'''
Python script to crunch sprite assets in Unity by a constant value (for reducing filesize with big images
This automatically applies to .meta files, so nothing changes in game, and it works on Multiple sprite assets as well.
PIL (Python Image Library) needs to be installed to use this.
@author: Brian Intile
'''
import os
from PIL import Image
import PIL
def get_immediate_subdirectories(directory):
return [
name for name in os.listdir(directory)
if os.path.isdir(os.path.join(directory, name))
]
def browse_for_traits(directory, recurse, mult):
for dir_0 in os.listdir(directory):
if (os.path.isfile(directory + u'\\' + dir_0) &
((dir_0.split('.')[-1].lower() == u'png'.lower()) |
(dir_0.split('.')[-1].lower() == u'jpg'.lower()))):
change_value_names(directory + '\\' + dir_0, mult)
if (recurse):
directories = get_immediate_subdirectories(directory)
for x in range(0, len(directories)):
def __init__(self, dataroot, train=True, augment=True):
self.images = []
self.bubbles = []
self.labels = []
dataset = [] # contains tuples of images and associated 360 bubbles
if train:
file = np.loadtxt(os.path.join(dataroot, "dataset_train.txt"),
dtype=str,
skiprows=3)
else:
file = np.loadtxt(os.path.join(dataroot, "dataset_test.txt"),
dtype=str,
skiprows=3)
# load image pairs and create training/validation labels
for pair in file:
if 'right' in pair[1]:
self.labels.extend([i for i in range(3, 6)])
elif 'left' in pair[1]:
self.labels.extend([i for i in range(0, 3)])
dataset.extend([pair for i in range(3)])
# calculate dataset length
self.data_len = len(dataset)
# transformations when loading images:
PIL = transforms.ToPILImage()
resize = transforms.Compose([transforms.Resize(300)])
bub_size = transforms.Resize(500)
if augment:
self.image_trans = transforms.Compose([
transforms.RandomCrop(224),
transforms.ColorJitter(brightness=0.5,
contrast=0.5,
saturation=0.5,
hue=0.05),
transforms.ToTensor()
])
self.bubble_trans = transforms.Compose([
transforms.ColorJitter(brightness=0.5,
contrast=0.5,
saturation=0.5,
hue=0.05),
transforms.ToTensor()
])
else:
self.image_trans = transforms.Compose(
[transforms.Resize(300),
transforms.ToTensor()])
self.bubble_trans = transforms.Compose([transforms.ToTensor()])
# load images and applying initial preprocessing
for i, (bubble, image) in enumerate(dataset):
image = io.imread(os.path.join(dataroot, image),
plugin='matplotlib')
bubble = io.imread(os.path.join(dataroot, bubble),
plugin='matplotlib')
label = torch.tensor(self.labels[i])
# cropping parameters in height and width, this assumes images of shape (2100, 2800)
params = [1300, 1300]
# set left pixel of the image crop depending on label
if label == 0 or label == 3:
width = 0
elif label == 1 or label == 4:
width = int((image.shape[1] - params[1]) / 2)
elif label == 2 or label == 5:
width = int(image.shape[1] - (params[1] + 1))
# set height of image crop
height = int((image.shape[0] - params[0]) / 2)
# pre-process image files
image = PIL(image)
image = TF.crop(image, height, width, params[0], params[1])
image = resize(image)
self.images.append(image)
# preprocess bubble and add the array
bubble = PIL(bubble)
bubble = bub_size(bubble)
self.bubbles.append(bubble)
