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make_nd_dataset.py
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make_nd_dataset.py
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# Written by Dr Daniel Buscombe, Marda Science LLC
# for the USGS Coastal Change Hazards Program
#
# MIT License
#
# Copyright (c) 2020-22, Marda Science LLC
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# utility to merge multiple coincident jpeg images into nd numpy arrays
import sys,os, time, json, shutil
# sys.path.insert(1, 'src')
from skimage.io import imread, imsave
import numpy as np
from tkinter import filedialog, messagebox
from tkinter import *
from glob import glob
from skimage.transform import rescale ## this is actually for resizing
from skimage.morphology import remove_small_objects, remove_small_holes
from tqdm import tqdm
from joblib import Parallel, delayed
###===========================================
#-----------------------------------
# custom 2d resizing functions for 2d discrete labels
def scale(im, nR, nC):
'''
for reszing 2d integer arrays
'''
nR0 = len(im) # source number of rows
nC0 = len(im[0]) # source number of columns
tmp = [[ im[int(nR0 * r / nR)][int(nC0 * c / nC)]
for c in range(nC)] for r in range(nR)]
return np.array(tmp).reshape((nR,nC))
#-----------------------------------
def scale_rgb(img, nR, nC, nD):
'''
for reszing 3d integer arrays
'''
imgout = np.zeros((nR, nC, nD))
for k in range(3):
im = img[:,:,k]
nR0 = len(im) # source number of rows
nC0 = len(im[0]) # source number of columns
tmp = [[ im[int(nR0 * r / nR)][int(nC0 * c / nC)]
for c in range(nC)] for r in range(nR)]
imgout[:,:,k] = np.array(tmp).reshape((nR,nC))
return imgout
#-----------------------------------
def do_pad_label(lfile, TARGET_SIZE):
### labels ------------------------------------
lab = imread(lfile)
try:
old_image_height, old_image_width, channels = lab.shape
except:
old_image_height, old_image_width = lab.shape
channels=0
# create new image of desired size and color (black) for padding
new_image_width = TARGET_SIZE[0]
new_image_height = TARGET_SIZE[0]
# compute center offset
x_center = (new_image_width - old_image_width) // 2
y_center = (new_image_height - old_image_height) // 2
color = (0)
result = np.full((new_image_height,new_image_width), color, dtype=np.uint8)
try: #image is smaller
# copy img image into center of result image
result[y_center:y_center+old_image_height,
x_center:x_center+old_image_width] = lab+1
except:
result = scale(lab,TARGET_SIZE[0],TARGET_SIZE[1])+1
##lab2 =rescale(lab,(sf,sf),anti_aliasing=True, preserve_range=True, order=0)
# result[y_center:y_center+old_image_height,
# x_center:x_center+old_image_width] = lab2+1
# del lab2
wend = lfile.split(os.sep)[-2]
fdir = os.path.dirname(lfile)
fdirout = fdir.replace(wend,'padded_'+wend)
# save result
#imsave(lfile.replace('labels','padded_labels').replace('.jpg','.png'), result.astype('uint8'), check_contrast=False, compression=0)
imsave(fdirout+os.sep+lfile.split(os.sep)[-1].replace('.jpg','.png'), result.astype('uint8'), check_contrast=False, compression=0)
#-----------------------------------
def do_pad_image(f, TARGET_SIZE):
img = imread(f)
try:
old_image_height, old_image_width, channels = img.shape
except:
old_image_height, old_image_width = img.shape
channels=0
# create new image of desired size and color (black) for padding
new_image_width = TARGET_SIZE[0]
new_image_height = TARGET_SIZE[0]
if channels>0:
color = (0,0,0)
result = np.full((new_image_height,new_image_width, channels), color, dtype=np.uint8)
else:
color = (0)
result = np.full((new_image_height,new_image_width), color, dtype=np.uint8)
