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lwir-nn
Commit ddd193c9 authored by Andrey Filippov's avatar Andrey Filippov
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more lwir-related updates

parent e3d871ba
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explore_data10.py 0 → 100644
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explore_data11.py 0 → 100644
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explore_data9.py
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......@@ -1693,7 +1693,7 @@ if __name__ == "__main__":
VARIANCE_SCALE_DISPARITY = 5.0 #Scale variance if average is above this
NUM_TRAIN_SETS = 32 # 8
FGBGMODE_TEST = 3 # 0 - average, 1 - FG, 2 - BG, 3 - AUX
FGBGMODE_TESTS = [1,3] # 0 - average, 1 - FG, 2 - BG, 3 - AUX
FGBGMODE_TRAIN = 1 # 0 - average, 1 - FG, 2 - BG
RND_AMPLIUDE_TEST = 0.5 # present corr2d rendered +/- this far from the GT
RND_AMPLIUDE_TRAIN_TILE = 0.5 # train with corr2d rendered +/- this far from the GT - independent for each tile component
......@@ -1725,12 +1725,12 @@ if __name__ == "__main__":
''' Prepare full image for testing '''
for model_ml_path in test_sets:
writeTFRecordsFromImageSet(
model_ml_path, # model/version/ml_dir
FGBGMODE_TEST, # 0, # expot_mode, # 0 - GT average, 1 - GT FG, 2 - GT BG, 3 - AUX disparity
RND_AMPLIUDE_TEST, # random_offset, # for modes 0..2 - add random offset of -random_offset to +random_offset, in mode 3 add random to GT average if no AUX data
pathTFR) # TFR directory
for fgbgmode_test in FGBGMODE_TESTS:
writeTFRecordsFromImageSet(
model_ml_path, # model/version/ml_dir
fgbgmode_test, # 0, # expot_mode, # 0 - GT average, 1 - GT FG, 2 - GT BG, 3 - AUX disparity
RND_AMPLIUDE_TEST, # random_offset, # for modes 0..2 - add random offset of -random_offset to +random_offset, in mode 3 add random to GT average if no AUX data
pathTFR) # TFR directory
# disp_bins = 20,
......
nn_ds_neibs30.py
View file @ ddd193c9
......@@ -49,6 +49,7 @@ 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
FGBG_MODE = 1 # 0 - do not filter by single-plane, 1 - remove split plabnes tiles, 2 - remove split planes and neighbors
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
......
nn_ds_neibs31.py
View file @ ddd193c9
......@@ -47,8 +47,10 @@ 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
CORR2D_LIMITS = [None, None]
SPREAD_CONVERGENCE, INTER_CONVERGENCE, HOR_FLIP, DISP_DIFF_CAP, DISP_DIFF_SLOPE = [None]*5
CLUSTER_RADIUS = None
FGBG_MODE = 1 # 0 - do not filter by single-plane, 1 - remove split plabnes tiles, 2 - remove split planes and neighbors
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
......@@ -64,7 +66,7 @@ globals().update(parameters)
TRAIN_BUFFER_SIZE = TRAIN_BUFFER_GPU * TRAIN_BUFFER_CPU # in merged (quad) batches
qsf.setCorr2Limits(CORR2D_LIMITS) # limit min/max 2d correlation tiles values
#exit(0)
......@@ -91,9 +93,10 @@ 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
# Tiff export slice labels
SLICE_LABELS = ["nn_out_ext","hier_out_ext","gt_disparity","gt_strength",
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"]
"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)
......@@ -126,6 +129,7 @@ if not USE_PARTIALS:
qsf.evaluateAllResults(result_files = files['result'],
absolute_disparity = ABSOLUTE_DISPARITY,
cluster_radius = CLUSTER_RADIUS,
fgbg_mode= FGBG_MODE,
labels = SLICE_LABELS,
logpath= LOGPATH)
......@@ -390,7 +394,7 @@ with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
merged = tf.summary.merge_all()
merged = tf.compat.v1.summary.merge_all()
tt_writers = []
for p in TT_PATHS:
tt_writers.append(tf.summary.FileWriter(p, sess.graph))
