code: """contours.py: Module is used to implement edge detection tecqniues using CV2 and apply Kernel estimations on the regions"""
  
  __author__ = "Chakraborty, S."
  __copyright__ = ""
  __credits__ = []
  __license__ = "MIT"
  __version__ = "1.0."
  __maintainer__ = "Chakraborty, S."
  __email__ = "shibaji7@vt.edu"
  __status__ = "Research"
  
  import os
  import matplotlib
  matplotlib.use("Agg")
  import matplotlib.pyplot as plt
  import numpy as np
  import cv2
  import datetime as dt
  import pandas as pd
  import json
  from scipy.stats import beta
  plt.style.context("seaborn")
  from matplotlib import rcParams
  rcParams["font.family"] = "sans-serif"
  
  import matplotlib.pyplot as plt
  import cv2
  import os
  
  class ProbabilisticContours(object):
      """ Probabilistic Contours detection by Open-CV """
      
      def __init__(self, aia):
          np.random.seed(0)
          self.aia = aia
          self.filename = aia.fname
          self.folder = aia.folder
          self.file = aia.folder + aia.fname
          print(" Proc file - ", self.file)
          self.extn = "." + aia.fname.split(".")[-1]
          self.binfile = self.folder + self.filename.replace(self.extn, "_binmaps" + self.extn)
          if os.path.exists(self.binfile): 
              self.binmaps = cv2.imread(cv2.samples.findFile(self.binfile), cv2.IMREAD_GRAYSCALE)
              self.fcontours = (self.binmaps > 0).astype(int)
              if os.path.exists(self.aia.folder + self.aia.fname): self.aia.normalized()
              self.norm_data = self.aia.m_normalized.data * self.fcontours
              self.fcontours_nan = np.copy(self.fcontours).astype(float)
              self.fcontours_nan[self.fcontours_nan == 0] = np.nan
          else: raise Exception("File does not exists:", self.binfile)
          return
      
      def probability_contours(self, v_range=(10, 300), v_step=10, pth=0.5, summary=True):
          _dict_ = {}
          for thd in range(v_range[0], v_range[1], v_step):
              _data = self.norm_data * self.fcontours_nan
              _intensity = (thd - _data).ravel()
              _intensity = _intensity[~np.isnan(_intensity)]
              _probs =  1./(1.+np.exp(_intensity))
              _hist, _bin_edges = np.histogram(_probs, bins=np.linspace(0,1,21), density=True)
              _idx = np.diff(_bin_edges)
              _f = pd.DataFrame(); _f["pr"], _f["edgs"], _f["idx"] = _hist, _bin_edges[:-1], _idx
              _f = _f[_f.edgs >= pth]
              _mask = (self.norm_data <= thd).astype(int) * self.fcontours
              _dict_[thd] = (np.sum(_f.idx*_f.pr), _mask)
          if summary: self.generate_analysis_summary("norm", _dict_)
          return _dict_
      
      def probability_contours_with_intensity_operation(self, v_range=(10, 300), v_step=10, pth=0.5,                                                       operations={"name":r"Logarithm: $10\times$ $log_{10}(x)$","fn":lambda x: 10*np.log10(x)}):
          _dict_ = {}
          
          return
      
      def generate_analysis_summary(self, kind="norm", _dict_ = {}):
          """
          Plot histograms for normalized intensity, thresholds and other parameters
          """
          summary_folder = self.folder + "summary/"
          if not os.path.exists(summary_folder): os.mkdir(summary_folder)
          if kind=="norm": norm_data = np.copy(self.norm_data)
          for key in _dict_.keys():
              _p, _mask = _dict_[key]
              fig, ax = plt.subplots(dpi=180, figsize=(4,4), nrows=1, ncols=1)
              ax.imshow(_mask*255, extent=[-1024,1024,-1024,1024], cmap="gray")
              ax.set_xticks([-1024,-512,0,512,1024])
              ax.set_yticks([-1024,-512,0,512,1024])
              ax.set_xticklabels([r"-$2^{10}$",r"-$2^{9}$","0",r"$2^{9}$",r"$2^{10}$"])
              ax.set_yticklabels([r"-$2^{10}$",r"-$2^{9}$","0",r"$2^{9}$",r"$2^{10}$"])
              ax.tick_params(axis="both", which="major", labelsize=8)
              ax.set_title(r"$\mathcal{F}(x_{\tau},I_{th})$=%.3f, $x_{\tau}$=0.5, $I_{th}$=%d"%(_p,key), fontdict={"size":8})
              fig.savefig(summary_folder + self.filename.replace(self.extn, "_binmaps_%04d"%key + self.extn), bbox_inches="tight")
              plt.close()
          os.system("zip -r summary.zip " + summary_folder)
          return
apis: numpy.sum numpy.diff pandas.DataFrame numpy.random.seed numpy.copy matplotlib.pyplot.subplots numpy.exp numpy.log10 matplotlib.pyplot.close numpy.isnan matplotlib.use numpy.linspace matplotlib.pyplot.style.context