# compute center offset
x_center = (new_image_width - old_image_width) // 2
y_center = (new_image_height - old_image_height) // 2
try:
# copy img image into center of result image
result[y_center:y_center+old_image_height,
x_center:x_center+old_image_width] = img
except:
## AN ALTERNATIVE WAY - DO NOT REMOVE
# sf = np.minimum(new_image_width/old_image_width,new_image_height/old_image_height)
# if channels>0:
# img = rescale(img,(sf,sf,1),anti_aliasing=True, preserve_range=True, order=1)
# else:
# img = rescale(img,(sf,sf),anti_aliasing=True, preserve_range=True, order=1)
# if channels>0:
# old_image_height, old_image_width, channels = img.shape
# else:
# old_image_height, old_image_width = img.shape
#
# x_center = (new_image_width - old_image_width) // 2
# y_center = (new_image_height - old_image_height) // 2
#
# result[y_center:y_center+old_image_height,
# x_center:x_center+old_image_width] = img.astype('uint8')
if channels>0:
result = scale_rgb(img,TARGET_SIZE[0],TARGET_SIZE[1],3)
else:
result = scale(img,TARGET_SIZE[0],TARGET_SIZE[1])
wend = f.split(os.sep)[-2]
fdir = os.path.dirname(f)
fdirout = fdir.replace(wend,'padded_'+wend)
# save result
imsave(fdirout+os.sep+f.split(os.sep)[-1].replace('.jpg','.png'), result.astype('uint8'), check_contrast=False, compression=0)
##========================================================
## USER INPUTS
##========================================================
root = Tk()
root.filename = filedialog.askdirectory(initialdir = os.getcwd(),title = "Select directory for OUTPUT files")
output_data_path = root.filename
print(output_data_path)
root.withdraw()
root = Tk()
root.filename = filedialog.askopenfilename(initialdir = output_data_path,title = "Select config file",filetypes = (("config files","*.json"),("all files","*.*")))
configfile = root.filename
print(configfile)
root.withdraw()
with open(configfile) as f:
config = json.load(f)
for k in config.keys():
exec(k+'=config["'+k+'"]')
USE_GPU = False #True
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
if USE_GPU == True:
if 'SET_GPU' in locals():
os.environ['CUDA_VISIBLE_DEVICES'] = str(SET_GPU)
else:
#use the first available GPU
os.environ['CUDA_VISIBLE_DEVICES'] = '0' #'1'
else:
## to use the CPU (not recommended):
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
root = Tk()
root.filename = filedialog.askdirectory(initialdir = output_data_path,title = "Select directory of LABEL files")
label_data_path = root.filename
print(label_data_path)
root.withdraw()
root = Tk()
root.filename = filedialog.askdirectory(initialdir = output_data_path,title = "Select FIRST directory of IMAGE files")
data_path = root.filename
print(data_path)
root.withdraw()
W=[]
W.append(data_path)
result = 'yes'
while result == 'yes':
result = messagebox.askquestion("More directories of images?", "More directories of images?", icon='warning')
if result == 'yes':
root = Tk()
root.filename = filedialog.askdirectory(initialdir = output_data_path,title = "Select directory of image files")
data_path = root.filename
print(data_path)
root.withdraw()
W.append(data_path)
##========================================================
## COLLATE FILES INTO LISTS
##========================================================
files = []
for data_path in W:
f = sorted(glob(data_path+os.sep+'*.jpg'))
if len(f)<1:
f = sorted(glob(data_path+os.sep+'images'+os.sep+'*.jpg'))
files.append(f)
# number of bands x number of samples
files = np.vstack(files).T
label_files = sorted(glob(label_data_path+os.sep+'*.jpg'))
if len(label_files)<1:
label_files = sorted(glob(label_data_path+os.sep+'labels'+os.sep+'*.jpg'))
print("Found {} image and {} label files".format(len(files), len(label_files)))
