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

nn_ds_neibs1_tmp.py

@@ -50,10 +50,12 @@ BATCH_SIZE =       2*1080//9 # == 120 Each batch of tiles has balanced D/S tiles
 SHUFFLE_EPOCH =    True
 NET_ARCH1 =          0 #0 # 4 # 3 # overwrite with argv?
 NET_ARCH2 =          0 # 0 # 3 # overwrite with argv?
+SYM8_SUB =        False #  True # False # True # False # True # False # enforce inputs from 2d correlation have symmetrical ones (groups of 8)
 ONLY_TILE =          None # 4 # None # 0 # 4# None # (remove all but center tile data), put None here for normal operation)
 ZIP_LHVAR =        True # combine _lvar and _hvar as odd/even elements
 
 #DEBUG_PACK_TILES = True
+WLOSS_LAMBDA =       0.001 # 5.0 # 1.0 # fraction of the W_loss (input layers weight non-uniformity) added to G_loss
 SUFFIX=str(NET_ARCH1)+'-'+str(NET_ARCH2)+ (["R","A"][ABSOLUTE_DISPARITY])
 # CLUSTER_RADIUS should match input data
 CLUSTER_RADIUS =     1 # 1 - 3x3, 2 - 5x5 tiles
@@ -250,10 +252,10 @@ files_train_hvar = ["/home/oleg/GIT/python3-imagej-tiff/data_sets/tf_data_rand2/
                     "/home/oleg/GIT/python3-imagej-tiff/data_sets/tf_data_rand2/train007_R1_GT_1.5.tfrecords",
 ]
 
-files_train_lvar = ["/home/oleg/GIT/python3-imagej-tiff/data_sets/tf_data_rand2/train000_R1_LE_1.5.tfrecords",
-                    ]
-files_train_hvar = ["/home/oleg/GIT/python3-imagej-tiff/data_sets/tf_data_rand2/train000_R1_GT_1.5.tfrecords",
-]
+#files_train_lvar = ["/home/oleg/GIT/python3-imagej-tiff/data_sets/tf_data_rand2/train000_R1_LE_1.5.tfrecords",
+#                    ]
+#files_train_hvar = ["/home/oleg/GIT/python3-imagej-tiff/data_sets/tf_data_rand2/train000_R1_GT_1.5.tfrecords",
+#]
 #files_train_hvar = []
 #file_test_lvar=     "/home/eyesis/x3d_data/data_sets/tf_data_3x3a/train000_R1_LE_1.5.tfrecords" # "/home/eyesis/x3d_data/data_sets/train-000_R1_LE_1.5.tfrecords"
 file_test_lvar=     "/home/oleg/GIT/python3-imagej-tiff/data_sets/tf_data_rand2/testTEST_R1_LE_1.5.tfrecords"
@@ -445,6 +447,7 @@ def network_summary_w_b(scope, in_shape, out_shape, layout, index, network_scope
     
     global test_op
     
+    # the scope is known
     with tf.variable_scope(scope,reuse=tf.AUTO_REUSE):
         # histograms
         w = tf.get_variable('weights',shape=[in_shape,out_shape])
@@ -462,8 +465,13 @@ def network_summary_w_b(scope, in_shape, out_shape, layout, index, network_scope
                 # red - the values will be automapped to 0-255 range
                 # grid = tf.stack([tf.reduce_max(w),tf.reduce_min(w),tf.reduce_min(w)])
                 # yellow - the values will be automapped to 0-255 range
-                grid_y = tf.stack([tf.reduce_max(w),tf.reduce_max(w),tf.reduce_max(w)/2])
-                grid_r = tf.stack([tf.reduce_max(w),tf.reduce_min(w),tf.reduce_min(w)])
+                #grid_y = tf.stack([tf.reduce_max(w),tf.reduce_max(w),tf.reduce_max(w)/2])
+                # black
+                grid_y = tf.stack([tf.reduce_min(w),tf.reduce_min(w),tf.reduce_min(w)])
+                
+                #grid_r = tf.stack([tf.reduce_max(w),tf.reduce_min(w),tf.reduce_min(w)])
+                # white
+                grid_r = tf.stack([tf.reduce_max(w),tf.reduce_max(w),tf.reduce_max(w)])
                 
