinimumDistance(self, centroidParkingSpace, listCentroids):
        idx = 0
        minDist = math.sqrt((listCentroids[0][0] - centroidParkingSpace[0])**2 + (listCentroids[0][1] - centroidParkingSpace[1])**2)
        i = 1
        while(i < len(listCentroids)):
            dist =  math.sqrt((listCentroids[i][0] - centroidParkingSpace[0])**2 + (listCentroids[i][1] - centroidParkingSpace[1])**2)
            if(dist < minDist):
                minDist = dist
                idx = i
            i += 1
        return idx, minDist

    def _computeCentroidVehicle(self, annotation):
        segmentation =  annotation[Constants.JSON_SEGMENTATION_KEY]
        cX = 0
        cY = 0
        for seg in segmentation:
            #force int. In case of floats, the moments assume an image and give wrong results
            npArray = np.array([seg], dtype=int) 
            npArray.shape = (-1,2)
            moments = cv2.moments(npArray)
            cX += int(moments["m10"] / moments["m00"])
            cY += int(moments["m01"] / moments["m00"])

        numSegs = len(segmentation)
        return (cX/numSegs,cY/numSegs)

    def _computeCentroidSpace(self, arrayNumPy):
        moments = cv2.moments(arrayNumPy)
        cX = int(moments["m10"] / moments["m00"])
        cY = int(moments["m01"] / moments["m00"])
        return (cX, cY)