Added function to plot grid of clusters

code/microscopy_analysis.py

@@ -11,7 +11,7 @@ import numpy as np
 import local_features as lf
 
 import matplotlib.pyplot as plt
-
+from math import ceil
 
 #%% General functions
 
@@ -83,7 +83,8 @@ def make_output_dirs(directory,subdirectories = False):
     #     os.makedirs(directory + disease + '/', exist_ok = True)
   
     
-#Reads images starting with base_name from the subdirectories of the input directory
+#Reads images starting with base_name from the subdirectories of the input directory.
+#Option for reading scaled down versions and in bnw or colour
 def read_max_imgs(dir_condition, base_name, sc_fac = 1, colour_mode = 'colour'):
     'monj@dtu.dk'
     
@@ -103,11 +104,16 @@ def read_max_imgs(dir_condition, base_name, sc_fac = 1, colour_mode = 'colour'):
             #Option to load in bnw or colour
             if colour_mode == 'bnw':
                 img = io.imread(frame_path, as_gray = True).astype('uint8')
-                frame_img_list += [skimage.transform.rescale(img, sc_fac, preserve_range = True).astype('uint8')] 
+                if sc_fac ==1:
+                    frame_img_list += [img]
+                else:
+                    frame_img_list += [skimage.transform.rescale(img, sc_fac, preserve_range = True).astype('uint8')] 
             else:
                 img = io.imread(frame_path).astype('uint8')
-                #print(img.dtype)
-                frame_img_list += [skimage.transform.rescale(img, sc_fac, preserve_range = True, multichannel=True).astype('uint8')]
+                if sc_fac == 1:
+                    frame_img_list += [img]
+                else:
+                    frame_img_list += [skimage.transform.rescale(img, sc_fac, preserve_range = True, multichannel=True).astype('uint8')]
         
         max_img_list += [frame_img_list] 
         #print(frame_img_list[0].dtype)
@@ -363,8 +369,35 @@ def ndim2col_pad(A, BSZ, stepsize=1, norm=False):
     tmp = np.squeeze(tmp)
     return ndim2col(tmp,BSZ,stepsize,norm)
 
+#%% Functions for visualisation of learnt features
+
+def plot_grid_cluster_centers(cluster_centers, cluster_order, patch_size, colour_mode = 'colour', occurrence = ''):
+    #grid dimensions
+    size_x = round(len(cluster_order)**(1/2))
+    size_y = ceil(len(cluster_order)/size_x)
+
+    #figure format
+    overhead = 1
+    w, h = plt.figaspect(size_x/size_y)
+    fig, axs = plt.subplots(size_x,size_y, figsize=(1.3*w,1.3*h*(1+overhead/2)), sharex=True, sharey=True)
+    #print('Grid size: ', grid_size[1], grid_size[2], 'Figure size: ', w, h)
+    
+    ax_list = axs.ravel()
+    for ind, cluster in enumerate(cluster_order):
+        #print(ind)
+        if colour_mode == 'bnw': #in bnw + colour give the clusters a uniform colour
+            cluster_centre = np.reshape(cluster_centers[cluster,:],(patch_size,patch_size))
+            ax_list[ind].imshow(cluster_centre.astype('uint8'),cmap='gray')
+        else:
+            cluster_centre = np.transpose((np.reshape(cluster_centers[cluster,:],(3,patch_size,patch_size))),(1,2,0))
+            ax_list[ind].imshow(cluster_centre.astype('uint8'))
+        if occurrence !='':
+            ax_list[ind].set_title(round(occurrence[ind],2))
+        else:
+            ax_list[ind].set_title(cluster)
+    plt.setp(axs, xticks=[], yticks=[])
 
-
+    
 # def plot_mapsAndimages(dir_condition, directory_list, map_img, max_img_list, base_name = 'frame', r = 1024, c = 1024):
 #     nr_list = 0
 #     for directory in directory_list: