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Commit d786da7b authored by Andrey Filippov's avatar Andrey Filippov
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      explore_data5m.py 0 → 100644
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      #!/usr/bin/env python3
      #from numpy import float64
      __copyright__ = "Copyright 2018, Elphel, Inc."
      __license__ = "GPL-3.0+"
      __email__ = "andrey@elphel.com"
      import os
      import sys
      import glob
      import imagej_tiff as ijt
      import numpy as np
      import resource
      #import timeit
      import matplotlib.pyplot as plt
      from scipy.ndimage.filters import gaussian_filter
      import time
      import tensorflow as tf
      #http://stackoverflow.com/questions/287871/print-in-terminal-with-colors-using-python
      class bcolors:
      HEADER = '\033[95m'
      OKBLUE = '\033[94m'
      OKGREEN = '\033[92m'
      WARNING = '\033[38;5;214m'
      FAIL = '\033[91m'
      ENDC = '\033[0m'
      BOLD = '\033[1m'
      BOLDWHITE = '\033[1;37m'
      UNDERLINE = '\033[4m'
      TIME_START = time.time()
      TIME_LAST = TIME_START
      def print_time(txt="",end="\n"):
      global TIME_LAST
      t = time.time()
      if txt:
      txt +=" "
      print(("%s"+bcolors.BOLDWHITE+"at %.4fs (+%.4fs)"+bcolors.ENDC)%(txt,t-TIME_START,t-TIME_LAST), end = end)
      TIME_LAST = t
      def _dtype_feature(ndarray):
      """match appropriate tf.train.Feature class with dtype of ndarray. """
      assert isinstance(ndarray, np.ndarray)
      dtype_ = ndarray.dtype
      if dtype_ == np.float64 or dtype_ == np.float32:
      return lambda array: tf.train.Feature(float_list=tf.train.FloatList(value=array))
      elif dtype_ == np.int64:
      return lambda array: tf.train.Feature(int64_list=tf.train.Int64List(value=array))
      else:
      raise ValueError("The input should be numpy ndarray. \
      Instead got {}".format(ndarray.dtype))
      def readTFRewcordsEpoch(train_filename):
      # filenames = [train_filename]
      # dataset = tf.data.TFRecordDataset(filenames)
      if not '.tfrecords' in train_filename:
      train_filename += '.tfrecords'
      record_iterator = tf.python_io.tf_record_iterator(path=train_filename)
      corr2d_list=[]
      target_disparity_list=[]
      gt_ds_list = []
      for string_record in record_iterator:
      example = tf.train.Example()
      example.ParseFromString(string_record)
      corr2d_list.append(np.array(example.features.feature['corr2d'] .float_list .value))
      target_disparity_list.append(np.array(example.features.feature['target_disparity'] .float_list .value[0]))
      gt_ds_list.append(np.array(example.features.feature['gt_ds'] .float_list .value))
      corr2d= np.array(corr2d_list)
      target_disparity = np.array(target_disparity_list)
      gt_ds = np.array(gt_ds_list)
      return corr2d, target_disparity, gt_ds
      def writeTFRewcordsImageTiles(img_path, tfr_filename): # test_set=False):
      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(img_path, corr_layers, all_image_tiles)
      """
      Values read from correlation file, it now may differ from the COMBO-DSI:
      1) The target disparities used for correlations are replaced if they are too far from the rig (GT) values and
      replaced by interpolation from available neighbors. If there are no suitable neighbors, target disparity is
      derived from the rig data by adding a random offset (specified in ImageJ plugin configuration ML section)
      2) correlation is performed around the defined tiles extrapolating disparity. rig data may be 0 disparity,
      0 strength if there is no rig data for those tiles. That means that such tiles can only be used as peripherals
      i (now 5x5) clusters, not for the cluster centers where GT is needed.
      """
      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))
      """
      Replace GT data with zero strength with nan, zero strength
      nan2 = np.array((np.nan,0), dtype=np.float32)
      gt_ds[np.where(gt_ds[:,1]==0)] = nan2
      """
      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:
      """
      TODO: add to constructor parameters
      """
      PATTERN = "*-DSI_COMBO.tiff"
      # ML_DIR = "ml"
      # ML_PATTERN = "*-ML_DATA*OFFS*.tiff"
      # ML_PATTERN = "*-ML_DATA*MAIN*.tiff"
      # ML_PATTERN = "*-ML_DATA*MAIN.tiff"
      # ML_PATTERN = "*-ML_DATA*MAIN_RND*.tiff"
      ## ML_PATTERN = "*-ML_DATA*RIG_RND*.tiff"
      # ML_PATTERN = "*-ML_DATA*OFFS-0.20000_0.20000.tiff"
      """
      1527182801_296892-ML_DATARND-32B-O-FZ0.05-OFFS-0.20000_0.20000.tiff
      1527182805_696892-ML_DATA-32B-O-FZ0.05-RIG_RND2.00000.tiff
      """
      def getComboList(self, top_dir, latest_version_only):
      if not top_dir:
      return []
      # patt = "*-DSI_COMBO.tiff"
      tlist = []
      for i in range(5):
      pp = top_dir#) ,'**', patt) # works
      for _ in range (i):
      pp = os.path.join(pp,'*')
      pp = os.path.join(pp, ExploreData.PATTERN)
      tlist += glob.glob(pp)
      if (self.debug_level > 0):
      print (pp+" "+str(len(tlist)))
      if (self.debug_level > 0):
      print("Found "+str(len(tlist))+" combo DSI files in "+top_dir+" :")
      if (self.debug_level > 1):
      print("\n".join(tlist))
      if latest_version_only:
      models = {}
      for p in tlist:
      model = os.path.dirname(os.path.dirname(p))
      if (not model in models) or ( models[model]< p):
      models[model] = p
      tlist = [v for v in models.values()]
      if (self.debug_level > 0):
      print("After filtering the latest versions only, left "+str(len(tlist))+" combo DSI files in "+top_dir+" :")
      if (self.debug_level > 1):
      print("\n".join(tlist))
      tlist.sort()
      return tlist
      def loadComboFiles(self, tlist):
      indx = 0
      images = []
      if (self.debug_level>2):
      print(str(resource.getrusage(resource.RUSAGE_SELF)))
      layers = ['disparity_rig','strength_rig','disparity_main']
      for combo_file in tlist:
      tiff = ijt.imagej_tiff(combo_file,layers)
      if not indx:
      images = np.empty((len(tlist), tiff.image.shape[0],tiff.image.shape[1],tiff.image.shape[2]), tiff.image.dtype)
      images[indx] = tiff.image
      if (self.debug_level>2):
      print(str(indx)+": "+str(resource.getrusage(resource.RUSAGE_SELF)))
      indx += 1
      return images
      def getHistogramDSI(
      self,
      list_rds,
      disparity_bins = 1000,
      strength_bins = 100,
      disparity_min_drop = -0.1,
      disparity_min_clip = -0.1,
      disparity_max_drop = 100.0,
      disparity_max_clip = 100.0,
      strength_min_drop = 0.1,
      strength_min_clip = 0.1,
      strength_max_drop = 1.0,
      strength_max_clip = 0.9,
      max_main_offset = 0.0,
      normalize = True,
      # no_histogram = False
      ):
      good_tiles_list=[]
      for combo_rds in list_rds:
      good_tiles = np.empty((combo_rds.shape[0], combo_rds.shape[1],combo_rds.shape[2]), dtype=bool)
      for ids in range (combo_rds.shape[0]): #iterate over all scenes ds[2][rows][cols]
      ds = combo_rds[ids]
      disparity = ds[...,0]
      strength = ds[...,1]
      good_tiles[ids] = disparity >= disparity_min_drop
      good_tiles[ids] &= disparity <= disparity_max_drop
      good_tiles[ids] &= strength >= strength_min_drop
      good_tiles[ids] &= strength <= strength_max_drop
      if max_main_offset > 0.0:
      disparity_main = ds[...,2]
      good_tiles[ids] &= disparity_main <= (disparity + max_main_offset)
      good_tiles[ids] &= disparity_main >= (disparity - max_main_offset)
      disparity = np.nan_to_num(disparity, copy = False) # to be able to multiply by 0.0 in mask | copy=False, then out=disparity all done in-place
      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(strength, strength_min_clip, strength_max_clip, out = strength)
      good_tiles_list.append(good_tiles)
      combo_rds = np.concatenate(list_rds)
      hist, xedges, yedges = np.histogram2d( # xedges, yedges - just for debugging
      x = combo_rds[...,1].flatten(),
      y = combo_rds[...,0].flatten(),
      bins= (strength_bins, disparity_bins),
      range= ((strength_min_clip,strength_max_clip),(disparity_min_clip,disparity_max_clip)),
      normed= normalize,
      weights= np.concatenate(good_tiles_list).flatten())
      for i, combo_rds in enumerate(list_rds):
      for ids in range (combo_rds.shape[0]): #iterate over all scenes ds[2][rows][cols]
      combo_rds[ids][...,1]*= good_tiles_list[i][ids]
      return hist, xedges, yedges
      def __init__(self,
      topdir_train,
      topdir_test,
      ml_pattern,
      latest_version_only,
      max_main_offset = 2.0, # > 0.0 - do not use main camera tiles with offset more than this
      debug_level = 0,
      disparity_bins = 1000,
      strength_bins = 100,
      disparity_min_drop = -0.1,
      disparity_min_clip = -0.1,
      disparity_max_drop = 100.0,
      disparity_max_clip = 100.0,
      strength_min_drop = 0.1,
      strength_min_clip = 0.1,
      strength_max_drop = 1.0,
      strength_max_clip = 0.9,
      hist_sigma = 2.0, # Blur log histogram
      hist_cutoff= 0.001 # of maximal
      ):
      # file name
      self.debug_level = debug_level
      self.ml_pattern = ml_pattern
      #self.testImageTiles()
      self.max_main_offset = max_main_offset
      self.disparity_bins = disparity_bins
      self.strength_bins = strength_bins
      self.disparity_min_drop = disparity_min_drop
      self.disparity_min_clip = disparity_min_clip
      self.disparity_max_drop = disparity_max_drop
      self.disparity_max_clip = disparity_max_clip
      self.strength_min_drop = strength_min_drop
      self.strength_min_clip = strength_min_clip
      self.strength_max_drop = strength_max_drop
      self.strength_max_clip = strength_max_clip
      self.hist_sigma = hist_sigma # Blur log histogram
      self.hist_cutoff= hist_cutoff # of maximal
      self.pre_log_offs = 0.001 # of histogram maximum
      self.good_tiles = None
      self.files_train = self.getComboList(topdir_train, latest_version_only)
      self.files_test = self.getComboList(topdir_test, latest_version_only)
      self.train_ds = self.loadComboFiles(self.files_train)
      self.test_ds = self.loadComboFiles(self.files_test)
      self.num_tiles = self.train_ds.shape[1]*self.train_ds.shape[2]
      """
      Traceback (most recent call last):
