simplified packing, next test

imagej_tiff.py

@@ -399,7 +399,7 @@ if __name__ == "__main__":
   #ijt.show_images(['X-corr','Y-corr'])
   #ijt.show_images(['R-vign',3])
 
-  ijt.show_images([0])
+  ijt.show_images()
   plt.show()
 
 

pack_tile.py

@@ -5,86 +5,134 @@ __license__   = "GPL-3.0+"
 __email__     = "oleg@elphel.com"
 
 import numpy as np
-import xml.etree.ElementTree as ET
-import ast
 
-class PackingTable:
+# pack from 9x9x4 to 25x1
+def pack_tile_type1(tile):
 
-  def __init__(self,filename,layers_of_interest):
-    e = ET.parse(filename).getroot()
-    #print(ET.tostring(e))
-    #reparsed = minidom.parseString(ET.tostring(e,""))
-    #print(reparsed.toprettyxml(indent="\t"))
-
-    # Parse xml:
-    # td = tmp_dict
-    td = {}
-    for table in e:
-      layer = table.get('layer')
-      td[layer] = []
-      for row in table:
-        # safe evaluation
-        td[layer].append(ast.literal_eval(row.text))
-
-    # order
-    LUT = []
-    for layer in layers_of_interest:
-      LUT.append(td[layer])
-
-    self.lut = LUT
-
-
-# A tile consists of layers
-# layer is packed from 9x9 to 25x1
-def pack_layer(layer,lut_row):
-
-  #print(layer.shape)
-
-  t = layer.flatten()
-
-  #out = np.array([])
-  out = np.empty(len(lut_row))
-
-  # iterate through rows
-  for i in range(len(lut_row)):
-
-    val = 0
-    # process row value
-    for j in lut_row[i]:
-      ## big slowdown
-      #if np.isnan(t[j[0]]):
-      # val = np.nan
-      #  break
-      #val += t[j[0]]*j[1]
-      val += t[j[0]]
+  out = np.empty(100)
 
