# Our imports

from Dicom import Dicom

from CvImage import CvImage

from Segment import Segment

from geometry import distanceToPolygon, isPointsInsidePolygon

import pandas as pd

from os import listdir

from os.path import isfile, join

# Our interest segments

# Bone

bone = Segment("Bone")

bone.setMinSegmentArea(200)

bone.setHUInterval(700, 3000)

bone.setRGB(0,0,255)

bone.setHSVFilter(100, 50, 0, 130, 255, 255)

# Blood

blood = Segment("Blood")

blood.setMinSegmentArea(100)

blood.setHUInterval(60, 100)

blood.setRGB(255,0,0)

blood.setHSVFilter(0, 60, 0, 10, 255, 255)

# Ventricle

ventricle = Segment("Ventricle")

ventricle.setMinSegmentArea(100)

ventricle.setHUInterval(-15, 15)

ventricle.setRGB(0,255,0)

ventricle.setHSVFilter(50,100, 0, 70, 255, 255)

# BrainMass

brainMass = Segment("BrainMass")

brainMass.setMinSegmentArea(200)

brainMass.setHUInterval(20, 50)

brainMass.setRGB(255,255,0)

brainMass.setHSVFilter(25, 50, 0, 35, 255, 255)

labels = pd.read_csv('D:/Downloads/stage2train.csv')

# Here it comes!

path = "D:/Downloads/stage2train/"

dcmFiles = []

dirFiles = listdir(path)

i = 0

while i < len(dirFiles) and i < 1:

  if isfile(join(path, dirFiles[i])):

    dcmFiles.append(dirFiles[i])

  i = i + 1

results = open("./results/results.txt", "a")

for filename in dcmFiles:

  ds = Dicom(path+filename)

  # Print labels and ID

  results.write(filename[:-4])

  for label in ["epidural", "intraparenchymal", "intraventricular", "subarachnoid", "subdural", "any"]:

    results.write("," + str(labels.loc[labels["ID"] == (filename[:-4]+"_"+label)].values[0][1]))

  # Filter by Hounsfield units (HU)

  segmentedRGB = ds.getSegmentedRGB([

    (bone.getLowerHU(), bone.getHigherHU(), bone.getBGR()), 

    (ventricle.getLowerHU(), ventricle.getHigherHU(), ventricle.getBGR()),

    (blood.getLowerHU(), blood.getHigherHU(), blood.getBGR()),

    (brainMass.getLowerHU(), brainMass.getHigherHU(), brainMass.getBGR())

  ])

  # Array of segments and their features

  segments = {

    bone.getName():{

      "segment": bone,

      "extractedFeatures": {}

    },

    brainMass.getName(): {

      "segment": brainMass,

      "extractedFeatures": {}

    },

    ventricle.getName(): {

      "segment": ventricle,

      "extractedFeatures": {}

    },

    blood.getName(): {

      "segment": blood,

      "extractedFeatures": {}

    }

  }

  for key in segments:

    segment = segments[key]["segment"]

    image = CvImage(segmentedRGB, segment.getName())

    image.hsvFilter(segment.getLowerHSV(), segment.gethigherHSV())

    # Perform some morph operations

    image.morphOperations()

    # Let's get all we want

    features = image.getContoursFeatures(segment.getMinSegmentArea())

    # RGB to see green contour

    image.gray2bgr()

    if (key == "Ventricle" or key == "BrainMass"):

      for atrib in ["area", "eccentricity"]:

        results.write("," + str(features[0][atrib]))

    for i in range(len(features)):

      # Blood only features

      if key == "Blood":

        distanceToBone = distanceToPolygon(features[i]["centroid"], segments["Bone"]["extractedFeatures"][0]["convexHull"])

        j = 0

        isInsideVentricle = False

        while isInsideVentricle == False and j < len(segments["Ventricle"]["extractedFeatures"]):

          isInsideVentricle = isPointsInsidePolygon(features[i]["convexHull"], segments["Ventricle"]["extractedFeatures"][j]["convexHull"]).all()

          j = j + 1

        isInsideBrainMass = False

        if isInsideVentricle == False:

          j = 0

          while isInsideBrainMass == False and j < len(segments["BrainMass"]["extractedFeatures"]):

            isInsideBrainMass = isPointsInsidePolygon(features[i]["convexHull"], segments["BrainMass"]["extractedFeatures"][j]["convexHull"]).all()

            j = j + 1

        features[i]["distanceToBone"] = distanceToBone

        features[i]["isInsideVentricle"] = isInsideVentricle

        features[i]["isInsideBrainMass"] = isInsideBrainMass

        results.write("\n\t")

        for atrib in ["area", "eccentricity", "distanceToBone", "isInsideVentricle", "isInsideBrainMass"]:

          if (features[i][atrib] == False or features[i][atrib] == True):

            if (features[i][atrib] == True):

              results.write(str(1))

            else:

              results.write(str(0))

          else:

            results.write(str(features[i][atrib]))

          if atrib != "isInsideBrainMass":

             results.write(",")

      image.drawCircle(features[i]["centroid"])

      image.drawContours([features[i]["convexHull"]])

    segments[key]["extractedFeatures"] = features

  results.write("\n")

results.close()