                 wt = tf.transpose(w,[1,0])
                 wt = wt[:,:-1]
@@ -521,8 +529,12 @@ def network_summary_w_b(scope, in_shape, out_shape, layout, index, network_scope
                 # red - the values will be automapped to 0-255 range
                 # grid = tf.stack([tf.reduce_max(w),tf.reduce_min(w),tf.reduce_min(w)])
                 # yellow - the values will be automapped to 0-255 range
-                grid_y = tf.stack([tf.reduce_max(w),tf.reduce_max(w),tf.reduce_max(w)/2])
-                grid_r = tf.stack([tf.reduce_max(w),tf.reduce_min(w),tf.reduce_min(w)])
+                # black
+                grid_y = tf.stack([tf.reduce_min(w),tf.reduce_min(w),tf.reduce_min(w)])
+                
+                #grid_r = tf.stack([tf.reduce_max(w),tf.reduce_min(w),tf.reduce_min(w)])
+                # white
+                grid_r = tf.stack([tf.reduce_max(w),tf.reduce_max(w),tf.reduce_max(w)])
                 
                 wt = tf.transpose(w,[1,0])
                 
@@ -592,22 +604,48 @@ def network_summary_w_b(scope, in_shape, out_shape, layout, index, network_scope
                 
                 
 
-def network_sub(input, layout, reuse):
+def network_sub(input, layout, reuse, sym8 = False):
     last_indx = None;
     fc = []
+    inp_weights = []
     for i, num_outs in enumerate (layout):
         if num_outs:
            if fc:
                inp = fc[-1]
+               fc.append(slim.fully_connected(inp,    num_outs, activation_fn=lrelu, scope='g_fc_sub'+str(i), reuse = reuse))
            else:
                inp = input
-               
-           fc.append(slim.fully_connected(inp,    num_outs, activation_fn=lrelu, scope='g_fc_sub'+str(i), reuse = reuse))
-           
+               if sym8:
+                   inp8 = sym_inputs8(inp)
+                   num_non_sum = num_outs %  len(inp8) # if number of first layer outputs is not multiple of 8
+                   num_sym8 =    num_outs // len(inp8) # number of symmetrical groups
+                   fc_sym = []
+                   for j in range (len(inp8)): # ==8
+                       reuse_this = reuse | (j > 0)
+                       scp = 'g_fc_sub'+str(i)
+                       fc_sym.append(slim.fully_connected(inp8[j],    num_sym8, activation_fn=lrelu, scope= scp,     reuse = reuse_this))
+                       if not reuse_this:
+                           with tf.variable_scope(scp,reuse=True) : # tf.AUTO_REUSE):
+                              inp_weights.append(tf.get_variable('weights')) # ,shape=[inp.shape[1],num_outs]))
+                           network_summary_w_b(scp, inp.shape[1], num_outs, layout, i, 'sub')
+                   if num_non_sum > 0:
+                       reuse_this = reuse
+                       scp = 'g_fc_sub'+str(i)+"r"
+                       fc_sym.append(slim.fully_connected(inp,     num_non_sum, activation_fn=lrelu, scope=scp, reuse = reuse_this))    
+                       if not reuse_this:
+                           with tf.variable_scope(scp,reuse=True) : # tf.AUTO_REUSE):
+                              inp_weights.append(tf.get_variable('weights')) # ,shape=[inp.shape[1],num_outs]))
+                           network_summary_w_b(scp, inp.shape[1], num_outs, layout, i, 'sub') 
+                   fc.append(tf.concat(fc_sym, 1, name='sym_input_layer'))
+               else:
+                   scp = 'g_fc_sub'+str(i)
+                   fc.append(slim.fully_connected(inp,    num_outs, activation_fn=lrelu, scope= scp, reuse = reuse))
                    if not reuse:
-               network_summary_w_b('g_fc_sub'+str(i), inp.shape[1], num_outs, layout, i, 'sub')
+                       with tf.variable_scope(scp, reuse=True) : # tf.AUTO_REUSE):
+                          inp_weights.append(tf.get_variable('weights')) # ,shape=[inp.shape[1],num_outs]))
+                       network_summary_w_b(scp, inp.shape[1], num_outs, layout, i, 'sub') 
            
-    return fc[-1]
+    return fc[-1], inp_weights
 
 def network_inter(input, layout):
     last_indx = None;
@@ -630,22 +668,31 @@ def network_inter(input, layout):
         #If using residual disparity, split last layer into 2 or remove activation and add rectifier to confidence only  
     return fc_out
 