-    #out = np.append(out,val)
-    #out[i] = val
-    out[i] = val
+  # pack diagm-pair
+  l = np.ravel(tile[:,:,0])
+  out[ 0] = 1.0*l[ 2]+1.0*l[ 3]+1.0*l[ 4]+1.0*l[ 5]+1.0*l[6]
+  out[ 1] = 1.0*l[10]+1.0*l[11]+1.0*l[19]+1.0*l[20]
+  out[ 2] = 1.0*l[12]+1.0*l[13]+1.0*l[14]
+  out[ 3] = 1.0*l[15]+1.0*l[16]+1.0*l[24]+1.0*l[25]
+  out[ 4] = 1.0*l[18]+1.0*l[27]+1.0*l[36]+1.0*l[45]+1.0*l[54]
+  out[ 5] = 1.0*l[21]+1.0*l[22]+1.0*l[23]
+  out[ 6] = 1.0*l[26]+1.0*l[35]+1.0*l[44]+1.0*l[53]+1.0*l[62]
+  out[ 7] = 1.0*l[28]+1.0*l[37]+1.0*l[46]
+  out[ 8] = 1.0*l[29]+1.0*l[38]+1.0*l[47]
+  out[ 9] = 1.0*l[30]
+  out[10] = 1.0*l[31]
+  out[11] = 1.0*l[32]
+  out[12] = 1.0*l[33]+1.0*l[42]+1.0*l[51]
+  out[13] = 1.0*l[34]+1.0*l[43]+1.0*l[52]
+  out[14] = 1.0*l[39]
+  out[15] = 1.0*l[40]
+  out[16] = 1.0*l[41]
+  out[17] = 1.0*l[48]
+  out[18] = 1.0*l[49]
+  out[19] = 1.0*l[50]
+  out[20] = 1.0*l[55]+1.0*l[56]+1.0*l[64]+1.0*l[65]
+  out[21] = 1.0*l[57]+1.0*l[58]+1.0*l[59]
+  out[22] = 1.0*l[60]+1.0*l[61]+1.0*l[69]+1.0*l[70]
+  out[23] = 1.0*l[66]+1.0*l[67]+1.0*l[68]
+  out[24] = 1.0*l[74]+1.0*l[75]+1.0*l[76]+1.0*l[77]+1.0*l[78]
+
+  # pack diago-pair
+  l = np.ravel(tile[:,:,1])
+  out[25] = 1.0*l[ 2]+1.0*l[ 3]+1.0*l[ 4]+1.0*l[ 5]+1.0*l[6]
+  out[26] = 1.0*l[10]+1.0*l[11]+1.0*l[19]+1.0*l[20]
+  out[27] = 1.0*l[12]+1.0*l[13]+1.0*l[14]
+  out[28] = 1.0*l[15]+1.0*l[16]+1.0*l[24]+1.0*l[25]
+  out[29] = 1.0*l[18]+1.0*l[27]+1.0*l[36]+1.0*l[45]+1.0*l[54]
+  out[30] = 1.0*l[21]+1.0*l[22]+1.0*l[23]
+  out[31] = 1.0*l[26]+1.0*l[35]+1.0*l[44]+1.0*l[53]+1.0*l[62]
+  out[32] = 1.0*l[28]+1.0*l[37]+1.0*l[46]
+  out[33] = 1.0*l[29]+1.0*l[38]+1.0*l[47]
+  out[34] = 1.0*l[30]
+  out[35] = 1.0*l[31]
+  out[36] = 1.0*l[32]
+  out[37] = 1.0*l[33]+1.0*l[42]+1.0*l[51]
+  out[38] = 1.0*l[34]+1.0*l[43]+1.0*l[52]
+  out[39] = 1.0*l[39]
+  out[40] = 1.0*l[40]
+  out[41] = 1.0*l[41]
+  out[42] = 1.0*l[48]
+  out[43] = 1.0*l[49]
+  out[44] = 1.0*l[50]
+  out[45] = 1.0*l[55]+1.0*l[56]+1.0*l[64]+1.0*l[65]
+  out[46] = 1.0*l[57]+1.0*l[58]+1.0*l[59]
+  out[47] = 1.0*l[60]+1.0*l[61]+1.0*l[69]+1.0*l[70]
+  out[48] = 1.0*l[66]+1.0*l[67]+1.0*l[68]