......@@ -614,9 +618,9 @@ with tf.Session() as sess:
files = files,
indx = ntest,
cluster_radius = CLUSTER_RADIUS,
tile_layers = TILE_LAYERS,
tile_side = TILE_SIDE,
width = IMG_WIDTH,
tile_layers = TILE_LAYERS, # 4
tile_side = TILE_SIDE, # 9
width = IMG_WIDTH, #160
replace_nans = True)
sess.run(iterator_tt.initializer, feed_dict={corr2d_train_placeholder: dataset_img['corr2d'],
......@@ -659,24 +663,41 @@ with tf.Session() as sess:
os.makedirs(os.path.dirname(result_file))
except:
pass
extra = dataset_img['t_extra']
if extra is None:
extra = np.array([dataset_img['gtruths'].shape[0],0])
rslt = np.concatenate(
[disp_out.reshape(-1,1),
dataset_img['t_disps'], #t_disps[ntest],
dataset_img['t_disps'], #t_disps[ntest], disp_out.shape[0],BATCH_SIZE
dataset_img['gtruths'], # gtruths[ntest],
dbg_cost_nw.reshape(-1,1),
dbg_cost_w.reshape(-1,1),
dbg_d.reshape(-1,1),
dbg_avg_disparity.reshape(-1,1),
dbg_gt_disparity.reshape(-1,1),
dbg_offs.reshape(-1,1)],1)
np.save(result_file, rslt.reshape(HEIGHT,WIDTH,-1))
rslt = qsf.eval_results(result_file, ABSOLUTE_DISPARITY, radius=CLUSTER_RADIUS, logfile=lf)
img_gain_test0 = rslt[0][0]/rslt[0][1]
img_gain_test9 = rslt[9][0]/rslt[9][1]
dbg_offs.reshape(-1,1),
extra, # len 3..6,
#adding extra data layers
],1)
num_slices = rslt.shape[1]
np.save(
result_file,
rslt.reshape(HEIGHT,WIDTH,-1))
eval_rslt = qsf.eval_results(
result_file,
ABSOLUTE_DISPARITY,
radius=0, # CLUSTER_RADIUS,
last_fgbg_mode = 1,
logfile=lf)
img_gain_test0 = eval_rslt[0][0]/eval_rslt[0][1]
img_gain_test9 = eval_rslt[9][0]/eval_rslt[9][1]
if SAVE_TIFFS:
qsf.result_npy_to_tiff(result_file, ABSOLUTE_DISPARITY, fix_nan = True,labels=SLICE_LABELS, logfile=lf)
qsf.result_npy_to_tiff(
result_file,
ABSOLUTE_DISPARITY,
fix_nan = True,
labels=SLICE_LABELS[0:num_slices],
logfile=lf)
"""
Remove dataset_img (if it is not [0] to reduce memory footprint
......
qcstereo_functions.py
View file @ ddd193c9
......@@ -11,7 +11,8 @@ import time
import imagej_tiffwriter
TIME_LAST = 0
TIME_START = 0
corr2_limits = None
MARGINS = 2 # disregard errors outside
class bcolors:
HEADER = '\033[95m'
OKBLUE = '\033[94m'
......@@ -58,7 +59,11 @@ def parseXmlConfig(conf_file, root_dir):
files[p.tag]=eval(p.text.strip())
dbg_parameters = {}
for p in root.find('dbg_parameters'):
dbg_parameters[p.tag]=eval(p.text.strip())
try:
dbg_parameters[p.tag]=eval(p.text.strip())
except:
print("Error in xml - p.tag = %s, p.text.strip()=%s"%(p.tag, p.text.strip()))
continue
return parameters, dirs, files, dbg_parameters
......@@ -312,7 +317,7 @@ def get_lengths(
cluster_side = 2 * cluster_radius + 1
cl = cluster_side * cluster_side * tile_layers * tile_side * tile_side
tl = cluster_side * cluster_side
gl = cluster_side * cluster_side * 2 # disparity+strength, resto goes to extra
gl = cluster_side * cluster_side * 2 # disparity+strength, rest goes to extra
return cl, tl, gl, cluster_side
......@@ -508,9 +513,16 @@ def readImageData(image_data,
width)
if replace_nans:
replace_nan([image_data[indx]])
if not (corr2_limits is None):
image_data[indx]['corr2d'] = np.clip(image_data[indx]['corr2d'], corr2_limits[0], corr2_limits[1])
return image_data[indx]
def setCorr2Limits(limits):
if not (limits is None) and ((not limits[0] is None) or (not limits[1] is None)):
globals()['corr2_limits'] = limits
else:
globals()['corr2_limits'] = None
def initImageData(files,
max_imgs,
cluster_radius,
......@@ -535,10 +547,10 @@ def initImageData(files,
replace_nans,
infer = infer,
keep_gt = keep_gt)
print_time(" Done")
return img_data
print_time(" Done")
return img_data
