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Commit 466ed6b1 authored by Andrey Filippov's avatar Andrey Filippov
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LWIR-related changes

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explore_data14.py
View file @ 466ed6b1
...@@ -180,7 +180,7 @@ def writeTFRecordsFromImageSet( ...@@ -180,7 +180,7 @@ def writeTFRecordsFromImageSet(
extra = np.concatenate(( extra = np.concatenate((
img_gt_aux.image[...,ijt.IJFGBG.AUX_DISP].reshape(-1,1), img_gt_aux.image[...,ijt.IJFGBG.AUX_DISP].reshape(-1,1),
img_gt_aux.image[...,ijt.IJFGBG.FG_STR].reshape(-1,1), img_gt_aux.image[...,ijt.IJFGBG.FG_DISP].reshape(-1,1),
img_gt_aux.image[...,ijt.IJFGBG.BG_DISP].reshape(-1,1), img_gt_aux.image[...,ijt.IJFGBG.BG_DISP].reshape(-1,1),
img_gt_aux.image[...,ijt.IJFGBG.RMS].reshape(-1,1), img_gt_aux.image[...,ijt.IJFGBG.RMS].reshape(-1,1),
img_gt_aux.image[...,ijt.IJFGBG.RMS_SPLIT].reshape(-1,1) img_gt_aux.image[...,ijt.IJFGBG.RMS_SPLIT].reshape(-1,1)
......
explore_data15.py 0 → 100644
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explore_data16.py 0 → 100644
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infer_qcds_01.py 0 → 100644
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infer_qcds_graph_01.py 0 → 100644
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#!/usr/bin/env python3
__copyright__ = "Copyright 2018, Elphel, Inc."
__license__ = "GPL-3.0+"
__email__ = "andrey@elphel.com"
'''
** Kind of obsolete now, can be used for testing **
Just inference, currently uses /data_ssd/data_sets/tf_data_5x5_main_13_heur/inference/
'''
import os
import sys
import numpy as np
import time
import shutil
##import qcstereo_network
import qcstereo_functions as qsf
import tensorflow as tf
#from tensorflow.python.ops import resource_variable_ops
#tf.ResourceVariable = resource_variable_ops.ResourceVariable
qsf.TIME_START = time.time()
qsf.TIME_LAST = qsf.TIME_START
IMG_WIDTH = 324 # tiles per image row
DEBUG_LEVEL= 1
try:
conf_file = sys.argv[1]
except IndexError:
print("Configuration path is required as a first argument. Optional second argument specifies root directory for data files")
exit(1)
try:
root_dir = sys.argv[2]
except IndexError:
root_dir = os.path.dirname(conf_file)
print ("Configuration file: " + conf_file)
parameters, dirs, files, _ = qsf.parseXmlConfig(conf_file, root_dir)
"""
Temporarily for backward compatibility
"""
if not "SLOSS_CLIP" in parameters:
parameters['SLOSS_CLIP'] = 0.5
print ("Old config, setting SLOSS_CLIP=", parameters['SLOSS_CLIP'])
"""
Defined in config file
"""
TILE_SIDE, TILE_LAYERS, TWO_TRAINS, NET_ARCH1, NET_ARCH2 = [None]*5