-def network_siam(input, # now [?:9,325]
+def network_siam(input, # now [?,9,325]-> [?,25,325]
                  layout1, 
                  layout2,
-                 only_tile=None): # just for debugging - feed only data from the center sub-network
+                 sym8 =        False,
+                 only_tile =   None): # just for debugging - feed only data from the center sub-network
     with tf.name_scope("Siam_net"):
+        inp_weights = []
         num_legs =  input.shape[1] # == 9
         inter_list = []
         reuse = False
         for i in range (num_legs):
             if (only_tile is None) or (i == only_tile):
-                inter_list.append(network_sub(input[:,i,:],
+#                inter_list.append(network_sub(input[:,i,:],
+#                                          layout= layout1,
+#                                          reuse= reuse,
+#                                          sym8 = sym8))
+                ns, ns_weights = network_sub(input[:,i,:],
                                           layout= layout1,
-                                          reuse= reuse))
+                                          reuse= reuse,
+                                          sym8 = sym8)
+                inter_list.append(ns)
+                inp_weights += ns_weights
                 reuse = True
         inter_tensor = tf.concat(inter_list, 1, name='inter_tensor')
-        return  network_inter (inter_tensor, layout2)
+        return  network_inter (inter_tensor, layout2),  inp_weights 
 #corr2d9x325 = tf.concat([tf.reshape(next_element_tt['corr2d'],[None,cluster_size,FEATURES_PER_TILE]) , tf.reshape(next_element_tt['target_disparity'], [None,cluster_size, 1])],2)
 
 def debug_gt_variance(
@@ -774,6 +821,36 @@ def batchLoss(out_batch,                   # [batch_size,(1..2)] tf_result
         else:
             return cost1b,  disp_slice, d_gt_slice, out_diff,out_diff2, w_norm, out_wdiff2, cost1
 
+
+
+def weightsLoss(inp_weights):       # [batch_size,(1..2)] tf_result
+#                weights_lambdas):  # single lambda or same length as inp_weights.shape[1]
+    """
+    Enforcing 'smooth' weights for the input 2d correlation tiles
+    @return mean squared difference for each weight and average of 8 neighbors divided by mean squared weights
+    """
+    weight_ortho = 1.0
+    weight_diag  = 0.7
+    sw = 4.0 * (weight_ortho + weight_diag)
+    weight_ortho /= sw
+    weight_diag /=  sw
+#    w_neib = tf.const([[weight_diag,  weight_ortho, weight_diag],
+#                       [weight_ortho, -1.0,         weight_ortho],
+#                       [weight_diag,  weight_ortho, weight_diag]])
+    with tf.name_scope("WeightsLoss"):
+        # Adding 1 tile border
+        tf_inp =     tf.reshape(inp_weights[:TILE_LAYERS * TILE_SIZE,:], [TILE_LAYERS, FILE_TILE_SIDE, FILE_TILE_SIDE, inp_weights.shape[1]], name = "tf_inp")
+        tf_inp_ext_h = tf.concat([tf_inp       [:, :,  :1, :], tf_inp,       tf_inp      [:,   :, -1:, :]], axis = 2, name ="tf_inp_ext_h")
+        tf_inp_ext   = tf.concat([tf_inp_ext_h [:, :1, :,  :], tf_inp_ext_h, tf_inp_ext_h[:, -1:,   :, :]], axis = 1, name ="tf_inp_ext")
+        s_ortho = tf_inp_ext[:,1:-1,:-2,:] + tf_inp_ext[:,1:-1, 2:,:] + tf_inp_ext[:,1:-1,:-2,:] + tf_inp_ext[:,1:-1, 2:, :] 
+        s_corn =  tf_inp_ext[:, :-2,:-2,:] + tf_inp_ext[:, :-2, 2:,:] + tf_inp_ext[:,2:,  :-2,:] + tf_inp_ext[:,2:  , 2:, :]
+        w_diff =  tf.subtract(tf_inp, s_ortho * weight_ortho + s_corn * weight_diag, name="w_diff") 
+        w_diff2 = tf.multiply(w_diff, w_diff,                                        name="w_diff2") 
+        w_var =   tf.reduce_mean(w_diff2,                                            name="w_var")
+        w2_mean = tf.reduce_mean(inp_weights * inp_weights,                          name="w2_mean")
+        w_rel =   tf.divide(w_var, w2_mean,                                          name= "w_rel")
+        return w_rel # scalar, cost for weights non-smoothness in 2d
+
 #In GPU - reformat inputs
 