##========================================================
## MAKING PADDED (RESIZED) COPIES OF IMAGERY
##========================================================
## neeed resizing?
szs = [imread(f).shape for f in files[:,0]]
szs = np.vstack(szs)[:,0]
if len(np.unique(szs))>1:
do_resize=True
else:
do_resize=False
## rersize / pad imagery so all a consistent size (TARGET_SIZE)
if do_resize:
## make padded direcs
for w in W:
wend = w.split(os.sep)[-1]
print(wend)
newdirec = w.replace(wend,'padded_'+wend)
try:
os.mkdir(newdirec)
except:
pass
if USEMASK:
newdireclabels = label_data_path.replace('masks','padded_masks')
else:
newdireclabels = label_data_path.replace('labels','padded_labels')
try:
os.mkdir(newdireclabels)
except:
pass
if len(W)==1:
w = Parallel(n_jobs=-2, verbose=0, max_nbytes=None)(delayed(do_pad_image)(f, TARGET_SIZE) for f in files.squeeze())
w = Parallel(n_jobs=-2, verbose=0, max_nbytes=None)(delayed(do_pad_label)(lfile, TARGET_SIZE) for lfile in label_files)
else:
## cycle through, merge and padd/resize if need to
for file,lfile in zip(files, label_files):
for f in file:
do_pad_image(f, TARGET_SIZE)
do_pad_label(lfile, TARGET_SIZE)
## write padded labels to file
if do_resize:
label_data_path = newdireclabels #label_data_path.replace('labels','padded_labels')
label_files = sorted(glob(label_data_path+os.sep+'*.png'))
if len(label_files)<1:
label_files = sorted(glob(label_data_path+os.sep+'images'+os.sep+'*.png'))
print("{} label files".format(len(label_files)))
W2 = []
for w in W:
wend = w.split(os.sep)[-1]
w = w.replace(wend,'padded_'+wend)
W2.append(w)
W = W2
del W2
files = []
for data_path in W:
f = sorted(glob(data_path+os.sep+'*.png'))
if len(f)<1:
f = sorted(glob(data_path+os.sep+'images'+os.sep+'*.png'))
files.append(f)
# number of bands x number of samples
files = np.vstack(files).T
print("{} sets of {} image files".format(len(W),len(files)))
else:
label_files = sorted(glob(label_data_path+os.sep+'*.jpg'))
if len(label_files)<1:
label_files = sorted(glob(label_data_path+os.sep+'images'+os.sep+'*.jpg'))
print("{} label files".format(len(label_files)))
files = sorted(glob(data_path+os.sep+'*.jpg'))
if len(f)<1:
files = sorted(glob(data_path+os.sep+'images'+os.sep+'*.jpg'))
###================================================
##========================================================
## NON-AUGMENTED FILES
##========================================================
# files = [f[0] for f in files]
print("Creating non-augmented subset")
## make non-aug subset first
# cycle through pairs of files and labels
for counter,(f,l) in enumerate(zip(files,label_files)):
if len(W)>1:
im=[] # read all images into a list
for k in f:
im.append(imread(k))
else:
try:
im = [imread(f)]
except:
im = [imread(f[0])]
datadict={}
try:
im=np.dstack(im)# create a dtack which takes care of different sized inputs
datadict['arr_0'] = im.astype(np.uint8)
lab = imread(l) # reac the label)
# if np.min(np.unique(lab))>0:
# lab -= 1
# if len(np.unique(lab))>NCLASSES+1:
# lab = (lab==0).astype('uint8')
if 'REMAP_CLASSES' in locals():
for k in REMAP_CLASSES.items():
lab[lab==int(k[0])] = int(k[1])
lab[lab>NCLASSES]=NCLASSES
if len(np.unique(lab))==1:
nx,ny = lab.shape
if NCLASSES==1:
lstack = np.zeros((nx,ny,NCLASSES+1))
else:
lstack = np.zeros((nx,ny,NCLASSES))
lstack[:,:,np.unique(lab)[0]]=np.ones((nx,ny))
else:
nx,ny = lab.shape
if NCLASSES==1:
lstack = np.zeros((nx,ny,NCLASSES+1))
lstack[:,:,:NCLASSES+1] = (np.arange(NCLASSES+1) == 1+lab[...,None]-1).astype(int) #one-hot encode
else:
lstack = np.zeros((nx,ny,NCLASSES))
lstack[:,:,:NCLASSES] = (np.arange(NCLASSES) == 1+lab[...,None]-1).astype(int) #one-hot encode
if FILTER_VALUE>1:
for kk in range(lstack.shape[-1]):
lab = remove_small_objects(lstack[:,:,kk].astype('uint8')>0, np.pi*(FILTER_VALUE**2))
lab = remove_small_holes(lstack[:,:,kk].astype('uint8')>0, np.pi*(FILTER_VALUE**2))
lstack[:,:,kk] = np.round(lab).astype(np.uint8)
del lab
datadict['arr_1'] = np.squeeze(lstack).astype(np.uint8)
datadict['num_bands'] = im.shape[-1]
datadict['files'] = [fi.split(os.sep)[-1] for fi in f]
segfile = output_data_path+os.sep+ROOT_STRING+'_noaug_nd_data_000000'+str(counter)+'.npz'
np.savez_compressed(segfile, **datadict)
del datadict, im, lstack
except:
print("Inconsistent inputs associated with label file: ".format(l))
###================================
from doodleverse_utils.imports import *
#---------------------------------------------------
#-----------------------------------
def load_npz(example):
with np.load(example.numpy()) as data:
image = data['arr_0'].astype('uint8')
image = standardize(image)
label = data['arr_1'].astype('uint8')
try:
file = [''.join(f) for f in data['files']]
except:
file = [f]
return image, label, file[0]
@tf.autograph.experimental.do_not_convert
#-----------------------------------
def read_seg_dataset_multiclass(example):
"""
"read_seg_dataset_multiclass(example)"
This function reads an example from a npz file into a single image and label
INPUTS:
* dataset example object (filename of npz)
OPTIONAL INPUTS: None
GLOBAL INPUTS: TARGET_SIZE
OUTPUTS:
* image [tensor array]
* class_label [tensor array]
"""
image, label, file = tf.py_function(func=load_npz, inp=[example], Tout=[tf.float32, tf.uint8, tf.string])
if NCLASSES==1:
label = tf.expand_dims(label,-1)
return image, label, file
###================================
##========================================================
## READ, VERIFY and PLOT NON-AUGMENTED FILES
##========================================================
# to deal with non-resized imaegry
BATCH_SIZE = 1
filenames = tf.io.gfile.glob(output_data_path+os.sep+ROOT_STRING+'_noaug*.npz')
dataset = tf.data.Dataset.list_files(filenames, shuffle=False)
print('{} files made'.format(len(filenames)))
