nanopyx.methods.squirrel.resolution

  1import numpy as np
  2from matplotlib import pyplot as plt
  3from ...core.analysis.decorr import DecorrAnalysis
  4from ...core.analysis.frc import FIRECalculator
  5
  6
  7def calculate_frc(
  8    frame_1: np.ndarray, frame_2: np.ndarray, pixel_size: float = 1, units: str = "pixel", plot_frc_curve: bool = False
  9):
 10    """
 11    Calculate the Fourier Ring Correlation (FRC) between two images to estimate their resolution.
 12
 13    Parameters
 14    ----------
 15    frame_1 : np.ndarray
 16        The first image frame as a 2D numpy array.
 17    frame_2 : np.ndarray
 18        The second image frame as a 2D numpy array.
 19    pixel_size : float, optional
 20        The physical size of a pixel in the specified units. Default is 1.
 21    units : str, optional
 22        The units of measurement for the pixel size (e.g., "pixel", "nm", "um"). Default is "pixel".
 23    plot_frc_curve : bool, optional
 24        If True, plots the FRC curve. Default is False.
 25
 26    Returns
 27    -------
 28    float
 29        The resolution estimate based on the FRC calculation.
 30
 31    Notes
 32    -----
 33    The Fourier Ring Correlation (FRC) is a method to estimate the resolution of images, particularly useful in microscopy. It compares the similarity of two images in the frequency domain, providing a measure of their resolution.
 34    """
 35    frc_calc = FIRECalculator(pixel_size=pixel_size, units=units)
 36    res = frc_calc.calculate_fire_number(frame_1, frame_2)
 37
 38    if plot_frc_curve:
 39        plt.axis("off")
 40        plt.subplots_adjust(left=0, right=1, top=1, bottom=0, wspace=0, hspace=0)
 41        plt.imshow(frc_calc.plot_frc_curve())
 42        plt.show()
 43
 44    return res
 45
 46
 47def calculate_decorr_analysis(
 48    frame: np.ndarray,
 49    rmin: float = 0,
 50    rmax: float = 1,
 51    n_r: int = 50,
 52    n_g: int = 10,
 53    pixel_size: float = 1,
 54    units: str = "pixel",
 55    roi: tuple = (0, 0, 0, 0),
 56    plot_decorr_analysis=False,
 57):
 58    """
 59    Perform decorrelation analysis on a given image frame to estimate its resolution.
 60
 61    Parameters
 62    ----------
 63    frame : np.ndarray
 64        The image frame as a 2D numpy array.
 65    rmin : float, optional
 66        The minimum radius for decorrelation analysis. Default is 0.
 67    rmax : float, optional
 68        The maximum radius for decorrelation analysis. Default is 1.
 69    n_r : int, optional
 70        The number of radial divisions for analysis. Default is 50.
 71    n_g : int, optional
 72        The number of angular divisions for analysis. Default is 10.
 73    pixel_size : float, optional
 74        The physical size of a pixel in the specified units. Default is 1.
 75    units : str, optional
 76        The units of measurement for the pixel size (e.g., "pixel", "nm", "um"). Default is "pixel".
 77    roi : tuple, optional
 78        The region of interest in the format (x_min, y_min, x_max, y_max). Default is (0, 0, 0, 0).
 79    plot_decorr_analysis : bool, optional
 80        If True, plots the results of the decorrelation analysis. Default is False.
 81
 82    Returns
 83    -------
 84    float
 85        The resolution estimate based on the decorrelation analysis.
 86
 87    Notes
 88    -----
 89    Decorrelation analysis is a technique used to estimate the resolution of images by analyzing the decorrelation of intensity values over different spatial scales. It is particularly useful in microscopy and imaging where direct resolution measurement is challenging.
 90    """
 91    decorr_calc = DecorrAnalysis(pixel_size=pixel_size, units=units, rmin=rmin, rmax=rmax, n_r=n_r, n_g=n_g, roi=roi)
 92    decorr_calc.run_analysis(frame)
 93
 94    if plot_decorr_analysis:
 95        plt.axis("off")
 96        plt.subplots_adjust(left=0, right=1, top=1, bottom=0, wspace=0, hspace=0)
 97        plt.imshow(decorr_calc.plot_results())
 98        plt.show()
 99
100    return decorr_calc.resolution