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data_utils.py
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data_utils.py
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# In[5]:
#################################################################
# Load Adobe MIT FiveK Dataset
#################################################################
import os
import sys
import glob
import numpy as np
import warnings
import shutil
import cv2
# import rawpy
import random
from skimage.io import imsave, imread
from skimage.transform import rescale
from skimage.transform import resize
from skimage.restoration import denoise_tv_chambolle, denoise_bilateral
from fnmatch import fnmatch
from multiprocessing import Pool
MAX_N_PROCESS = 6
def center_crop(im, tw, th):
w, h = im.shape[:2]
if w == tw and h == th:
return im
w_p = int(round((w - tw) / 2))
h_p = int(round((h - th) / 2))
return im[w_p:w_p+tw, h_p:h_p+th]
def load_raw(path, scale=1.0):
# Read RAW Image
# Returns an image in the 0--1 float range
rgb = rescale(rawpy.imread(path).postprocess(), scale, preserve_range=False)
return rgb
def multiply_and_clip(rgb, threshold = [-.25, .75]):
# Color
rgb_clipped = np.clip(rgb, threshold[0], threshold[1])
rgb_clipped = rgb_clipped *(1+(threshold[0])+(1-threshold[1]))
rgb_clipped = np.clip(rgb_clipped, .0, 1.)
return rgb_clipped
def saturate(rgb, threshold = [0, .6]):
rgb_clipped = np.clip(rgb, threshold[0], threshold[1])
rgb_clipped = rgb_clipped- np.min(rgb_clipped)
rgb_clipped = rgb_clipped/np.max(rgb_clipped)
rgb_clipped = np.clip(rgb_clipped, .0, 1.)
return rgb_clipped
def add_noise(image, noise_typ = "gauss", mean = 0, var = 0.1, exp_sigma = 0.5, percent = 1):
if noise_typ == "gauss":
row,col,ch= image.shape
#mean = 0
#var = 0.1
sigma = ((percent**2)*var)**exp_sigma
gauss = np.random.normal(mean,sigma,(row,col,ch))
gauss = gauss.reshape(row,col,ch)
noisy = image + gauss
noisy = np.clip(noisy, .0, 1.)
return noisy
elif noise_typ == "s&p":
row,col,ch = image.shape
s_vs_p = 0.5
amount = 0.004
out = np.copy(image)
# Salt mode
num_salt = np.ceil(amount * image.size * s_vs_p)
coords = [np.random.randint(0, i - 1, int(num_salt))
for i in image.shape]
out[coords] = 1
# Pepper mode
num_pepper = np.ceil(amount* image.size * (1. - s_vs_p))
coords = [np.random.randint(0, i - 1, int(num_pepper))
for i in image.shape]
out[coords] = 0
out = np.clip(out, .0, 1.)
return out
elif noise_typ == "poisson":
vals = len(np.unique(image))
vals = 2 ** np.ceil(np.log2(vals))
noisy = np.random.poisson(image * vals) / float(vals)
noisy = np.clip(noisy, .0, 1.)
return noisy
elif noise_typ =="speckle":
row,col,ch = image.shape
gauss = np.random.randn(row,col,ch)
gauss = gauss.reshape(row,col,ch)
noisy = image + image * gauss
noisy = np.clip(noisy, .0, 1.)
return noisy
elif noise_typ == "glare":
print('Not yet implemented')
class DataLoaderDistort():
def __init__(self, datases_path, dataset_name, img_res=(256, 256), train_split = .7,
distortion=['low_range','saturate', 'underexpose', 'underexpose_p25','gauss', 'speckle', 'poisson', 's&p'],
filetype = "*.dng", percent_noise_gt = 0.0,
debug=False):
np.random.RandomState(seed=42)
self.raw_types = ["*.dng","*.DNG","*.arw",".ARW"]
self.filetype = filetype
self.dataset_name = dataset_name
self.distortion = distortion
self.img_res = img_res
self.datases_path = datases_path
self.debug = debug
self.path = os.path.join(datases_path, dataset_name)
self.imgs_path = []
self.percent_noise_gt = percent_noise_gt
for paths, subdirs, files in os.walk(self.path):
for name in files:
if fnmatch(name, self.filetype):
self.imgs_path.append(os.path.join(paths, name))
self.train_imgs_path = self.imgs_path[:int(np.floor(train_split*len(self.imgs_path)))]
self.test_imgs_path = self.imgs_path[int(np.floor((1.0-train_split)*len(self.imgs_path))):]
final_train_path = os.path.join(self.path, 'tif', 'train')
final_test_path = os.path.join(self.path, 'tif', 'test')
if not os.path.isdir(final_train_path):
os.makedirs(final_train_path)
for train_img in self.train_imgs_path:
img = train_img.split("/")[-1]
#print(os.path.join(final_train_path, img))
os.rename(train_img, os.path.join(final_train_path, img))
if not os.path.isdir(final_test_path):
self.test_imgs_path = glob.glob(os.path.join(self.path, 'tif', '*.tif') )
os.makedirs(final_test_path)
for test_img in self.test_imgs_path:
img = test_img.split("/")[-1]
#print(os.path.join(final_test_path, img))
os.rename(test_img, os.path.join(final_test_path, img))
#def separate_data(self):
def load_data(self, batch_size=1, is_testing=False):
imgs_A = []
imgs_B = []
if is_testing:
rnd = np.random.randint(0, len(self.test_imgs_path)- batch_size)
rnd = range(rnd, rnd+int(np.ceil(batch_size/len(self.distortion))))
else:
rnd = np.random.randint(0, len(self.train_imgs_path)- batch_size)
rnd = range(rnd, rnd+int(np.ceil(batch_size/len(self.distortion))))
for j in rnd:
flag_pass = 1
if is_testing:
try:
if self.filetype in self.raw_types:
img_A = resize(load_raw(self.test_imgs_path[j]), self.img_res)
else:
img_A = resize(imread(self.test_imgs_path[j])/255., self.img_res)
except:
print("Deu ruim por causa imagem corrompida nos testes!!!!")
