diff --git a/code/local_features.py b/code/local_features.py
deleted file mode 100755
index 11b5b3b8f8e1ecdb0cd2941d0dc5b050d040a4d6..0000000000000000000000000000000000000000
--- a/code/local_features.py
+++ /dev/null
@@ -1,117 +0,0 @@
-"""
-Helping functions for extracting features from images
-"""
-
-import skimage.io
-import numpy as np
-import matplotlib.pyplot as plt
-import scipy.ndimage
-
-def get_gauss_feat_im(im, sigma=1, normalize=False):
- """Gauss derivative feaures for every image pixel.
- Arguments:
- image: a 2D image, shape (r,c).
- sigma: standard deviation for Gaussian derivatives.
- normalize: flag indicating normalization of features.
- Returns:
- imfeat: a 3D array of size (r,c,15) with a 15-dimentional feature
- vector for every image pixel.
- Author: vand@dtu.dk, 2020
- """
-
- r,c = im.shape
- imfeat = np.zeros((r,c,15))
- imfeat[:,:,0] = scipy.ndimage.gaussian_filter(im,sigma,order=0)
- imfeat[:,:,1] = scipy.ndimage.gaussian_filter(im,sigma,order=[0,1])
- imfeat[:,:,2] = scipy.ndimage.gaussian_filter(im,sigma,order=[1,0])
- imfeat[:,:,3] = scipy.ndimage.gaussian_filter(im,sigma,order=[0,2])
- imfeat[:,:,4] = scipy.ndimage.gaussian_filter(im,sigma,order=[1,1])
- imfeat[:,:,5] = scipy.ndimage.gaussian_filter(im,sigma,order=[2,0])
- imfeat[:,:,6] = scipy.ndimage.gaussian_filter(im,sigma,order=[0,3])
- imfeat[:,:,7] = scipy.ndimage.gaussian_filter(im,sigma,order=[1,2])
- imfeat[:,:,8] = scipy.ndimage.gaussian_filter(im,sigma,order=[2,1])
- imfeat[:,:,9] = scipy.ndimage.gaussian_filter(im,sigma,order=[3,0])
- imfeat[:,:,10] = scipy.ndimage.gaussian_filter(im,sigma,order=[0,4])
- imfeat[:,:,11] = scipy.ndimage.gaussian_filter(im,sigma,order=[1,3])
- imfeat[:,:,12] = scipy.ndimage.gaussian_filter(im,sigma,order=[2,2])
- imfeat[:,:,13] = scipy.ndimage.gaussian_filter(im,sigma,order=[3,1])
- imfeat[:,:,14] = scipy.ndimage.gaussian_filter(im,sigma,order=[4,0])
-
- if normalize:
- imfeat -= np.mean(imfeat, axis=(0,1))
- imfeat *= (1/np.std(imfeat, axis=(0,1)))
-
- return imfeat
-
-def im2col(im, patch_size=[3,3], stepsize=1):
- """Rearrange image patches into columns
- Arguments:
- image: a 2D image, shape (r,c).
- patch size: size of extracted paches.
- stepsize: patch step size.
- Returns:
- patches: a 2D array which in every column has a patch associated
- with one image pixel. For stepsize 1, number of returned column
- is (r-patch_size[0]+1)*(c-patch_size[0]+1) due to bounary. The
- length of columns is pathc_size[0]*patch_size[1].