# Set `num_parallel_calls` so multiple images are loaded/processed in parallel.
dataset = dataset.map(read_seg_dataset_multiclass, num_parallel_calls=AUTO)
dataset = dataset.repeat()
dataset = dataset.batch(BATCH_SIZE, drop_remainder=True) # drop_remainder will be needed on TPU
dataset = dataset.prefetch(AUTO)
try:
os.mkdir(output_data_path+os.sep+'noaug_sample')
except:
pass
#blue,red, yellow,green, etc
class_label_colormap = ['#3366CC','#DC3912','#FF9900','#109618','#990099','#0099C6','#DD4477',
'#66AA00','#B82E2E', '#316395','#0d0887', '#46039f', '#7201a8',
'#9c179e', '#bd3786', '#d8576b', '#ed7953', '#fb9f3a', '#fdca26', '#f0f921']
if NCLASSES>1:
class_label_colormap = class_label_colormap[:NCLASSES]
else:
class_label_colormap = class_label_colormap[:NCLASSES+1]
print('.....................................')
print('Printing examples to file ...')
counter=0
for imgs,lbls,files in dataset.take(20):
for count,(im,lab, file) in enumerate(zip(imgs, lbls, files)):
im = rescale_array(im.numpy(), 0, 1)
if im.shape[-1]:
im = im[:,:,:3]
plt.imshow(im)
lab = np.argmax(lab.numpy().squeeze(),-1)
color_label = label_to_colors(np.squeeze(lab), tf.cast(im[:,:,0]==0,tf.uint8),
alpha=128, colormap=class_label_colormap,
color_class_offset=0, do_alpha=False)
if NCLASSES==1:
plt.imshow(color_label, alpha=0.5)#, vmin=0, vmax=NCLASSES)
else:
#lab = np.argmax(lab,-1)
plt.imshow(color_label, alpha=0.5)#, vmin=0, vmax=NCLASSES)
file = file.numpy()
plt.axis('off')
plt.title(file)
plt.savefig(output_data_path+os.sep+'noaug_sample'+os.sep+ ROOT_STRING + 'noaug_ex'+str(counter)+'.png', dpi=200, bbox_inches='tight')
#counter +=1
plt.close('all')
counter += 1
##========================================================
## AUGMENTED FILES
##========================================================
print("Creating augmented files")
# we create two instances with the same arguments
data_gen_args = dict(featurewise_center=False,
featurewise_std_normalization=False,
rotation_range=AUG_ROT,
width_shift_range=AUG_WIDTHSHIFT,
height_shift_range=AUG_HEIGHTSHIFT,
fill_mode='reflect', #'nearest',
zoom_range=AUG_ZOOM,
horizontal_flip=AUG_HFLIP,
vertical_flip=AUG_VFLIP)
null_data_gen_args = dict(featurewise_center=False,
featurewise_std_normalization=False,
rotation_range=0,
width_shift_range=0,
height_shift_range=0,
fill_mode='reflect',
zoom_range=0,
horizontal_flip=False,
vertical_flip=False)
#get image dimensions
NX = TARGET_SIZE[0]
NY = TARGET_SIZE[1]
null_image_datagen = tf.keras.preprocessing.image.ImageDataGenerator(**null_data_gen_args)
mask_datagen = tf.keras.preprocessing.image.ImageDataGenerator(**data_gen_args)
null_mask_datagen = tf.keras.preprocessing.image.ImageDataGenerator(**null_data_gen_args)
# important that each band has the same image generator
image_datagen = tf.keras.preprocessing.image.ImageDataGenerator(**data_gen_args)
## put images in subfolders
for counter,w in enumerate(W):
n_im = len(glob(w+os.sep+'*.png')+glob(w+os.sep+'*.jpg'))
if n_im>0:
try:
os.mkdir(w+os.sep+'images')
except:
pass
for file in glob(w+os.sep+'*.png'):
try:
shutil.move(file,w+os.sep+'images')
except:
pass
for file in glob(w+os.sep+'*.jpg'):
try:
shutil.move(file,w+os.sep+'images')
except:
pass
n_im = len(glob(w+os.sep+'images'+os.sep+'*.*'))