      File "explore_data5.py", line 1036, in <module>
      hist_cutoff= 0.001) # of maximal
      File "explore_data5.py", line 286, in __init__
      self.num_tiles = self.train_ds.shape[1]*self.train_ds.shape[2]
      AttributeError: 'list' object has no attribute 'shape'
      """
      ## self.hist, xedges, yedges = self.getHistogramDSI(
      self.hist, _, _ = self.getHistogramDSI(
      list_rds = [self.train_ds,self.test_ds], # combo_rds,
      disparity_bins = self.disparity_bins,
      strength_bins = self.strength_bins,
      disparity_min_drop = self.disparity_min_drop,
      disparity_min_clip = self.disparity_min_clip,
      disparity_max_drop = self.disparity_max_drop,
      disparity_max_clip = self.disparity_max_clip,
      strength_min_drop = self.strength_min_drop,
      strength_min_clip = self.strength_min_clip,
      strength_max_drop = self.strength_max_drop,
      strength_max_clip = self.strength_max_clip,
      max_main_offset = self.max_main_offset,
      normalize = True
      # no_histogram = False
      )
      log_offset = self.pre_log_offs * self.hist.max()
      h_cutoff = hist_cutoff * self.hist.max()
      lhist = np.log(self.hist + log_offset)
      blurred_lhist = gaussian_filter(lhist, sigma = self.hist_sigma)
      self.blurred_hist = np.exp(blurred_lhist) - log_offset
      self.good_tiles = self.blurred_hist >= h_cutoff
      self.blurred_hist *= self.good_tiles # set bad ones to zero
      def exploreNeibs(self,
      data_ds, # disparity/strength data for all files (train or test)
      radius, # how far to look from center each side ( 1- 3x3, 2 - 5x5)
      disp_thesh = 5.0): # reduce effective variance for higher disparities
      """
      For each tile calculate difference between max and min among neighbors and number of qualifying neighbors (bad center is not removed)
      data_ds may maismatch with the correlation files - correlation filas have data in extrapolated areas and replaced for large difference with GT
      """
      disp_min = np.empty_like(data_ds[...,0], dtype = np.float)
      disp_max = np.empty_like(disp_min, dtype = np.float)
      tile_neibs = np.zeros_like(disp_min, dtype = np.int)
      dmin = data_ds[...,0].min()
      dmax = data_ds[...,0].max()
      good_tiles = self.getBB(data_ds) >= 0
      side = 2 * radius + 1
      for nf, ds in enumerate(data_ds):
      disp = ds[...,0]
      height = disp.shape[0]
      width = disp.shape[1]
      bad_max = np.ones((height+side, width+side), dtype=float) * dmax
      bad_min = np.ones((height+side, width+side), dtype=float) * dmin
      good = np.zeros((height+side, width+side), dtype=int)
      #Assign centers of the array, replace bad tiles with max/min (so they will not change min/max)
      bad_max[radius:height+radius,radius:width+radius] = np.select([good_tiles[nf]],[disp],default = dmax)
      bad_min[radius:height+radius,radius:width+radius] = np.select([good_tiles[nf]],[disp],default = dmin)
      good [radius:height+radius,radius:width+radius] = good_tiles[nf]
      disp_min [nf,...] = disp
      disp_max [nf,...] = disp
      tile_neibs[nf,...] = good_tiles[nf]
      for offset_y in range(-radius, radius+1):
      oy = offset_y+radius
      for offset_x in range(-radius, radius+1):
      ox = offset_x+radius
      if offset_y or offset_x: # Skip center - already copied
      np.minimum(disp_min[nf], bad_max[oy:oy+height, ox:ox+width], out=disp_min[nf])
      np.maximum(disp_max[nf], bad_min[oy:oy+height, ox:ox+width], out=disp_max[nf])
      tile_neibs[nf] += good[oy:oy+height, ox:ox+width]
      pass
      pass
      pass
      pass
      #disp_thesh
      disp_avar = disp_max - disp_min
      disp_rvar = disp_avar * disp_thesh / np.maximum(disp_max, 0.001) # removing division by 0 error - those tiles will be anyway discarded
      disp_var = np.select([disp_max >= disp_thesh, disp_max < disp_thesh],[disp_rvar,disp_avar])
      return disp_var, tile_neibs
      def assignBatchBins(self,
      disp_bins,
      str_bins,
      files_per_scene = 5, # not used here, will be used when generating batches
      min_batch_choices=10, # not used here, will be used when generating batches
      max_batch_files = 10): # not used here, will be used when generating batches
      """
      for each disparity/strength combination (self.disparity_bins * self.strength_bins = 1000*100) provide number of "large"
      variable-size disparity/strength bin, or -1 if this disparity/strength combination does not seem right
      """
      self.files_per_scene = files_per_scene
      self.min_batch_choices=min_batch_choices
      self.max_batch_files = max_batch_files
      hist_to_batch = np.zeros((self.blurred_hist.shape[0],self.blurred_hist.shape[1]),dtype=int) #zeros_like?
      ## hist_to_batch_multi = np.ones((self.blurred_hist.shape[0],self.blurred_hist.shape[1]),dtype=int) #zeros_like?
      scale_hist= (disp_bins * str_bins)/self.blurred_hist.sum()
      norm_b_hist = self.blurred_hist * scale_hist
      ## disp_list = [] # last disparity hist
      # disp_multi = [] # number of disp rows to fit
      disp_run_tot = 0.0
      disp_batch = 0
      disp=0
      num_batch_bins = disp_bins * str_bins
      disp_hist = np.linspace(0, num_batch_bins, disp_bins+1)
      batch_index = 0
      num_members = np.zeros((num_batch_bins,),int)
      while disp_batch < disp_bins:
      #disp_multi.append(1)
      # while (disp < self.disparity_bins):
      # disp_target_tot =disp_hist[disp_batch+1]
      disp_run_tot_new = disp_run_tot
      disp0 = disp # start disaprity matching disp_run_tot
      while (disp_run_tot_new < disp_hist[disp_batch+1]) and (disp < self.disparity_bins):
      disp_run_tot_new += norm_b_hist[:,disp].sum()
      disp+=1;
      disp_multi = 1
      while (disp_batch < (disp_bins - 1)) and (disp_run_tot_new >= disp_hist[disp_batch+2]):
      disp_batch += 1 # only if large disp_bins and very high hist value
      disp_multi += 1
      # now disp_run_tot - before this batch disparity col
      str_bins_corr = str_bins * disp_multi # if too narrow disparity column - multiply number of strength columns
      str_bins_corr_last = str_bins_corr -1
      str_hist = np.linspace(disp_run_tot, disp_run_tot_new, str_bins_corr + 1)
      str_run_tot_new = disp_run_tot
      # str_batch = 0
      str_index=0
      # wide_col = norm_b_hist[:,disp0:disp] #disp0 - first column, disp - last+ 1
      #iterate in linescan along the column
      for si in range(self.strength_bins):
      for di in range(disp0, disp,1):
      if norm_b_hist[si,di] > 0.0 :
      str_run_tot_new += norm_b_hist[si,di]
      # do not increment after last to avoid precision issues
      if (batch_index < num_batch_bins) and (num_members[batch_index] > 0) and (str_index < str_bins_corr_last) and (str_run_tot_new > str_hist[str_index+1]):
      batch_index += 1
      str_index += 1
      if batch_index < num_batch_bins :
      hist_to_batch[si,di] = batch_index
      num_members[batch_index] += 1
      else:
      pass
      else:
      hist_to_batch[si,di] = -1
      batch_index += 1 # it was not incremented afterthe last in the column to avoid rounding error
      disp_batch += 1
      disp_run_tot = disp_run_tot_new
      pass
      self.hist_to_batch = hist_to_batch
      return hist_to_batch
      def getBB(self, data_ds):
      """
      for each file, each tile get histogram index (or -1 for bad tiles)
      """
      ## hist_to_batch = self.hist_to_batch
      ## files_batch_list = []
      disp_step = ( self.disparity_max_clip - self.disparity_min_clip )/ self.disparity_bins
      str_step = ( self.strength_max_clip - self.strength_min_clip )/ self.strength_bins
      bb = np.empty_like(data_ds[...,0],dtype=int)
      for findx in range(data_ds.shape[0]):
      ds = data_ds[findx]
      gt = ds[...,1] > 0.0 # OK
      db = (((ds[...,0] - self.disparity_min_clip)/disp_step).astype(int))*gt
      sb = (((ds[...,1] - self.strength_min_clip)/ str_step).astype(int))*gt
      np.clip(db, 0, self.disparity_bins-1, out = db)
      np.clip(sb, 0, self.strength_bins-1, out = sb)
      bb[findx] = (self.hist_to_batch[sb.reshape(self.num_tiles),db.reshape(self.num_tiles)]) .reshape(db.shape[0],db.shape[1]) + (gt -1)
      return bb
      def makeBatchLists(self,
      data_ds = None, # (disparity,strength) per scene, per tile
      disp_var = None, # difference between maximal and minimal disparity for each scene, each tile
      disp_neibs = None, # number of valid tiles around each center tile (for 3x3 (radius = 1) - maximal is 9
      min_var = None, # Minimal tile variance to include
      max_var = None, # Maximal tile variance to include
      ## scale_disp = 5.0,
      min_neibs = None):# Minimal number of valid tiles to include
      if data_ds is None:
      data_ds = self.train_ds
      ## hist_to_batch = self.hist_to_batch
      num_batch_tiles = np.empty((data_ds.shape[0],self.hist_to_batch.max()+1),dtype = int)
      bb = self.getBB(data_ds)
      use_neibs = not ((disp_var is None) or (disp_neibs is None) or (min_var is None) or (max_var is None) or (min_neibs is None))
      list_of_file_lists=[]
      for findx in range(data_ds.shape[0]):
      foffs = findx * self.num_tiles
      lst = []
      for i in range (self.hist_to_batch.max()+1):
      lst.append([])
      # bb1d = bb[findx].reshape(self.num_tiles)
      if use_neibs:
      disp_var_tiles = disp_var[findx].reshape(self.num_tiles)
      disp_neibs_tiles = disp_neibs[findx].reshape(self.num_tiles)
      for n, indx in enumerate(bb[findx].reshape(self.num_tiles)):
      if indx >= 0:
      if use_neibs:
      # disp_var_tiles = disp_var[findx].reshape(self.num_tiles)
      # disp_neibs_tiles = disp_neibs[findx].reshape(self.num_tiles)
      if disp_neibs_tiles[n] < min_neibs:
      continue # too few neighbors
      if not disp_var_tiles[n] >= min_var:
      continue #too small variance
      if not disp_var_tiles[n] < max_var:
      continue #too large variance
      lst[indx].append(foffs + n)
      lst_arr=[]
      for i,l in enumerate(lst):
      # lst_arr.append(np.array(l,dtype = int))
      lst_arr.append(l)
      num_batch_tiles[findx,i] = len(l)
      list_of_file_lists.append(lst_arr)
      self.list_of_file_lists= list_of_file_lists
      self.num_batch_tiles = num_batch_tiles
      return list_of_file_lists, num_batch_tiles
      #todo: only use other files if there are no enough choices in the main file!