ABSOLUTE_DISPARITY,SYM8_SUB, WLOSS_LAMBDA, SLOSS_LAMBDA, SLOSS_CLIP = [None]*5
SPREAD_CONVERGENCE, INTER_CONVERGENCE, HOR_FLIP, DISP_DIFF_CAP, DISP_DIFF_SLOPE = [None]*5
CLUSTER_RADIUS = None
PARTIALS_WEIGHTS, MAX_IMGS_IN_MEM, MAX_FILES_PER_GROUP, BATCH_WEIGHTS, ONLY_TILE = [None] * 5
USE_CONFIDENCE, WBORDERS_ZERO, EPOCHS_TO_RUN, FILE_UPDATE_EPOCHS = [None] * 4
LR600,LR400,LR200,LR100,LR = [None]*5
SHUFFLE_FILES, EPOCHS_FULL_TEST, SAVE_TIFFS = [None] * 3
CHECKPOINT_PERIOD = None
TRAIN_BUFFER_GPU, TRAIN_BUFFER_CPU = [None]*2
TEST_TITLES = None
USE_SPARSE_ONLY = True
LOGFILE="results-infer.txt"
"""
Next gets globals from the config file
"""
globals().update(parameters)
WIDTH = 324
HEIGHT = 242
TILE_SIZE = TILE_SIDE* TILE_SIDE # == 81
FEATURES_PER_TILE = TILE_LAYERS * TILE_SIZE# == 324
BATCH_SIZE = ([1,2][TWO_TRAINS])*2*1000//25 # == 80 Each batch of tiles has balanced D/S tiles, shuffled batches but not inside batches
SUFFIX=(str(NET_ARCH1)+'-'+str(NET_ARCH2)+
(["R","A"][ABSOLUTE_DISPARITY]) +
(["NS","S8"][SYM8_SUB])+
"WLAM"+str(WLOSS_LAMBDA)+
"SLAM"+str(SLOSS_LAMBDA)+
"SCLP"+str(SLOSS_CLIP)+
(['_nG','_G'][SPREAD_CONVERGENCE])+
(['_nI','_I'][INTER_CONVERGENCE]) +
(['_nHF',"_HF"][HOR_FLIP]) +
('_CP'+str(DISP_DIFF_CAP)) +
('_S'+str(DISP_DIFF_SLOPE))
)
##NN_LAYOUT1 = qcstereo_network.NN_LAYOUTS[NET_ARCH1]
##NN_LAYOUT2 = qcstereo_network.NN_LAYOUTS[NET_ARCH2]
# Tiff export slice labels
SLICE_LABELS = ["nn_out_ext","hier_out_ext","gt_disparity","gt_strength"]#,
# "cutcorn_cost_nw","cutcorn_cost",
# "gt-avg_dist","avg8_disp","gt_disp","out-avg"]
##############################################################################
cluster_size = (2 * CLUSTER_RADIUS + 1) * (2 * CLUSTER_RADIUS + 1)
center_tile_index = 2 * CLUSTER_RADIUS * (CLUSTER_RADIUS + 1)
qsf.prepareFiles(dirs,
files,
suffix = SUFFIX)
"""
Next is tag for pb (pb == protocol buffer) model
"""
#PB_TAGS = ["model_pb"]
print ("Copying config files to results directory:\n ('%s' -> '%s')"%(conf_file,dirs['result']))
try:
os.makedirs(dirs['result'])
except:
pass
shutil.copy2(conf_file,dirs['result'])
LOGPATH = os.path.join(dirs['result'],LOGFILE)
image_data = qsf.initImageData( # just use image_data[0]
files = files,
max_imgs = MAX_IMGS_IN_MEM,
cluster_radius = 0, # CLUSTER_RADIUS,
tile_layers = TILE_LAYERS,
tile_side = TILE_SIDE,
width = IMG_WIDTH,
replace_nans = True,
infer = True,
keep_gt = True) # to generate same output files
cluster_radius = CLUSTER_RADIUS
ROOT_PATH = './attic/infer_qcds_graph'+SUFFIX+"/" # for tensorboard
try:
os.makedirs(os.path.dirname(files['inference']))
print ("Created directory ",os.path.dirname(files['inference']))
except:
pass
with tf.Session() as sess:
# Actually, refresh all the time and have an extra script to restore from it.
# use_Saved_Model = False
#if os.path.isdir(dirs['exportdir']):
# # check if dir contains "Saved Model" model
# use_saved_model = tf.saved_model.loader.maybe_saved_model_directory(dirs['exportdir'])
#if use_saved_model:
# print("Model restore: using Saved_Model model MetaGraph protocol buffer")
# meta_graph_source = tf.saved_model.loader.load(sess, [tf.saved_model.tag_constants.SERVING], dirs['exportdir'])
#else:
meta_graph_source = files["inference"]+'.meta'
print("Model restore: using conventionally saved model, but saving Saved Model for the next run")
print("MetaGraph source = "+str(meta_graph_source))
infer_saver = tf.train.import_meta_graph(meta_graph_source)
graph=tf.get_default_graph()
ph_corr2d = graph.get_tensor_by_name('ph_corr2d:0')
ph_target_disparity = graph.get_tensor_by_name('ph_target_disparity:0')
ph_ntile = graph.get_tensor_by_name('ph_ntile:0')
ph_ntile_out = graph.get_tensor_by_name('ph_ntile_out:0')
stage1done = graph.get_tensor_by_name('Disparity_net/stage1done:0') #<tf.Operation 'Siam_net/stage1done' type=Const>,
stage2_out_sparse = graph.get_tensor_by_name('Disparity_net/stage2_out_sparse:0')#not found
if not USE_SPARSE_ONLY: #Does it reduce the graph size?
stage2_out_full = graph.get_tensor_by_name('Disparity_net/stage2_out_full:0')
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
infer_saver.restore(sess, files["inference"])
merged = tf.summary.merge_all()
writer = tf.summary.FileWriter(ROOT_PATH, sess.graph)
lf = None
if LOGPATH:
lf=open(LOGPATH,"w") #overwrite previous (or make it "a"?
for nimg,_ in enumerate(image_data):
dataset_img = qsf.readImageData(
image_data = image_data,
files = files,
indx = nimg,
cluster_radius = 0, # CLUSTER_RADIUS,
tile_layers = TILE_LAYERS,
tile_side = TILE_SIDE,
width = IMG_WIDTH,
replace_nans = True,
infer = True,
keep_gt = True) # to generate same output files
img_corr2d = dataset_img['corr2d'] # (?,324)
img_target = dataset_img['target_disparity'] # (?,1)
img_ntile = dataset_img['ntile'].reshape([-1]) # (?) - 0...78k int32
#run first stage network
qsf.print_time("Running inferred model, stage1", end=" ")
_ = sess.run([stage1done],
feed_dict={ph_corr2d: img_corr2d,
ph_target_disparity: img_target,
ph_ntile: img_ntile })
qsf.print_time("Done.")
qsf.print_time("Running inferred model, stage2", end=" ")
disp_out, = sess.run([stage2_out_sparse],
feed_dict={ph_ntile_out: img_ntile })
qsf.print_time("Done.")
result_file = files['result'][nimg].replace('.npy','-infer.npy') #not to overwrite training result files that are more complete
try:
os.makedirs(os.path.dirname(result_file))
except:
pass
rslt = np.concatenate(
[disp_out.reshape(-1,1),
dataset_img['t_disps'], #t_disps[ntest],
dataset_img['gtruths'], # gtruths[ntest],
],1)
np.save(result_file, rslt.reshape(HEIGHT,WIDTH,-1))
rslt = qsf.eval_results(result_file, ABSOLUTE_DISPARITY, radius=CLUSTER_RADIUS, logfile=lf) # (re-loads results). Only uses first 4 layers
if SAVE_TIFFS:
qsf.result_npy_to_tiff(result_file, ABSOLUTE_DISPARITY, fix_nan = True,labels=SLICE_LABELS, logfile=lf)
"""
Remove dataset_img (if it is not [0] to reduce memory footprint
"""
image_data[nimg] = None
"""
Save MetaGraph to Saved_Model in *.pb (protocol buffer) format to
be able to use from Java
"""
# force clean
shutil.rmtree(dirs['exportdir'], ignore_errors=True)
builder = tf.saved_model.builder.SavedModelBuilder(dirs['exportdir'])
builder.add_meta_graph_and_variables(sess,[tf.saved_model.tag_constants.SERVING],main_op=tf.local_variables_initializer())
builder.save(False) # True = *.pbtxt, False = *.pb
if lf:
lf.close()
writer.close()