## put label images in subfolders
n_im = len(glob(label_data_path+os.sep+'*.png')+glob(label_data_path+os.sep+'*.jpg'))
if n_im>0:
try:
os.mkdir(label_data_path+os.sep+'images')
except:
pass
for file in glob(label_data_path+os.sep+'*.jpg'):
try:
shutil.move(file,label_data_path+os.sep+'images')
except:
pass
for file in glob(label_data_path+os.sep+'*.png'):
try:
shutil.move(file,label_data_path+os.sep+'images')
except:
pass
n_im = len(glob(label_data_path+os.sep+'images'+os.sep+'*.*'))
#### make training generators directly, and in advance
train_generators = []
null_train_generators = []
for counter,w in enumerate(W):
print("folder: {}".format(w.split(os.sep)[-1]))
img_generator = image_datagen.flow_from_directory(
w,
target_size=(NX, NY),
batch_size=int(n_im/AUG_LOOPS),
class_mode=None, seed=SEED, shuffle=False)
null_img_generator = null_image_datagen.flow_from_directory(
w,
target_size=(NX, NY),
batch_size=int(n_im/AUG_LOOPS),
class_mode=None, seed=SEED, shuffle=False)
print("folder: {}".format(label_data_path.split(os.sep)[-1]))
#the seed must be the same as for the training set to get the same images
mask_generator = mask_datagen.flow_from_directory(
label_data_path,
target_size=(NX, NY),
batch_size=int(n_im/AUG_LOOPS),
class_mode=None, seed=SEED, shuffle=False, color_mode="grayscale", interpolation="nearest")
null_mask_generator = null_mask_datagen.flow_from_directory(
label_data_path,
target_size=(NX, NY),
batch_size=int(n_im/AUG_LOOPS),
class_mode=None, seed=SEED, shuffle=False, color_mode="grayscale", interpolation="nearest")
train_generator = (pair for pair in zip(img_generator, mask_generator))
train_generators.append([img_generator,mask_generator,train_generator])
null_train_generator = (pair for pair in zip(null_img_generator, null_mask_generator))
null_train_generators.append([null_img_generator, null_mask_generator,null_train_generator])
######################## generate and print files
i = 0
for copy in tqdm(range(AUG_COPIES)):
for k in range(AUG_LOOPS):
# print("Working on copy number {} out of {}".format(copy,AUG_COPIES))
# print("Working on loop {} out of {}".format(k,AUG_LOOPS))
# print("Starting from augmented sample {}".format(i))
X=[]; Y=[]; F=[]
for counter,train_generator in enumerate(train_generators):
#grab a batch of images and label images
x, y = next(train_generator[-1])
y = np.round(y)
idx = np.maximum((train_generator[0].batch_index - 1) * train_generator[0].batch_size, 0)
filenames = train_generator[0].filenames[idx : idx + train_generator[0].batch_size]
X.append(x)
del x
Y.append(y)
del y
F.append(filenames)
del filenames
## for 1-band inputs, the generator will make 3-band inputs
## this is so
## that means for 3+ band inputs where the extra files encode just 1 band each
## single bands are triplicated and the following code removes the redundancy
## so check for bands 0 and 1 being the same and if so, use only bans 0
X3 = []
for x in X:
x3=[]
for im in x:
if np.all(im[:,:,0]==im[:,:,1]):
im = im[:,:,0]
x3.append(im)
X3.append(x3)
del X
Y = Y[0]
# wrute them to file and increment the counter
for counter,lab in enumerate(Y):
im = np.dstack([x[counter] for x in X3])
# try:
files = np.dstack([x[counter] for x in F])