      def augmentBatchFileIndices(self,
      seed_index,
      min_choices=None,
      max_files = None,
      set_ds = None
      ):
      if min_choices is None:
      min_choices = self.min_batch_choices
      if max_files is None:
      max_files = self.max_batch_files
      if set_ds is None:
      set_ds = self.train_ds
      full_num_choices = self.num_batch_tiles[seed_index].copy()
      flist = [seed_index]
      all_choices = list(range(self.num_batch_tiles.shape[0]))
      all_choices.remove(seed_index)
      for _ in range (max_files-1):
      if full_num_choices.min() >= min_choices:
      break
      findx = np.random.choice(all_choices)
      flist.append(findx)
      all_choices.remove(findx)
      full_num_choices += self.num_batch_tiles[findx]
      file_tiles_sparse = [[] for _ in set_ds] #list of empty lists for each train scene (will be sparse)
      for nt in range(self.num_batch_tiles.shape[1]): #number of tiles per batch (not counting ml file variant) // radius2 - 40
      tl = []
      nchoices = 0
      for findx in flist:
      if (len(self.list_of_file_lists[findx][nt])):
      tl.append(self.list_of_file_lists[findx][nt])
      nchoices+= self.num_batch_tiles[findx][nt]
      if nchoices >= min_choices: # use minimum of extra files
      break;
      while len(tl)==0:
      print("** BUG! could not find a single candidate from files ",flist," for cell ",nt)
      print("trying to use some other cell")
      nt1 = np.random.randint(0,self.num_batch_tiles.shape[1])
      for findx in flist:
      if (len(self.list_of_file_lists[findx][nt1])):
      tl.append(self.list_of_file_lists[findx][nt1])
      nchoices+= self.num_batch_tiles[findx][nt1]
      if nchoices >= min_choices: # use minimum of extra files
      break;
      tile = np.random.choice(np.concatenate(tl))
      """
      Traceback (most recent call last):
      File "explore_data2.py", line 1041, in <module>
      ex_data.writeTFRewcordsEpoch(fpath, ml_list = ml_list_train, files_list = ex_data.files_train, set_ds= ex_data.train_ds, radius = RADIUS)
      File "explore_data2.py", line 761, in writeTFRewcordsEpoch
      corr2d_batch, target_disparity_batch, gt_ds_batch = ex_data.prepareBatchData(ml_list, seed_index, min_choices=None, max_files = None, ml_num = None, set_ds = set_ds, radius = radius)
      File "explore_data2.py", line 556, in prepareBatchData
      flist,tiles = self.augmentBatchFileIndices(seed_index, min_choices, max_files, set_ds)
      File "explore_data2.py", line 494, in augmentBatchFileIndices
      tile = np.random.choice(np.concatenate(tl))
      ValueError: need at least one array to concatenate
      """
      # print (nt, tile, tile//self.num_tiles, tile % self.num_tiles)
      if not type (tile) is np.int64:
      print("tile=",tile)
      file_tiles_sparse[tile//self.num_tiles].append(tile % self.num_tiles)
      file_tiles = []
      for findx in flist:
      file_tiles.append(np.sort(np.array(file_tiles_sparse[findx],dtype=int)))
      return flist, file_tiles # file indices, list if tile indices for each file
      def getMLList(self, ml_subdir, flist):
      ml_list = []
      for fn in flist:
      # ml_patt = os.path.join(os.path.dirname(fn), ml_subdir, ExploreData.ML_PATTERN)
      ## if isinstance(ml_subdir,list)
      ml_patt = os.path.join(os.path.dirname(fn), ml_subdir, self.ml_pattern)
      ml_list.append(glob.glob(ml_patt))
      ## self.ml_list = ml_list
      return ml_list
      def getBatchData(
      self,
      flist,
      ## tiles,
      ml_list,
      ml_num = None ): # 0 - use all ml files for the scene, >0 select random number
      if ml_num is None:
      ml_num = self.files_per_scene
      ml_all_files = []
      for findx in flist:
      mli = list(range(len(ml_list[findx])))
      if (ml_num > 0) and (ml_num < len(mli)):
      mli_left = mli
      mli = []
      for _ in range(ml_num):
      ml = np.random.choice(mli_left)
      mli.append(ml)
      mli_left.remove(ml)
      ml_files = []
      for ml_index in mli:
      ml_files.append(ml_list[findx][ml_index])
      ml_all_files.append(ml_files)
      return ml_all_files
      def prepareBatchData(self,
      ml_list,
      seed_index,
      min_choices=None,
      max_files = None,
      ml_num = None,
      set_ds = None,
      radius = 0):
      """
      set_ds (from COMBO_DSI) is used to select tile clusters, exported values come from correlation files.
      target_disparity for correlation files may be different than data_ds - replaced dureing ImageJ plugin
      export if main camera and the rig (GT) converged on different objects fro the same tile
      """
      if min_choices is None:
      min_choices = self.min_batch_choices
      if max_files is None:
      max_files = self.max_batch_files
      if ml_num is None:
      ml_num = self.files_per_scene
      if set_ds is None:
      set_ds = self.train_ds
      tiles_in_sample = (2 * radius + 1) * (2 * radius + 1)
      height = set_ds.shape[1]
      width = set_ds.shape[2]
      width_m1 = width-1
      height_m1 = height-1
      # set_ds = [self.train_ds, self.test_ds][test_set]
      corr_layers = ['hor-pairs', 'vert-pairs','diagm-pair', 'diago-pair']
      flist,tiles = self.augmentBatchFileIndices(seed_index, min_choices, max_files, set_ds)
      # ml_all_files = self.getBatchData(flist, tiles, ml_list, ml_num) # 0 - use all ml files for the scene, >0 select random number
      ml_all_files = self.getBatchData(
      flist,
      ## tiles,
      ml_list,
      0) # ml_num) # 0 - use all ml files for the scene, >0 select random number
      if self.debug_level > 1:
      print ("==============",seed_index, flist)
      for i, _ in enumerate(flist):
      print(i,"\n".join(ml_all_files[i]))
      print(tiles[i])
      total_tiles = 0
      for i, t in enumerate(tiles):
      ## total_tiles += len(t)*len(ml_all_files[i]) # tiles per scene * offset files per scene
      total_tiles += len(t) # tiles per scene * offset files per scene
      if self.debug_level > 1:
      print("Tiles in the batch=",total_tiles)
      corr2d_batch = None # np.empty((total_tiles, len(corr_layers),81))
      gt_ds_batch = np.empty((total_tiles * tiles_in_sample, 2), dtype=float)
      target_disparity_batch = np.empty((total_tiles * tiles_in_sample, ), dtype=float)
      start_tile = 0
      for nscene, scene_files in enumerate(ml_all_files):
      '''
      Create tiles list including neighbors
      '''
      full_tiles = np.empty([len(tiles[nscene]) * tiles_in_sample], dtype = int)
      indx = 0;
      for i, nt in enumerate(tiles[nscene]):
      ty = nt // width
      tx = nt % width
      for dy in range (-radius, radius+1):
      y = np.clip(ty+dy,0,height_m1)
      for dx in range (-radius, radius+1):
      x = np.clip(tx+dx,0,width_m1)
      full_tiles[indx] = y * width + x
      indx += 1
      """
      Assign tiles to several correlation files
      """
      file_tiles = []
      file_indices = []
      for _ in scene_files:
      file_tiles.append([])
      num_scene_files = len(scene_files)
      for t in full_tiles:
      fi = np.random.randint(0, num_scene_files) #error here - probably wrong ml file pattern (no files matched)
      file_tiles[fi].append(t)
      file_indices.append(fi)
      corr2d_list = []
      target_disparity_list = []
      gt_ds_list = []
      for fi, path in enumerate (scene_files):
      img = ijt.imagej_tiff(path, corr_layers, tile_list=file_tiles[fi])
      corr2d_list.append (img.corr2d)
      target_disparity_list.append(img.target_disparity)
      gt_ds_list.append (img.gt_ds)
      img_indices = [0] * len(scene_files)
      for i, fi in enumerate(file_indices):
      ti = img_indices[fi]
      img_indices[fi] += 1
      if corr2d_batch is None:
      corr2d_batch = np.empty((total_tiles * tiles_in_sample, len(corr_layers), corr2d_list[fi].shape[-1]))
      gt_ds_batch [start_tile] = gt_ds_list[fi][ti]
      target_disparity_batch [start_tile] = target_disparity_list[fi][ti]
      corr2d_batch [start_tile] = corr2d_list[fi][ti]
      start_tile += 1
      """
      Sometimes get bad tile in ML file that was not bad in COMBO-DSI
      Need to recover
      np.argwhere(np.isnan(target_disparity_batch))
      """
      bad_tiles = np.argwhere(np.isnan(target_disparity_batch))
      if (len(bad_tiles)>0):
      print ("*** Got %d bad tiles in a batch, no code to replace :-("%(len(bad_tiles)))
      # for now - just repeat some good tile
      """
      for ibt in bad_tiles:
      while np.isnan(target_disparity_batch[ibt]):
      irt = np.random.randint(0,total_tiles)
      if not np.isnan(target_disparity_batch[irt]):
      target_disparity_batch[ibt] = target_disparity_batch[irt]
      corr2d_batch[ibt] = corr2d_batch[irt]
      gt_ds_batch[ibt] = gt_ds_batch[irt]
      break
      print (" done replacing")
      """
      self.corr2d_batch = corr2d_batch
      self.target_disparity_batch = target_disparity_batch
      self.gt_ds_batch = gt_ds_batch
      return corr2d_batch, target_disparity_batch, gt_ds_batch
      def writeTFRewcordsEpoch(self, tfr_filename, ml_list, files_list = None, set_ds= None, radius = 0, num_scenes = None): # test_set=False):
      # train_filename = 'train.tfrecords' # address to save the TFRecords file
      # open the TFRecords file
      if not '.tfrecords' in tfr_filename:
      tfr_filename += '.tfrecords'
      tfr_filename=tfr_filename.replace(' ','_')
      if files_list is None:
      files_list = self.files_train
      if set_ds is None:
      set_ds = self.train_ds
      try:
      os.makedirs(os.path.dirname(tfr_filename))
      print("Created directory "+os.path.dirname(tfr_filename))
      except:
      print("Directory "+os.path.dirname(tfr_filename)+" already exists, using it")
      pass
      #skip writing if file exists - it will be possible to continue or run several instances
      if os.path.exists(tfr_filename):
      print(tfr_filename+" already exists, skipping generation. Please remove and re-run this program if you want to regenerate the file")
      return
      writer = tf.python_io.TFRecordWriter(tfr_filename)
      #$ files_list = [self.files_train, self.files_test][test_set]
      if num_scenes is None:
      num_scenes = len(files_list)
      if len(files_list) <= num_scenes:
      seed_list = np.arange(num_scenes) % len(files_list)
      np.random.shuffle(seed_list)
      else:
      seed_list = np.arange(len(files_list))
      np.random.shuffle(seed_list)
      seed_list = seed_list[:num_scenes]
      # seed_list = np.arange(len(files_list))
      np.random.shuffle(seed_list)
      cluster_size = (2 * radius + 1) * (2 * radius + 1)
      for nscene, seed_index in enumerate(seed_list):
      corr2d_batch, target_disparity_batch, gt_ds_batch = ex_data.prepareBatchData(
      ml_list,
      seed_index,
      min_choices=None,
      max_files = None,
      ml_num = None,
      set_ds = set_ds,
      radius = radius)
      #shuffles tiles in a batch
      # tiles_in_batch = len(target_disparity_batch)
      tiles_in_batch = corr2d_batch.shape[0]
      clusters_in_batch = tiles_in_batch // cluster_size
      # permut = np.random.permutation(tiles_in_batch)
      permut = np.random.permutation(clusters_in_batch)
      corr2d_clusters = corr2d_batch. reshape((clusters_in_batch,-1))
      target_disparity_clusters = target_disparity_batch.reshape((clusters_in_batch,-1))
      gt_ds_clusters = gt_ds_batch. reshape((clusters_in_batch,-1))
      # corr2d_batch_shuffled = corr2d_batch[permut].reshape((corr2d_batch.shape[0], corr2d_batch.shape[1]*corr2d_batch.shape[2]))
      # target_disparity_batch_shuffled = target_disparity_batch[permut].reshape((tiles_in_batch,1))
      # gt_ds_batch_shuffled = gt_ds_batch[permut]
      corr2d_batch_shuffled = corr2d_clusters[permut]. reshape((tiles_in_batch, -1))
      target_disparity_batch_shuffled = target_disparity_clusters[permut].reshape((tiles_in_batch, -1))
      gt_ds_batch_shuffled = gt_ds_clusters[permut]. reshape((tiles_in_batch, -1))
      if nscene == 0:
      dtype_feature_corr2d = _dtype_feature(corr2d_batch_shuffled)
      dtype_target_disparity = _dtype_feature(target_disparity_batch_shuffled)
      dtype_feature_gt_ds = _dtype_feature(gt_ds_batch_shuffled)
      for i in range(tiles_in_batch):
      x = corr2d_batch_shuffled[i].astype(np.float32)
      y = target_disparity_batch_shuffled[i].astype(np.float32)
      z = gt_ds_batch_shuffled[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())
      if (self.debug_level > 0):
      print_time("Scene %d (%d) of %d -> %s"%(nscene, seed_index, len(seed_list), tfr_filename))
      writer.close()
      sys.stdout.flush()
      def showVariance(self,
      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
      num_neibs_list, # list of number of tile neibs files. Same shape(but last dim) as rds_list
      variance_min = 0.0,
      variance_max = 1.5,
      neibs_min = 9,
      #Same parameters as for the histogram
      # disparity_bins = 1000,
      # strength_bins = 100,
      # disparity_min_drop = -0.1,
      # disparity_min_clip = -0.1,
      # disparity_max_drop = 100.0,
      # disparity_max_clip = 100.0,
      # strength_min_drop = 0.1,
      # strength_min_clip = 0.1,
      # strength_max_drop = 1.0,
      # strength_max_clip = 0.9,
      normalize = False): # True):
      good_tiles_list=[]
      for nf, combo_rds in enumerate(rds_list):
      disp_var = disp_var_list[nf]
      num_neibs = num_neibs_list[nf]
      good_tiles = np.empty((combo_rds.shape[0], combo_rds.shape[1],combo_rds.shape[2]), dtype=bool)
      for ids in range (combo_rds.shape[0]): #iterate over all scenes ds[2][rows][cols]
      ds = combo_rds[ids]
      disparity = ds[...,0]
      strength = ds[...,1]
      variance = disp_var[ids]
      neibs = num_neibs[ids]
      good_tiles[ids] = disparity >= self.disparity_min_drop
      good_tiles[ids] &= disparity <= self.disparity_max_drop
      good_tiles[ids] &= strength >= self.strength_min_drop
      good_tiles[ids] &= strength <= self.strength_max_drop
      good_tiles[ids] &= neibs >= neibs_min
      good_tiles[ids] &= variance >= variance_min
      good_tiles[ids] &= variance < variance_max
      disparity = np.nan_to_num(disparity, copy = False) # to be able to multiply by 0.0 in mask | copy=False, then out=disparity all done in-place
      strength = np.nan_to_num(strength, copy = False) # likely should never happen
      # np.clip(disparity, self.disparity_min_clip, self.disparity_max_clip, out = disparity)
      # np.clip(strength, self.strength_min_clip, self.strength_max_clip, out = strength)
      good_tiles_list.append(good_tiles)
      combo_rds = np.concatenate(rds_list)
      # hist, xedges, yedges = np.histogram2d( # xedges, yedges - just for debugging
      hist, _, _ = np.histogram2d( # xedges, yedges - just for debugging
      x = combo_rds[...,1].flatten(),
      y = combo_rds[...,0].flatten(),
      bins= (self.strength_bins, self.disparity_bins),
      range= ((self.strength_min_clip,self.strength_max_clip),(self.disparity_min_clip,self.disparity_max_clip)),
      normed= normalize,
      weights= np.concatenate(good_tiles_list).flatten())
      mytitle = "Disparity_Strength variance histogram"
      fig = plt.figure()
      fig.canvas.set_window_title(mytitle)
      fig.suptitle("Min variance = %f, max variance = %f, min neibs = %d"%(variance_min, variance_max, neibs_min))
      # plt.imshow(hist, vmin=0, vmax=.1 * hist.max())#,vmin=-6,vmax=-2) # , vmin=0, vmax=.01)
      plt.imshow(hist, vmin=0.0, vmax=300.0)#,vmin=-6,vmax=-2) # , vmin=0, vmax=.01)
      plt.colorbar(orientation='horizontal') # location='bottom')
      # for i, combo_rds in enumerate(rds_list):
      # for ids in range (combo_rds.shape[0]): #iterate over all scenes ds[2][rows][cols]
      # combo_rds[ids][...,1]*= good_tiles_list[i][ids]
      # return hist, xedges, yedges
      #MAIN
      if __name__ == "__main__":
      LATEST_VERSION_ONLY = True
      try:
      topdir_train = sys.argv[1]
      except IndexError:
      # topdir_train = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/train"#test" #all/"
      ## topdir_train = "/data_ssd/data_sets/train_mlr32_18d"
      ## topdir_train = '/data_ssd/data_sets/test_only'# ''
      topdir_train = '/data_ssd/data_sets/train_set2'# ''
      # tf_data_5x5_main_10_heur
      try:
      topdir_test = sys.argv[2]
      except IndexError:
      # topdir_test = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/test"#test" #all/"
      # topdir_test = "/data_ssd/data_sets/test_mlr32_18d"
      ## topdir_test = '/data_ssd/data_sets/test_only'
      topdir_test = '/data_ssd/data_sets/test_set2'
      try:
      pathTFR = sys.argv[3]
      except IndexError:
      # pathTFR = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/tf_data_3x3b" #no trailing "/"
      # pathTFR = "/home/eyesis/x3d_data/data_sets/tf_data_5x5" #no trailing "/"
      pathTFR = "/data_ssd/data_sets/tf_data_5x5_main_13_heur"
      ## pathTFR = "/data_ssd/data_sets/tf_data_5x5_main_11_rnd"
      ## pathTFR = "/data_ssd/data_sets/tf_data_5x5_main_12_rigrnd"
      try:
      ml_subdir = sys.argv[4]
      except IndexError:
      # ml_subdir = "ml"
      # ml_subdir = "mlr32_18a"
      # ml_subdir = "mlr32_18d"
      # ml_subdir = "{ml32,mlr32_18d}"
      ml_subdir = "ml*"
      try:
      ml_pattern = sys.argv[5]
      except IndexError:
      ml_pattern = "*-ML_DATA*MAIN.tiff" ## pathTFR = "/data_ssd/data_sets/tf_data_5x5_main_10_heur"
      ## ml_pattern = "*-ML_DATA*MAIN_RND*.tiff" ## pathTFR = "/data_ssd/data_sets/tf_data_5x5_main_11_rnd"
      ## ml_pattern = "*-ML_DATA*RIG_RND*.tiff" ## pathTFR = "/data_ssd/data_sets/tf_data_5x5_main_12_rigrnd"
      ## ML_PATTERN = "*-ML_DATA*RIG_RND*.tiff"
      #1527182801_296892-ML_DATA-32B-O-FZ0.05-MAIN_RND2.00000.tiff
      # 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' # overlook
      # test_corr = '/home/eyesis/x3d_data/data_sets/test_mlr32_18a/1527256816_150165/v02/mlr32_18a/1527256816_150165-ML_DATA-32B-O-FZ0.05-MAIN.tiff' # State Street
      # test_corr = '/home/eyesis/x3d_data/models/dsi_combo_and_ml_all/state_street/1527256858_150165/v01/mlr32_18a/1527256858_150165-ML_DATA-32B-O-FZ0.05-MAIN.tiff' # State Street
      """
      /data_ssd/models/plane_1527182801/1527182805_696892/v02/mlr32_18d/1527182805_696892-ML_DATA-32B-O-FZ0.05-RIG_RND2.00000.tiff
      /data_ssd/models/plane_1527182801/1527182805_696892/v02/mlr32_18d/1527182805_696892-ML_DATA-32B-O-FZ0.05-MAIN_RND2.00000.tiff
      /data_ssd/models/plane_1527182801/1527182805_696892/v02/mlr32_18d/1527182805_696892-ML_DATA-32B-O-FZ0.05-MAIN.tiff
      test_corrs = [
      '/home/eyesis/x3d_data/data_sets/test_mlr32_18a/1527257933_150165/v04/mlr32_18a/1527257933_150165-ML_DATA-32B-O-FZ0.05-MAIN.tiff', # overlook
      '/home/eyesis/x3d_data/data_sets/test_mlr32_18a/1527256816_150165/v02/mlr32_18a/1527256816_150165-ML_DATA-32B-O-FZ0.05-MAIN.tiff', # State Street