print(self.train_imgs_path[j])
flag_pass = 0 #if didn't load the image, don't pass through noise addition.
pass
if not is_testing:
try:
if self.filetype in self.raw_types:
img_A = resize(load_raw(self.train_imgs_path[j]), self.img_res)
else:
img_A = resize(imread(self.train_imgs_path[j])/255., self.img_res)
except:
print("Deu ruim por causa imagem corrompida no treinamento!!!!")
print(self.train_imgs_path[j])
flag_pass = 0 #if didn't load the image, don't pass through noise addition.
pass
# print(self.train_imgs_path[j])
#img_A = resize(load_raw(self.train_imgs_path[j]), self.img_res)
# print(img_A)
#noise addition
if flag_pass:
#Choose between ground truth with noise or not. Always gausian noise.
if self.percent_noise_gt > 0.:
imgs_A.append(add_noise(img_A, 'gauss', percent = self.percent_noise_gt))
else:
imgs_A.append(img_A)
if 'low_range' in self.distortion:
imgs_B.append(multiply_and_clip(img_A))
if 'saturate' in self.distortion:
imgs_B.append(saturate(img_A,[.0,.6]))
if 'underexpose' in self.distortion:
imgs_B.append(saturate(img_A,[.4,1.]))
if 'underexpose_p5' in self.distortion:
imgs_B.append(saturate(img_A,[np.percentile(img_A,5),1.]))
if 'underexpose_p10' in self.distortion:
imgs_B.append(saturate(img_A,[np.percentile(img_A,10),1.]))
if 'underexpose_p15' in self.distortion:
imgs_B.append(saturate(img_A,[np.percentile(img_A,15),1.]))
if 'underexpose_p25' in self.distortion:
imgs_B.append(saturate(img_A,[np.percentile(img_A,25),1.]))
if 'saturate_p5' in self.distortion:
imgs_B.append(saturate(img_A,[0.,np.percentile(img_A,95)]))
if 'saturate_p10' in self.distortion:
imgs_B.append(saturate(img_A,[0.,np.percentile(img_A,90)]))
if 'saturate_p15' in self.distortion:
imgs_B.append(saturate(img_A,[0.,np.percentile(img_A,85)]))
if 'saturate_p25' in self.distortion:
imgs_B.append(saturate(img_A,[0.,np.percentile(img_A,75)]))
if 'denoise_tv_chambolle' in self.distortion:
imgs_B.append(denoise_tv_chambolle(img_A, weight=10))
if 'denoise_bilateral' in self.distortion:
imgs_B.append(denoise_bilateral(img_A, weight=10))
if 'gauss' in self.distortion:
imgs_B.append(add_noise(img_A, 'gauss'))
if 'speckle' in self.distortion:
imgs_B.append(add_noise(img_A, 'speckle'))
if 'poisson' in self.distortion:
imgs_B.append(add_noise(img_A, 'poisson'))
if 's&p' in self.distortion:
imgs_B.append(add_noise(img_A, 's&p'))
# if not flag_pass:
# j = j-1 #disconsider the actual interaction
imgs_A = np.array(imgs_A[:batch_size])
imgs_B = np.array(imgs_B[:batch_size])
if self.debug:
print(imgs_A.shape)
print(imgs_B.shape)
return imgs_B, imgs_A
def load_batch(self, batch_size=1, is_testing=False):
data_type = "train" if not is_testing else "val"
pathA = '%s/%s/%s/A/*' % (self.datases_path, self.dataset_name, data_type)
if self.debug:
print(pathA)
pathA = glob(pathA)
pathB = '%s/%s/%s/B/*' % (self.datases_path, self.dataset_name, data_type)
if self.debug:
print(pathB)
pathB = glob(pathB)
if self.debug:
print(pathA)
print(pathB)
self.n_batches = int(len(pathA) / batch_size)
for i in range(self.n_batches-1):
batchIdx = range(i*batch_size,(i+1)*batch_size,1)
imgs_A, imgs_B = [], []
for j in batchIdx:
img_A = self.imread(pathA[j])
img_B = self.imread(pathB[j])
img_A = scipy.misc.imresize(img_A, self.img_res)
img_B = scipy.misc.imresize(img_B, self.img_res)
#print(img_A.shape, img_B.shape)
if not is_testing and np.random.random() > 0.5:
img_A = np.fliplr(img_A)
img_B = np.fliplr(img_B)
imgs_A.append(img_A)
imgs_B.append(img_B)
imgs_A = np.array(imgs_A)/127.5 - 1.
