saliency-detection-demo / joint_transforms.py
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# joint_transforms.py
import math
import random
import numpy as np
from PIL import Image, ImageFilter
class Compose(object):
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, img, gt):
for t in self.transforms:
img, gt = t(img, gt)
return img, gt
class RandomScaleCrop(object):
"""多尺度缩放裁剪(同时处理图像和标签)"""
def __init__(self, base_size=352, crop_size=352, scale_factor=[0.75, 1.0, 1.25]):
self.base_size = base_size
self.crop_size = crop_size
self.scale_factor = scale_factor
def __call__(self, img, gt):
# 随机选择缩放比例
sf = random.choice(self.scale_factor)
new_size = int(self.base_size * sf)
# 缩放
img = img.resize((new_size, new_size), Image.BILINEAR)
gt = gt.resize((new_size, new_size), Image.NEAREST)
# 随机裁剪
x = random.randint(0, new_size - self.crop_size)
y = random.randint(0, new_size - self.crop_size)
img = img.crop((x, y, x+self.crop_size, y+self.crop_size))
gt = gt.crop((x, y, x+self.crop_size, y+self.crop_size))
return img, gt
class RandomRotate(object):
"""随机旋转(保持图像和标签同步)"""
def __init__(self, degree=30):
self.degree = degree
def __call__(self, img, gt):
rotate_degree = random.uniform(-self.degree, self.degree)
img = img.rotate(rotate_degree, Image.BILINEAR)
gt = gt.rotate(rotate_degree, Image.NEAREST)
return img, gt
class RandomGaussianBlur(object):
"""随机高斯模糊(仅对图像处理)"""
def __init__(self, p=0.5):
self.p = p
def __call__(self, img, gt):
if random.random() < self.p:
img = img.filter(ImageFilter.GaussianBlur(
radius=random.uniform(0.5, 2.0)))
return img, gt
class RandomHorizontallyFlip(object):
"""随机水平翻转"""
def __init__(self, p=0.5):
self.p = p
def __call__(self, img, gt):
if random.random() < self.p:
img = img.transpose(Image.FLIP_LEFT_RIGHT)
gt = gt.transpose(Image.FLIP_LEFT_RIGHT)
return img, gt