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python3-imagej-tiff
Commit bcf427de authored by Andrey Filippov's avatar Andrey Filippov
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Modified to use newer Tiff write

parent 38b9513f
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Showing with 36 additions and 56 deletions
nn_ds_neibs13.py
View file @ bcf427de
......@@ -420,7 +420,7 @@ def result_npy_to_tiff(npy_path, absolute, fix_nan):
else:
data[...,0] += data[...,1]
data = data.transpose(2,0,1)
imagej_tiffwriter.save(tiff_path,data[...,np.newaxis])
imagej_tiffwriter.save(tiff_path,data)
def eval_results(rslt_path, absolute,
......
nn_ds_neibs14.py
View file @ bcf427de
......@@ -425,7 +425,7 @@ def result_npy_to_tiff(npy_path, absolute, fix_nan):
else:
data[...,0] += data[...,1]
data = data.transpose(2,0,1)
imagej_tiffwriter.save(tiff_path,data[...,np.newaxis])
imagej_tiffwriter.save(tiff_path,data)
def eval_results(rslt_path, absolute,
......
nn_ds_neibs15.py
View file @ bcf427de
......@@ -424,7 +424,7 @@ def result_npy_to_tiff(npy_path, absolute, fix_nan):
else:
data[...,0] += data[...,1]
data = data.transpose(2,0,1)
imagej_tiffwriter.save(tiff_path,data[...,np.newaxis])
imagej_tiffwriter.save(tiff_path,data)
def eval_results(rslt_path, absolute,
......@@ -1161,6 +1161,7 @@ with tf.Session() as sess:
num_train_variants = len(datasets_train)
thr=None;
thr_result = None
trains_to_update = [train_next[n_train]['files'] > train_next[n_train]['slots'] for n_train in range(len(train_next))]
for epoch in range (EPOCHS_TO_RUN):
"""
......
nn_ds_neibs17.py
View file @ bcf427de
This diff is collapsed.
nn_eval_01.py
View file @ bcf427de
......@@ -5,13 +5,13 @@ __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 numpy as np
import time
......
qcstereo_functions.py
View file @ bcf427de
......@@ -3,7 +3,6 @@ __copyright__ = "Copyright 2018, Elphel, Inc."
__license__ = "GPL-3.0+"
__email__ = "andrey@elphel.com"
#from numpy import float64
import os
import numpy as np
import tensorflow as tf
......@@ -46,7 +45,6 @@ def parseXmlConfig(conf_file, root_dir):
files={}
for p in root.find('files'):
files[p.tag]=eval(p.text.strip())
# globals().update(parameters)
dbg_parameters = {}
for p in root.find('dbg_parameters'):
dbg_parameters[p.tag]=eval(p.text.strip())
......@@ -94,9 +92,6 @@ def readTFRewcordsEpoch(train_filename, cluster_radius):
file_all = os.path.join(npy_dir,filebasename + '.npy')
if os.path.exists(file_all):
data = np.load (file_all)
# corr2d= np.load (file_corr2d)
# target_disparity = np.load(file_target_disparity)
# gt_ds = np.load(file_gt_ds)
else:
record_iterator = tf.python_io.tf_record_iterator(path=train_filename)
corr2d_list=[]
......@@ -152,16 +147,12 @@ def read_and_decode(filename_queue, featrures_per_tile):
target_disparity = features['target_disparity'] # tf.decode_raw(features['target_disparity'], tf.float32)
gt_ds = tf.cast(features['gt_ds'], tf.float32) # tf.decode_raw(features['gt_ds'], tf.float32)
in_features = tf.concat([corr2d,target_disparity],0)
