more lwir-related updates

explore_data9.py

@@ -1693,7 +1693,7 @@ if __name__ == "__main__":
     VARIANCE_SCALE_DISPARITY = 5.0 #Scale variance if average is above this
     NUM_TRAIN_SETS =           32 # 8
     
-    FGBGMODE_TEST =             3 # 0 - average, 1 - FG, 2 - BG, 3 - AUX
+    FGBGMODE_TESTS =            [1,3] # 0 - average, 1 - FG, 2 - BG, 3 - AUX
     FGBGMODE_TRAIN =            1 # 0 - average, 1 - FG, 2 - BG
     RND_AMPLIUDE_TEST =         0.5 # present corr2d rendered +/- this far from the GT
     RND_AMPLIUDE_TRAIN_TILE =   0.5 # train with corr2d rendered +/- this far from the GT - independent for each tile component
@@ -1725,14 +1725,14 @@ if __name__ == "__main__":
     ''' Prepare full image for testing '''
     
     for model_ml_path in test_sets:
+        for fgbgmode_test in FGBGMODE_TESTS:
             writeTFRecordsFromImageSet(
                 model_ml_path,     # model/version/ml_dir
-            FGBGMODE_TEST,     # 0, # expot_mode,    # 0 - GT average, 1 - GT FG, 2 - GT BG, 3 - AUX disparity
+                fgbgmode_test,     # 0, # expot_mode,    # 0 - GT average, 1 - GT FG, 2 - GT BG, 3 - AUX disparity
                 RND_AMPLIUDE_TEST, # random_offset, # for modes 0..2 - add random offset of -random_offset to +random_offset, in mode 3 add random to  GT average if no AUX data
                 pathTFR)           # TFR directory
     
     
-    
 #        disp_bins = 20,
 #      str_bins=10)
 

nn_ds_neibs30.py

@@ -49,6 +49,7 @@ TILE_SIDE, TILE_LAYERS, TWO_TRAINS, NET_ARCH1, NET_ARCH2 = [None]*5
 ABSOLUTE_DISPARITY,SYM8_SUB, WLOSS_LAMBDA,  SLOSS_LAMBDA, SLOSS_CLIP  = [None]*5
 SPREAD_CONVERGENCE, INTER_CONVERGENCE, HOR_FLIP, DISP_DIFF_CAP, DISP_DIFF_SLOPE  = [None]*5
 CLUSTER_RADIUS = None
+FGBG_MODE = 1 # 0 - do not filter by single-plane, 1 - remove split plabnes tiles, 2 - remove split planes and neighbors
 PARTIALS_WEIGHTS, MAX_IMGS_IN_MEM, MAX_FILES_PER_GROUP,  BATCH_WEIGHTS, ONLY_TILE = [None] * 5  
 USE_CONFIDENCE, WBORDERS_ZERO, EPOCHS_TO_RUN, FILE_UPDATE_EPOCHS = [None] * 4
 LR600,LR400,LR200,LR100,LR = [None]*5

nn_ds_neibs31.py

@@ -47,8 +47,10 @@ Defined in config file
 """
 TILE_SIDE, TILE_LAYERS, TWO_TRAINS, NET_ARCH1, NET_ARCH2 = [None]*5
 ABSOLUTE_DISPARITY,SYM8_SUB, WLOSS_LAMBDA,  SLOSS_LAMBDA, SLOSS_CLIP  = [None]*5
+CORR2D_LIMITS = [None, None]
 SPREAD_CONVERGENCE, INTER_CONVERGENCE, HOR_FLIP, DISP_DIFF_CAP, DISP_DIFF_SLOPE  = [None]*5
 CLUSTER_RADIUS = None
+FGBG_MODE = 1 # 0 - do not filter by single-plane, 1 - remove split plabnes tiles, 2 - remove split planes and neighbors
 PARTIALS_WEIGHTS, MAX_IMGS_IN_MEM, MAX_FILES_PER_GROUP,  BATCH_WEIGHTS, ONLY_TILE = [None] * 5  
 USE_CONFIDENCE, WBORDERS_ZERO, EPOCHS_TO_RUN, FILE_UPDATE_EPOCHS = [None] * 4
 LR600,LR400,LR200,LR100,LR = [None]*5
@@ -64,7 +66,7 @@ globals().update(parameters)
 
 
 TRAIN_BUFFER_SIZE = TRAIN_BUFFER_GPU * TRAIN_BUFFER_CPU # in merged (quad) batches
-
+qsf.setCorr2Limits(CORR2D_LIMITS) # limit min/max 2d correlation tiles values
 
 
 #exit(0)
@@ -91,9 +93,10 @@ NN_LAYOUT2 = qcstereo_network.NN_LAYOUTS[NET_ARCH2]
 USE_PARTIALS =      not PARTIALS_WEIGHTS is None # False - just a single Siamese net, True - partial outputs that use concentric squares of the first level subnets
 
