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python3-imagej-tiff
Commit 3519f5ec authored by Andrey Filippov's avatar Andrey Filippov
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next versions, some cleanup

parent 51cc074c
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Showing with 1270 additions and 103 deletions
explore_data5.py 0 → 100644
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nn_ds_neibs16.py
View file @ 3519f5ec
#!/usr/bin/env python3
from numpy import float64
from tensorflow.contrib.losses.python.metric_learning.metric_loss_ops import npairs_loss
from debian.deb822 import PdiffIndex
##from numpy import float64
##from tensorflow.contrib.losses.python.metric_learning.metric_loss_ops import npairs_loss
##from debian.deb822 import PdiffIndex
__copyright__ = "Copyright 2018, Elphel, Inc."
__license__ = "GPL-3.0+"
__email__ = "andrey@elphel.com"
from PIL import Image
##from PIL import Image
import os
import sys
import glob
##import glob
import numpy as np
import itertools
##import itertools
import time
import matplotlib.pyplot as plt
##import matplotlib.pyplot as plt
import shutil
from threading import Thread
......@@ -49,7 +49,7 @@ except IndexError:
root_dir = os.path.dirname(conf_file)
print ("Configuration file: " + conf_file)
parameters, dirs, files = qsf.parseXmlConfig(conf_file, root_dir)
parameters, dirs, files, _ = qsf.parseXmlConfig(conf_file, root_dir)
"""
Temporarily for backward compatibility
"""
......@@ -221,7 +221,7 @@ if SPREAD_CONVERGENCE:
else:
outs, inp_weights = qcstereo_network.networks_siam(
input= corr2d_Nx325,
input_tensor= corr2d_Nx325,
input_global = None,
layout1 = NN_LAYOUT1,
layout2 = NN_LAYOUT2,
......@@ -247,7 +247,7 @@ G_losses[0], _disp_slice, _d_gt_slice, _out_diff, _out_diff2, _w_norm, _out_wdif
absolute_disparity = ABSOLUTE_DISPARITY,
use_confidence = USE_CONFIDENCE, # True,
lambda_conf_avg = 0.01,
lambda_conf_pwr = 0.1,
## lambda_conf_pwr = 0.1,
conf_pwr = 2.0,
gt_conf_offset = 0.08,
gt_conf_pwr = 2.0,
......@@ -268,7 +268,7 @@ for n in range (1,len(partials)):
absolute_disparity = ABSOLUTE_DISPARITY,
use_confidence = USE_CONFIDENCE, # True,
lambda_conf_avg = 0.01,
lambda_conf_pwr = 0.1,
# lambda_conf_pwr = 0.1,
conf_pwr = 2.0,
gt_conf_offset = 0.08,
gt_conf_pwr = 2.0,
......@@ -702,7 +702,7 @@ with tf.Session() as sess:
if ntest > 0:
image_data[ntest] = None
# Close writers
# Close writers
train_writer.close()
test_writer.close()
test_writer1.close()
......
qcstereo_functions.py
View file @ 3519f5ec
......@@ -30,13 +30,14 @@ def print_time(txt="",end="\n"):
txt +=" "
print(("%s"+bcolors.BOLDWHITE+"at %.4fs (+%.4fs)"+bcolors.ENDC)%(txt,t-TIME_START,t-TIME_LAST), end = end, flush=True)
TIME_LAST = t
def parseXmlConfig(conf_file, root_dir):
tree = ET.parse(conf_file)
root = tree.getroot()
parameters = {}
for p in root.find('parameters'):
parameters[p.tag]=eval(p.text.strip())
globals
# globals
dirs={}
for p in root.find('directories'):
dirs[p.tag]=eval(p.text.strip())
......@@ -46,7 +47,11 @@ def parseXmlConfig(conf_file, root_dir):
for p in root.find('files'):
files[p.tag]=eval(p.text.strip())
# globals().update(parameters)
return parameters, dirs, files
dbg_parameters = {}
for p in root.find('dbg_parameters'):
dbg_parameters[p.tag]=eval(p.text.strip())
return parameters, dirs, files, dbg_parameters
......@@ -84,7 +89,8 @@ def readTFRewcordsEpoch(train_filename):
npy_dir_name = "npy"
dirname = os.path.dirname(train_filename)
npy_dir = os.path.join(dirname, npy_dir_name)
filebasename, file_extension = os.path.splitext(train_filename)
# filebasename, file_extension = os.path.splitext(train_filename)
filebasename, _ = os.path.splitext(train_filename)
filebasename = os.path.basename(filebasename)
file_corr2d = os.path.join(npy_dir,filebasename + '_corr2d.npy')
file_target_disparity = os.path.join(npy_dir,filebasename + '_target_disparity.npy')
......@@ -179,7 +185,7 @@ def add_neibs(npa_ext,radius):
height = npa_ext.shape[0]-2*radius
width = npa_ext.shape[1]-2*radius