      '/home/eyesis/x3d_data/data_sets/test_mlr32_18a/1527256858_150165/v01/mlr32_18a/1527256858_150165-ML_DATA-32B-O-FZ0.05-MAIN.tiff', # State Street
      '/home/eyesis/x3d_data/data_sets/test_mlr32_18a/1527182802_096892/v02/mlr32_18a/1527182802_096892-ML_DATA-32B-O-FZ0.05-MAIN.tiff', # near plane"
      '/home/eyesis/x3d_data/data_sets/test_mlr32_18a/1527182805_096892/v02/mlr32_18a/1527182805_096892-ML_DATA-32B-O-FZ0.05-MAIN.tiff', # medium plane"
      '/home/eyesis/x3d_data/data_sets/test_mlr32_18a/1527182810_096892/v02/mlr32_18a/1527182810_096892-ML_DATA-32B-O-FZ0.05-MAIN.tiff', # far plane
      ]
      test_corrs = [
      '/home/eyesis/x3d_data/data_sets/test_mlr32_18a/1527257933_150165/v04/mlr32_18c/1527257933_150165-ML_DATA-32B-O-FZ0.05-MAIN.tiff', # overlook
      '/home/eyesis/x3d_data/data_sets/test_mlr32_18a/1527256816_150165/v02/mlr32_18c/1527256816_150165-ML_DATA-32B-O-FZ0.05-MAIN.tiff', # State Street
      '/home/eyesis/x3d_data/data_sets/test_mlr32_18a/1527256858_150165/v01/mlr32_18c/1527256858_150165-ML_DATA-32B-O-FZ0.05-MAIN.tiff', # State Street
      '/home/eyesis/x3d_data/data_sets/test_mlr32_18a/1527182802_096892/v02/mlr32_18c/1527182802_096892-ML_DATA-32B-O-FZ0.05-MAIN.tiff', # near plane"
      '/home/eyesis/x3d_data/data_sets/test_mlr32_18a/1527182805_096892/v02/mlr32_18c/1527182805_096892-ML_DATA-32B-O-FZ0.05-MAIN.tiff', # medium plane"
      '/home/eyesis/x3d_data/data_sets/test_mlr32_18a/1527182810_096892/v02/mlr32_18c/1527182810_096892-ML_DATA-32B-O-FZ0.05-MAIN.tiff', # far plane
      ]
      test_corrs = [
      '/data_ssd/data_sets/test_mlr32_18d/1527257933_150165/v04/mlr32_18d/1527257933_150165-ML_DATA-32B-O-FZ0.05-MAIN.tiff', # overlook
      '/data_ssd/data_sets/test_mlr32_18d/1527256816_150165/v02/mlr32_18d/1527256816_150165-ML_DATA-32B-O-FZ0.05-MAIN.tiff', # State Street
      '/data_ssd/data_sets/test_mlr32_18d/1527256858_150165/v01/mlr32_18d/1527256858_150165-ML_DATA-32B-O-FZ0.05-MAIN.tiff', # State Street
      '/data_ssd/data_sets/test_mlr32_18d/1527182802_096892/v02/mlr32_18d/1527182802_096892-ML_DATA-32B-O-FZ0.05-MAIN.tiff', # near plane"
      '/data_ssd/data_sets/test_mlr32_18d/1527182805_096892/v02/mlr32_18d/1527182805_096892-ML_DATA-32B-O-FZ0.05-MAIN.tiff', # medium plane"
      '/data_ssd/data_sets/test_mlr32_18d/1527182810_096892/v02/mlr32_18d/1527182810_096892-ML_DATA-32B-O-FZ0.05-MAIN.tiff', # far plane
      ]
      test_corrs = [
      '/data_ssd/data_sets/test_mlr32_18d/1527257933_150165/v04/mlr32_18d/1527257933_150165-ML_DATA-32B-O-FZ0.05-MAIN_RND2.00000.tiff', # overlook
      '/data_ssd/data_sets/test_mlr32_18d/1527256816_150165/v02/mlr32_18d/1527256816_150165-ML_DATA-32B-O-FZ0.05-MAIN_RND2.00000.tiff', # State Street
      '/data_ssd/data_sets/test_mlr32_18d/1527256858_150165/v01/mlr32_18d/1527256858_150165-ML_DATA-32B-O-FZ0.05-MAIN_RND2.00000.tiff', # State Street
      '/data_ssd/data_sets/test_mlr32_18d/1527182802_096892/v02/mlr32_18d/1527182802_096892-ML_DATA-32B-O-FZ0.05-MAIN_RND2.00000.tiff', # near plane"
      '/data_ssd/data_sets/test_mlr32_18d/1527182805_096892/v02/mlr32_18d/1527182805_096892-ML_DATA-32B-O-FZ0.05-MAIN_RND2.00000.tiff', # medium plane"
      '/data_ssd/data_sets/test_mlr32_18d/1527182810_096892/v02/mlr32_18d/1527182810_096892-ML_DATA-32B-O-FZ0.05-MAIN_RND2.00000.tiff', # far plane
      ]
      test_corrs = [
      '/data_ssd/data_sets/test_mlr32_18d/1527257933_150165/v04/mlr32_18d/1527257933_150165-ML_DATA-32B-O-FZ0.05-RIG_RND2.00000.tiff', # overlook
      '/data_ssd/data_sets/test_mlr32_18d/1527256816_150165/v02/mlr32_18d/1527256816_150165-ML_DATA-32B-O-FZ0.05-RIG_RND2.00000.tiff', # State Street
      '/data_ssd/data_sets/test_mlr32_18d/1527256858_150165/v01/mlr32_18d/1527256858_150165-ML_DATA-32B-O-FZ0.05-RIG_RND2.00000.tiff', # State Street
      '/data_ssd/data_sets/test_mlr32_18d/1527182802_096892/v02/mlr32_18d/1527182802_096892-ML_DATA-32B-O-FZ0.05-RIG_RND2.00000.tiff', # near plane"
      '/data_ssd/data_sets/test_mlr32_18d/1527182805_096892/v02/mlr32_18d/1527182805_096892-ML_DATA-32B-O-FZ0.05-RIG_RND2.00000.tiff', # medium plane"
      '/data_ssd/data_sets/test_mlr32_18d/1527182810_096892/v02/mlr32_18d/1527182810_096892-ML_DATA-32B-O-FZ0.05-RIG_RND2.00000.tiff', # far plane
      ]
      """
      # These images are made with large random offset
      '''
      test_corrs = [
      '/data_ssd/data_sets/test_only/1527258897_071435/v02/ml32/1527258897_071435-ML_DATA-32B-O-FZ0.05-MAIN.tiff',
      '/data_ssd/data_sets/test_only/1527257894_750165/v02/ml32/1527257894_750165-ML_DATA-32B-O-FZ0.05-MAIN.tiff',
      '/data_ssd/data_sets/test_only/1527257406_950165/v02/ml32/1527257406_950165-ML_DATA-32B-O-FZ0.05-MAIN.tiff',
      '/data_ssd/data_sets/test_only/1527257757_950165/v02/ml32/1527257757_950165-ML_DATA-32B-O-FZ0.05-MAIN.tiff',
      '/data_ssd/data_sets/test_only/1527257370_950165/v02/ml32/1527257370_950165-ML_DATA-32B-O-FZ0.05-MAIN.tiff',
      '/data_ssd/data_sets/test_only/1527257235_950165/v02/ml32/1527257235_950165-ML_DATA-32B-O-FZ0.05-MAIN.tiff',
      '/data_ssd/data_sets/test_only/1527257235_350165/v02/ml32/1527257235_350165-ML_DATA-32B-O-FZ0.05-MAIN.tiff',
      '/data_ssd/data_sets/test_only/1527259003_271435/v02/ml32/1527259003_271435-ML_DATA-32B-O-FZ0.05-MAIN.tiff',
      '/data_ssd/data_sets/test_only/1527257787_950165/v02/ml32/1527257787_950165-ML_DATA-32B-O-FZ0.05-MAIN.tiff',
      '/data_ssd/data_sets/test_only/1527257235_150165/v02/ml32/1527257235_150165-ML_DATA-32B-O-FZ0.05-MAIN.tiff',
      '/data_ssd/data_sets/test_only/1527257235_750165/v02/ml32/1527257235_750165-ML_DATA-32B-O-FZ0.05-MAIN.tiff',
      '/data_ssd/data_sets/test_only/1527258936_671435/v02/ml32/1527258936_671435-ML_DATA-32B-O-FZ0.05-MAIN.tiff',
      '/data_ssd/data_sets/test_only/1527257244_350165/v02/ml32/1527257244_350165-ML_DATA-32B-O-FZ0.05-MAIN.tiff',
      '/data_ssd/data_sets/test_only/1527257235_550165/v02/ml32/1527257235_550165-ML_DATA-32B-O-FZ0.05-MAIN.tiff',
      ]
      '''
      test_corrs = []
      #1527257933_150165-ML_DATA-32B-O-FZ0.05-MAIN-RND2.00000.tiff
      #/home/eyesis/x3d_data/data_sets/test_mlr32_18a/1527257933_150165/v04/mlr32_18c/1527257933_150165-ML_DATA-32B-O-FZ0.05-MAIN.tiff
      #Parameters to generate neighbors data. Set radius to 0 to generate single-tile
      TEST_SAME_LENGTH_AS_TRAIN = False # True # make test to have same number of entries as train ones
      FIXED_TEST_LENGTH = None # put number of test scenes to output (used when making test only from few or single test file
      RADIUS = 2 # 5x5
      MIN_NEIBS = (2 * RADIUS + 1) * (2 * RADIUS + 1) # All tiles valid == 9
      VARIANCE_THRESHOLD = 0.4 # 1.5
      VARIANCE_SCALE_DISPARITY = 5.0 #Scale variance if average is above this
      NUM_TRAIN_SETS = 32 # 8
      if not topdir_train:
      NUM_TRAIN_SETS = 0
      if RADIUS == 0:
      BATCH_DISP_BINS = 50 # 1000 * 1
      BATCH_STR_BINS = 20 # 10
      elif RADIUS == 1:
      BATCH_DISP_BINS = 15 # 120 * 9
      BATCH_STR_BINS = 8
      else: # RADIUS = 2
      BATCH_DISP_BINS = 10 # 40 * 25
      BATCH_STR_BINS = 4
      train_filenameTFR = pathTFR+"/train"
      test_filenameTFR = pathTFR+"/test"
      # disp_bins = 20,
      # str_bins=10)
      # corr2d, target_disparity, gt_ds = readTFRewcordsEpoch(train_filenameTFR)
      # print_time("Read %d tiles"%(corr2d.shape[0]))
      # exit (0)
      ex_data = ExploreData(
      topdir_train = topdir_train,
      topdir_test = topdir_test,
      ml_pattern = ml_pattern,
      latest_version_only = LATEST_VERSION_ONLY,
      debug_level = 1, #3, ##0, #3,
      disparity_bins = 200, #1000,
      strength_bins = 100,
      disparity_min_drop = -0.1,
      disparity_min_clip = -0.1,
      disparity_max_drop = 20.0, #100.0,
      disparity_max_clip = 20.0, #100.0,
      strength_min_drop = 0.1,
      strength_min_clip = 0.1,
      strength_max_drop = 1.0,
      strength_max_clip = 0.9,
      hist_sigma = 2.0, # Blur log histogram
      hist_cutoff= 0.001) # of maximal
      mytitle = "Disparity_Strength histogram"
      fig = plt.figure()
      fig.canvas.set_window_title(mytitle)
      fig.suptitle(mytitle)
      # plt.imshow(lhist,vmin=-6,vmax=-2) # , vmin=0, vmax=.01)
      plt.imshow(ex_data.blurred_hist, vmin=0, vmax=.1 * ex_data.blurred_hist.max())#,vmin=-6,vmax=-2) # , vmin=0, vmax=.01)
      plt.colorbar(orientation='horizontal') # location='bottom')
      hist_to_batch = ex_data.assignBatchBins(
      disp_bins = BATCH_DISP_BINS,
      str_bins = BATCH_STR_BINS)
      bb_display = hist_to_batch.copy()
      bb_display = ( 1+ (bb_display % 2) + 2 * ((bb_display % 20)//10)) * (hist_to_batch > 0) #).astype(float)
      fig2 = plt.figure()
      fig2.canvas.set_window_title("Batch indices")
      fig2.suptitle("Batch index for each disparity/strength cell")
      plt.imshow(bb_display) #, vmin=0, vmax=.1 * ex_data.blurred_hist.max())#,vmin=-6,vmax=-2) # , vmin=0, vmax=.01)
      """ prepare test dataset """
      for test_corr in test_corrs:
      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)
      print_time("Saving test image %s as tiles..."%(img_filenameTFR),end = " ")
      writeTFRewcordsImageTiles(test_corr, img_filenameTFR)
      print_time("Done")
      pass
      if (RADIUS > 0):
      disp_var_test, num_neibs_test = ex_data.exploreNeibs(ex_data.test_ds, RADIUS, VARIANCE_SCALE_DISPARITY)
      disp_var_train, num_neibs_train = ex_data.exploreNeibs(ex_data.train_ds, RADIUS, VARIANCE_SCALE_DISPARITY)