 ##corr2d325 = tf.concat([next_element_tt['corr2d'], next_element_tt['target_disparity']],1)
@@ -781,6 +858,9 @@ def batchLoss(out_batch,                   # [batch_size,(1..2)] tf_result
 #Should have shape (?,9,325)
 corr2d9x325 = tf.concat([tf.reshape(next_element_tt['corr2d'],[-1,cluster_size,FEATURES_PER_TILE]) , tf.reshape(next_element_tt['target_disparity'], [-1,cluster_size, 1])],2)
 
+corr2d_Nx325 = tf.concat([tf.reshape(next_element_tt['corr2d'],[-1,cluster_size,FEATURES_PER_TILE], name="coor2d_cluster"),
+                          tf.reshape(next_element_tt['target_disparity'], [-1,cluster_size, 1], name="targdisp_cluster")], axis=2, name = "corr2d_Nx325")
+
 #corr2d9x324 = tf.reshape( next_element_tt['corr2d'],          [-1, cluster_size, FEATURES_PER_TILE], name = 'corr2d9x324')
 #td9x1 =       tf.reshape(next_element_tt['target_disparity'], [-1, cluster_size, 1],    name = 'td9x1')
 #corr2d9x325 = tf.concat([corr2d9x324 , td9x1],2, name = 'corr2d9x325')
@@ -789,9 +869,10 @@ corr2d9x325 = tf.concat([tf.reshape(next_element_tt['corr2d'],[-1,cluster_size,F
 #    in_features = tf.concat([corr2d,target_disparity],0)
 
 #out =       network_fc_simple(input=corr2d325, arch = NET_ARCH1)
-out =       network_siam(input=corr2d9x325,
+out, inp_weights =       network_siam(input=corr2d_Nx325,
                                       layout1 =   NN_LAYOUT1, 
                                       layout2 =   NN_LAYOUT2,
+                                      sym8 =      SYM8_SUB,
                                       only_tile = ONLY_TILE) #Remove/put None for normal operation
 #            w_slice = tf.reshape(gt_ds_batch[:,1],[-1],                     name = "w_gt_slice")
 
@@ -809,11 +890,18 @@ G_loss, _disp_slice, _d_gt_slice, _out_diff, _out_diff2, _w_norm, _out_wdiff2, _
               conf_pwr =               2.0,
               gt_conf_offset =         0.08,
               gt_conf_pwr =            2.0,
-              error2_offset =          0.0025, # (0.05^2)
+              error2_offset =          0, # 0.0025, # (0.05^2)
               disp_wmin =              1.0,    # minimal disparity to apply weight boosting for small disparities
               disp_wmax =              8.0,    # maximal disparity to apply weight boosting for small disparities
               use_out =                False)  # use calculated disparity for disparity weight boosting (False - use target disparity)
 
+if WLOSS_LAMBDA > 0.0:   
+    W_loss =     weightsLoss(inp_weights[0]) #    inp_weights - list of tensors, currently - just [0]
+    GW_loss =    tf.add(G_loss, WLOSS_LAMBDA * W_loss, name = "GW_loss")
+else:
+    GW_loss = G_loss
+    W_loss =  tf.constant(0.0)
+
 #debug
 GT_variance =  debug_gt_variance(indx = 0,        # This tile index (0..8)
                                  center_indx = 4, # center tile index
@@ -834,7 +922,9 @@ with tf.name_scope('epoch_average'):
 
 t_vars=tf.trainable_variables()
 lr=tf.placeholder(tf.float32)
-G_opt=tf.train.AdamOptimizer(learning_rate=lr).minimize(G_loss)
+
+#G_opt=tf.train.AdamOptimizer(learning_rate=lr).minimize(G_loss)
+G_opt=tf.train.AdamOptimizer(learning_rate=lr).minimize(GW_loss)
 
 saver=tf.train.Saver()
 
@@ -855,18 +945,18 @@ with tf.Session()  as sess:
 
     merged = tf.summary.merge_all()
     