def evaluateAllResults(result_files, absolute_disparity, cluster_radius, labels=None, logpath=None):
def evaluateAllResults(result_files, absolute_disparity, cluster_radius, labels=None, logpath=None, fgbg_mode=1):
if logpath:
lf=open(logpath,"w")
else:
......@@ -546,7 +558,7 @@ def evaluateAllResults(result_files, absolute_disparity, cluster_radius, labels=
for result_file in result_files:
try:
print_time("Reading resuts from "+result_file, end=" ")
eval_results(result_file, absolute_disparity, radius=cluster_radius, logfile=lf)
eval_results(result_file, absolute_disparity, radius=cluster_radius, last_fgbg_mode = fgbg_mode, logfile=lf)
except:
print_time(" - does not exist")
continue
......@@ -686,6 +698,129 @@ def result_npy_to_tiff(npy_path,
imagej_tiffwriter.save(tiff_path,data,labels=labels)
def eval_results(rslt_path, absolute,
min_disp = -0.1, #minimal GT disparity
max_disp = 20.0, # maximal GT disparity
max_ofst_target = 1.0,
max_ofst_result = 1.0,
str_pow = 1.0,
last_fgbg_mode = 1, # 0 - no fgbg filter, 1 exclude tiles with fg/bg, 2 exclude fg/bg tiles and neighbors
radius = 0,
logfile = None):
variants = [[ -0.1, 3.0, 0.5, 0.5, 0.0, 1],
[ -0.1, 3.0, 0.5, 0.5, 1.0, 1],
[ -0.1, 3.0, 0.5, 0.5, 0.0, 2],
[ -0.1, 3.0, 0.5, 0.5, 1.0, 2],
[ -0.1, 3.0, 0.5, 0.5, 1.0, 0],
[ -0.1, 10.0, 5.0, 5.0, 0.0, 1],
[ -0.1, 10.0, 5.0, 5.0, 1.0, 1],
[ -0.1, 10.0, 5.0, 5.0, 1.0, 2],
[ -0.1, 10.0, 5.0, 5.0, 1.0, 0],
[ min_disp, max_disp, max_ofst_target, max_ofst_result, str_pow, last_fgbg_mode]]
stack = np.load(rslt_path)
layers = {
"nn_out_ext": stack[..., 0],
"target_disp": stack[..., 1], # used as target disparity, it is not heuristic data!
"gt_disparity": stack[..., 2],
"gt_strength": stack[..., 3],
"cutcorn_cost_nw": stack[..., 4],
"cutcorn_cost": stack[..., 5],
"gt_avg_dist": stack[..., 6],
"avg8_disp": stack[..., 7],
"gt_disp": stack[..., 8],
"out_avg": stack[..., 9],
"aux_disp": stack[...,10],
"fg_disp": stack[...,11],
"bg_disp": stack[...,12],
"gt_rms": stack[...,13],
"gt_rms_split": stack[...,14],
}
'''
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"]
MARGINS = 2 # disregard errors outside
'''
fgbg_single = layers["gt_rms"] <= layers["gt_rms_split"]
fgbg_ext = 1
fgbg_single_ext = np.ones((stack.shape[0] + 2 * fgbg_ext, stack.shape[1] + 2 * fgbg_ext),dtype=np.bool)
fgbg_single_ext[fgbg_ext:-fgbg_ext, fgbg_ext:-fgbg_ext] = fgbg_single
for dy in range(2*fgbg_ext+1):
for dx in range(2*fgbg_ext+1):
fgbg_single_ext[dy:dy+fgbg_single.shape[0], dx:dx+fgbg_single.shape[1]] &= fgbg_single
fgbg_single2 = fgbg_single_ext[fgbg_ext:-fgbg_ext,fgbg_ext:-fgbg_ext] #
not_nan = ~np.isnan(layers["nn_out_ext"]) # nn_out_ext
not_nan &= ~np.isnan(layers["target_disp"]) # target_disp
not_nan &= ~np.isnan(layers["gt_disparity"]) # gt_disparity
not_nan &= ~np.isnan(layers["gt_strength"]) # gt_strength
# pessimistic - make not_nan to have no NaN-s in 5x5 clusters. Maybe too strict for LWIR - nothing will remain
if radius > 0:
not_nan_ext = np.zeros((stack.shape[0] + 2*radius,stack.shape[1] + 2 * radius),dtype=np.bool)
not_nan_ext[radius:-radius,radius:-radius] = not_nan
for dy in range(2*radius+1):
for dx in range(2*radius+1):
not_nan_ext[dy:dy+not_nan.shape[0], dx:dx+not_nan.shape[1]] &= not_nan
not_nan = not_nan_ext[radius:-radius,radius:-radius]
wo_margins = np.zeros((stack.shape[0],stack.shape[1]), dtype=bool)
wo_margins[MARGINS:-MARGINS, MARGINS:-MARGINS] = True
not_nan &= wo_margins
if not absolute:
stack[...,0] += stack[...,1]
nn_disparity = np.nan_to_num(stack[...,0], copy = False)
target_disparity = np.nan_to_num(stack[...,1], copy = False)
heurist_disparity = np.nan_to_num(layers["aux_disp"], copy = False)
