original_docstring: 
      Detect features using circular Hough transfrom.
      
      Arguments:
          image (2D array): Image data. \n
          sigma (float): Sigma value for the Canny edge detector. \n
          low_threshold (int): Minimum radius in pixels. \n
          high_threshold (int): Maximum radius in pixels. \n
          min_radius (int): Minimum radius in pixels. \n
          max_radius (int): Maximum radius in pixels. \n
          threshold (int): Threshold for selecting the most prominent circles.
      Returns:
          features (pandas DataFrame): Pandas DataFrame with the detected features. \n
          image_out (2D array): The output image.  
      
docstring: Detect features using circular Hough transfrom.
docstring_tokens: Detect features using circular Hough transfrom .
code: def hough_transform(image, sigma, low_threshold, high_threshold, min_radius, max_radius, threshold):
      """
      Detect features using circular Hough transfrom.
      
      Arguments:
          image (2D array): Image data. \n
          sigma (float): Sigma value for the Canny edge detector. \n
          low_threshold (int): Minimum radius in pixels. \n
          high_threshold (int): Maximum radius in pixels. \n
          min_radius (int): Minimum radius in pixels. \n
          max_radius (int): Maximum radius in pixels. \n
          threshold (int): Threshold for selecting the most prominent circles.
      Returns:
          features (pandas DataFrame): Pandas DataFrame with the detected features. \n
          image_out (2D array): The output image.  
      """
      features = pd.DataFrame()
      
      edges = canny(image, sigma, low_threshold, high_threshold) # , low_threshold=80, high_threshold=150
      hough_radii = np.arange(min_radius, max_radius, 1) # np.linspace(20, 45, 50) 
      hough_transform = hough_circle(edges, hough_radii)
  
      # Select the most prominent circles
      _, x_centers, y_centers, radii = hough_circle_peaks(hough_transform, hough_radii, threshold=threshold)  
  
      features = pd.DataFrame()
      for x, y, r in zip(x_centers, y_centers, radii):
           features = features.append([{'x': x,
                                        'y': y,
                                        #'max_intensity': image[mask==1].max(),
                                        'radius': r,
                                       }])
          
              
      return features, edges.astype('int')
code_tokens: def hough_transform ( image , sigma , low_threshold , high_threshold , min_radius , max_radius , threshold ) : features = pd . DataFrame ( ) edges = canny ( image , sigma , low_threshold , high_threshold ) hough_radii = np . arange ( min_radius , max_radius , 1 ) hough_transform = hough_circle ( edges , hough_radii ) _ , x_centers , y_centers , radii = hough_circle_peaks ( hough_transform , hough_radii , threshold = threshold ) features = pd . DataFrame ( ) for x , y , r in zip ( x_centers , y_centers , radii ) : features = features . append ( [ { 'x' : x , 'y' : y , 'radius' : r , } ] ) return features , edges . astype ( 'int' )
short_docstring: Detect features using circular Hough transfrom.
short_docstring_tokens: Detect features using circular Hough transfrom .
comment: """
      Detect features using circular Hough transfrom.
      
      Arguments:
          image (2D array): Image data. \n
          sigma (float): Sigma value for the Canny edge detector. \n
          low_threshold (int): Minimum radius in pixels. \n
          high_threshold (int): Maximum radius in pixels. \n
          min_radius (int): Minimum radius in pixels. \n
          max_radius (int): Maximum radius in pixels. \n
          threshold (int): Threshold for selecting the most prominent circles.
      Returns:
          features (pandas DataFrame): Pandas DataFrame with the detected features. \n
          image_out (2D array): The output image.  
      """$SEP$# , low_threshold=80, high_threshold=150$SEP$# np.linspace(20, 45, 50) $SEP$# Select the most prominent circles$SEP$#'max_intensity': image[mask==1].max(),