side = 2 * radius + 1
size = side * side
# size = side * side
npa_neib = np.empty((height, width, side, side, npa_ext.shape[2]), dtype = npa_ext.dtype)
for dy in range (side):
for dx in range (side):
......@@ -187,8 +193,8 @@ def add_neibs(npa_ext,radius):
return npa_neib.reshape(height, width, -1)
def extend_img_to_clusters(datasets_img,radius, width): # = 324):
side = 2 * radius + 1
size = side * side
# side = 2 * radius + 1
# size = side * side
if len(datasets_img) ==0:
return
num_tiles = datasets_img[0]['corr2d'].shape[0]
......@@ -210,7 +216,7 @@ def reformat_to_clusters(datasets_data, cluster_radius):
def flip_horizontal(datasets_data, cluster_radius, tile_layers, tile_side):
cluster_side = 2 * cluster_radius + 1
cluster_size = cluster_side * cluster_side
# cluster_size = cluster_side * cluster_side
"""
TILE_LAYERS = 4
TILE_SIDE = 9 # 7
......@@ -238,8 +244,8 @@ TILE_SIZE = TILE_SIDE* TILE_SIDE # == 81
rec['target_disparity'] = target_disparity.reshape((target_disparity.shape[0],-1))
rec['gt_ds'] = gt_ds.reshape((gt_ds.shape[0],-1))
def replace_nan(datasets_data, cluster_radius):
cluster_size = (2 * cluster_radius + 1) * (2 * cluster_radius + 1)
def replace_nan(datasets_data): # , cluster_radius):
# cluster_size = (2 * cluster_radius + 1) * (2 * cluster_radius + 1)
# Reformat input data
for rec in datasets_data:
if not rec is None:
......@@ -259,7 +265,7 @@ def permute_to_swaps(perm):
def shuffle_in_place(datasets_data, indx, period):
swaps = permute_to_swaps(np.random.permutation(len(datasets_data)))
num_entries = datasets_data[0]['corr2d'].shape[0] // period
# num_entries = datasets_data[0]['corr2d'].shape[0] // period
for swp in swaps:
ds0 = datasets_data[swp[0]]
ds1 = datasets_data[swp[1]]
......@@ -279,9 +285,10 @@ def shuffle_chunks_in_place(datasets_data, tiles_groups_per_chunk):
"""
Improve shuffling by preserving indices inside batches (0 <->0, ... 39 <->39 for 40 tile group batches)
"""
num_files = len(datasets_data)
# num_files = len(datasets_data)
#chunks_per_file = datasets_data[0]['target_disparity']
for nf, ds in enumerate(datasets_data):
# for nf, ds in enumerate(datasets_data):
for ds in datasets_data:
groups_per_file = ds['corr2d'].shape[0]
chunks_per_file = groups_per_file//tiles_groups_per_chunk
permut = np.random.permutation(chunks_per_file)
......@@ -327,7 +334,8 @@ def zip_lvar_hvar(datasets_all_data, del_src = True):
'target_disparity': np.empty((recs[0]['target_disparity'].shape[0]*num_sets_to_combine,recs[0]['target_disparity'].shape[1]),dtype=np.float32),
'gt_ds': np.empty((recs[0]['gt_ds'].shape[0]*num_sets_to_combine, recs[0]['gt_ds'].shape[1]),dtype=np.float32)}
for nset, reci in enumerate(recs):
# for nset, reci in enumerate(recs):
for nset, _ in enumerate(recs):
rec['corr2d'] [nset::num_sets_to_combine] = recs[nset]['corr2d']
rec['target_disparity'][nset::num_sets_to_combine] = recs[nset]['target_disparity']
rec['gt_ds'] [nset::num_sets_to_combine] = recs[nset]['gt_ds']
......@@ -356,10 +364,10 @@ def initTrainTestData(
max_files_per_group, # shuffling buffer for files
two_trains,
train_next):
datasets_train_lvar = []
datasets_train_hvar = []
datasets_train_lvar1 = []
datasets_train_hvar1 = []
# datasets_train_lvar = []
# datasets_train_hvar = []
# datasets_train_lvar1 = []
# datasets_train_hvar1 = []
datasets_train_all = [[],[],[],[]]
for n_train, f_train in enumerate(files['train']):
if len(f_train) and ((n_train<2) or two_trains):
......@@ -445,7 +453,8 @@ def readImageData(image_data,
cluster_radius,
width)
if replace_nans:
replace_nan([image_data[indx]], cluster_radius)
# replace_nan([image_data[indx]], cluster_radius)
replace_nan([image_data[indx]])
return image_data[indx]
......@@ -477,7 +486,7 @@ def evaluateAllResults(result_files, absolute_disparity, cluster_radius):
def result_npy_to_tiff(npy_path, absolute, fix_nan, insert_deltas=True):
def result_npy_prepare(npy_path, absolute, fix_nan, insert_deltas=True):
"""
@param npy_path full path to the npy file with 4-layer data (242,324,4) - nn_disparity(offset), target_disparity, gt disparity, gt strength