-    vis_placeholder = tf.placeholder(tf.float32, [1,32,325,3])
-    some_image2 = tf.summary.image('custom_test', vis_placeholder)
+    # display weights, part 1 begin
+    import numpy_visualize_weights as npw
     
     l1 = NN_LAYOUT1.index(next(filter(lambda x: x!=0, NN_LAYOUT1)))
     l2 = NN_LAYOUT2.index(next(filter(lambda x: x!=0, NN_LAYOUT2)))
-    with tf.variable_scope('g_fc_sub'+str(l1),reuse=tf.AUTO_REUSE):
-        w = tf.get_variable('weights',shape=[325,32])
-        wd = w[...,tf.newaxis]
-        wds = tf.stack([wd]*3,axis=0)
-        #print(wd.shape)
-        #some_image = tf.summary.image("tfsi_test",wds.eval(),max_outputs=1)
-        some_image = tf.summary.image("tfsi_test",wds,max_outputs=1)
+    
+    wimg1_placeholder = tf.placeholder(tf.float32, [1,160,80,3])
+    wimg1 = tf.summary.image('weights/sub_'+str(l1), wimg1_placeholder)
+    
+    wimg2_placeholder = tf.placeholder(tf.float32, [1,120,60,3])
+    wimg2 = tf.summary.image('weights/inter_'+str(l2), wimg2_placeholder)
+    # display weights, part 1 end
         
     train_writer =    tf.summary.FileWriter(TRAIN_PATH, sess.graph)
     test_writer  =    tf.summary.FileWriter(TEST_PATH, sess.graph)
@@ -899,8 +989,8 @@ with tf.Session()  as sess:
         for i in range(dataset_train_size):
             try:
 #                train_summary,_, G_loss_trained,  output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, corr2d325_out  = sess.run(
-                _, train_summary,_, G_loss_trained,  output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, gt_variance  = sess.run(
-                    [   test_op, merged,
+                train_summary,_, G_loss_trained,  output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, gt_variance  = sess.run(
+                    [   merged,
                         G_opt,
                         G_loss,
                         out,
@@ -970,23 +1060,27 @@ with tf.Session()  as sess:
 #        _,_=sess.run([tf_ph_G_loss,tf_ph_sq_diff],feed_dict={tf_ph_G_loss:test_avg, tf_ph_sq_diff:test2_avg})
             
         #train_writer.add_summary(some_image.eval(), epoch)
+        
+        # display weights, part 2 begin
         l1 = NN_LAYOUT1.index(next(filter(lambda x: x!=0, NN_LAYOUT1)))
         l2 = NN_LAYOUT2.index(next(filter(lambda x: x!=0, NN_LAYOUT2)))
-        with tf.variable_scope('g_fc_sub'+str(l1),reuse=tf.AUTO_REUSE):
-            w = tf.get_variable('weights',shape=[325,32])
-            wd = w[tf.newaxis,...]
-            wds = tf.stack([wd]*3,axis=-1)
         
-            timg_min = tf.reduce_min(w).eval()
-            timg_max = tf.reduce_max(w).eval()
+        with tf.variable_scope('g_fc_sub'+str(l1),reuse=tf.AUTO_REUSE):
+            w = tf.get_variable('weights',shape=[325,NN_LAYOUT1[l1]])
+            w = tf.transpose(w,(1,0))            
+            img1 = npw.tiles(npw.coldmap(w.eval(),zero_span=0.0002),(1,4,9,9),tiles_per_line=2,borders=True)
+            img1 = img1[np.newaxis,...]
             
-            timg = wds.eval()
+        train_writer.add_summary(wimg1.eval(feed_dict={wimg1_placeholder: img1}), epoch)
 
-            timg[:,:,:,0] = timg_min
-            timg[:,:,:,1] = timg_min
-            timg = np.transpose(timg,(0,2,1,3))
+        with tf.variable_scope('g_fc_inter'+str(l2),reuse=tf.AUTO_REUSE):
+            w = tf.get_variable('weights',shape=[144,NN_LAYOUT1[l2]])
+            w = tf.transpose(w,(1,0))            
+            img2 = npw.tiles(npw.coldmap(w.eval(),zero_span=0.0002),(3,3,4,4),tiles_per_line=4,borders=True)
+            img2 = img2[np.newaxis,...]
             
-        train_writer.add_summary(some_image2.eval(feed_dict={vis_placeholder: timg}), epoch)
+        train_writer.add_summary(wimg2.eval(feed_dict={wimg2_placeholder: img2}), epoch)
+        # display weights, part 2 end
 
         train_writer.add_summary(train_summary, epoch)
         test_writer.add_summary(test_summaries[0], epoch)

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()
\ No newline at end of file