      # show varinace histogram
      # for var_thresh in [0.1, 1.0, 1.5, 2.0, 5.0]:
      for var_thresh in [1.5]:
      ex_data.showVariance(
      rds_list = [ex_data.train_ds, ex_data.test_ds], # list of disparity/strength files, suchas training, testing
      disp_var_list = [disp_var_train, disp_var_test], # list of disparity variance files. Same shape(but last dim) as rds_list
      num_neibs_list = [num_neibs_train, num_neibs_test], # list of number of tile neibs files. Same shape(but last dim) as rds_list
      variance_min = 0.0,
      variance_max = var_thresh,
      neibs_min = MIN_NEIBS)
      ex_data.showVariance(
      rds_list = [ex_data.train_ds, ex_data.test_ds], # list of disparity/strength files, suchas training, testing
      disp_var_list = [disp_var_train, disp_var_test], # list of disparity variance files. Same shape(but last dim) as rds_list
      num_neibs_list = [num_neibs_train, num_neibs_test], # list of number of tile neibs files. Same shape(but last dim) as rds_list
      variance_min = var_thresh,
      variance_max = 1000.0,
      neibs_min = MIN_NEIBS)
      pass
      pass
      else:
      disp_var_test, num_neibs_test = None, None
      disp_var_train, num_neibs_train = None, None
      ml_list_train=ex_data.getMLList(ml_subdir, ex_data.files_train)
      ml_list_test= ex_data.getMLList(ml_subdir, ex_data.files_test)
      if FIXED_TEST_LENGTH is None:
      num_test_scenes = len([ex_data.files_test, ex_data.files_train][TEST_SAME_LENGTH_AS_TRAIN])
      else:
      num_test_scenes = FIXED_TEST_LENGTH
      if RADIUS == 0 :
      list_of_file_lists_train, num_batch_tiles_train = ex_data.makeBatchLists( # results are also saved to self.*
      data_ds = ex_data.train_ds,
      disp_var = disp_var_train, # difference between maximal and minimal disparity for each scene, each tile
      disp_neibs = num_neibs_train, # number of valid tiles around each center tile (for 3x3 (radius = 1) - macximal is 9
      min_var = 0.0, # Minimal tile variance to include
      max_var = VARIANCE_THRESHOLD, # Maximal tile variance to include
      scale_disp = VARIANCE_SCALE_DISPARITY,
      min_neibs = MIN_NEIBS) # Minimal number of valid tiles to include
      pass
      # ex_data.makeBatchLists(data_ds = ex_data.train_ds)
      for train_var in range (NUM_TRAIN_SETS):
      fpath = train_filenameTFR+("%03d"%(train_var,))
      ex_data.writeTFRewcordsEpoch(fpath, ml_list = ml_list_train, files_list = ex_data.files_train, set_ds= ex_data.train_ds)
      list_of_file_lists_test, num_batch_tiles_test = ex_data.makeBatchLists( # results are also saved to self.*
      data_ds = ex_data.test_ds,
      disp_var = disp_var_test, # difference between maximal and minimal disparity for each scene, each tile
      disp_neibs = num_neibs_test, # number of valid tiles around each center tile (for 3x3 (radius = 1) - macximal is 9
      min_var = 0.0, # Minimal tile variance to include
      max_var = VARIANCE_THRESHOLD, # Maximal tile variance to include
      ## scale_disp = VARIANCE_SCALE_DISPARITY,
      min_neibs = MIN_NEIBS) # Minimal number of valid tiles to include
      fpath = test_filenameTFR # +("-%03d"%(train_var,))
      ex_data.writeTFRewcordsEpoch(fpath, ml_list = ml_list_test, files_list = ex_data.files_test, set_ds= ex_data.test_ds, num_scenes = num_test_scenes)
      pass
      else: # RADIUS > 0
      # test
      list_of_file_lists_test, num_batch_tiles_test = ex_data.makeBatchLists( # results are also saved to self.*
      data_ds = ex_data.test_ds,
      disp_var = disp_var_test, # difference between maximal and minimal disparity for each scene, each tile
      disp_neibs = num_neibs_test, # number of valid tiles around each center tile (for 3x3 (radius = 1) - macximal is 9
      min_var = 0.0, # Minimal tile variance to include
      max_var = VARIANCE_THRESHOLD, # Maximal tile variance to include
      ## scale_disp = VARIANCE_SCALE_DISPARITY,
      min_neibs = MIN_NEIBS) # Minimal number of valid tiles to include
      num_le_test = num_batch_tiles_test.sum()
      print("Number of <= %f disparity variance tiles: %d (est)"%(VARIANCE_THRESHOLD, num_le_test))
      fpath = test_filenameTFR +("TEST_R%d_LE%4.1f"%(RADIUS,VARIANCE_THRESHOLD))
      ex_data.writeTFRewcordsEpoch(fpath, ml_list = ml_list_test, files_list = ex_data.files_test, set_ds= ex_data.test_ds, radius = RADIUS, num_scenes = num_test_scenes)
      list_of_file_lists_test, num_batch_tiles_test = ex_data.makeBatchLists( # results are also saved to self.*
      data_ds = ex_data.test_ds,
      disp_var = disp_var_test, # difference between maximal and minimal disparity for each scene, each tile
      disp_neibs = num_neibs_test, # number of valid tiles around each center tile (for 3x3 (radius = 1) - macximal is 9
      min_var = VARIANCE_THRESHOLD, # Minimal tile variance to include
      max_var = 1000.0, # Maximal tile variance to include
      ## scale_disp = VARIANCE_SCALE_DISPARITY,
      min_neibs = MIN_NEIBS) # Minimal number of valid tiles to include
      num_gt_test = num_batch_tiles_test.sum()
      high_fract_test = 1.0 * num_gt_test / (num_le_test + num_gt_test)
      print("Number of > %f disparity variance tiles: %d, fraction = %f (test)"%(VARIANCE_THRESHOLD, num_gt_test, high_fract_test))
      fpath = test_filenameTFR +("TEST_R%d_GT%4.1f"%(RADIUS,VARIANCE_THRESHOLD))
      ex_data.writeTFRewcordsEpoch(fpath, ml_list = ml_list_test, files_list = ex_data.files_test, set_ds= ex_data.test_ds, radius = RADIUS, num_scenes = num_test_scenes)
      #fake
      if NUM_TRAIN_SETS > 0:
      list_of_file_lists_fake, num_batch_tiles_fake = ex_data.makeBatchLists( # results are also saved to self.*
      data_ds = ex_data.train_ds,
      disp_var = disp_var_train, # difference between maximal and minimal disparity for each scene, each tile
      disp_neibs = num_neibs_train, # number of valid tiles around each center tile (for 3x3 (radius = 1) - macximal is 9
      min_var = 0.0, # Minimal tile variance to include
      max_var = VARIANCE_THRESHOLD, # Maximal tile variance to include
      ## scale_disp = VARIANCE_SCALE_DISPARITY,
      min_neibs = MIN_NEIBS) # Minimal number of valid tiles to include
      num_le_fake = num_batch_tiles_fake.sum()
      print("Number of <= %f disparity variance tiles: %d (test)"%(VARIANCE_THRESHOLD, num_le_fake))
      fpath = test_filenameTFR +("FAKE_R%d_LE%4.1f"%(RADIUS,VARIANCE_THRESHOLD))
      ex_data.writeTFRewcordsEpoch(fpath, ml_list = ml_list_train, files_list = ex_data.files_train, set_ds= ex_data.train_ds, radius = RADIUS, num_scenes = num_test_scenes)
      list_of_file_lists_fake, num_batch_tiles_fake = ex_data.makeBatchLists( # results are also saved to self.*
      data_ds = ex_data.train_ds,
      disp_var = disp_var_train, # difference between maximal and minimal disparity for each scene, each tile
      disp_neibs = num_neibs_train, # number of valid tiles around each center tile (for 3x3 (radius = 1) - macximal is 9
      min_var = VARIANCE_THRESHOLD, # Minimal tile variance to include
      max_var = 1000.0, # Maximal tile variance to include
      ## scale_disp = VARIANCE_SCALE_DISPARITY,
      min_neibs = MIN_NEIBS) # Minimal number of valid tiles to include
      num_gt_fake = num_batch_tiles_fake.sum()
      high_fract_fake = 1.0 * num_gt_fake / (num_le_fake + num_gt_fake)
      print("Number of > %f disparity variance tiles: %d, fraction = %f (test)"%(VARIANCE_THRESHOLD, num_gt_fake, high_fract_fake))
      fpath = test_filenameTFR +("FAKE_R%d_GT%4.1f"%(RADIUS,VARIANCE_THRESHOLD))
      ex_data.writeTFRewcordsEpoch(fpath, ml_list = ml_list_train, files_list = ex_data.files_train, set_ds= ex_data.train_ds, radius = RADIUS, num_scenes = num_test_scenes)
      # train
      for train_var in range (NUM_TRAIN_SETS): # Recalculate list for each file - slower, but will alternate lvar/hvar
      list_of_file_lists_train, num_batch_tiles_train = ex_data.makeBatchLists( # results are also saved to self.*
      data_ds = ex_data.train_ds,
      disp_var = disp_var_train, # difference between maximal and minimal disparity for each scene, each tile
      disp_neibs = num_neibs_train, # number of valid tiles around each center tile (for 3x3 (radius = 1) - macximal is 9
      min_var = 0.0, # Minimal tile variance to include
      max_var = VARIANCE_THRESHOLD, # Maximal tile variance to include
      ## scale_disp = VARIANCE_SCALE_DISPARITY,
      min_neibs = MIN_NEIBS) # Minimal number of valid tiles to include
      num_le_train = num_batch_tiles_train.sum()
      print("Number of <= %f disparity variance tiles: %d (train)"%(VARIANCE_THRESHOLD, num_le_train))
      # for train_var in range (NUM_TRAIN_SETS):
      fpath = train_filenameTFR+("%03d_R%d_LE%4.1f"%(train_var,RADIUS,VARIANCE_THRESHOLD))
      ex_data.writeTFRewcordsEpoch(fpath, ml_list = ml_list_train, files_list = ex_data.files_train, set_ds= ex_data.train_ds, radius = RADIUS)
      list_of_file_lists_train, num_batch_tiles_train = ex_data.makeBatchLists( # results are also saved to self.*
      data_ds = ex_data.train_ds,
      disp_var = disp_var_train, # difference between maximal and minimal disparity for each scene, each tile
      disp_neibs = num_neibs_train, # number of valid tiles around each center tile (for 3x3 (radius = 1) - macximal is 9
      min_var = VARIANCE_THRESHOLD, # Minimal tile variance to include
      max_var = 1000.0, # Maximal tile variance to include
      ## scale_disp = VARIANCE_SCALE_DISPARITY,
