H-BEMD-detail-1

# Reference from **Bidimensional Empirical Mode Decomposition** code by Dawid Laszuk (laszukdawid@gmail.com).
# This version is modified by H-BEMD for sin and cos value and optimize Extrema detection and Normolization value
# By Trong-An Bui (trongan93@gmail.com - http://buitrongan.com)
class BEMD:
  def __init__(self):
      # ProtoIMF related
      self.mse_thr = 0.01
      self.mean_thr = 0.01

      self.FIXE = 1  # Single iteration by default, otherwise results are terrible
      self.FIXE_H = 0
      self.MAX_ITERATION = 5

  def __call__(self, image, max_imf=-1):
      return self.bemd(image, max_imf=max_imf)
      
  def extract_max_min_spline(self, image, min_peaks_pos, max_peaks_pos):
      xi, yi = np.meshgrid(np.arange(image.shape[0]), np.arange(image.shape[1]))
          min_val = np.array([image[x,y] for x, y in zip(*min_peaks_pos)])
          max_val = np.array([image[x,y] for x, y in zip(*max_peaks_pos)])
          min_env = self.spline_points(min_peaks_pos[0], min_peaks_pos[1], min_val, xi, yi)
          max_env = self.spline_points(max_peaks_pos[0], max_peaks_pos[1], max_val, xi, yi)
          return min_env, max_env
  @classmethod
  def spline_points(cls, X, Y, Z, xi, yi):
      """Creates a spline for given set of points.

      Uses Radial-basis function to extrapolate surfaces. It's not the best but gives something.
      Griddata algorithm didn't work.
      """
      spline = Rbf(X, Y, Z, function='cubic')
      return spline(xi, yi)
  
  @classmethod
  def find_extrema_positions(cls, image):
      max_peaks_pos = BEMD.extract_maxima_positions(image)
      min_peaks_pos = BEMD.extract_minima_positions(image)
      return min_peaks_pos, max_peaks_pos
  
  @classmethod
  def extract_minima_positions(cls, image):
      return BEMD.extract_maxima_positions(-image)

  @classmethod
  def extract_maxima_positions(cls, image):
      seed_min = image - 0.000001
      dilated = reconstruction(seed_min, image, method='dilation')
      cleaned_image = image - dilated
      maxima_positions = np.where(cleaned_image>0)[::-1]
      return maxima_positions
  
  @classmethod
  def end_condition(cls, image, IMFs):
      rec = np.sum(IMFs, axis=0)
      if np.allclose(image, rec):
          return True
      return False
  
  def check_proto_imf(self, proto_imf, proto_imf_prev, mean_env):
    if np.all(np.abs(mean_env-mean_env.mean())<self.mean_thr):
        return True

    if np.allclose(proto_imf, proto_imf_prev, rtol=0.01):
        return True
        
    if np.mean(np.abs(proto_imf)) < self.mean_thr:
        return True

    mse_proto_imf = np.mean(proto_imf*proto_imf)
    if mse_proto_imf > self.mse_thr:
        return False

    return False
        
  def bemd(self, image, max_imf=-1):
      image_s = image.copy()
      imf = np.zeros(image.shape)
      imf_old = imf.copy()
      imfNo = 0
      IMF = np.empty((imfNo,)+image.shape)
      notFinished = True

      while(notFinished):
          res = image_s - np.sum(IMF[:imfNo], axis=0)
          saveLogFile('residue_' + str(imfNo) + '.csv',res)
          imf = res.copy()
          mean_env = np.zeros(image.shape)
          stop_sifting = False

          n = 0   
          n_h = 0

          while(not stop_sifting and n<self.MAX_ITERATION):
              n += 1

              min_peaks_pos, max_peaks_pos = self.find_extrema_positions(imf)
              if len(min_peaks_pos[0])>1 and len(max_peaks_pos[0])>1:
                  min_env, max_env = self.extract_max_min_spline(imf, min_peaks_pos, max_peaks_pos)
                  mean_env = 0.5*(min_env+max_env)

                  imf_old = imf.copy()
                  imf = imf - mean_env

                  if self.FIXE:
                      if n>=self.FIXE+1:
                          stop_sifting = True

                  elif self.FIXE_H:
                      if n == 1: continue
                      if self.check_proto_imf(imf, imf_old, mean_env):
                          n_h += 1
                      else:
                          n_h = 0

                      # STOP if enough n_h
                      if n_h >= self.FIXE_H:
                          stop_sifting = True

                  else:
                      if self.check_proto_imf(imf, imf_old, mean_env):
                          stop_sifting = True

              else:
                  stop_sifting = True

          IMF = np.vstack((IMF, imf.copy()[None,:]))
          imfNo += 1

          if self.end_condition(image, IMF) or (max_imf>0 and imfNo>=max_imf):
              notFinished = False
              break

      res = image_s - np.sum(IMF[:imfNo], axis=0)
      if not np.allclose(res, 0):
          IMF = np.vstack((IMF, res[None,:]))
          imfNo += 1

      return IMF