# except:
# files = 'error'
## this happens because the last few indices get chopped off on the last yield of the generator
##============================================ label
if NCLASSES==1:
lab=lab.squeeze()
#lab[lab>0]=1
if NCLASSES==1:
l = lab.astype(np.uint8)
else:
l = np.round(lab[:,:,0]).astype(np.uint8)
if 'REMAP_CLASSES' in locals():
for k in REMAP_CLASSES.items():
l[l==int(k[0])] = int(k[1])
l[l>NCLASSES]=NCLASSES
if len(np.unique(l))==1:
nx,ny = l.shape
if NCLASSES==1:
lstack = np.zeros((nx,ny,NCLASSES+1))
else:
lstack = np.zeros((nx,ny,NCLASSES))
lstack[:,:,np.unique(l)[0]]=np.ones((nx,ny))
else:
nx,ny = l.shape
if NCLASSES==1:
lstack = np.zeros((nx,ny,NCLASSES+1))
lstack[:,:,:NCLASSES+1] = (np.arange(NCLASSES+1) == 1+l[...,None]-1).astype(int) #one-hot encode
else:
lstack = np.zeros((nx,ny,NCLASSES))
lstack[:,:,:NCLASSES] = (np.arange(NCLASSES) == 1+l[...,None]-1).astype(int) #one-hot encode
if FILTER_VALUE>1:
for kk in range(lstack.shape[-1]):
#l = median(lstack[:,:,kk], disk(FILTER_VALUE))
l = remove_small_objects(lstack[:,:,kk].astype('uint8')>0, np.pi*(FILTER_VALUE**2))
l = remove_small_holes(lstack[:,:,kk].astype('uint8')>0, np.pi*(FILTER_VALUE**2))
lstack[:,:,kk] = np.round(l).astype(np.uint8)
del l
datadict={}
datadict['arr_0'] = im.astype(np.uint8)
datadict['arr_1'] = np.squeeze(lstack).astype(np.uint8)
datadict['num_bands'] = im.shape[-1]
try:
datadict['files'] = [fi.split(os.sep)[-1] for fi in files.squeeze()]
except:
datadict['files'] = [files]
np.savez_compressed(output_data_path+os.sep+ROOT_STRING+'_aug_nd_data_000000'+str(i),
**datadict)
del lstack, l, im
i += 1
##========================================================
## READ, VERIFY and PLOT AUGMENTED FILES
##========================================================
filenames = tf.io.gfile.glob(output_data_path+os.sep+ROOT_STRING+'_aug*.npz')
dataset = tf.data.Dataset.list_files(filenames, shuffle=False)
print('{} files made'.format(len(filenames)))
# Set `num_parallel_calls` so multiple images are loaded/processed in parallel.
dataset = dataset.map(read_seg_dataset_multiclass, num_parallel_calls=AUTO)
dataset = dataset.repeat()
dataset = dataset.batch(BATCH_SIZE, drop_remainder=True) # drop_remainder will be needed on TPU
dataset = dataset.prefetch(AUTO)
try:
os.mkdir(output_data_path+os.sep+'aug_sample')
except:
pass
print('.....................................')
print('Printing examples to file ...')
counter=0
for imgs,lbls,files in dataset.take(20):
for count,(im,lab, file) in enumerate(zip(imgs, lbls, files)):
im = rescale_array(im.numpy(), 0, 1)
if im.shape[-1]:
im = im[:,:,:3] #just show the first 3 bands
plt.imshow(im)
# print(lab.shape)
lab = np.argmax(lab.numpy().squeeze(),-1)
color_label = label_to_colors(np.squeeze(lab), tf.cast(im[:,:,0]==0,tf.uint8),
alpha=128, colormap=class_label_colormap,
color_class_offset=0, do_alpha=False)
if NCLASSES==1:
plt.imshow(color_label, alpha=0.5)#, vmin=0, vmax=NCLASSES)
else:
#lab = np.argmax(lab,-1)
plt.imshow(color_label, alpha=0.5)#, vmin=0, vmax=NCLASSES)
try:
file = file.numpy().split(os.sep)[-1]
plt.title(file)
del file
except:
pass
plt.axis('off')
plt.savefig(output_data_path+os.sep+'aug_sample'+os.sep+ ROOT_STRING + 'aug_ex'+str(counter)+'.png', dpi=200, bbox_inches='tight')
#counter +=1
plt.close('all')
counter += 1
#boom.