      min_neibs = MIN_NEIBS) # Minimal number of valid tiles to include
      num_gt_train = num_batch_tiles_train.sum()
      high_fract_train = 1.0 * num_gt_train / (num_le_train + num_gt_train)
      print("Number of > %f disparity variance tiles: %d, fraction = %f (train)"%(VARIANCE_THRESHOLD, num_gt_train, high_fract_train))
      # for train_var in range (NUM_TRAIN_SETS):
      fpath = (train_filenameTFR+("%03d_R%d_GT%4.1f"%(train_var,RADIUS,VARIANCE_THRESHOLD)))
      ex_data.writeTFRewcordsEpoch(fpath, ml_list = ml_list_train, files_list = ex_data.files_train, set_ds= ex_data.train_ds, radius = RADIUS)
      ## if train_var < 1: # make test files immediately after the train ones
      plt.show()
      """
      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)
      print_time("Saving test image %s as tiles..."%(img_filenameTFR),end = " ")
      writeTFRewcordsImageTiles(test_corr, img_filenameTFR)
      print_time("Done")
      pass
      """
      pass
      nn_eval_01.py
      View file @ d786da7b
      ......@@ -13,11 +13,14 @@ import sys
      #import numpy as np
      import imagej_tiffwriter
      import time
      import matplotlib.pyplot as plt
      from matplotlib.backends.backend_pdf import PdfPages
      import qcstereo_functions as qsf
      import numpy as np
      #import xml.etree.ElementTree as ET
      ......@@ -132,27 +135,143 @@ fig_params = get_fig_params(dbg_parameters['disparity_ranges'])
      pass
      #temporary:
      TIFF_ONLY = False # True
      #max_bad = 2.5 # excludes only direct bad
      max_bad = 2.5 #2.5 # 1.5 # excludes only direct bad
      max_diff = 1.5 # 2.0 # 5.0 # maximal max-min difference
      max_target_err = 1.0 # 0.5 # maximal max-min difference
      max_disp = 5.0
      min_strength = 0.18 #ignore tiles below
      min_neibs = 1
      max_log_to_mm = 0.5 # difference between center average and center should be under this fraction of max-min (0 - disables feature)
      #num_bins = 256 # number of histogram bins
      num_bins = 15 # 50 # number of histogram bins
      use_gt_weights = True # False # True
      index_gt = 2
      index_gt_weight = 3
      index_heur_err = 7
      index_nn_err = 6
      index_mm = 8 # max-min
      index_log = 9
      index_bad = 10
      index_num_neibs = 11
      """
      Debugging high 9-tile variations, removing error for all tiles with lower difference between max and min
      """
      #min_diff = 0.25 # remove all flat tiles with spread less than this (do not show on heuristic/network disparity errors subplots
      min_diff = 0 # remove all flat tiles with spread less than this
      max_target_err2 = max_target_err * max_target_err
      if not 'show' in FIGS_SAVESHOW:
      plt.ioff()
      for mode in ['train','infer']:
      #for mode in ['train','infer']:
      for mode in ['infer']:
      figs = []
      ffiles = [] # no ext
      def setlimsxy(lim_xy):
      if not lim_xy is None:
      plt.xlim(min(lim_xy[:2]),max(lim_xy[:2]))
      plt.ylim(max(lim_xy[2:]),min(lim_xy[2:]))
      cumul_weights = None
      for nfile, fpars in enumerate(fig_params):
      if not fpars is None:
      img_file = files['result'][nfile]
      if mode == 'infer':
      img_file = img_file.replace('.npy','-infer.npy')
      """
      try:
      data,_ = qsf.result_npy_prepare(img_file, ABSOLUTE_DISPARITY, fix_nan=True, insert_deltas=True)
      # data,_ = qsf.result_npy_prepare(img_file, ABSOLUTE_DISPARITY, fix_nan=True, insert_deltas=True)
      # data,_ = qsf.result_npy_prepare(img_file, ABSOLUTE_DISPARITY, fix_nan=True, insert_deltas=3)
      data,labels = qsf.result_npy_prepare(img_file, ABSOLUTE_DISPARITY, fix_nan=True, insert_deltas=3)
      except:
      print ("Image file does not exist:", img_file)
      continue
      """
      pass
      data,labels = qsf.result_npy_prepare(img_file, ABSOLUTE_DISPARITY, fix_nan=True, insert_deltas=3)
      if True: #TIFF_ONLY:
      tiff_path = img_file.replace('.npy','-test.tiff')
      data = data.transpose(2,0,1)
      print("Saving results to TIFF: "+tiff_path)
      imagej_tiffwriter.save(tiff_path,data,labels=labels)
      """
      Calculate histograms
      """
      err_heur2 = data[index_heur_err]*data[index_heur_err]
      err_nn2 = data[index_nn_err]* data[index_nn_err]
      diff_log2 = data[index_log]* data[index_log]
      weights = (
      (data[index_gt] < max_disp) &
      (err_heur2 < max_target_err2) &
      (data[index_bad] < max_bad) &
      (data[index_gt_weight] >= min_strength) &
      (data[index_num_neibs] >= min_neibs)&
      #max_log_to_mm = 0.5 # difference between center average and center should be under this fraction of max-min (0 - disables feature)
      (data[index_log] < max_log_to_mm * np.sqrt(data[index_mm]) )
      ).astype(data.dtype) # 0.0/1.1
      #max_disp
      #max_target_err
      if use_gt_weights:
      weights *= data[index_gt_weight]
      mm = data[index_mm]
      weh = np.nan_to_num(weights*err_heur2)
      wen = np.nan_to_num(weights*err_nn2)
      wel = np.nan_to_num(weights*diff_log2)
      hist_weights,bin_vals = np.histogram(a=mm, bins = num_bins, range = (0.0, max_diff), weights = weights, density = False)
      hist_err_heur2,_ = np.histogram(a=mm, bins = num_bins, range = (0.0, max_diff), weights = weh, density = False)
      hist_err_nn2,_ = np.histogram(a=mm, bins = num_bins, range = (0.0, max_diff), weights = wen, density = False)
      hist_diff_log2,_ = np.histogram(a=mm, bins = num_bins, range = (0.0, max_diff), weights = wel, density = False)
      if cumul_weights is None:
      cumul_weights = hist_weights
      cumul_err_heur2 = hist_err_heur2
      cumul_err_nn2 = hist_err_nn2
      cumul_diff_log2 = hist_diff_log2
      else:
      cumul_weights += hist_weights
      cumul_err_heur2 += hist_err_heur2
      cumul_err_nn2 += hist_err_nn2
      cumul_diff_log2 += hist_diff_log2
      hist_err_heur2 = np.nan_to_num(hist_err_heur2/hist_weights)
      hist_err_nn2 = np.nan_to_num(hist_err_nn2/hist_weights)
      hist_gain2 = np.nan_to_num(hist_err_heur2/hist_err_nn2)
      hist_gain = np.sqrt(hist_gain2)
      hist_diff_log2 = np.nan_to_num(hist_diff_log2/hist_weights)
      print("hist_err_heur2", end = " ")
      print(np.sqrt(hist_err_heur2))
      print("hist_err_nn2", end = " ")
      print(np.sqrt(hist_err_nn2))
      print("hist_gain", end = " ")
      print(hist_gain)
      print("hist_diff_log2", end = " ")
      print(np.sqrt(hist_diff_log2))
      if min_diff> 0.0:
      pass
      good = (mm > min_diff).astype(mm.dtype)
      good /= good # good - 1, bad - nan
      data[index_heur_err] *= good
      data[index_nn_err] *= good
      data = data.transpose(1,2,0)
      if TIFF_ONLY:
      continue
      for subindex, rng in enumerate(fpars['ranges']):
      lim_val = rng['lim_val']
      ......@@ -214,7 +333,68 @@ for mode in ['train','infer']:
      fb_noext+="-"+str(subindex)
      ffiles.append(fb_noext)
      pass
      if True:
      cumul_err_heur2 = np.nan_to_num(cumul_err_heur2/cumul_weights)
      cumul_err_nn2 = np.nan_to_num(cumul_err_nn2/cumul_weights)
      cumul_gain2 = np.nan_to_num(cumul_err_heur2/cumul_err_nn2)
      cumul_gain = np.sqrt(cumul_gain2)
      cumul_diff_log2 = np.nan_to_num(cumul_diff_log2/cumul_weights)
      print("cumul_weights", end = " ")
      print(cumul_weights)
      print("cumul_err_heur", end = " ")
      print(np.sqrt(cumul_err_heur2))
      print("cumul_err_nn", end = " ")
      print(np.sqrt(cumul_err_nn2))
      print("cumul_gain", end = " ")
      print(cumul_gain)
      print("cumul_diff_log2", end = " ")
      print(np.sqrt(cumul_diff_log2))
      fig, ax1 = plt.subplots()
      ax1.set_xlabel('3x3 tiles ground truth disparity max-min (pix)')
      ax1.set_ylabel('RMSE\n(pix)', color='black', rotation='horizontal')
      ax1.yaxis.set_label_coords(-0.045,0.92)
      ax1.plot(bin_vals[0:-1], np.sqrt(cumul_err_nn2), 'tab:red',label="network disparity RMSE")
      ax1.plot(bin_vals[0:-1], np.sqrt(cumul_err_heur2), 'tab:green',label="heuristic disparity RMSE")
      ax1.plot(bin_vals[0:-1], np.sqrt(cumul_diff_log2), 'tab:cyan',label="ground truth LoG")
      ax1.tick_params(axis='y', labelcolor='black')
      ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis
      ax2.set_ylabel('weight', color='black', rotation='horizontal') # we already handled the x-label with ax1
      ax2.yaxis.set_label_coords(1.06,1.0)
      ax2.plot(bin_vals[0:-1], cumul_weights,color='grey',dashes=[6, 2],label='weights = n_tiles * gt_confidence')
      ax1.legend(loc="upper left", bbox_to_anchor=(0.2,1.0))
      ax2.legend(loc="lower right", bbox_to_anchor=(1.0,0.1))
      """
      fig = plt.figure(figsize=FIGSIZE)
      fig.canvas.set_window_title('Cumulative')
      fig.suptitle('Difference to GT')
      # ax_conf=plt.subplot(322)
      ax_conf=plt.subplot(211)
      ax_conf.set_title("RMS vs max9-min9")
      plt.plot(bin_vals[0:-1], np.sqrt(cumul_err_heur2),'red',
      bin_vals[0:-1], np.sqrt(cumul_err_nn2),'green',
      bin_vals[0:-1], np.sqrt(cumul_diff_log2),'blue')
      figs.append(fig)
      ffiles.append('cumulative')
      ax_conf=plt.subplot(212)
      ax_conf.set_title("weights vs max9-min9")
      plt.plot(bin_vals[0:-1], cumul_weights,'black')
      """
      figs.append(fig)
      ffiles.append('cumulative')
      pass
      #bin_vals[0:-1]
      # fig.suptitle("Groud truth confidence")
      #
      #whow to allow adjustment before applying tight_layout?