......@@ -485,10 +494,9 @@ def result_npy_to_tiff(npy_path, absolute, fix_nan, insert_deltas=True):
@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
"""
tiff_path = npy_path.replace('.npy','.tiff')
data = np.load(npy_path) #(324,242,4) [nn_disp, target_disp,gt_disp, gt_conf]
nn_out = 0
target_disparity = 1
# target_disparity = 1
gt_disparity = 2
gt_strength = 3
if not absolute:
......@@ -501,20 +509,28 @@ def result_npy_to_tiff(npy_path, absolute, fix_nan, insert_deltas=True):
data = np.concatenate([data[...,0:4],data[...,0:2],data[...,0:2],data[...,4:]], axis = 2)
data[...,6] -= data[...,gt_disparity]
data[...,7] -= data[...,gt_disparity]
for l in [4,5,6,7]:
for l in [2, 4, 5, 6, 7]:
data[...,l] = np.select([data[...,gt_strength]==0.0, data[...,gt_strength]>0.0], [np.nan,data[...,l]])
# 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]])
# data[...,4] = np.select([data[...,3]==0.0, data[...,3]>0.0], [np.nan,data[...,4]])
# data[...,5] = np.select([data[...,3]==0.0, data[...,3]>0.0], [np.nan,data[...,5]])
return data
def result_npy_to_tiff(npy_path, absolute, fix_nan, insert_deltas=True):
"""
@param npy_path full path to the npy file with 4-layer data (242,324,4) - nn_disparity(offset), target_disparity, gt disparity, gt strength
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
"""
data = result_npy_prepare(npy_path, absolute, fix_nan, insert_deltas)
tiff_path = npy_path.replace('.npy','.tiff')
data = data.transpose(2,0,1)
print("Saving results to TIFF: "+tiff_path)
imagej_tiffwriter.save(tiff_path,data[...,np.newaxis])
def eval_results(rslt_path, absolute,
min_disp = -0.1, #minimal GT disparity
max_disp = 20.0, # maximal GT disparity
......
qcstereo_losses.py
View file @ 3519f5ec
......@@ -4,7 +4,7 @@ __license__ = "GPL-3.0+"
__email__ = "andrey@elphel.com"
#from numpy import float64
import numpy as np
#import numpy as np
import tensorflow as tf
def smoothLoss(out_batch, # [batch_size,(1..2)] tf_result
......@@ -76,7 +76,7 @@ def batchLoss(out_batch, # [batch_size,(1..2)] tf_result
absolute_disparity = False, #when false there should be no activation on disparity output !
use_confidence = False,
lambda_conf_avg = 0.01,
lambda_conf_pwr = 0.1,
## lambda_conf_pwr = 0.1,
conf_pwr = 2.0,
gt_conf_offset = 0.08,
gt_conf_pwr = 1.0,
......@@ -90,14 +90,14 @@ def batchLoss(out_batch, # [batch_size,(1..2)] tf_result
Here confidence should be after relU. Disparity - may be also if absolute, but no activation if output is residual disparity
"""
tf_lambda_conf_avg = tf.constant(lambda_conf_avg, dtype=tf.float32, name="tf_lambda_conf_avg")
tf_lambda_conf_pwr = tf.constant(lambda_conf_pwr, dtype=tf.float32, name="tf_lambda_conf_pwr")
tf_conf_pwr = tf.constant(conf_pwr, dtype=tf.float32, name="tf_conf_pwr")
## tf_lambda_conf_pwr = tf.constant(lambda_conf_pwr, dtype=tf.float32, name="tf_lambda_conf_pwr")
## tf_conf_pwr = tf.constant(conf_pwr, dtype=tf.float32, name="tf_conf_pwr")
tf_gt_conf_offset = tf.constant(gt_conf_offset, dtype=tf.float32, name="tf_gt_conf_offset")
tf_gt_conf_pwr = tf.constant(gt_conf_pwr, dtype=tf.float32, name="tf_gt_conf_pwr")
tf_num_tiles = tf.shape(gt_ds_batch)[0]
tf_0f = tf.constant(0.0, dtype=tf.float32, name="tf_0f")
tf_1f = tf.constant(1.0, dtype=tf.float32, name="tf_1f")
tf_maxw = tf.constant(1.0, dtype=tf.float32, name="tf_maxw")
## tf_maxw = tf.constant(1.0, dtype=tf.float32, name="tf_maxw")
tf_disp_diff_cap2= tf.constant(disp_diff_cap*disp_diff_cap, dtype=tf.float32, name="disp_diff_cap2")
tf_disp_diff_slope= tf.constant(disp_diff_slope, dtype=tf.float32, name="disp_diff_slope")
......@@ -197,7 +197,7 @@ def weightsLoss(inp_weights,
tile_side,
wborders_zero):
# [batch_size,(1..2)] tf_result
# [batch_size,(1..2)] tf_result
# weights_lambdas): # single lambda or same length as inp_weights.shape[1]
"""
Enforcing 'smooth' weights for the input 2d correlation tiles
......
qcstereo_network.py
View file @ 3519f5ec
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