imgs_B = np.array(imgs_B)/127.5 - 1.
yield imgs_A, imgs_B
# a = DataLoaderFiveK(DATASET_FOLDER, DATASET_NAME)
# y,x = a.load_data()
# for i in range(0,len(x)):
# imsave(os.path.join(LOG_FOLDER, str(i).zfill(6)+'x.png'), x[i])
# imsave(os.path.join(LOG_FOLDER, str(i).zfill(6)+'y.png'), y[i])
################################################################################
# Data Loader
################################################################################
class DataLoader():
def __init__(self, datases_path, dataset_name, img_res=(256, 256), debug=False):
self.dataset_name = dataset_name
self.img_res = img_res
self.datases_path = datases_path
self.debug = debug
def load_data(self, batch_size=1, is_testing=False):
data_type = "train" if not is_testing else "test"
pathA = '%s/%s/%s/A/*' % (self.datases_path, self.dataset_name, data_type)
if self.debug:
print(pathA)
pathA = glob(pathA)
if self.debug:
print(pathA)
pathB = '%s/%s/%s/B/*' % (self.datases_path, self.dataset_name, data_type)
if self.debug:
print(pathB)
pathB = glob(pathB)
imgs_A = []
imgs_B = []
rnd = np.random.randint(0, len(pathA), batch_size)
for j in rnd:
img_A = self.imread(pathA[j])
img_B = self.imread(pathB[j])
img_A = scipy.misc.imresize(img_A, self.img_res)
img_B = scipy.misc.imresize(img_B, self.img_res)
# If training => do random flip
if not is_testing and np.random.random() < 0.5:
img_A = np.fliplr(img_A)
img_B = np.fliplr(img_B)
imgs_A.append(img_A)
imgs_B.append(img_B)
imgs_A = np.array(imgs_A)/127.5 - 1.
imgs_B = np.array(imgs_B)/127.5 - 1.
if self.debug:
print(imgs_A.shape)
print(imgs_B.shape)
return imgs_A, imgs_B
def load_batch(self, batch_size=1, is_testing=False):
data_type = "train" if not is_testing else "val"
pathA = '%s/%s/%s/A/*' % (self.datases_path, self.dataset_name, data_type)
if self.debug:
print(pathA)
pathA = glob(pathA)
pathB = '%s/%s/%s/B/*' % (self.datases_path, self.dataset_name, data_type)
if self.debug:
print(pathB)
pathB = glob(pathB)
if self.debug:
print(pathA)
print(pathB)
self.n_batches = int(len(pathA) / batch_size)
for i in range(self.n_batches-1):
batchIdx = range(i*batch_size,(i+1)*batch_size,1)
imgs_A, imgs_B = [], []
for j in batchIdx:
img_A = self.imread(pathA[j])
img_B = self.imread(pathB[j])
img_A = scipy.misc.imresize(img_A, self.img_res)
img_B = scipy.misc.imresize(img_B, self.img_res)
if not is_testing and np.random.random() > 0.5:
img_A = np.fliplr(img_A)
img_B = np.fliplr(img_B)
imgs_A.append(img_A)
imgs_B.append(img_B)
imgs_A = np.array(imgs_A)/127.5 - 1.
imgs_B = np.array(imgs_B)/127.5 - 1.