+  out[49] = 1.0*l[74]+1.0*l[75]+1.0*l[76]+1.0*l[77]+1.0*l[78]
+
+  # pack hor-pairs
+  l = np.ravel(tile[:,:,2])
+  out[50] = 1.0*l[ 2]+1.0*l[ 3]+1.0*l[ 4]+1.0*l[ 5]+1.0*l[6]
+  out[51] = 1.0*l[10]+1.0*l[11]+1.0*l[19]+1.0*l[20]
+  out[52] = 1.0*l[12]+1.0*l[13]+1.0*l[14]
+  out[53] = 1.0*l[15]+1.0*l[16]+1.0*l[24]+1.0*l[25]
+  out[54] = 1.0*l[18]+1.0*l[27]+1.0*l[36]+1.0*l[45]+1.0*l[54]
+  out[55] = 1.0*l[21]+1.0*l[22]+1.0*l[23]
+  out[56] = 1.0*l[26]+1.0*l[35]+1.0*l[44]+1.0*l[53]+1.0*l[62]
+  out[57] = 1.0*l[28]+1.0*l[37]+1.0*l[46]
+  out[58] = 1.0*l[29]+1.0*l[38]+1.0*l[47]
+  out[59] = 1.0*l[30]
+  out[60] = 1.0*l[31]
+  out[61] = 1.0*l[32]
+  out[62] = 1.0*l[33]+1.0*l[42]+1.0*l[51]
+  out[63] = 1.0*l[34]+1.0*l[43]+1.0*l[52]
+  out[64] = 1.0*l[39]
+  out[65] = 1.0*l[40]
+  out[66] = 1.0*l[41]
+  out[67] = 1.0*l[48]
+  out[68] = 1.0*l[49]
+  out[69] = 1.0*l[50]
+  out[70] = 1.0*l[55]+1.0*l[56]+1.0*l[64]+1.0*l[65]
+  out[71] = 1.0*l[57]+1.0*l[58]+1.0*l[59]
+  out[72] = 1.0*l[60]+1.0*l[61]+1.0*l[69]+1.0*l[70]
+  out[73] = 1.0*l[66]+1.0*l[67]+1.0*l[68]
+  out[74] = 1.0*l[74]+1.0*l[75]+1.0*l[76]+1.0*l[77]+1.0*l[78]
+
+  # pack vert-pairs
+  l = np.ravel(tile[:,:,3])
+  out[75] = 1.0*l[ 2]+1.0*l[ 3]+1.0*l[ 4]+1.0*l[ 5]+1.0*l[6]
+  out[76] = 1.0*l[10]+1.0*l[11]+1.0*l[19]+1.0*l[20]
+  out[77] = 1.0*l[12]+1.0*l[13]+1.0*l[14]
+  out[78] = 1.0*l[15]+1.0*l[16]+1.0*l[24]+1.0*l[25]
+  out[79] = 1.0*l[18]+1.0*l[27]+1.0*l[36]+1.0*l[45]+1.0*l[54]
+  out[80] = 1.0*l[21]+1.0*l[22]+1.0*l[23]
+  out[81] = 1.0*l[26]+1.0*l[35]+1.0*l[44]+1.0*l[53]+1.0*l[62]
+  out[82] = 1.0*l[28]+1.0*l[37]+1.0*l[46]
+  out[83] = 1.0*l[29]+1.0*l[38]+1.0*l[47]
+  out[84] = 1.0*l[30]
+  out[85] = 1.0*l[31]
+  out[86] = 1.0*l[32]
+  out[87] = 1.0*l[33]+1.0*l[42]+1.0*l[51]
+  out[88] = 1.0*l[34]+1.0*l[43]+1.0*l[52]
+  out[89] = 1.0*l[39]
+  out[90] = 1.0*l[40]
+  out[91] = 1.0*l[41]
+  out[92] = 1.0*l[48]
+  out[93] = 1.0*l[49]
+  out[94] = 1.0*l[50]
+  out[95] = 1.0*l[55]+1.0*l[56]+1.0*l[64]+1.0*l[65]
+  out[96] = 1.0*l[57]+1.0*l[58]+1.0*l[59]
+  out[97] = 1.0*l[60]+1.0*l[61]+1.0*l[69]+1.0*l[70]
+  out[98] = 1.0*l[66]+1.0*l[67]+1.0*l[68]
+  out[99] = 1.0*l[74]+1.0*l[75]+1.0*l[76]+1.0*l[77]+1.0*l[78]
 