infer_qcds_graph_01_pbtest.py 0 → 100644
View file @ 466ed6b1
#!/usr/bin/env python3
__copyright__ = "Copyright 2018, Elphel, Inc."
__license__ = "GPL-3.0+"
__email__ = "andrey@elphel.com"
# Just inference, currently uses /data_ssd/data_sets/tf_data_5x5_main_13_heur/inference/
# TODO: Updatew for LWIR !
import os
import sys
import numpy as np
import time
import shutil
##import qcstereo_network
import qcstereo_functions as qsf
import tensorflow as tf
#from tensorflow.python.ops import resource_variable_ops
#tf.ResourceVariable = resource_variable_ops.ResourceVariable
qsf.TIME_START = time.time()
qsf.TIME_LAST = qsf.TIME_START
IMG_WIDTH = 324 # tiles per image row
DEBUG_LEVEL= 1
try:
conf_file = sys.argv[1]
except IndexError:
print("Configuration path is required as a first argument. Optional second argument specifies root directory for data files")
exit(1)
try:
root_dir = sys.argv[2]
except IndexError:
root_dir = os.path.dirname(conf_file)
print ("Configuration file: " + conf_file)
parameters, dirs, files, _ = qsf.parseXmlConfig(conf_file, root_dir)
"""
Temporarily for backward compatibility
"""
if not "SLOSS_CLIP" in parameters:
parameters['SLOSS_CLIP'] = 0.5
print ("Old config, setting SLOSS_CLIP=", parameters['SLOSS_CLIP'])
"""
Defined in config file
"""
TILE_SIDE, TILE_LAYERS, TWO_TRAINS, NET_ARCH1, NET_ARCH2 = [None]*5
ABSOLUTE_DISPARITY,SYM8_SUB, WLOSS_LAMBDA, SLOSS_LAMBDA, SLOSS_CLIP = [None]*5
SPREAD_CONVERGENCE, INTER_CONVERGENCE, HOR_FLIP, DISP_DIFF_CAP, DISP_DIFF_SLOPE = [None]*5
CLUSTER_RADIUS = None
PARTIALS_WEIGHTS, MAX_IMGS_IN_MEM, MAX_FILES_PER_GROUP, BATCH_WEIGHTS, ONLY_TILE = [None] * 5
USE_CONFIDENCE, WBORDERS_ZERO, EPOCHS_TO_RUN, FILE_UPDATE_EPOCHS = [None] * 4
LR600,LR400,LR200,LR100,LR = [None]*5
SHUFFLE_FILES, EPOCHS_FULL_TEST, SAVE_TIFFS = [None] * 3
CHECKPOINT_PERIOD = None
TRAIN_BUFFER_GPU, TRAIN_BUFFER_CPU = [None]*2
TEST_TITLES = None
USE_SPARSE_ONLY = True
LOGFILE="results-infer.txt"
"""
Next gets globals from the config file
"""
globals().update(parameters)
WIDTH = 324
HEIGHT = 242
TILE_SIZE = TILE_SIDE* TILE_SIDE # == 81
FEATURES_PER_TILE = TILE_LAYERS * TILE_SIZE# == 324
BATCH_SIZE = ([1,2][TWO_TRAINS])*2*1000//25 # == 80 Each batch of tiles has balanced D/S tiles, shuffled batches but not inside batches
SUFFIX=(str(NET_ARCH1)+'-'+str(NET_ARCH2)+
(["R","A"][ABSOLUTE_DISPARITY]) +
(["NS","S8"][SYM8_SUB])+
"WLAM"+str(WLOSS_LAMBDA)+
"SLAM"+str(SLOSS_LAMBDA)+
"SCLP"+str(SLOSS_CLIP)+
(['_nG','_G'][SPREAD_CONVERGENCE])+
(['_nI','_I'][INTER_CONVERGENCE]) +
(['_nHF',"_HF"][HOR_FLIP]) +
('_CP'+str(DISP_DIFF_CAP)) +
('_S'+str(DISP_DIFF_SLOPE))
)
##NN_LAYOUT1 = qcstereo_network.NN_LAYOUTS[NET_ARCH1]
##NN_LAYOUT2 = qcstereo_network.NN_LAYOUTS[NET_ARCH2]
# Tiff export slice labels
SLICE_LABELS = ["nn_out_ext","hier_out_ext","gt_disparity","gt_strength"]#,
# "cutcorn_cost_nw","cutcorn_cost",
# "gt-avg_dist","avg8_disp","gt_disp","out-avg"]
##############################################################################