- """
-
- r,c = im.shape
- s0, s1 = im.strides
- nrows =r-patch_size[0]+1
- ncols = c-patch_size[1]+1
- shp = patch_size[0],patch_size[1],nrows,ncols
- strd = s0,s1,s0,s1
-
- out_view = np.lib.stride_tricks.as_strided(im, shape=shp, strides=strd)
- return out_view.reshape(patch_size[0]*patch_size[1],-1)[:,::stepsize]
-
-
-def ndim2col(im, block_size=[3,3], stepsize=1):
- """Rearrange image blocks into columns for N-D image (e.g. RGB image)"""""
- if(im.ndim == 2):
- return im2col(im, block_size, stepsize)
- else:
- r,c,l = im.shape
- patches = np.zeros((l*block_size[0]*block_size[1],
- (r-block_size[0]+1)*(c-block_size[1]+1)))
- for i in range(l):
- patches[i*block_size[0]*block_size[1]:(i+1)*block_size[0]*block_size[1],
- :] = im2col(im[:,:,i],block_size,stepsize)
- return patches
-
-#%%
-
-if __name__ == '__main__':
-
- filename = '../example_data/training_image.png'
- I = skimage.io.imread(filename)
- I = I.astype(np.float)
-
-
- # features based on gaussian derivatives
- sigma = 1;
- gf = get_gauss_feat_im(I, sigma)
-
- fig,ax = plt.subplots(3,5)
- for j in range(5):
- for i in range(3):
- ax[i][j].imshow(gf[:,:,5*i+j])
- ax[i][j].set_title(f'layer {5*i+j}')
-
-
- # features based on image patches
- I = I[I.shape[0]//2:I.shape[0]//2+20,I.shape[1]//2:I.shape[1]//2+20] # smaller image such that we can see the difference in layers
- pf = im2col(I,[3,3])
- pf = pf.reshape((9,I.shape[0]-2,I.shape[1]-2))
-
- fig, ax = plt.subplots()
- ax.imshow(I)
-
- fig,ax = plt.subplots(3,3)
- for j in range(3):
- for i in range(3):
- ax[i][j].imshow(pf[3*i+j])
- ax[i][j].set_title(f'layer {3*i+j}')
-
-
diff --git a/code/microscopy_analysis.py b/code/microscopy_analysis.py
index 394ea5f76edd06f7306ab44c7451b725f6e4a5f8..f5505e708cf7af40176f2aa9b1815c3f4f555cf4 100755
--- a/code/microscopy_analysis.py
+++ b/code/microscopy_analysis.py
@@ -10,7 +10,6 @@ import os
import skimage.io as io
import skimage.transform
import numpy as np
-import local_features as lf
import matplotlib.pyplot as plt
from math import ceil
@@ -310,37 +309,6 @@ def rgb2cmy(img):
#%%Functions for feature analysis and visualisation
-# computes the max projection image and the features into a list
-def compute_max_img_feat(in_dir, ext, base_name, sigma, sc_fac, save = False, abs_intensity = True):
- dir_list = [dI for dI in os.listdir(in_dir) if (os.path.isdir(os.path.join(in_dir,dI)) and dI[0:len(base_name)]==base_name)]
- dir_list.sort()
- max_img_list = []
- for d in dir_list:
- image_dir = in_dir + d + '/'
- max_img = get_max_img(image_dir, ext)
-
- if save:
- dir_name_out_img = in_dir.replace('data','maxProjImages')
- os.makedirs(dir_name_out_img, exist_ok = True)
-
- io.imsave( dir_name_out_img + d + '.png', max_img.astype('uint8'))
-
- max_img_list += [skimage.transform.rescale(max_img, sc_fac, multichannel=True)]
-
- feat_list = []
- for max_img in max_img_list:
- r,c = max_img.shape[:2]
- feat = np.zeros((r*c,45))
- for i in range(0,3):
- feat_tmp = lf.get_gauss_feat_im(max_img[:,:,i], sigma)
- feat[:,i*15:(i+1)*15] = feat_tmp.reshape(-1,feat_tmp.shape[2])
- if not(abs_intensity):
- feat_list += [np.concatenate((feat[:,1:15],feat[:,16:30],feat[:,31:45]),axis = 1)]
- else:
- feat_list += [feat]
- return max_img_list, feat_list
-
-
# computes a histogram of features from a kmeans object
def compute_assignment_hist(feat_list, kmeans, background_feat = None):
assignment_list = []