yield imgs_A, imgs_B
def imread(self, path):
return scipy.misc.imread(path, mode='RGB').astype(np.float)
################################################################################
# Data Loader for Learning to See in the Dark Dataset
################################################################################
class DataLoaderSeeInTheDark():
def __init__(self, dataset_path, dataset_name, img_res=(256, 256), debug=False):
self.dataset_name = dataset_name
self.img_res = img_res
self.dataset_path = dataset_path
self.debug = debug
self.path = os.path.join(dataset_path, dataset_name)
with open(os.path.join(self.path,dataset_name+'_train_list.txt')) as f:
self.train_imgs_path = [line.split() for line in f]
with open(os.path.join(self.path,dataset_name+'_test_list.txt')) as f:
self.test_imgs_path = [line.split() for line in f]
def load_data(self, batch_size=1, is_testing=False):
imgs_A = []
imgs_B = []
img_B = None
img_A = None
if is_testing:
rnd = np.random.random_integers(0, len(self.test_imgs_path)-1, batch_size)
else:
rnd = np.random.random_integers(0, len(self.train_imgs_path)-1, batch_size)
for j in rnd:
flag_pass = 1
if is_testing:
try:
img_A = load_raw(os.path.join(self.path, self.test_imgs_path[j][0]))
img_B = load_raw(os.path.join(self.path, self.test_imgs_path[j][1]))
except:
print("Deu ruim por causa imagem corrompida nos testes!!!!")
print(os.path.join(self.path, self.test_imgs_path[j][0]))
print(os.path.join(self.path, self.test_imgs_path[j][1]))
flag_pass = 0 #if didn't load the image, don't pass through noise addition.
pass
if not is_testing:
try:
img_A = load_raw(os.path.join(self.path, self.train_imgs_path[j][0]))
img_B = load_raw(os.path.join(self.path, self.train_imgs_path[j][1]))
except:
print("Deu ruim por causa imagem corrompida no treinamento!!!!")
print(os.path.join(self.path, self.train_imgs_path[j][0]))
print(os.path.join(self.path, self.train_imgs_path[j][1]))
flag_pass = 0 #if didn't load the image, don't pass through noise addition.
pass
if flag_pass == 1:
if not self.img_res is None:
img_A = resize(img_A, self.img_res)
img_B = resize(img_B, self.img_res)
imgs_A.append(img_A)
imgs_B.append(img_B)
return np.array(imgs_A), np.array(imgs_B)
class DataLoaderSteffens2018():
def __init__(self, datases_path, dataset_name='all_small', img_res=(256, 256), train_split = .7, filetype = "*.JPG", a_folder='u', b_folder='hdr', debug=False, get_all = False):
self.dataset_name = dataset_name
self.img_res = img_res
self.datases_path = os.path.join(datases_path, dataset_name)
self.debug = debug
self.filetype = filetype
print(self.datases_path)
pathA = []
pathB = []
for paths, subdirs, files in os.walk(os.path.join(self.datases_path,a_folder)):
for name in files:
if fnmatch(name, self.filetype):
pathA.append(os.path.join(paths, name))
for paths, subdirs, files in os.walk(os.path.join(self.datases_path,b_folder)):
for name in files:
if fnmatch(name, self.filetype):
pathB.append(os.path.join(paths, name))
pathA = sorted(pathA)
pathB = sorted(pathB)
if get_all:
train_split = 1
print('train_split:',train_split)
self.imgs_path = list(zip(pathA, pathB))
self.train_imgs_path = self.imgs_path[:int(np.floor(train_split*len(self.imgs_path)))]
self.test_imgs_path = self.imgs_path[len(self.imgs_path)-int(np.ceil((1.0-train_split)*len(self.imgs_path))):]
if self.debug:
print(self.train_imgs_path)
print(self.test_imgs_path)
assert(len(pathA) == len(pathB))
def load_data(self, batch_size=1, is_testing=False, get_all = False):
#self.test_imgs_path =
imgs_A = []
imgs_B = []
if get_all:
is_testing = False
#print(is_testing)
if not get_all:
if is_testing:
rnd = np.random.randint(0, len(self.test_imgs_path)- (1+batch_size))
rnd = range(rnd, rnd+int(np.ceil(batch_size)))
else:
rnd = np.random.randint(0, len(self.train_imgs_path)- (1+batch_size))
rnd = range(rnd, rnd+int(np.ceil(batch_size)))
else:
#rnd = np.random.randint(0, batch_size-1)
#rnd = range(rnd, rnd+int(np.ceil(batch_size)))
rnd = range(batch_size)
print(rnd)
for j in rnd:
flag_pass = 1
if is_testing:
#print('1')
try:
img_A = imread(self.test_imgs_path[j][0])/255. if self.img_res==None else resize(imread(self.test_imgs_path[j][0])/255., self.img_res)
img_B = imread(self.test_imgs_path[j][1])/255. if self.img_res==None else resize(imread(self.test_imgs_path[j][1])/255., self.img_res)
except:
print(sys.exc_info()[0])
print("Deu ruim por causa imagem corrompida nos testes!!!!")
print(self.train_imgs_path[j])
flag_pass = 0 #if didn't load the image, don't pass through noise addition.
pass
if not is_testing:
#print('2')
try:
img_A = imread(self.train_imgs_path[j][0])/255. if self.img_res==None else resize(imread(self.train_imgs_path[j][0])/255., self.img_res)
img_B = imread(self.train_imgs_path[j][1])/255. if self.img_res==None else resize(imread(self.train_imgs_path[j][1])/255., self.img_res)
except:
print(sys.exc_info())
print("Deu ruim por causa imagem corrompida no treinamento!!!!")