cluster_size = (2 * CLUSTER_RADIUS + 1) * (2 * CLUSTER_RADIUS + 1)
center_tile_index = 2 * CLUSTER_RADIUS * (CLUSTER_RADIUS + 1)
qsf.prepareFiles(dirs,
files,
suffix = SUFFIX)
"""
Next is tag for pb (pb == protocol buffer) model
"""
#PB_TAGS = ["model_pb"]
print ("Copying config files to results directory:\n ('%s' -> '%s')"%(conf_file,dirs['result']))
try:
os.makedirs(dirs['result'])
except:
pass
shutil.copy2(conf_file,dirs['result'])
LOGPATH = os.path.join(dirs['result'],LOGFILE)
image_data = qsf.initImageData( # just use image_data[0]
files = files,
max_imgs = MAX_IMGS_IN_MEM,
cluster_radius = 0, # CLUSTER_RADIUS,
tile_layers = TILE_LAYERS,
tile_side = TILE_SIDE,
width = IMG_WIDTH,
replace_nans = True,
infer = True,
keep_gt = True) # to generate same output files
cluster_radius = CLUSTER_RADIUS
ROOT_PATH = './attic/infer_qcds_graph'+SUFFIX+"/" # for tensorboard
try:
os.makedirs(os.path.dirname(files['inference']))
print ("Created directory ",os.path.dirname(files['inference']))
except:
pass
with tf.Session() as sess:
# Actually, refresh all the time and have an extra script to restore from it.
# use_Saved_Model = False
#if os.path.isdir(dirs['exportdir']):
# # check if dir contains "Saved Model" model
# use_saved_model = tf.saved_model.loader.maybe_saved_model_directory(dirs['exportdir'])
#if use_saved_model:
# print("Model restore: using Saved_Model model MetaGraph protocol buffer")
# meta_graph_source = tf.saved_model.loader.load(sess, [tf.saved_model.tag_constants.SERVING], dirs['exportdir'])
#else:
use_saved_model = tf.saved_model.loader.maybe_saved_model_directory(dirs['exportdir'])
if not use_saved_model:
print("ERROR: Saved_Model not found. Run previous script to create it.")
sys.exit()
meta_graph_source = tf.saved_model.loader.load(sess, [tf.saved_model.tag_constants.SERVING], dirs['exportdir'])
infer_saver = tf.train.import_meta_graph(meta_graph_source)
graph=tf.get_default_graph()
ph_corr2d = graph.get_tensor_by_name('ph_corr2d:0')
ph_target_disparity = graph.get_tensor_by_name('ph_target_disparity:0')
ph_ntile = graph.get_tensor_by_name('ph_ntile:0')
ph_ntile_out = graph.get_tensor_by_name('ph_ntile_out:0')
stage1done = graph.get_tensor_by_name('Disparity_net/stage1done:0') #<tf.Operation 'Siam_net/stage1done' type=Const>,
stage2_out_sparse = graph.get_tensor_by_name('Disparity_net/stage2_out_sparse:0')#not found
if not USE_SPARSE_ONLY: #Does it reduce the graph size?
stage2_out_full = graph.get_tensor_by_name('Disparity_net/stage2_out_full:0')
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
#infer_saver.restore(sess, dirs['exportdir'])
#infer_saver.restore(sess, files["inference"])
infer_saver.restore(sess,dirs['exportdir']+'/variables/variables')
merged = tf.summary.merge_all()
writer = tf.summary.FileWriter(ROOT_PATH, sess.graph)
lf = None
if LOGPATH:
lf=open(LOGPATH,"w") #overwrite previous (or make it "a"?
