Source code for PyEMD.EMD2d

#!/usr/bin/python
# coding: UTF-8
#
# Author:   Dawid Laszuk
# Contact:  https://github.com/laszukdawid/PyEMD/issues
#
# Edited:   07/07/2017
#
# Feel free to contact for any information.

from __future__ import division, print_function

import logging
import numpy as np

try:
    from scipy.ndimage.filters import maximum_filter
    from scipy.ndimage.morphology import generate_binary_structure, binary_erosion
    from scipy.interpolate import SmoothBivariateSpline as SBS
except ImportError:
    raise ImportError("EMD2D and BEMD are not supported. Feel free to play around and improve them. " + \
            "Required depdenecies are in `requriements-extra`.")

[docs]class EMD2D: """ **Empirical Mode Decomposition** on images. **Important** This is an experimental module. Experiments performed using this module didn't provide acceptable results, either in actual output nor in computation performance. The author is not an expert in image processing so it's very likely that the code could have been improved. Take your best shot. Method decomposes images into 2D representations of loose Intrinsic Mode Functions (IMFs). The current version of the algorithm detects local extrema, separately minima and maxima, and then connects them to create envelopes. These are then used to create a mean trend and subtracted from the input. Threshold values that control goodness of the decomposition: * `mse_thr` --- proto-IMF check whether small mean square error. * `mean_thr` --- proto-IMF chekc whether small mean value. """ logger = logging.getLogger(__name__)
[docs] def __init__(self, **config): # ProtoIMF related self.mse_thr = 0.01 self.mean_thr = 0.01 self.FIXE = 0 self.FIXE_H = 0 self.MAX_ITERATION = 1000 # Update based on options for key in config.keys(): if key in self.__dict__.keys(): self.__dict__[key] = config[key]
[docs] def __call__(self, image, max_imf=-1): return self.emd(image, max_imf=max_imf)
[docs] def extract_max_min_spline(self, image): """Calculates top and bottom envelopes for image. Parameters ---------- image : numpy 2D array Returns ------- min_env : numpy 2D array Bottom envelope in form of an image. max_env : numpy 2D array Top envelope in form of an image. """ big_image = self.prepare_image(image) big_min_peaks, big_max_peaks = self.find_extrema(big_image) # Prepare grid for interpolation. Doesn't seem necessary. xi = np.arange(image.shape[0],image.shape[0]*2) yi = np.arange(image.shape[1],image.shape[1]*2) big_min_image_val = big_image[big_min_peaks] big_max_image_val = big_image[big_max_peaks] min_env = self.spline_points(big_min_peaks[0], big_min_peaks[1], big_min_image_val, xi, yi) max_env = self.spline_points(big_max_peaks[0], big_max_peaks[1], big_max_image_val, xi, yi) return min_env, max_env
[docs] @classmethod def prepare_image(cls, image): """Prepares image for edge extrapolation. Method bloats image by mirroring it along all axes. This turns extrapolation on edges into interpolation within bigger image. Parameters ---------- image : numpy 2D array Image for which interpolation is required, Returns ------- image : numpy 2D array Big image based on the input. Grid 3x3 where the center block is input and neighbouring panels are respective mirror images. """ #TODO: This is nasty. Instead of bloating whole image and then trying to # find all extrema, it's better to deal directly with indices. shape = image.shape big_image = np.zeros((shape[0]*3, shape[1]*3)) image_lr = np.fliplr(image) image_ud = np.flipud(image) image_ud_lr = np.flipud(image_lr) image_lr_ud = np.fliplr(image_ud) # Fill center with default image big_image[shape[0]:2*shape[0], shape[1]:2*shape[1]] = image # Fill left center big_image[shape[0]:2*shape[0], :shape[1]] = image_lr # Fill right center big_image[shape[0]:2*shape[0], 2*shape[1]:] = image_lr # Fill center top big_image[:shape[0], shape[1]:shape[1]*2] = image_ud # Fill center bottom big_image[2*shape[0]:, shape[1]:2*shape[1]] = image_ud # Fill left top big_image[:shape[0], :shape[1]] = image_ud_lr # Fill left bottom big_image[2*shape[0]:, :shape[1]] = image_ud_lr # Fill right top big_image[:shape[0], 2*shape[1]:] = image_lr_ud # Fill right bottom big_image[2*shape[0]:, 2*shape[1]:] = image_lr_ud return big_image
[docs] @classmethod def spline_points(cls, X, Y, Z, xi, yi): """Interpolates for given set of points""" # SBS requires at least m=(kx+1)*(ky+1) points, # where kx=ky=3 (default) is the degree of bivariate spline. # Thus, if less than 16=(3+1)*(3+1) points, adjust kx & ky. spline = SBS(X, Y, Z) return spline(xi, yi)
[docs] @classmethod def find_extrema(cls, image): """ Finds extrema, both mininma and maxima, based on local maximum filter. Returns extrema in form of two rows, where the first and second are positions of x and y, respectively. Parameters ---------- image : numpy 2D array Monochromatic image or any 2D array. Returns ------- min_peaks : numpy array Minima positions. max_peaks : numpy array Maxima positions. """ # define an 3x3 neighborhood neighborhood = generate_binary_structure(2, 2) # apply the local maximum filter; all pixel of maximal value # in their neighborhood are set to 1 local_min = maximum_filter(-image, footprint=neighborhood) == -image local_max = maximum_filter(image, footprint=neighborhood) == image # can't distinguish between background zero and filter zero background = (image == 0) #appear along the bg border (artifact of the local max filter) eroded_background = binary_erosion(background, structure=neighborhood, border_value=1) # we obtain the final mask, containing only peaks, # by removing the background from the local_max mask (xor operation) min_peaks = local_min ^ eroded_background max_peaks = local_max ^ eroded_background min_peaks = local_min max_peaks = local_max min_peaks[[0, -1], :] = False min_peaks[:, [0, -1]] = False max_peaks[[0, -1], :] = False max_peaks[:, [0, -1]] = False min_peaks = np.nonzero(min_peaks) max_peaks = np.nonzero(max_peaks) return min_peaks, max_peaks
