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WiggleGAN / dataloader.py

Rodrigo_Cobo

modified how to work with depth images in dataloader

3c20dc8 over 1 year ago

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	from torch.utils.data import DataLoader
	from torchvision import datasets, transforms
	from torch.utils.data import Dataset
	import torch
	from configparser import ConfigParser
	import matplotlib.pyplot as plt
	import os
	import torch as th
	from PIL import Image
	import numpy as np
	import random
	from PIL import ImageMath
	import random

	def dataloader(dataset, input_size, batch_size,dim,split='train', trans=False):
	#transform = transforms.Compose([transforms.Resize((input_size, input_size)), transforms.ToTensor(),
	# transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))])
	if dataset == 'mnist':
	data_loader = DataLoader(
	datasets.MNIST('data/mnist', train=True, download=True, transform=transform),
	batch_size=batch_size, shuffle=True)
	elif dataset == 'fashion-mnist':
	data_loader = DataLoader(
	datasets.FashionMNIST('data/fashion-mnist', train=True, download=True, transform=transform),
	batch_size=batch_size, shuffle=True)
	elif dataset == 'cifar10':
	data_loader = DataLoader(
	datasets.CIFAR10('data/cifar10', train=True, download=True, transform=transform),
	batch_size=batch_size, shuffle=True)
	elif dataset == 'svhn':
	data_loader = DataLoader(
	datasets.SVHN('data/svhn', split=split, download=True, transform=transform),
	batch_size=batch_size, shuffle=True)
	elif dataset == 'stl10':
	data_loader = DataLoader(
	datasets.STL10('data/stl10', split=split, download=True, transform=transform),
	batch_size=batch_size, shuffle=True)
	elif dataset == 'lsun-bed':
	data_loader = DataLoader(
	datasets.LSUN('data/lsun', classes=['bedroom_train'], transform=transform),
	batch_size=batch_size, shuffle=True)
	elif dataset == '4cam':
	if split == 'score':
	cams = ScoreDataset(root_dir=os.getcwd() + '/Images/Score-Test', dim=dim, name=split, cant_images=300) #hardcode is bad but quick
	return DataLoader(cams, batch_size=batch_size, shuffle=False, num_workers=0)
	if split != 'test':
	cams = ImagesDataset(root_dir=os.getcwd() + '/Images/ActualDataset', dim=dim, name=split, transform=trans)
	return DataLoader(cams, batch_size=batch_size, shuffle=True, num_workers=0)
	else:
	cams = TestingDataset(root_dir=os.getcwd() + '/Images/Input-Test', dim=dim, name=split)
	return DataLoader(cams, batch_size=batch_size, shuffle=False, num_workers=0)

	return data_loader


	class ImagesDataset(Dataset):
	"""My dataset."""

	def __init__(self, root_dir, dim, name, transform):
	"""
	Args:
	root_dir (string): Directory with all the images.
	transform (callable, optional): Optional transform to be applied
	on a sample.
	"""
	self.root_dir = root_dir
	self.nCameras = 2
	self.imageDim = dim
	self.name = name
	self.parser = ConfigParser()
	self.parser.read('config.ini')
	self.transform = transform

	def __len__(self):

	return self.parser.getint(self.name, 'total')
	#oneCameRoot = self.root_dir + '\CAM1'
	#return int(len([name for name in os.listdir(oneCameRoot) if os.path.isfile(os.path.join(oneCameRoot, name))])/2) #por el depth


	def __getitem__(self, idx):
	if th.is_tensor(idx):
	idx = idx.tolist()
	idx = self.parser.get(self.name, str(idx))
	if self.transform:
	brighness = random.uniform(0.7, 1.2)
	saturation = random.uniform(0, 2)
	contrast = random.uniform(0.4, 2)
	gamma = random.uniform(0.7, 1.3)
	hue = random.uniform(-0.3, 0.3) # 0.01

	oneCameRoot = self.root_dir + '/CAM0'