for nimg,_ in enumerate(image_data):
dataset_img = qsf.readImageData(
image_data = image_data,
files = files,
indx = nimg,
cluster_radius = 0, # CLUSTER_RADIUS,
tile_layers = TILE_LAYERS,
tile_side = TILE_SIDE,
width = IMG_WIDTH,
replace_nans = True,
infer = True,
keep_gt = True) # to generate same output files
img_corr2d = dataset_img['corr2d'] # (?,324)
img_target = dataset_img['target_disparity'] # (?,1)
img_ntile = dataset_img['ntile'].reshape([-1]) # (?) - 0...78k int32
#run first stage network
qsf.print_time("Running inferred model, stage1", end=" ")
_ = sess.run([stage1done],
feed_dict={ph_corr2d: img_corr2d,
ph_target_disparity: img_target,
ph_ntile: img_ntile })
qsf.print_time("Done.")
qsf.print_time("Running inferred model, stage2", end=" ")
disp_out, = sess.run([stage2_out_sparse],
feed_dict={ph_ntile_out: img_ntile })
qsf.print_time("Done.")
result_file = files['result'][nimg].replace('.npy','-infer.npy') #not to overwrite training result files that are more complete
try:
os.makedirs(os.path.dirname(result_file))
except:
pass
rslt = np.concatenate(
[disp_out.reshape(-1,1),
dataset_img['t_disps'], #t_disps[ntest],
dataset_img['gtruths'], # gtruths[ntest],
],1)
np.save(result_file, rslt.reshape(HEIGHT,WIDTH,-1))
rslt = qsf.eval_results(result_file, ABSOLUTE_DISPARITY, radius=CLUSTER_RADIUS, logfile=lf) # (re-loads results). Only uses first 4 layers
if SAVE_TIFFS:
qsf.result_npy_to_tiff(result_file, ABSOLUTE_DISPARITY, fix_nan = True,labels=SLICE_LABELS, logfile=lf)
"""
Remove dataset_img (if it is not [0] to reduce memory footprint
"""
image_data[nimg] = None
if lf:
lf.close()
writer.close()
nn_ds_neibs31.py
View file @ 466ed6b1
...@@ -20,7 +20,7 @@ import tensorflow as tf ...@@ -20,7 +20,7 @@ import tensorflow as tf
qsf.TIME_START = time.time() qsf.TIME_START = time.time()
qsf.TIME_LAST = qsf.TIME_START qsf.TIME_LAST = qsf.TIME_START
IMG_WIDTH = 20 # 324 # tiles per image row #IMG_WIDTH = 20 # 324 # tiles per image row Defined in config
DEBUG_LEVEL= 1 DEBUG_LEVEL= 1
try: try:
...@@ -59,6 +59,8 @@ CHECKPOINT_PERIOD = None ...@@ -59,6 +59,8 @@ CHECKPOINT_PERIOD = None
TRAIN_BUFFER_GPU, TRAIN_BUFFER_CPU = [None]*2 TRAIN_BUFFER_GPU, TRAIN_BUFFER_CPU = [None]*2
TEST_TITLES = None TEST_TITLES = None
LOGFILE="results.txt" LOGFILE="results.txt"
IMG_WIDTH = 20
IMG_HEIGHT = 15
""" """
Next gets globals from the config file Next gets globals from the config file
""" """
...@@ -70,8 +72,8 @@ qsf.setCorr2Limits(CORR2D_LIMITS) # limit min/max 2d correlation tiles values ...@@ -70,8 +72,8 @@ qsf.setCorr2Limits(CORR2D_LIMITS) # limit min/max 2d correlation tiles values
#exit(0) #exit(0)
WIDTH = 20 # 324 #WIDTH = 20 # 324
HEIGHT = 15 # 242 #HEIGHT = 15 # 242
TILE_SIZE = TILE_SIDE* TILE_SIDE # == 81 TILE_SIZE = TILE_SIDE* TILE_SIDE # == 81
FEATURES_PER_TILE = TILE_LAYERS * TILE_SIZE# == 324 FEATURES_PER_TILE = TILE_LAYERS * TILE_SIZE# == 324