      pass
      for fig in figs:
      ......@@ -229,9 +409,9 @@ for mode in ['train','infer']:
      pp=None
      if 'pdf' in FIGS_EXTENSIONS:
      if mode == 'infer':
      pdf_path = os.path.join(dirs['figures'],"figures-infer.pdf")
      pdf_path = os.path.join(dirs['figures'],"figures-infer%s.pdf"%str(min_diff))
      else:
      pdf_path = os.path.join(dirs['figures'],"figures-train.pdf")
      pdf_path = os.path.join(dirs['figures'],"figures-train%s.pdf"%str(min_diff))
      pp= PdfPages(pdf_path)
      for fb_noext, fig in zip(ffiles,figs):
      ......@@ -259,8 +439,8 @@ if 'show' in FIGS_SAVESHOW:
      #FIGS_ESXTENSIONS
      #qsf.evaluateAllResults(result_files = files['result'],
      # absolute_disparity = ABSOLUTE_DISPARITY,
      # cluster_radius = CLUSTER_RADIUS)
      qsf.evaluateAllResults(result_files = files['result'],
      absolute_disparity = ABSOLUTE_DISPARITY,
      cluster_radius = CLUSTER_RADIUS)
      print("All done")
      exit (0)
      qcstereo_functions.py
      View file @ d786da7b
      ......@@ -31,14 +31,14 @@ def print_time(txt="",end="\n"):
      TIME_LAST = t
      DEFAULT_TITLES = [
      ['test_lvar','Test_flat_heuristic'],
      ['test_hvar','Test_edge_heuristic'],
      ['test_lvar1','Test_flat_random'],
      ['test_hvar1','Test_edge_random'],
      ['fake_lvar','Fake_flat_heuristic'],
      ['fake_hvar','Fake_edge_heuristic'],
      ['fake_lvar1','Fake_flat_random'],
      ['fake_hvar1','Fake_edge_random']]
      ['test_lvar', 'Test_flat_heuristic'],
      ['test_hvar', 'Test_edge_heuristic'],
      ['test_lvar1', 'Test_flat_random'],
      ['test_hvar1', 'Test_edge_random'],
      ['fake_lvar', 'Fake_flat_heuristic'],
      ['fake_hvar', 'Fake_edge_heuristic'],
      ['fake_lvar1', 'Fake_flat_random'],
      ['fake_hvar1', 'Fake_edge_random']]
      def parseXmlConfig(conf_file, root_dir):
      tree = ET.parse(conf_file)
      ......@@ -517,6 +517,7 @@ def result_npy_prepare(npy_path, absolute, fix_nan, insert_deltas=True,labels=No
      data will be written as 4-layer tiff, extension '.npy' replaced with '.tiff'
      @param absolute - True - the first layer contains absolute disparity, False - difference from target_disparity
      @param fix_nan - replace nan in target_disparity with 0 to apply offset, target_disparity will still contain nan
      @parame insert_deltas: +1 - add delta layers, +2 - add variance (max - min of this and 8 neighbors)
      """
      data = np.load(npy_path) #(324,242,4) [nn_disp, target_disp,gt_disp, gt_conf]
      if labels is None:
      ......@@ -526,12 +527,17 @@ def result_npy_prepare(npy_path, absolute, fix_nan, insert_deltas=True,labels=No
      # target_disparity = 1
      gt_disparity = 2
      gt_strength = 3
      heur_err = 7
      min_heur_err = 0.001
      height = data.shape[0]
      width = data.shape[1]
      nocenter9 = np.array([[[1,1,1,1,np.nan,1,1,1,1]]], dtype = data.dtype)
      if not absolute:
      if fix_nan:
      data[...,nn_out] += np.nan_to_num(data[...,1], copy=True)
      else:
      data[...,nn_out] += data[...,1]
      if insert_deltas:
      if (insert_deltas & 1):
      np.nan_to_num(data[...,gt_strength], copy=False)
      data = np.concatenate([data[...,0:4],data[...,0:2],data[...,0:2],data[...,4:]], axis = 2) # data[...,4:] may be empty
      labels = labels[:4]+["nn_out","hier_out","nn_err","hier_err"]+labels[4:]
      ......@@ -543,6 +549,69 @@ def result_npy_prepare(npy_path, absolute, fix_nan, insert_deltas=True,labels=No
      # All other layers - mast too
      for l in range(8,data.shape[2]):
      data[...,l] = np.select([data[...,gt_strength]==0.0, data[...,gt_strength]>0.0], [np.nan,data[...,l]])
      """
      Calculate bad tiles where ggt was used as a master, to remove them from the results (later versions add random error)
      """
      bad1 = abs(data[...,heur_err]) < min_heur_err
      bad1_ext = np.concatenate([bad1 [0:1,:], bad1 [0:1,:], bad1[:,:], bad1 [-1:height,:], bad1 [-1:height,:]],axis = 0)
      bad1_ext = np.concatenate([bad1_ext[:,0:1], bad1_ext[:,0:1], bad1_ext[:,:], bad1_ext[:,-1:width], bad1_ext[:,-1:width]], axis = 1)
      bad25 = np.empty(shape=[height, width, 25], dtype=bad1.dtype)
      bm25=np.array([[[1,1,1,1,1, 1,1,1,1,1, 1,1,1,1,1, 1,1,1,1,1, 1,1,1,1,1]]])
      bm09=np.array([[[0,0,0,0,0, 0,1,1,1,0, 0,1,1,1,0, 0,1,1,1,0, 0,0,0,0,0]]])
      bm01=np.array([[[0,0,0,0,0, 0,0,0,0,0, 0,0,1,0,0, 0,0,0,0,0, 0,0,0,0,0]]])
      for row in range(5):
      for col in range(5):
      pass
      bad25 [...,row*5+col]= bad1_ext[row:height+row, col:width+col]
      bad_num1=(np.sum(bad25*bm25,axis=2) > 0).astype(data.dtype)
      bad_num2=(np.sum(bad25*bm09,axis=2) > 0).astype(data.dtype)
      bad_num3=(np.sum(bad25*bm01,axis=2) > 0).astype(data.dtype)
      bad_num = bad_num1 + bad_num2 + bad_num3
      if (insert_deltas & 2):
      wo = 0.7 # ortho
      wc = 0.5 #corner
      w8=np.array([wc,wo,wc,wo,0.0,wo,wc,wo,wc], dtype=data.dtype)
      w8/=np.sum(w8) #normalize
      gt_ext = np.concatenate([data[0:1,:,gt_disparity],data[:,:,gt_disparity],data[-1:height,:,gt_disparity]],axis = 0)
      gt_ext = np.concatenate([gt_ext[:,0:1], gt_ext[:,:], gt_ext[:,-1:width]],axis = 1)
      gs_ext = np.concatenate([data[0:1,:,gt_strength], data[:,:,gt_strength], data[-1:height,:,gt_strength]],axis = 0)
      gs_ext = np.concatenate([gs_ext[:,0:1], gs_ext[:,:], gs_ext[:,-1:width]],axis = 1)
      data9 = np.empty(shape=[height, width, 9], dtype=data.dtype)
      weight9 = np.empty(shape=[height, width, 9], dtype=data.dtype)
      for row in range(3):
      for col in range(3):
      pass
      data9 [...,row*3+col]= gt_ext[row:height+row, col:width+col]
      weight9[...,row*3+col]= gs_ext[row:height+row, col:width+col]
      data9 *= weight9/weight9 # make data=nan where wigth is 0
      # data = np.concatenate([data[...],np.empty_like(data[...,-1])], axis = 2) # data[...,4:] may be empty
      data = np.concatenate([data[...],np.empty(shape=[height,width,4],dtype=data.dtype)], axis = 2) # data[...,4:] may be empty
      data[...,-4] = np.nanmax(data9*nocenter9, axis=2)-np.nanmin(data9*nocenter9,axis=2)# will ignore nan
      np.nan_to_num(data9,copy=False) # replace all nan in data9 with 0.
      weight9 *= w8
      w_center = np.sum(weight9, axis=2)
      dw_center = np.sum(data9*weight9, axis=2)
      dw_center /= w_center # now dw_center - weighted average in the center
      data[...,-3] = np.abs(data[...,gt_disparity]- dw_center)
      # data[...,-2] = data[...,gt_disparity]- dw_center
      #data[...,-3] *= (data[...,-4] < 1.0) # just temporary
      #data[...,-3] *= (data[...,gt_disparity] < 5) #just temporary
      data[...,-2] =bad_num.astype(data.dtype)
      data [...,-1]= np.sum(np.nan_to_num(weight9/weight9),axis=2).astype(data.dtype)
      # data[...,-1] = dw_center
      labels +=["max-min","abs-center","badness","neibs"]
      #neib = np.concatenate([gt_ext[:height,:width,:],],axis = )
      pass
      return data, labels
      def result_npy_to_tiff(npy_path,
      ......
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