gt_disparity = np.nan_to_num(stack[...,2], copy = False)
gt_strength = np.nan_to_num(stack[...,3], copy = False)
rrslt = []
print ("--------------- %s ---------------"%(rslt_path))
if logfile:
print ("--------------- %s ---------------"%(rslt_path), file=logfile)
for min_disparity, max_disparity, max_offset_target, max_offset_result, strength_pow, fgbg_mode in variants:
good_tiles = not_nan.copy();
if fgbg_mode == 1:
good_tiles &= fgbg_single
elif fgbg_mode == 2:
good_tiles &= fgbg_single2
good_tiles &= (gt_disparity >= min_disparity)
good_tiles &= (gt_disparity <= max_disparity)
# good_tiles &= (target_disparity != gt_disparity)
good_tiles &= (np.abs(heurist_disparity - gt_disparity) <= max_offset_target)
good_tiles &= (np.abs(target_disparity - gt_disparity) <= max_offset_target)
good_tiles &= (np.abs(target_disparity - nn_disparity) <= max_offset_result)
gt_w = gt_strength * good_tiles
if strength_pow > 0: # power (0,0) = 1.0
gt_w = np.power(gt_w,strength_pow)
else:
gt_w = good_tiles * 1.0
sw = gt_w.sum()
diff0 = heurist_disparity - gt_disparity
diff1 = nn_disparity - gt_disparity
diff0_2w = gt_w*diff0*diff0
diff1_2w = gt_w*diff1*diff1
rms0 = np.sqrt(diff0_2w.sum()/sw)
rms1 = np.sqrt(diff1_2w.sum()/sw)
print ("%7.3f<disp<%7.3f, offs_tgt<%5.2f, offs_rslt<%5.2f pwr=%05.3f, fgbg=%1d, rms0=%7.4f, rms1=%7.4f (gain=%7.4f) num good tiles = %5d"%(
min_disparity, max_disparity, max_offset_target, max_offset_result, strength_pow, fgbg_mode, rms0, rms1, rms0/rms1, good_tiles.sum() ))
if logfile:
print ("%7.3f<disp<%7.3f, offs_tgt<%5.2f, offs_rslt<%5.2f pwr=%05.3f, fgbg=%1d, rms0=%7.4f, rms1=%7.4f (gain=%7.4f) num good tiles = %5d"%(
min_disparity, max_disparity, max_offset_target, max_offset_result, strength_pow, fgbg_mode, rms0, rms1, rms0/rms1, good_tiles.sum() ),file=logfile)
rrslt.append([rms0,rms1])
return rrslt
def eval_results_eo(rslt_path, absolute,
min_disp = -0.1, #minimal GT disparity
max_disp = 20.0, # maximal GT disparity
max_ofst_target = 1.0,
......@@ -706,10 +841,13 @@ def eval_results(rslt_path, absolute,
rslt = np.load(rslt_path)
not_nan = ~np.isnan(rslt[...,0])
not_nan &= ~np.isnan(rslt[...,1])
not_nan &= ~np.isnan(rslt[...,2])
not_nan &= ~np.isnan(rslt[...,3])
not_nan = ~np.isnan(rslt[...,0]) # nn_out_ext
not_nan &= ~np.isnan(rslt[...,1]) # hier_out_ext
not_nan &= ~np.isnan(rslt[...,2]) # gt_disparity
not_nan &= ~np.isnan(rslt[...,3]) # gt_strength
# pessimistic - make not_nan to have no NaN-s in 5x5 clusters. Maybe too strict for LWIR - nothing will remain
not_nan_ext = np.zeros((rslt.shape[0] + 2*radius,rslt.shape[1] + 2 * radius),dtype=np.bool)
not_nan_ext[radius:-radius,radius:-radius] = not_nan
for dy in range(2*radius+1):
......@@ -723,7 +861,7 @@ def eval_results(rslt_path, absolute,
target_disparity = np.nan_to_num(rslt[...,1], copy = False)
gt_disparity = np.nan_to_num(rslt[...,2], copy = False)
gt_strength = np.nan_to_num(rslt[...,3], copy = False)
rslt = []
rrslt = []
print ("--------------- %s ---------------"%(rslt_path))
if logfile:
print ("--------------- %s ---------------"%(rslt_path), file=logfile)
......@@ -750,8 +888,10 @@ def eval_results(rslt_path, absolute,
print ("%7.3f<disp<%7.3f, offs_tgt<%5.2f, offs_rslt<%5.2f pwr=%05.3f, rms0=%7.4f, rms1=%7.4f (gain=%7.4f) num good tiles = %5d"%(
min_disparity, max_disparity, max_offset_target, max_offset_result, strength_pow, rms0, rms1, rms0/rms1, good_tiles.sum() ),file=logfile)
rslt.append([rms0,rms1])
return rslt
rrslt.append([rms0,rms1])
return rrslt
def concentricSquares(radius):
side = 2 * radius + 1
......
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