print(self.train_imgs_path[j])
flag_pass = 0 #if didn't load the image, don't pass through noise addition.
pass
if flag_pass:
imgs_A.append(img_A)
imgs_B.append(img_B)
imgs_A = np.array(imgs_A)
imgs_B = np.array(imgs_B)
if self.debug:
print(imgs_A.shape)
print(imgs_B.shape)
return imgs_A, imgs_B
def imread(self, path):
return scipy.misc.imread(path, mode='RGB').astype(np.float)
class DataLoaderCai2018():
def __init__(self, datases_path, dataset_name='Cai2018_Part1', img_res=None, dark_bright = .1, train_split = .8, debug=False, sortway = 1):
self.dataset_name = dataset_name
self.img_res = img_res
self.datases_path = os.path.join(datases_path, dataset_name)
self.debug = debug
self.filetype = ".JPG"
self.dark_bright = dark_bright
print(self.datases_path)
self.pathA = os.path.join(self.datases_path,'input')
self.pathB = os.path.join(self.datases_path,'Label')
self.imgs_path = os.listdir(self.pathA)
if sortway == 1:
self.test_imgs_path = self.imgs_path[int(np.floor((1.0-train_split)*len(self.imgs_path))):]
elif sortway == 2:
self.train_imgs_path = []
self.test_imgs_path = []
files_n = len(self.imgs_path)
train_n = train_split*files_n
stride = files_n//train_n
j = 0
#print(self.imgs_path)
#print(train_n)
#print(files_n)
#print(stride)
flag = 0
for i in range(len(self.imgs_path)):
#print(self.test_imgs_path)
#print(str(self.imgs_path[i]))
if i == j*stride and flag == 0:
#print(i)
j = j+1
self.train_imgs_path.append(str(self.imgs_path[i]))
#os.rename(dataset+files[i], dataset+'/test/'+files[i])
if j >= train_n:
flag = 1
else:
self.test_imgs_path.append(str(self.imgs_path[i]))
if self.debug:
print('train:')
print(self.train_imgs_path)
print('test:')
print(self.test_imgs_path)
input_path = os.path.join(datases_path, self.dataset_name, "input")
label_path = os.path.join(datases_path, self.dataset_name, "Label")
paths = [input_path, label_path]
flag = 1
for path in paths:
final_train_path = os.path.join(path, "train")
final_test_path = os.path.join(path, "test")
#print(final_train_path)
if not os.path.isdir(final_train_path):
os.makedirs(final_train_path)
for train_img in self.train_imgs_path:
if flag:
shutil.move(os.path.join(path, train_img), final_train_path)
else:
shutil.move(os.path.join(path, train_img)+self.filetype, final_train_path)
#print(self.test_imgs_path)
if not os.path.isdir(final_test_path):
os.makedirs(final_test_path)
for test_img in self.test_imgs_path:
if flag:
shutil.move(os.path.join(path, test_img), final_test_path)
else:
shutil.move(os.path.join(path, test_img)+self.filetype, final_test_path)
flag = 0
def load_data2(self, batch_size=1, is_testing=False):
imgs_A = []
imgs_B = []
if is_testing:
test_path = os.listdir(os.path.join(self.pathA, 'test'))
#print(test_path)
rnd = np.random.randint(0, len(test_path)- (1+batch_size))
rnd = range(rnd, rnd+int(np.ceil(batch_size)))
#print(rnd)
else:
train_path = os.listdir(os.path.join(self.pathA, 'train'))
#print(train_path)
rnd = np.random.randint(0, len(train_path)- (1+batch_size))
rnd = range(rnd, rnd+int(np.ceil(batch_size)))
#print(rnd)
#print(rnd)
for j in rnd:
flag_pass = 1
if is_testing:
#print(test_path[j])
#print(os.path.join(os.path.join(self.pathA, 'test'), test_path[j]))
files_in_folder = len(os.listdir(os.path.join(os.path.join(self.pathA, 'test'), test_path[j])))
file_path = [os.path.join(os.path.join(self.pathA, 'test'), test_path[j], str(1+int(files_in_folder*self.dark_bright)).zfill(3)+self.filetype), os.path.join(self.pathB, 'test',test_path[j]+self.filetype)]
try:
img_A = imread(file_path[0])/255. if self.img_res==None else resize(imread(file_path[0])/255., self.img_res)
img_B = imread(file_path[1])/255. if self.img_res==None else resize(imread(file_path[1])/255., self.img_res)
except:
print(sys.exc_info()); print("Deu ruim por causa imagem corrompida nos testes!!!!"); print(file_path)
flag_pass = 0 #if didn't load the image, don't pass through noise addition.
