explore_data5m.py 68.1 KB
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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