import numpy as np

import pydicom as dicom

def getSegmentedPixelColor (value, intervals):

  for i in range(len(intervals)):

    if value >= intervals[i][0] and value <= intervals[i][1]:

      return intervals[i][2]

  return [0,0,0]

# Linear transformation : from [bt < pxvalue < wt] linear to [0 <pyvalue< 255] !important: has loss of information

def linearTransform(pxvalue, bt, wt):

    if pxvalue < bt:

        y=0

    elif pxvalue > wt:

        y=255

    else:

        y=pxvalue*255/(wt-bt)-255*bt/(wt-bt)

    return y

# Linear transformation : convert to Hounsfield units (HU)

def getHuPixels (pixels_array, rows, cols, intercept, slope):

    m = np.zeros((rows, cols), np.int16)

    for i in range(rows):

      for j in range(cols):

        m[i][j] = pixels_array[i][j] * slope + intercept

    return m

class Dicom:

  def __init__(self, src):

    self.__ds = dicom.dcmread(src)

    self.__patientId = self.__ds.PatientID

    self.__cols = self.__ds.Columns

    self.__rows = self.__ds.Rows

    self.__huPixelsArray = getHuPixels(self.__ds.pixel_array, self.__rows, self.__cols, self.__ds.RescaleIntercept, self.__ds.RescaleSlope)

  def getPatientId (self):

    return self.__patientId

  def getPixelsArray (self):

    return self.__huPixelsArray

  def getRawPixelsArray (self):

    return self.__ds.pixel_array

  def getLinearRGB (self, bt, wt):

    image = np.zeros((self.__rows, self.__cols, 3), np.uint8)

    for i in range(self.__rows):

      for j in range(self.__cols):

        color = linearTransform(self.getPixelsArray()[i][j], bt, wt)

        image[i][j] = (color,color,color)

    return image

  # Remember, open-cv uses BGR color system

  def getSegmentedRGB (self, intervals = [(-3000,-1000,[0,0,0]),(-15,15,[255,0,128]),(20,50,[255,0,0]),(60,100,[0,0,255]),(101,3000,[255,255,255])]): # List of intervals to filter

    image = np.zeros((self.__rows, self.__cols, 3), np.uint8)

    for i in range(self.__rows):

      for j in range(self.__cols):

          image[i][j] = getSegmentedPixelColor(self.getPixelsArray()[i][j], intervals)

    return image

  def showHistogram (self):

    import matplotlib.pyplot as plt

    plt.hist(self.getPixelsArray().flatten(), bins=50, color='c')

    plt.xlabel("Hounsfield Units (HU)")

    plt.ylabel("Frequency")

    plt.show()