pass
#print(img_A)
#print(img_B
else:
#print(train_path[j])
#print(os.path.join(os.path.join(self.pathA, 'train'), train_path[j]))
files_in_folder = len(os.listdir(os.path.join(os.path.join(self.pathA, 'train'), train_path[j])))
file_path = [os.path.join(os.path.join(self.pathA, 'train'), train_path[j], str(1+int(files_in_folder*self.dark_bright)).zfill(3)+self.filetype), os.path.join(self.pathB, 'train',train_path[j]+self.filetype)]
try:
img_A = imread(file_path[0])/255. if self.img_res==None else resize(imread(file_path[0])/255., self.img_res)
img_B = imread(file_path[1])/255. if self.img_res==None else resize(imread(file_path[1])/255., self.img_res)
except:
print(sys.exc_info()); print("Deu ruim por causa imagem corrompida no traino!!!!"); print(file_path)
flag_pass = 0 #if didn't load the image, don't pass through noise addition.
pass
#print(img_A)
#print(img_B)
if flag_pass:
imgs_A.append(img_A)
imgs_B.append(img_B)
imgs_A = np.array(imgs_A)
imgs_B = np.array(imgs_B)
if self.debug:
print(imgs_A.shape)
print(imgs_B.shape)
return imgs_A, imgs_B
def load_data(self, batch_size=1, is_testing=False):
imgs_A = []
imgs_B = []
if is_testing:
rnd = np.random.randint(0, len(self.test_imgs_path)- (1+batch_size))
rnd = range(rnd, rnd+int(np.ceil(batch_size)))
else:
rnd = np.random.randint(0, len(self.train_imgs_path)- (1+batch_size))
rnd = range(rnd, rnd+int(np.ceil(batch_size)))
for j in rnd:
flag_pass = 1
if is_testing:
files_in_folder = len(os.listdir(os.path.join(self.pathA, self.test_imgs_path[j])))
file_path = [os.path.join(self.pathA, self.test_imgs_path[j], str(1+int(files_in_folder*self.dark_bright)).zfill(3)+self.filetype), os.path.join(self.pathB, self.test_imgs_path[j]+self.filetype)]
try:
img_A = imread(file_path[0])/255. if self.img_res==None else resize(imread(file_path[0])/255., self.img_res)
img_B = imread(file_path[1])/255. if self.img_res==None else resize(imread(file_path[1])/255., self.img_res)
except:
print(sys.exc_info()); print("Deu ruim por causa imagem corrompida nos testes!!!!"); print(file_path)
flag_pass = 0 #if didn't load the image, don't pass through noise addition.
pass
if not is_testing:
files_in_folder = len(os.listdir(os.path.join(self.pathA, self.train_imgs_path[j])))
file_path = [os.path.join(self.pathA, self.train_imgs_path[j], str(1+int(files_in_folder*self.dark_bright)).zfill(3)+self.filetype), os.path.join(self.pathB, self.train_imgs_path[j]+self.filetype)]
try:
img_A = imread(file_path[0])/255. if self.img_res==None else resize(imread(file_path[0])/255., self.img_res)
img_B = imread(file_path[1])/255. if self.img_res==None else resize(imread(file_path[1])/255., self.img_res)
except:
print(sys.exc_info()); print("Deu ruim por causa imagem corrompida no treinamento!!!!"); print(file_path)
flag_pass = 0 #if didn't load the image, don't pass through noise addition.