 # Tiff export slice labels
-SLICE_LABELS =  ["nn_out_ext","hier_out_ext","gt_disparity","gt_strength",
+SLICE_LABELS =  ["nn_out_ext","target_disp","gt_disparity","gt_strength",
                  "cutcorn_cost_nw","cutcorn_cost",
-                 "gt-avg_dist","avg8_disp","gt_disp","out-avg"]
+                 "gt_avg_dist","avg8_disp","gt_disp","out_avg",
+                 "aux_disp","fg_disp","bg_disp","gt_rms","gt_rms_split"]
 
 ##############################################################################
 cluster_size = (2 * CLUSTER_RADIUS + 1) * (2 * CLUSTER_RADIUS + 1)
@@ -126,6 +129,7 @@ if not USE_PARTIALS:
 qsf.evaluateAllResults(result_files = files['result'],
                        absolute_disparity = ABSOLUTE_DISPARITY,
                        cluster_radius =     CLUSTER_RADIUS,
+                       fgbg_mode=           FGBG_MODE,
                        labels =             SLICE_LABELS,
                        logpath=             LOGPATH)
 
@@ -390,7 +394,7 @@ with tf.Session()  as sess:
     
     sess.run(tf.global_variables_initializer())
     sess.run(tf.local_variables_initializer())
-    merged = tf.summary.merge_all()
+    merged = tf.compat.v1.summary.merge_all()
     tt_writers = []
     for p in TT_PATHS:
         tt_writers.append(tf.summary.FileWriter(p, sess.graph))
@@ -614,9 +618,9 @@ with tf.Session()  as sess:
                     files =          files,
                     indx =           ntest,
                     cluster_radius = CLUSTER_RADIUS,
-                    tile_layers =    TILE_LAYERS,
-                    tile_side =      TILE_SIDE,
-                    width =          IMG_WIDTH,
+                    tile_layers =    TILE_LAYERS, # 4
+                    tile_side =      TILE_SIDE,   # 9
+                    width =          IMG_WIDTH,   #160
                     replace_nans =   True)
 
                 sess.run(iterator_tt.initializer, feed_dict={corr2d_train_placeholder:      dataset_img['corr2d'],
@@ -659,24 +663,41 @@ with tf.Session()  as sess:
                     os.makedirs(os.path.dirname(result_file))
                 except:
                     pass     
-
+                extra = dataset_img['t_extra']
+                if extra is None:
+                    extra = np.array([dataset_img['gtruths'].shape[0],0])
                 rslt = np.concatenate(
                     [disp_out.reshape(-1,1),
-                     dataset_img['t_disps'], #t_disps[ntest],
+                     dataset_img['t_disps'], #t_disps[ntest], disp_out.shape[0],BATCH_SIZE
                      dataset_img['gtruths'], # gtruths[ntest],
                      dbg_cost_nw.reshape(-1,1),
                      dbg_cost_w.reshape(-1,1),
                      dbg_d.reshape(-1,1),
                      dbg_avg_disparity.reshape(-1,1),
                      dbg_gt_disparity.reshape(-1,1),
-                     dbg_offs.reshape(-1,1)],1)
-
-                np.save(result_file,           rslt.reshape(HEIGHT,WIDTH,-1))
-                rslt = qsf.eval_results(result_file, ABSOLUTE_DISPARITY, radius=CLUSTER_RADIUS, logfile=lf)                
-                img_gain_test0 = rslt[0][0]/rslt[0][1]   
-                img_gain_test9 = rslt[9][0]/rslt[9][1]   
+                     dbg_offs.reshape(-1,1),
+                     extra, # len 3..6,
+#adding    extra data layers                  
+                     ],1)
+                num_slices = rslt.shape[1]
+                np.save(
+                    result_file,
+                    rslt.reshape(HEIGHT,WIDTH,-1))
+                eval_rslt = qsf.eval_results(
+                    result_file,
+                    ABSOLUTE_DISPARITY,
+                    radius=0, # CLUSTER_RADIUS,
+                    last_fgbg_mode = 1,
+                    logfile=lf)                
+                img_gain_test0 = eval_rslt[0][0]/eval_rslt[0][1]   
+                img_gain_test9 = eval_rslt[9][0]/eval_rslt[9][1]   
                 if SAVE_TIFFS:
-                    qsf.result_npy_to_tiff(result_file, ABSOLUTE_DISPARITY, fix_nan = True,labels=SLICE_LABELS, logfile=lf)
+                    qsf.result_npy_to_tiff(
+                        result_file,
+                        ABSOLUTE_DISPARITY,
+                        fix_nan = True,
+                        labels=SLICE_LABELS[0:num_slices],
+                        logfile=lf)
                     
                 """
                 Remove dataset_img (if it is not [0] to reduce memory footprint