   return out
 
+# pack single
+def pack_tile(tile):
+  return pack_tile_type1(tile)
 
-# tile and lut already ordered and indices match
-def pack_tile(tile,lut):
-
-  #out = np.array([])
-  #s = 0
-  #for i in range(len(lut)):
-  #  s += len(lut[i])
-
-  #out = np.empty(s)
-  out = np.empty(100)
-
-  ptr = 0
-  for i in range(len(lut)):
-    layer = pack_layer(tile[:,:,i],lut[i])
-    #out = np.append(out,layer)
-    out[ptr:ptr+len(lut[i])] = layer
-    #out[ptr:ptr+len(lut[i])] = np.empty(25)
-    ptr += len(lut[i])
-
-  return out
+# pack all tiles
+def pack(tiles):
+  output = np.array([[pack_tile(tiles[i,j]) for j in range(tiles.shape[1])] for i in range(tiles.shape[0])])
+  return output
 
 
 # tiles are already packed
@@ -114,13 +162,3 @@ def get_tile_with_neighbors(tiles,i,j,radius):
       out = np.append(out,tiles[y,x])
 
   return out
-
-
-
-
-
-
-
-
-
-

test_nn_feed.py

@@ -141,67 +141,14 @@ if not IS_TEST:
 
   # test later
 
-  # now pack from 9x9 to 1x25
-  # tiles and values
-
-  # Parse packing table
-  # packing table name
-  ptab_name = "tile_packing_table.xml"
-  ptab = pile.PackingTable(ptab_name,LAYERS_OF_INTEREST).lut
-
   # might not need it because going to loop through anyway
-  packed_tiles = np.array([[pile.pack_tile(tiles[i,j],ptab) for j in range(tiles.shape[1])] for i in range(tiles.shape[0])])
+  packed_tiles = pile.pack(tiles)
   packed_tiles = np.dstack((packed_tiles,values[:,:,0]))
 
   print("Packed (81x4 -> 1x(25*4+1)) tiled input shape: "+str(packed_tiles.shape))
   print("Values shape "+str(values.shape))
   print_time()
 
-else:
-  print("Init test data")
-
-  ptab_name = "tile_packing_table.xml"
-  pt = pile.PackingTable(ptab_name,LAYERS_OF_INTEREST).lut
-
-  # 9x9 2 layers, no neighbors
-  l = np.zeros((9,9))
-  for y,x in itertools.product(range(l.shape[0]),range(l.shape[1])):
-    l[y,x] = 9*y + x
-
-  l_value = np.array([2.54,3.54,0.5])
-
-  #print(l)
-
-  l1 = l
-  l2 = l*2
-  l3 = l*3
-  l4 = l*4
-
-  ls = np.dstack((l1,l2,l3,l4))
-  #print(ls.shape)
-
-  l_packed_pre = pile.pack_tile(ls,pt)
-  #print(l_packed_pre.shape)
-  #print(l_packed_pre)
-
-  l_packed = np.hstack((l_packed_pre,l_value[0]))
-
-  #print(l_packed.shape)
-  #print(l_packed)
-  # use l_packed
-
-  packed_tiles = np.empty([1,1,l_packed.shape[0]])
-  values =       np.empty([1,1,2])
-  print(packed_tiles.shape)
-  print(values.shape)
-
-  packed_tiles[0,0] = l_packed
-  values[0,0] = l_value[1:3]
-
-  print(packed_tiles[0,0])
-  print(values[0,0])
-
-
 # END IF IS_TEST
 
 
@@ -341,13 +288,8 @@ for epoch in range(lastepoch,lastepoch+len(tlist)):
   tmp_tiles = tmp_tiff.getstack(labels,shape_as_tiles=True)
   tmp_vals  = tmp_tiff.getvalues(label=VALUES_LAYER_NAME)
 
-  # Parse packing table
-  # packing table name
-  ptab_name = "tile_packing_table.xml"
-  ptab = pile.PackingTable(ptab_name,LAYERS_OF_INTEREST).lut
-
   # might not need it because going to loop through anyway
-  packed_tiles = np.array([[pile.pack_tile(tmp_tiles[i,j],ptab) for j in range(tmp_tiles.shape[1])] for i in range(tmp_tiles.shape[0])])
+  packed_tiles = pile.pack(tmp_tiles)
   packed_tiles = np.dstack((packed_tiles,tmp_vals[:,:,0]))
 
   #if epoch > 2000:

test_nn_infer.py

@@ -139,13 +139,8 @@ for item in tlist:
   # now pack from 9x9 to 1x25
   # tiles and values
 
-  # Parse packing table
-  # packing table name
-  ptab_name = "tile_packing_table.xml"
-  ptab = pile.PackingTable(ptab_name,LAYERS_OF_INTEREST).lut
-
   # might not need it because going to loop through anyway
-  packed_tiles = np.array([[pile.pack_tile(tiles[i,j],ptab) for j in range(tiles.shape[1])] for i in range(tiles.shape[0])])
+  packed_tiles = pile.pack(tiles)
   packed_tiles = np.dstack((packed_tiles,values[:,:,0]))
 
   print(packed_tiles.shape)