BATCH_SIZE = ([1,2][TWO_TRAINS])*2*1000//25 # == 80 Each batch of tiles has balanced D/S tiles, shuffled batches but not inside batches BATCH_SIZE = ([1,2][TWO_TRAINS])*2*1000//25 # == 80 Each batch of tiles has balanced D/S tiles, shuffled batches but not inside batches
...@@ -93,11 +95,6 @@ NN_LAYOUT2 = qcstereo_network.NN_LAYOUTS[NET_ARCH2] ...@@ -93,11 +95,6 @@ NN_LAYOUT2 = qcstereo_network.NN_LAYOUTS[NET_ARCH2]
USE_PARTIALS = not PARTIALS_WEIGHTS is None # False - just a single Siamese net, True - partial outputs that use concentric squares of the first level subnets USE_PARTIALS = not PARTIALS_WEIGHTS is None # False - just a single Siamese net, True - partial outputs that use concentric squares of the first level subnets
# Tiff export slice labels # Tiff export slice labels
SLICE_LABELS = ["nn_out_ext","target_disp","gt_disparity","gt_strength",
"cutcorn_cost_nw","cutcorn_cost",
"gt_avg_dist","avg8_disp","gt_disp","out_avg",
"aux_disp","fg_disp","bg_disp","gt_rms","gt_rms_split"]
############################################################################## ##############################################################################
cluster_size = (2 * CLUSTER_RADIUS + 1) * (2 * CLUSTER_RADIUS + 1) cluster_size = (2 * CLUSTER_RADIUS + 1) * (2 * CLUSTER_RADIUS + 1)
center_tile_index = 2 * CLUSTER_RADIUS * (CLUSTER_RADIUS + 1) center_tile_index = 2 * CLUSTER_RADIUS * (CLUSTER_RADIUS + 1)
...@@ -130,7 +127,7 @@ qsf.evaluateAllResults(result_files = files['result'], ...@@ -130,7 +127,7 @@ qsf.evaluateAllResults(result_files = files['result'],
absolute_disparity = ABSOLUTE_DISPARITY, absolute_disparity = ABSOLUTE_DISPARITY,
cluster_radius = CLUSTER_RADIUS, cluster_radius = CLUSTER_RADIUS,
fgbg_mode= FGBG_MODE, fgbg_mode= FGBG_MODE,
labels = SLICE_LABELS, labels = qsf.SLICE_LABELS,
logpath= LOGPATH) logpath= LOGPATH)
image_data = qsf.initImageData( image_data = qsf.initImageData(
...@@ -328,8 +325,8 @@ with tf.name_scope('epoch_average'): ...@@ -328,8 +325,8 @@ with tf.name_scope('epoch_average'):
tf.compat.v1.summary.scalar("sq_diff_epoch", tf_ph_sq_diff) tf.compat.v1.summary.scalar("sq_diff_epoch", tf_ph_sq_diff)
tf.compat.v1.summary.scalar("gtvar_diff", tf_gtvar_diff) tf.compat.v1.summary.scalar("gtvar_diff", tf_gtvar_diff)
tf.compat.v1.summary.scalar("img_test0", tf_img_test0) tf.compat.v1.summary.scalar("Disparity error", tf_img_test0)
tf.compat.v1.summary.scalar("img_test9", tf_img_test9) tf.compat.v1.summary.scalar("NN gain over heuristic", tf_img_test9)
trainable_vars= tf.trainable_variables() trainable_vars= tf.trainable_variables()
lr= tf.compat.v1.placeholder(tf.float32) lr= tf.compat.v1.placeholder(tf.float32)
...@@ -425,7 +422,7 @@ with tf.Session() as sess: ...@@ -425,7 +422,7 @@ with tf.Session() as sess:
gtvar_test_hist= np.empty(dataset_test_size, dtype=np.float32) gtvar_test_hist= np.empty(dataset_test_size, dtype=np.float32)
gtvar_train = 0.0 gtvar_train = 0.0
gtvar_test = 0.0 gtvar_test = 0.0
img_gain_test0 = 1.0 img_gain_test0 = 0.2