	# foto normal
	img_name = os.path.join(oneCameRoot, "n_" + idx + ".png")
	img = Image.open(img_name).convert('RGB') # .convert('L')
	if (img.size[0] != self.imageDim or img.size[1] != self.imageDim):
	img = img.resize((self.imageDim, self.imageDim))
	if self.transform:
	img = transforms.functional.adjust_gamma(img, gamma)
	img = transforms.functional.adjust_brightness(img, brighness)
	img = transforms.functional.adjust_contrast(img, contrast)
	img = transforms.functional.adjust_saturation(img, saturation)
	img = transforms.functional.adjust_hue(img, hue)
	x1 = transforms.ToTensor()(img)
	x1 = (x1 * 2) - 1

	# foto produndidad
	img_name = os.path.join(oneCameRoot, "d_" + idx + ".png")
	img = Image.open(img_name).convert('I')
	img = convert_I_to_L(img)
	if (img.size[0] != self.imageDim or img.size[1] != self.imageDim):
	img = img.resize((self.imageDim, self.imageDim))
	x1_dep = transforms.ToTensor()(img)
	x1_dep = (x1_dep * 2) - 1

	oneCameRoot = self.root_dir + '/CAM1'

	# foto normal
	img_name = os.path.join(oneCameRoot, "n_" + idx + ".png")
	img = Image.open(img_name).convert('RGB') # .convert('L')
	if (img.size[0] != self.imageDim or img.size[1] != self.imageDim):
	img = img.resize((self.imageDim, self.imageDim))
	if self.transform:
	img = transforms.functional.adjust_gamma(img, gamma)
	img = transforms.functional.adjust_brightness(img, brighness)
	img = transforms.functional.adjust_contrast(img, contrast)
	img = transforms.functional.adjust_saturation(img, saturation)
	img = transforms.functional.adjust_hue(img, hue)
	x2 = transforms.ToTensor()(img)
	x2 = (x2 * 2) - 1

	# foto produndidad
	img_name = os.path.join(oneCameRoot, "d_" + idx + ".png")
	img = Image.open(img_name).convert('I')
	img = convert_I_to_L(img)
	if (img.size[0] != self.imageDim or img.size[1] != self.imageDim):
	img = img.resize((self.imageDim, self.imageDim))
	x2_dep = transforms.ToTensor()(img)
	x2_dep = (x2_dep * 2) - 1


	#random izq o derecha
	if (bool(random.getrandbits(1))):
	sample = {'x_im': x1, 'x_dep': x1_dep, 'y_im': x2, 'y_dep': x2_dep, 'y_': torch.ones(1, self.imageDim, self.imageDim)}
	else:
	sample = {'x_im': x2, 'x_dep': x2_dep, 'y_im': x1, 'y_dep': x1_dep, 'y_': torch.zeros(1, self.imageDim, self.imageDim)}

	return sample

	def __iter__(self):

	for i in range(this.__len__()):
	list.append(this.__getitem__(i))
	return iter(list)

	class TestingDataset(Dataset):
	"""My dataset."""

	def __init__(self, root_dir, dim, name):
	"""
	Args:
	root_dir (string): Directory with all the images.
	transform (callable, optional): Optional transform to be applied
	on a sample.
	"""
	self.root_dir = root_dir
	self.imageDim = dim
	self.name = name
	files = os.listdir(self.root_dir)
	self.files = [ele for ele in files if not ele.endswith('_d.png')]

	def __len__(self):

	#return self.parser.getint(self.name, 'total')
	#oneCameRoot = self.root_dir + '\CAM1'
	#return int(len([name for name in os.listdir(self.root_dir) if os.path.isfile(os.path.join(self.root_dir, name))])/2) #por el depth
	return len(self.files)


	def __getitem__(self, idx):
	if th.is_tensor(idx):
	idx = idx.tolist()

	# foto normal
	img_name = os.path.join(self.root_dir, self.files[idx])
	img = Image.open(img_name).convert('RGB') # .convert('L')
	if (img.size[0] != self.imageDim or img.size[1] != self.imageDim):
	img = img.resize((self.imageDim, self.imageDim))
	x1 = transforms.ToTensor()(img)
	x1 = (x1 * 2) - 1