[docs] @classmethod def end_condition(cls, image, IMFs): """Determins whether decomposition should be stopped. Parameters ---------- image : numpy 2D array Input image which is decomposed. IMFs : numpy 3D array Array for which first dimensions relates to respective IMF, i.e. (numIMFs, imageX, imageY). """ rec = np.sum(IMFs, axis=0) # If reconstruction is perfect, no need for more tests if np.allclose(image, rec): return True return False
[docs] def check_proto_imf(self, proto_imf, proto_imf_prev, mean_env): """Check whether passed (proto) IMF is actual IMF. Current condition is solely based on checking whether the mean is below threshold. Parameters ---------- proto_imf : numpy 2D array Current iteration of proto IMF. proto_imf_prev : numpy 2D array Previous iteration of proto IMF. mean_env : numpy 2D array Local mean computed from top and bottom envelopes. Returns ------- boolean Whether current proto IMF is actual IMF. """ #TODO: Sifiting is very sensitive and subtracting const val can often flip # maxima with minima in decompoisition and thus repeating above/below # behaviour. For now, mean_env is checked whether close to zero excluding # its offset. if np.all(np.abs(mean_env-mean_env.mean()) < self.mean_thr): #if np.all(np.abs(mean_env)<self.mean_thr): return True # If very little change with sifting if np.allclose(proto_imf, proto_imf_prev): return True # If IMF mean close to zero (below threshold) if np.mean(np.abs(proto_imf)) < self.mean_thr: return True # Everything relatively close to 0 mse_proto_imf = np.mean(proto_imf*proto_imf) if mse_proto_imf < self.mse_thr: return True return False
[docs] def emd(self, image, max_imf=-1): """Performs EMD on input image with specified parameters. Parameters ---------- image : numpy 2D array, Image which will be decomposed. max_imf : int, (default: -1) IMF number to which decomposition should be performed. Negative value means *all*. Returns ------- IMFs : numpy 3D array Set of IMFs in form of numpy array where the first dimension relates to IMF's ordinary number. """ image_min, image_max = np.min(image), np.max(image) offset = image_min scale = image_max-image_min image_s = (image-offset)/scale imf = np.zeros(image.shape) imf_old = imf.copy() imfNo = 0 IMF = np.empty((imfNo,)+image.shape) notFinished = True while(notFinished): self.logger.debug('IMF -- '+str(imfNo)) res = image_s - np.sum(IMF[:imfNo], axis=0) imf = res.copy() mean_env = np.zeros(image.shape) stop_sifting = False # Counters n = 0 # All iterations for current imf. n_h = 0 # counts when mean(proto_imf) < threshold while(not stop_sifting and n < self.MAX_ITERATION): n += 1 self.logger.debug("Iteration: "+str(n)) min_peaks, max_peaks = self.find_extrema(imf) self.logger.debug("min_peaks = %i | max_peaks = %i", len(min_peaks[0]), len(max_peaks[0])) if len(min_peaks[0]) > 4 and len(max_peaks[0]) > 4: imf_old = imf.copy() imf = imf - mean_env min_env, max_env = self.extract_max_min_spline(imf) mean_env = 0.5*(min_env+max_env) imf_old = imf.copy() imf = imf - mean_env # Fix number of iterations if self.FIXE: if n >= self.FIXE+1: stop_sifting = True # Fix number of iterations after number of zero-crossings # and extrema differ at most by one. 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 # Stops after default stopping criteria are met else: if self.check_proto_imf(imf, imf_old, mean_env): stop_sifting = True else: notFinished = False 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 IMF = IMF*scale IMF[-1] += offset return IMF
######################################## if __name__ == "__main__": print("Running example on EMD2D") PLOT = True logging.basicConfig(level=logging.DEBUG) # Generate image print("Generating image... ", end="") rows, cols = 1024, 1024 row_scale, col_scale = 256, 256 x = np.arange(rows)/float(row_scale) y = np.arange(cols).reshape((-1,1))/float(col_scale) pi2 = 2*np.pi img = np.zeros((rows,cols)) img = img + np.sin(2*pi2*x)*np.cos(y*4*pi2+4*x*pi2) img = img + 3*np.sin(2*pi2*x)+2 img = img + 5*x*y + 2*(y-0.2)*y print("Done") # Perform decomposition print("Performing decomposition... ", end="") emd2d = EMD2D() #emd2d.FIXE_H = 5 IMFs = emd2d.emd(img, max_imf=4) imfNo = IMFs.shape[0] print("Done") if PLOT: print("Plotting results... ", end="") import pylab as plt # Save image for preview plt.figure(figsize=(4,4*(imfNo+1))) plt.subplot(imfNo+1, 1, 1) plt.imshow(img) plt.colorbar() plt.title("Input image") # Save reconstruction for n, imf in enumerate(IMFs): plt.subplot(imfNo+1, 1, n+2) plt.imshow(imf) plt.colorbar() plt.title("IMF %i"%(n+1)) plt.savefig("image_decomp") print("Done")