img_gain_test9 = 1.0 img_gain_test9 = 1.0
thr=None thr=None
...@@ -603,14 +600,14 @@ with tf.Session() as sess: ...@@ -603,14 +600,14 @@ with tf.Session() as sess:
# Read the full image # Read the full image
################################################### ###################################################
## test_summaries_img = [0.0]*len(ind_img) # datasets_img) ## test_summaries_img = [0.0]*len(ind_img) # datasets_img)
disp_out= np.empty((WIDTH*HEIGHT), dtype=np.float32) disp_out= np.empty((IMG_WIDTH * IMG_HEIGHT), dtype=np.float32)
dbg_cost_nw= np.empty((WIDTH*HEIGHT), dtype=np.float32) dbg_cost_nw= np.empty((IMG_WIDTH * IMG_HEIGHT), dtype=np.float32)
dbg_cost_w= np.empty((WIDTH*HEIGHT), dtype=np.float32) dbg_cost_w= np.empty((IMG_WIDTH * IMG_HEIGHT), dtype=np.float32)
dbg_d= np.empty((WIDTH*HEIGHT), dtype=np.float32) dbg_d= np.empty((IMG_WIDTH * IMG_HEIGHT), dtype=np.float32)
dbg_avg_disparity = np.empty((WIDTH*HEIGHT), dtype=np.float32) dbg_avg_disparity = np.empty((IMG_WIDTH * IMG_HEIGHT), dtype=np.float32)
dbg_gt_disparity = np.empty((WIDTH*HEIGHT), dtype=np.float32) dbg_gt_disparity = np.empty((IMG_WIDTH * IMG_HEIGHT), dtype=np.float32)
dbg_offs = np.empty((WIDTH*HEIGHT), dtype=np.float32) dbg_offs = np.empty((IMG_WIDTH * IMG_HEIGHT), dtype=np.float32)
for ntest in ind_img: # datasets_img): for ntest in ind_img: # datasets_img):
dataset_img = qsf.readImageData( dataset_img = qsf.readImageData(
...@@ -676,27 +673,27 @@ with tf.Session() as sess: ...@@ -676,27 +673,27 @@ with tf.Session() as sess:
dbg_avg_disparity.reshape(-1,1), dbg_avg_disparity.reshape(-1,1),
dbg_gt_disparity.reshape(-1,1), dbg_gt_disparity.reshape(-1,1),
dbg_offs.reshape(-1,1), dbg_offs.reshape(-1,1),
extra, # len 3..6, extra, # len 3..6, #adding extra data layers
#adding extra data layers
],1) ],1)
num_slices = rslt.shape[1] num_slices = rslt.shape[1]
np.save( np.save(
result_file, result_file,
rslt.reshape(HEIGHT,WIDTH,-1)) rslt.reshape(IMG_HEIGHT, IMG_WIDTH,-1))
eval_rslt = qsf.eval_results( eval_rslt = qsf.eval_results(
result_file, result_file,
ABSOLUTE_DISPARITY, ABSOLUTE_DISPARITY,
radius=0, # CLUSTER_RADIUS, radius=0, # CLUSTER_RADIUS,
last_fgbg_mode = 1, last_fgbg_mode = 1,
logfile=lf) logfile=lf)
img_gain_test0 = eval_rslt[0][0]/eval_rslt[0][1] # img_gain_test0 = eval_rslt[0][0]/eval_rslt[0][1]
img_gain_test0 = eval_rslt[9][1]
img_gain_test9 = eval_rslt[9][0]/eval_rslt[9][1] img_gain_test9 = eval_rslt[9][0]/eval_rslt[9][1]
if SAVE_TIFFS: if SAVE_TIFFS:
qsf.result_npy_to_tiff( qsf.result_npy_to_tiff(
result_file, result_file,
ABSOLUTE_DISPARITY, ABSOLUTE_DISPARITY,
fix_nan = True, fix_nan = True,
labels=SLICE_LABELS[0:num_slices], labels=qsf.SLICE_LABELS[0:num_slices],
logfile=lf) logfile=lf)
""" """
......
nn_eval_01.py 0 → 100644
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nn_eval_lwir.py 0 → 100644
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qcstereo_functions.py
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