	# foto produndidad
	img_name = os.path.join(self.root_dir , self.files[idx][:-4] + "_d.png")
	img = Image.open(img_name)
	if (img.size[0] != self.imageDim or img.size[1] != self.imageDim):
	img = img.resize((self.imageDim, self.imageDim))
	x1_dep = transforms.ToTensor()(img)
	x1_dep = (x1_dep * 2) - 1

	sample = {'x_im': x1, 'x_dep': x1_dep}

	return sample

	def __iter__(self):

	for i in range(this.__len__()):
	list.append(this.__getitem__(i))
	return iter(list)


	def show_image(t_data, grey=False):

	#from numpy
	t_data2 = t_data.transpose(1, 2, 0)
	t_data2 = t_data2 * 255.0
	t_data2 = t_data2.astype(np.uint8)
	if (not grey):
	outIm = Image.fromarray(t_data2, mode='RGB')
	else:
	t_data2 = np.squeeze(t_data2, axis=2)
	outIm = Image.fromarray(t_data2, mode='L')
	outIm.show()

	def convert_I_to_L(img):
	array = np.uint8(np.array(img) / 256) #el numero esta bien, sino genera espacios en negro en la imagen
	return Image.fromarray(array)

	class ScoreDataset(Dataset):
	"""My dataset."""

	def __init__(self, root_dir, dim, name, cant_images):
	"""
	Args:
	root_dir (string): Directory with all the images.
	transform (callable, optional): Optional transform to be applied
	on a sample.
	"""
	self.root_dir = root_dir
	self.nCameras = 2
	self.imageDim = dim
	self.name = name
	self.size = cant_images

	def __len__(self):

	return self.size


	def __getitem__(self, idx):

	oneCameRoot = self.root_dir + '/CAM0'

	idx = "{:04d}".format(idx)
	# foto normal
	img_name = os.path.join(oneCameRoot, "n_" + idx + ".png")
	img = Image.open(img_name).convert('RGB') # .convert('L')
	if (img.size[0] != self.imageDim or img.size[1] != self.imageDim):
	img = img.resize((self.imageDim, self.imageDim))
	x1 = transforms.ToTensor()(img)
	x1 = (x1 * 2) - 1

	# foto produndidad
	img_name = os.path.join(oneCameRoot, "d_" + idx + ".png")
	img = Image.open(img_name).convert('I')
	img = convert_I_to_L(img)
	if (img.size[0] != self.imageDim or img.size[1] != self.imageDim):
	img = img.resize((self.imageDim, self.imageDim))
	x1_dep = transforms.ToTensor()(img)
	x1_dep = (x1_dep * 2) - 1

	oneCameRoot = self.root_dir + '/CAM1'

	# foto normal
	img_name = os.path.join(oneCameRoot, "n_" + idx + ".png")
	img = Image.open(img_name).convert('RGB') # .convert('L')
	if (img.size[0] != self.imageDim or img.size[1] != self.imageDim):
	img = img.resize((self.imageDim, self.imageDim))
	x2 = transforms.ToTensor()(img)
	x2 = (x2 * 2) - 1

	# foto produndidad
	img_name = os.path.join(oneCameRoot, "d_" + idx + ".png")
	img = Image.open(img_name).convert('I')
	img = convert_I_to_L(img)
	if (img.size[0] != self.imageDim or img.size[1] != self.imageDim):
	img = img.resize((self.imageDim, self.imageDim))
	x2_dep = transforms.ToTensor()(img)
	x2_dep = (x2_dep * 2) - 1


	sample = {'x_im': x1, 'x_dep': x1_dep, 'y_im': x2, 'y_dep': x2_dep, 'y_': torch.ones(1, self.imageDim, self.imageDim)}
	return sample

	def __iter__(self):

	for i in range(self.__len__()):
	list.append(self.__getitem__(i))
	return iter(list)