# still some nan-s in correlation data?
# in_features_clean = tf.where(tf.is_nan(in_features), tf.zeros_like(in_features), in_features)
# corr2d_out, target_disparity_out, gt_ds_out = tf.train.shuffle_batch( [in_features_clean, target_disparity, gt_ds],
corr2d_out, target_disparity_out, gt_ds_out = tf.train.shuffle_batch( [in_features, target_disparity, gt_ds],
batch_size=1000, # 2,
capacity=30,
num_threads=2,
min_after_dequeue=10)
return corr2d_out, target_disparity_out, gt_ds_out
#http://adventuresinmachinelearning.com/introduction-tensorflow-queuing/
def add_margins(npa,radius, val = np.nan):
npa_ext = np.empty((npa.shape[0]+2*radius, npa.shape[1]+2*radius, npa.shape[2]), dtype = npa.dtype)
npa_ext[radius:radius + npa.shape[0],radius:radius + npa.shape[1]] = npa
......@@ -304,7 +295,6 @@ def initTrainTestData(
"""
num_trains = len(files['train'])
num_entries = num_trains * buffer_size
# dataset_train_all = None
dataset_train_merged = None
train_next = [None]*num_trains
for n_train, f_train in enumerate(files['train']):
......@@ -402,7 +392,7 @@ def initImageData(files,
print_time(" Done")
return img_data
def evaluateAllResults(result_files, absolute_disparity, cluster_radius):
def evaluateAllResults(result_files, absolute_disparity, cluster_radius, labels=None):
for result_file in result_files:
try:
print_time("Reading resuts from "+result_file, end=" ")
......@@ -412,12 +402,12 @@ def evaluateAllResults(result_files, absolute_disparity, cluster_radius):
continue
print_time("Done")
print_time("Saving resuts to tiff", end=" ")
result_npy_to_tiff(result_file, absolute_disparity, fix_nan = True)
result_npy_to_tiff(result_file, absolute_disparity, fix_nan = True, labels=labels)
print_time("Done")
def result_npy_prepare(npy_path, absolute, fix_nan, insert_deltas=True):
def result_npy_prepare(npy_path, absolute, fix_nan, insert_deltas=True,labels=None):
"""
@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
......@@ -426,6 +416,9 @@ def result_npy_prepare(npy_path, absolute, fix_nan, insert_deltas=True):
@param fix_nan - replace nan in target_disparity with 0 to apply offset, target_disparity will still contain nan
"""
data = np.load(npy_path) #(324,242,4) [nn_disp, target_disp,gt_disp, gt_conf]
if labels is None:
labels = ["chn%d"%(i) for i in range(data.shape[0])]
# labels = ["nn_out","hier_out","gt_disparity","gt_strength"]
nn_out = 0
# target_disparity = 1
gt_disparity = 2
......@@ -438,6 +431,7 @@ def result_npy_prepare(npy_path, absolute, fix_nan, insert_deltas=True):
if insert_deltas:
np.nan_to_num(data[...,gt_strength], copy=False)
data = np.concatenate([data[...,0:4],data[...,0:2],data[...,0:2],data[...,4:]], axis = 2)
labels = labels[:4]+["nn_out","hier_out","nn_err","hier_err"]+labels[4:]
data[...,6] -= data[...,gt_disparity]
data[...,7] -= data[...,gt_disparity]
for l in [2, 4, 5, 6, 7]:
......@@ -445,9 +439,13 @@ def result_npy_prepare(npy_path, absolute, fix_nan, insert_deltas=True):
# 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]])
return data
return data, labels
def result_npy_to_tiff(npy_path, absolute, fix_nan, insert_deltas=True):
def result_npy_to_tiff(npy_path,
absolute,
fix_nan,
insert_deltas=True,
labels = None):
"""
@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
......@@ -455,12 +453,12 @@ 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
"""
data = result_npy_prepare(npy_path, absolute, fix_nan, insert_deltas)
data,labels = result_npy_prepare(npy_path, absolute, fix_nan, insert_deltas, labels=labels)
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])
imagej_tiffwriter.save(tiff_path,data,labels=labels)
def eval_results(rslt_path, absolute,
min_disp = -0.1, #minimal GT disparity
......@@ -469,7 +467,6 @@ def eval_results(rslt_path, absolute,
max_ofst_result = 1.0,
str_pow = 2.0,
radius = 0):
# for min_disparity, max_disparity, max_offset_target, max_offset_result, strength_pow in [
variants = [[ -0.1, 5.0, 0.5, 0.5, 1.0],
[ -0.1, 5.0, 0.5, 0.5, 2.0],
[ -0.1, 5.0, 0.2, 0.2, 1.0],
......
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