pass
if flag_pass:
imgs_A.append(img_A)
imgs_B.append(img_B)
imgs_A = np.array(imgs_A)
imgs_B = np.array(imgs_B)
if self.debug:
print(imgs_A.shape)
print(imgs_B.shape)
return imgs_A, imgs_B
def imread(self, path):
skimage.io.imread(path, plugin='matplotlib')
#return scipy.misc.imread(path, mode='RGB').astype(np.float)
def adjust_gamma(image, gamma=1.0):
# build a lookup table mapping the pixel values [0, 255] to
# their adjusted gamma values
# invGamma = 1.0 / gamma
# table = np.array([((i / 255.0) ** invGamma) * 255
# for i in np.arange(0, 256)]).astype("uint8")
# apply gamma correction using the lookup table
return (image)**(1/gamma)
class dataloader():
def __init__(self, dataset_name: object, saturation: object, dataset_path: object = None, img_res: object = None, filetype: object = None,
percent_saturation: object = 0,
gamma = 1.0,
percent_noise: object = 0,
debug: object = False) -> object:
# TODO: recive a saturation list
self.datasets = {"Cai2018_Part1" : "/mnt/dados/datasets/Cai2018_Part1",
"fivek" : "/mnt/dados/datasets/fivek_dataset/tif",
"hdr+burst" : "/mnt/dados/datasets/hdr+burst",
"a6300" : "/mnt/dados/datasets/a6300_exposure/all_small"}
self.raw_types = ["*.dng", "*.DNG", "*.arw", ".ARW"]
self.dataset_name = dataset_name
self.img_res = img_res
self.filetype = filetype
self.percent_saturation = percent_saturation
self.gamma = gamma
self.saturation = saturation
self.percent_noise_gt = percent_noise
self.debug = debug
if self.filetype == None:
if self.dataset_name == "Cai2018_Part1":
self.filetype = ".JPG"
elif self.dataset_name == "fivek":
self.filetype = ".tif"
elif self.dataset_name == "hdr+burst":
self.filetype = ".jpg"
elif self.dataset_name == "a6300":
self.filetype = ".JPG"
if (self.dataset_name == "fivek" or self.dataset_name == "hdr+burst"):
if not isinstance(self.saturation, (list,)):
self.saturation = [self.saturation]
if not isinstance(self.gamma, (list,)):
self.gamma = [self.gamma]
if not dataset_path == None:
self.datasets[self.dataset_name] = os.path.join(dataset_path, dataset_name)
if self.debug:
print("Dataset name:", self.dataset_name)
print("Dataset path:", self.datasets.get(self.dataset_name))
def load_data (self, batch_size = 1, is_testing = False, path_pass = False):
if self.dataset_name == "Cai2018_Part1":
return self.load_cai(batch_size = batch_size, is_testing = is_testing, path_pass = path_pass)
elif self.dataset_name == "a6300":
return self.load_a6300(batch_size = batch_size, is_testing = is_testing, path_pass = path_pass)
else:
return self.load_default(batch_size = batch_size, is_testing = is_testing, path_pass = path_pass)
def load_default(self, batch_size, is_testing=False, path_pass = False):
imgs_A = []
imgs_B = []
print("batch_size:",batch_size)
if is_testing:
test_imgs_path = glob.glob(os.path.join(self.datasets[self.dataset_name], 'test', '*'+self.filetype))
#print(os.path.join(self.datasets[self.dataset_name], 'test', '*'+self.filetype))
#print(test_imgs_path)
if (len(test_imgs_path) - batch_size > 0):
rnd = np.random.randint(0, len(test_imgs_path) - batch_size)
rnd = range(rnd, rnd + int(np.ceil(batch_size / len(self.saturation))))
else:
rnd = range(0, len(test_imgs_path))
#rnd = range(len(test_imgs_path)-1, len(test_imgs_path))
else:
train_imgs_path = glob.glob(os.path.join(self.datasets[self.dataset_name], 'train', '*'+self.filetype))
rnd = np.random.randint(0, len(train_imgs_path) - batch_size)
rnd = range(rnd, rnd + int(np.ceil(batch_size / len(self.saturation))))
print("rnd:", rnd)
for j in rnd:
# print('J:', j)
# print(len(self.saturation))
flag_pass = 1
if is_testing:
try:
if not path_pass:
if self.filetype in self.raw_types:
#img_A = imread(os.path.join(pathA, 'test',test_path[j])) / 255. if self.img_res == None else resize(imread(os.path.join(pathA,'test',test_path[j])) / 255., self.img_res)
img_A = resize(load_raw(test_imgs_path[j]), self.img_res) if self.img_res != None else load_raw(test_imgs_path[j])
else:
img_A = resize(imread(test_imgs_path[j]) / 255., self.img_res) if self.img_res != None else imread(test_imgs_path[j]) / 255.
else:
img_A = test_imgs_path[j]
except:
print("Deu ruim por causa imagem corrompida nos testes!!!!")
print(test_imgs_path[j])
flag_pass = 0 # if didn't load the image, don't pass through noise addition.
pass
if not is_testing:
try:
if not path_pass:
if self.filetype in self.raw_types:
img_A = resize(load_raw(train_imgs_path[j]), self.img_res) if self.img_res != None else load_raw(train_imgs_path[j])
else:
img_A = resize(imread(train_imgs_path[j]) / 255.,self.img_res) if self.img_res != None else imread(train_imgs_path[j]) / 255.
else:
img_A = test_imgs_path[j]
except:
print("Deu ruim por causa imagem corrompida no treinamento!!!!")
print(train_imgs_path[j])
flag_pass = 0 # if didn't load the image, don't pass through noise addition.
