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from email.mime import base
import numpy as np
from PIL import Image
import os
import sys
import torch
import torch.nn.functional as F
from PIL import Image, ImageOps, ImageFilter, ImageEnhance
from typing import Optional
filepath = os.path.split(__file__)[0]
repopath = os.path.split(filepath)[0]
sys.path.append(repopath)
from utils.misc import *
class static_resize:
# Resize for training
# size: h x w
def __init__(self, size=[384, 384], base_size=None):
self.size = size[::-1]
self.base_size = base_size[::-1] if base_size is not None else None
def __call__(self, sample):
sample['image'] = sample['image'].resize(self.size, Image.BILINEAR)
if 'gt' in sample.keys():
sample['gt'] = sample['gt'].resize(self.size, Image.NEAREST)
if self.base_size is not None:
sample['image_resized'] = sample['image'].resize(self.size, Image.BILINEAR)
if 'gt' in sample.keys():
sample['gt_resized'] = sample['gt'].resize(self.size, Image.NEAREST)
return sample
class dynamic_resize:
# base_size: h x w
def __init__(self, L=1280, base_size=[384, 384]):
self.L = L
self.base_size = base_size[::-1]
def __call__(self, sample):
size = list(sample['image'].size)
if (size[0] >= size[1]) and size[1] > self.L:
size[0] = size[0] / (size[1] / self.L)
size[1] = self.L
elif (size[1] > size[0]) and size[0] > self.L:
size[1] = size[1] / (size[0] / self.L)
size[0] = self.L
size = (int(round(size[0] / 32)) * 32, int(round(size[1] / 32)) * 32)
if 'image' in sample.keys():
sample['image_resized'] = sample['image'].resize(self.base_size, Image.BILINEAR)
sample['image'] = sample['image'].resize(size, Image.BILINEAR)
if 'gt' in sample.keys():
sample['gt_resized'] = sample['gt'].resize(self.base_size, Image.NEAREST)
sample['gt'] = sample['gt'].resize(size, Image.NEAREST)
return sample
class random_scale_crop:
def __init__(self, range=[0.75, 1.25]):
self.range = range
def __call__(self, sample):
scale = np.random.random() * (self.range[1] - self.range[0]) + self.range[0]
if np.random.random() < 0.5:
for key in sample.keys():
if key in ['image', 'gt']:
base_size = sample[key].size
scale_size = tuple((np.array(base_size) * scale).round().astype(int))
sample[key] = sample[key].resize(scale_size)
lf = (sample[key].size[0] - base_size[0]) // 2
up = (sample[key].size[1] - base_size[1]) // 2
rg = (sample[key].size[0] + base_size[0]) // 2
lw = (sample[key].size[1] + base_size[1]) // 2
border = -min(0, min(lf, up))
sample[key] = ImageOps.expand(sample[key], border=border)
sample[key] = sample[key].crop((lf + border, up + border, rg + border, lw + border))
return sample
class random_flip:
def __init__(self, lr=True, ud=True):
self.lr = lr
self.ud = ud
def __call__(self, sample):
lr = np.random.random() < 0.5 and self.lr is True
ud = np.random.random() < 0.5 and self.ud is True
for key in sample.keys():
if key in ['image', 'gt']:
sample[key] = np.array(sample[key])
if lr:
sample[key] = np.fliplr(sample[key])
if ud:
sample[key] = np.flipud(sample[key])
sample[key] = Image.fromarray(sample[key])
return sample
class random_rotate:
def __init__(self, range=[0, 360], interval=1):
self.range = range
self.interval = interval
def __call__(self, sample):
rot = (np.random.randint(*self.range) // self.interval) * self.interval
rot = rot + 360 if rot < 0 else rot
if np.random.random() < 0.5:
for key in sample.keys():
if key in ['image', 'gt']:
base_size = sample[key].size
sample[key] = sample[key].rotate(rot, expand=True, fillcolor=255 if key == 'depth' else None)
sample[key] = sample[key].crop(((sample[key].size[0] - base_size[0]) // 2,
(sample[key].size[1] - base_size[1]) // 2,
(sample[key].size[0] + base_size[0]) // 2,
(sample[key].size[1] + base_size[1]) // 2))
return sample
class random_image_enhance:
def __init__(self, methods=['contrast', 'brightness', 'sharpness']):
self.enhance_method = []
if 'contrast' in methods:
self.enhance_method.append(ImageEnhance.Contrast)
if 'brightness' in methods:
self.enhance_method.append(ImageEnhance.Brightness)
if 'sharpness' in methods:
self.enhance_method.append(ImageEnhance.Sharpness)
def __call__(self, sample):
if 'image' in sample.keys():
np.random.shuffle(self.enhance_method)
for method in self.enhance_method:
if np.random.random() > 0.5:
enhancer = method(sample['image'])
factor = float(1 + np.random.random() / 10)
sample['image'] = enhancer.enhance(factor)
return sample
class tonumpy:
def __init__(self):
pass
def __call__(self, sample):
for key in sample.keys():
if key in ['image', 'image_resized', 'gt', 'gt_resized']:
sample[key] = np.array(sample[key], dtype=np.float32)
return sample
class normalize:
def __init__(self, mean: Optional[list]=None, std: Optional[list]=None, div=255):
self.mean = mean if mean is not None else 0.0
self.std = std if std is not None else 1.0
self.div = div
def __call__(self, sample):
if 'image' in sample.keys():
sample['image'] /= self.div
sample['image'] -= self.mean
sample['image'] /= self.std
if 'image_resized' in sample.keys():
sample['image_resized'] /= self.div
sample['image_resized'] -= self.mean
sample['image_resized'] /= self.std
if 'gt' in sample.keys():
sample['gt'] /= self.div
if 'gt_resized' in sample.keys():
sample['gt_resized'] /= self.div
return sample
class totensor:
def __init__(self):
pass
def __call__(self, sample):
if 'image' in sample.keys():
sample['image'] = sample['image'].transpose((2, 0, 1))
sample['image'] = torch.from_numpy(sample['image']).float()
if 'image_resized' in sample.keys():
sample['image_resized'] = sample['image_resized'].transpose((2, 0, 1))
sample['image_resized'] = torch.from_numpy(sample['image_resized']).float()
if 'gt' in sample.keys():
sample['gt'] = torch.from_numpy(sample['gt'])
sample['gt'] = sample['gt'].unsqueeze(dim=0)
if 'gt_resized' in sample.keys():
sample['gt_resized'] = torch.from_numpy(sample['gt_resized'])
sample['gt_resized'] = sample['gt_resized'].unsqueeze(dim=0)
return sample
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