Merge branch 'master' of git@git.elphel.com:Elphel/python3-imagej-tiff.git

numpy_visualize_weights.py

+#!/usr/bin/env python3
+
+import numpy as np
+import matplotlib.pyplot as plt
+import math
+
+# input: np.array(a,b) - 1 channel
+# output: np.array(a,b,3) - 3 color channels
+def coldmap(img,zero_span=0.2):
+
+  out = np.dstack(3*[img])
+
+  img_min = np.nanmin(img)
+  img_max = np.nanmax(img)
+
+  #print("min: "+str(img_min)+", max: "+str(img_max))
+
+  ch_r = out[...,0]
+  ch_g = out[...,1]
+  ch_b = out[...,2]
+
+  # blue for <0
+  ch_r[img<0] = 0
+  ch_g[img<0] = 0
+  ch_b[img<0] = -ch_b[img<0]
+
+  # red for >0
+  ch_r[img>0] = ch_b[img>0]
+  ch_g[img>0] = 0
+  ch_b[img>0] = 0
+
+  # green for 0
+  ch_r[img==0] = 0
+  ch_g[img==0] = img_max
+  ch_b[img==0] = 0
+
+  # green for zero vicinity
+  ch_r[abs(img)<zero_span/2] = 0
+  ch_g[abs(img)<zero_span/2] = img_max/2
+  ch_b[abs(img)<zero_span/2] = 0
+
+  return out
+
+# has to be pre transposed
+# it just suppose to match
+def tiles(img,shape,tiles_per_line=1,borders=True):
+
+  # shape is (n0,n1,n2,n3)
+  # n0*n1*n2*n3 = img.shape[1]
+  img_min = np.nanmin(img)
+  img_max = np.nanmax(img)
+
+  outer_color = [img_max,img_max,img_min]
+  outer_color = [img_max,img_max,img_max]
+
+  inner_color = [img_max/4,img_max/4,img_min]
+  inner_color = [img_min,img_min,img_min]
+  #inner_color = [img_max,img_max,img_min]
+
+  group_h = shape[0]
+  group_w = shape[1]
+  group_size = group_h*group_w
+
+  tile_h = shape[2]
+  tile_w = shape[3]
+  tile_size = tile_h*tile_w
+
+  tpl = tiles_per_line
+
+  # main
+
+  tmp1 = []
+
+  for i in range(img.shape[0]):
+
+    if i%tpl==0:
+      tmp2 = []
+
+    tmp3 = []
+
+    for igh in range(group_h):
+
+      tmp4 = []
+
+      for igw in range(group_w):
+
+        si = (group_w*igh + igw + 0)*tile_size
+        ei = (group_w*igh + igw + 1)*tile_size
+
+        tile = img[i,si:ei]
+        tile = np.reshape(tile,(tile_h,tile_w,tile.shape[1]))
+
+        if borders:
+
+          if igw==group_w-1:
+
+            b_h_inner = [[inner_color]*(tile_w+0)]*(       1)
+            b_h_outer = [[outer_color]*(tile_w+0)]*(       1)
+            b_v_outer = [[outer_color]*(       1)]*(tile_h+1)
+
+            # outer hor
+            if igh==group_h-1:
+              tile = np.concatenate([tile,b_h_outer],axis=0)
+            # inner hor
+            else:
+              tile = np.concatenate([tile,b_h_inner],axis=0)
+            # outer vert
+            tile = np.concatenate([tile,b_v_outer],axis=1)
+
+          else:
+
+            b_v_inner = [[inner_color]*(       1)]*(tile_h+0)
+            b_h_inner = [[inner_color]*(tile_w+1)]*(       1)
+            b_h_outer = [[outer_color]*(tile_w+1)]*(       1)
+
+            # inner vert
+            tile = np.concatenate([tile,b_v_inner],axis=1)
+
+            # outer hor
+            if igh==group_h-1:
+              tile = np.concatenate([tile,b_h_outer],axis=0)
+            # inner hor
+            else:
+              tile = np.concatenate([tile,b_h_inner],axis=0)
+
+        tmp4.append(tile)
+
+      tmp3.append(np.concatenate(tmp4,axis=1))
+
+    tmp2.append(np.concatenate(tmp3,axis=0))
+
+    if i%tpl==(tpl-1):
+      tmp1.append(np.concatenate(tmp2,axis=1))
+
+  out = np.concatenate(tmp1,axis=0)
+  #out = img
+  return out
+
+if __name__=="__main__":
+
+  #image = np.zeros((32,144))
+  image = np.random.rand(32,144)
+  rgb_img_0 = tiles(coldmap(image),(3,3,4,4),tiles_per_line=8,borders=True)
+
+  fig = plt.figure()
+  fig.suptitle("Test")
+  plt.imshow(rgb_img_0)
+  plt.show()
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