pass
# print(self.train_imgs_path[j])
# img_A = resize(load_raw(self.train_imgs_path[j]), self.img_res)
# print(img_A)
if not path_pass:
# noise addition
img_A_gt = img_A
#img_A = adjust_gamma(img_A, gamma=self.gamma)
gamma = random.choice(self.gamma)
if self.debug:
print("LIST: ", self.gamma)
print("GaMMA: ", gamma)
img_A = (img_A)**(1/gamma)
if flag_pass:
# Choose between ground truth with noise or not. Always gausian noise.
if self.percent_noise_gt > 0.:
imgs_A.append(add_noise(img_A_gt, 'gauss', percent=self.percent_noise_gt))
else:
imgs_A.append(img_A_gt)
if 'low_range' in self.saturation:
imgs_B.append(multiply_and_clip(img_A))
# if 'saturate' in self.saturation:
# imgs_B.append(saturate(img_A, [.0, .6]))
# if 'underexpose' in self.saturation:
# imgs_B.append(saturate(img_A, [.4, 1.]))
# if 'underexpose_p5' in self.saturation:
# imgs_B.append(saturate(img_A, [np.percentile(img_A, 5), 1.]))
# if 'underexpose_p10' in self.saturation:
# imgs_B.append(saturate(img_A, [np.percentile(img_A, 10), 1.]))
# if 'underexpose_p15' in self.saturation:
# imgs_B.append(saturate(img_A, [np.percentile(img_A, 15), 1.]))
# if 'underexpose_p25' in self.saturation:
# imgs_B.append(saturate(img_A, [np.percentile(img_A, 25), 1.]))
# if 'saturate_p5' in self.saturation:
# imgs_B.append(saturate(img_A, [0., np.percentile(img_A, 95)]))
# if 'saturate_p10' in self.saturation:
# imgs_B.append(saturate(img_A, [0., np.percentile(img_A, 90)]))
# if 'saturate_p15' in self.saturation:
# imgs_B.append(saturate(img_A, [0., np.percentile(img_A, 85)]))
# if 'saturate_p25' in self.saturation:
# imgs_B.append(saturate(img_A, [0., np.percentile(img_A, 75)]))
if 'over' in self.saturation:
imgs_B.append(saturate(img_A, [0., np.percentile(img_A, 100 - self.percent_saturation)]))
if 'under' in self.saturation:
imgs_B.append(saturate(img_A, [np.percentile(img_A, self.percent_saturation), 1.]))
if 'denoise_tv_chambolle' in self.saturation:
imgs_B.append(denoise_tv_chambolle(img_A, weight=10))
if 'denoise_bilateral' in self.saturation:
imgs_B.append(denoise_bilateral(img_A, weight=10))
if 'gauss' in self.saturation:
imgs_B.append(add_noise(img_A, 'gauss'))
if 'speckle' in self.saturation:
imgs_B.append(add_noise(img_A, 'speckle'))
if 'poisson' in self.saturation:
imgs_B.append(add_noise(img_A, 'poisson'))
if 's&p' in self.saturation:
imgs_B.append(add_noise(img_A, 's&p'))
# if not flag_pass:
# j = j-1 #disconsider the actual interaction
else:
imgs_A.append(img_A)
imgs_B.append(img_A)
imgs_A = np.array(imgs_A[:batch_size])
imgs_B = np.array(imgs_B[:batch_size])
if self.debug:
print(imgs_A.shape)
print(imgs_B.shape)
return imgs_B, imgs_A
def load_a6300(self, batch_size=1, is_testing=False, path_pass = False):
imgs_A = []
imgs_B = []
if self.saturation == 'over':
folder_name = 'o'
elif self.saturation == 'under':
folder_name = 'u'
pathA = os.path.join(self.datasets[self.dataset_name], folder_name)
pathB = os.path.join(self.datasets[self.dataset_name], 'c')
if is_testing:
test_path = os.listdir(os.path.join(pathA, 'test'))
if (len(test_path) - (1 + batch_size) > 0):
#rnd = np.random.randint(0, len(test_path) - (1 + batch_size))
rnd = np.random.randint(0, len(test_path) - batch_size)
rnd = range(rnd, rnd + int(np.ceil(batch_size)))
else:
rnd = range(0, len(test_path))
#rnd = range(len(test_path)-1, len(test_path))
else:
train_path = os.listdir(os.path.join(pathA, 'train'))
rnd = np.random.randint(0, len(train_path) - (1 + batch_size))
rnd = range(rnd, rnd + int(np.ceil(batch_size)))
print(rnd)
for j in rnd:
flag_pass = 1
if is_testing:
# print('1')
try:
if not path_pass:
img_A = imread(os.path.join(pathA, 'test',test_path[j])) / 255. if self.img_res == None else resize(
imread(os.path.join(pathA,'test',test_path[j])) / 255., self.img_res)
img_B = imread(os.path.join(pathB,'test',test_path[j])) / 255. if self.img_res == None else resize(
imread(os.path.join(pathB,'test',test_path[j])) / 255., self.img_res)
else:
img_A = os.path.join(pathA, 'test',test_path[j])
img_B = os.path.join(pathB,'test',test_path[j])
except:
print(sys.exc_info()[0])
print("Deu ruim por causa imagem corrompida nos testes!!!!")
print(test_path[j])
flag_pass = 0 # if didn't load the image, don't pass through noise addition.
pass
if not is_testing: