Testing with full image data

    gt_ds =            np.array(gt_ds_list)

    gt_ds =            np.array(gt_ds_list)

    return corr2d, target_disparity, gt_ds   

    return corr2d, target_disparity, gt_ds   

def writeTFRewcordsImageTiles(img_path, tfr_filename): # test_set=False):

#        train_filename = 'train.tfrecords'  # address to save the TFRecords file

      # open the TFRecords file

      num_tiles = 242*324 # fixme

      all_image_tiles = np.array(range(num_tiles))

      corr_layers =  ['hor-pairs', 'vert-pairs','diagm-pair', 'diago-pair']

      img =          ijt.imagej_tiff(test_corr, corr_layers, all_image_tiles)

      corr2d =           img.corr2d.reshape((num_tiles,-1))

      target_disparity = img.target_disparity.reshape((num_tiles,-1))

      gt_ds =            img.gt_ds.reshape((num_tiles,-1))

      if not  '.tfrecords' in tfr_filename:

          tfr_filename += '.tfrecords'

      tfr_filename=tfr_filename.replace(' ','_')

      try:

          os.makedirs(os.path.dirname(tfr_filename))

      except:

          pass     

      writer = tf.python_io.TFRecordWriter(tfr_filename)

      dtype_feature_corr2d =   _dtype_feature(corr2d)

      dtype_target_disparity = _dtype_feature(target_disparity)

      dtype_feature_gt_ds =    _dtype_feature(gt_ds)

      for i in range(num_tiles):

          x = corr2d[i].astype(np.float32)

          y = target_disparity[i].astype(np.float32)

          z = gt_ds[i].astype(np.float32)

          d_feature = {'corr2d':          dtype_feature_corr2d(x),

                       'target_disparity':dtype_target_disparity(y),

                       'gt_ds':           dtype_feature_gt_ds(z)}

          example = tf.train.Example(features=tf.train.Features(feature=d_feature))

          writer.write(example.SerializeToString())

          pass

      writer.close()

      sys.stdout.flush()        

class ExploreData:

class ExploreData:

    PATTERN = "*-DSI_COMBO.tiff"

    PATTERN = "*-DSI_COMBO.tiff"

                strength =  np.nan_to_num(strength, copy = False)  # likely should never happen

                strength =  np.nan_to_num(strength, copy = False)  # likely should never happen

                np.clip(disparity, disparity_min_clip, disparity_max_clip, out = disparity)

                np.clip(disparity, disparity_min_clip, disparity_max_clip, out = disparity)

                np.clip(strength, strength_min_clip, strength_max_clip, out = strength)

                np.clip(strength, strength_min_clip, strength_max_clip, out = strength)

#                if no_histogram:

#                    strength *= good_tiles[ids]

#            if no_histogram:

#                return None # no histogram, just condition data

            good_tiles_list.append(good_tiles)

            good_tiles_list.append(good_tiles)

        combo_rds = np.concatenate(list_rds)

        combo_rds = np.concatenate(list_rds)

        hist, xedges, yedges = np.histogram2d( # xedges, yedges - just for debugging

        hist, xedges, yedges = np.histogram2d( # xedges, yedges - just for debugging

            weights= np.concatenate(good_tiles_list).flatten())

            weights= np.concatenate(good_tiles_list).flatten())

        for i, combo_rds in enumerate(list_rds):

        for i, combo_rds in enumerate(list_rds):

            for ids in range (combo_rds.shape[0]): #iterate over all scenes ds[2][rows][cols]

            for ids in range (combo_rds.shape[0]): #iterate over all scenes ds[2][rows][cols]

#                strength =  combo_rds[ids][...,1]

#                strength *= good_tiles_list[i][ids]

                combo_rds[ids][...,1]*= good_tiles_list[i][ids]

                combo_rds[ids][...,1]*= good_tiles_list[i][ids]

        return hist, xedges, yedges

        return hist, xedges, yedges

        writer.close()

        writer.close()

        sys.stdout.flush()        

        sys.stdout.flush()        

    def showVariance(self,

    def showVariance(self,

            rds_list,           # list of disparity/strength files, suchas training, testing 

            rds_list,           # list of disparity/strength files, suchas training, testing 

            disp_var_list,      # list of disparity variance files. Same shape(but last dim) as rds_list

            disp_var_list,      # list of disparity variance files. Same shape(but last dim) as rds_list

  except IndexError:

  except IndexError:

      ml_subdir =   "ml"

      ml_subdir =   "ml"

#  pathTFR = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/tf_data_3x3b" #no trailing "/"

  test_corr = '/home/eyesis/x3d_data/models/var_main/www/html/x3domlet/models/all-clean/overlook/1527257933_150165/v04/mlr32_18a/1527257933_150165-ML_DATA-32B-O-FZ0.05-MAIN.tiff'

  scene = os.path.basename(test_corr)[:17]

  scene_version= os.path.basename(os.path.dirname(os.path.dirname(test_corr)))

  fname =scene+'-'+scene_version 

  img_filenameTFR = os.path.join(pathTFR,'img',fname)        

  writeTFRewcordsImageTiles(test_corr, img_filenameTFR)

  pass

  exit(0)

  #Parameters to generate neighbors data. Set radius to 0 to generate single-tile     

  #Parameters to generate neighbors data. Set radius to 0 to generate single-tile     

  RADIUS = 1

  RADIUS = 1

  MIN_NEIBS = (2 * RADIUS + 1) * (2 * RADIUS + 1) # All tiles valid == 9

  MIN_NEIBS = (2 * RADIUS + 1) * (2 * RADIUS + 1) # All tiles valid == 9