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import os
import cv2
import random
import numpy as np
from PIL import Image
from torchvision import transforms
import torchtask
def add_parser_arguments(parser):
torchtask.data_template.add_parser_arguments(parser)
def harmonizer_iharmony4():
return HarmonizerIHarmony4
def original_iharmony4():
return OriginalIHarmony4
def resize(img, size):
interp = cv2.INTER_LINEAR
return Image.fromarray(
cv2.resize(np.array(img).astype('uint8'), size, interpolation=interp))
im_train_transform = transforms.Compose([
transforms.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.03),
transforms.ToTensor(),
])
im_val_transform = transforms.Compose([
transforms.ToTensor(),
])
class HarmonizerIHarmony4(torchtask.data_template.TaskDataset):
def __init__(self, args, is_train):
super(HarmonizerIHarmony4, self).__init__(args, is_train)
self.im_dir = os.path.join(self.root_dir, 'image')
self.mask_dir = os.path.join(self.root_dir, 'mask')
if not os.path.exists(self.mask_dir):
self.mask_dir = os.path.join(self.root_dir, 'matte')
self.sample_list = [_ for _ in os.listdir(self.im_dir)]
self.idxs = [_ for _ in range(0, len(self.sample_list))]
self.im_size = self.args.im_size
self.rotation = True if self.is_train else False
self.fliplr = True if self.is_train else False
def __getitem__(self, idx):
image_path = os.path.join(self.im_dir, self.sample_list[idx])
mask_path = os.path.join(self.mask_dir, self.sample_list[idx])
image = self.im_loader.load(image_path)
mask = self.im_loader.load(mask_path)
width, height = image.size
# resize to self.im_size
image = resize(image, (self.im_size, self.im_size))
mask = resize(mask, (self.im_size, self.im_size))
# convert to np array and scale to [0, 1]
image = np.array(image).astype('float32') / 255.0
mask = np.array(mask).astype('float32') / 255.0
# check image shape
if len(mask.shape) == 3:
mask = mask[:, :, -1]
if len(image.shape) == 2:
image = image[:, :, None]
if image.shape[2] == 1:
image = np.repeat(image, 3, axis=2)
elif image.shape[2] == 4:
image = image[:, :, 0:3]
# random rotate
rerotation = 0
if self.rotation and random.randint(0, 1) == 0:
rotate_num = random.randint(1, 3)
rerotation = 4 - rotate_num
image = np.rot90(image, k=rotate_num).copy()
mask = np.rot90(mask, k=rotate_num).copy()
# random flip
if self.fliplr and (random.randint(0, 1) == 0):
image = np.fliplr(image).copy()
mask = np.fliplr(mask).copy()
image = Image.fromarray((image * 255.0).astype('uint8'))
if self.is_train:
image = im_train_transform(image)
else:
image = im_val_transform(image)
mask = mask[None, :, :]
adjusted = image.numpy() * -1
return (adjusted, mask), (image, )
class OriginalIHarmony4(torchtask.data_template.TaskDataset):
def __init__(self, args, is_train):
super(OriginalIHarmony4, self).__init__(args, is_train)
self.adjusted_dir = os.path.join(self.root_dir, 'comp')
self.mask_dir = os.path.join(self.root_dir, 'mask')
self.im_dir = os.path.join(self.root_dir, 'image')
self.sample_list = [_ for _ in os.listdir(self.adjusted_dir)]
self.idxs = [_ for _ in range(0, len(self.sample_list))]
self.im_size = self.args.im_size
self.rotation = True if self.is_train else False
self.fliplr = True if self.is_train else False
def __getitem__(self, idx):
sname = self.sample_list[idx]
adjusted_path = os.path.join(self.adjusted_dir, sname)
image_path = os.path.join(self.im_dir, sname)
mask_path = os.path.join(self.mask_dir, sname)
if not os.path.exists(image_path):
prefix = '_'.join(sname.split('_')[:-1])
image_path = os.path.join(self.im_dir, '{0}.jpg'.format(prefix))
mask_path = os.path.join(self.mask_dir, '{0}.jpg'.format(prefix))
adjusted = self.im_loader.load(adjusted_path)
image = self.im_loader.load(image_path)
mask = self.im_loader.load(mask_path)
width, height = image.size
# resize to self.im_size
adjusted = resize(adjusted, (self.im_size, self.im_size))
image = resize(image, (self.im_size, self.im_size))
mask = resize(mask, (self.im_size, self.im_size))
# convert to np array and scale to [0, 1]
adjusted = np.array(adjusted).astype('float32') / 255.0
image = np.array(image).astype('float32') / 255.0
mask = np.array(mask).astype('float32') / 255.0
# check image shape
if len(mask.shape) == 3:
mask = mask[:, :, -1]
if len(image.shape) == 2:
image = image[:, :, None]
if image.shape[2] == 1:
image = np.repeat(image, 3, axis=2)
elif image.shape[2] == 4:
image = image[:, :, 0:3]
if len(adjusted.shape) == 2:
adjusted = adjusted[:, :, None]
if adjusted.shape[2] == 1:
adjusted = np.repeat(adjusted, 3, axis=2)
elif adjusted.shape[2] == 4:
adjusted = adjusted[:, :, 0:3]
# random rotate
rerotation = 0
if self.rotation and random.randint(0, 1) == 0:
rotate_num = random.randint(1, 3)
rerotation = 4 - rotate_num
adjusted = np.rot90(adjusted, k=rotate_num).copy()
image = np.rot90(image, k=rotate_num).copy()
mask = np.rot90(mask, k=rotate_num).copy()
# random flip
if self.fliplr and (random.randint(0, 1) == 0):
adjusted = np.fliplr(adjusted).copy()
image = np.fliplr(image).copy()
mask = np.fliplr(mask).copy()
adjusted = Image.fromarray((adjusted * 255.0).astype('uint8'))
image = Image.fromarray((image * 255.0).astype('uint8'))
# NOTE: do not add random color adjustement here
adjusted = im_val_transform(adjusted)
image = im_val_transform(image)
mask = mask[None, :, :]
return (adjusted, mask), (image, )
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