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transformations.py
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transformations.py
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#!/usr/bin/env python3
# Detailed description about image augmentation and how to use the code:
# https://medium.com/@stefan.herdy/how-to-augment-images-for-semantic-segmentation-2d7df97544de
import numpy as np
from sklearn.externals._pilutil import bytescale
import random
import matplotlib.pyplot as plt
import cv2
import random
import torch
import torchvision.transforms as transforms
def create_dense_target(tar: np.ndarray):
dense_tar = tar[:,:]
return dense_tar
def normalize_01(inp: np.ndarray):
inp_out = (inp - np.min(inp)) / np.ptp(inp)
return inp_out
def normalize(inp: np.ndarray, mean: float, std: float):
inp_out = (inp - mean) / std
return inp_out
def re_normalize(inp: np.ndarray,
low: int = 0,
high: int = 255
):
"""Normalize the data to a certain range. Default: [0-255]"""
inp_out = bytescale(inp, low=low, high=high)
return inp_out
class Compose:
"""
Composes several transformations together.
"""
def __init__(self, transforms: list):
self.transforms = transforms
def __call__(self, inp, target):
for t in self.transforms:
inp, target = t(inp, target)
return inp, target
def __repr__(self): return str([transform for transform in self.transforms])
class MoveAxis:
"""From [H, W, C] to [C, H, W]"""
def __init__(self, transform_input: bool = True, transform_target: bool = False):
self.transform_input = transform_input
self.transform_target = transform_target
def __call__(self, inp: np.ndarray, tar: np.ndarray):
inp = np.moveaxis(inp, -1, 0)
return inp, tar
def __repr__(self):
return str({self.__class__.__name__: self.__dict__})
class ColorTransformations:
def __init__(self):
pass
def __call__(self, inp: np.ndarray, tar: np.ndarray):
inp_tensor = torch.from_numpy(inp)
tar_tensor = torch.from_numpy(tar)
color_transform = transforms.Compose([
transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.2),
])
inp_tensor = color_transform(inp_tensor)
inp = inp_tensor.numpy()
tar = tar_tensor.numpy()
return inp, tar
class ColorNoise:
def __init__(self, noise_std=0.05):
self.noise_std = noise_std
def __call__(self, inp: np.ndarray, tar: np.ndarray):
inp_tensor = torch.from_numpy(inp)
tar_tensor = torch.from_numpy(tar)
noise = torch.randn_like(inp_tensor) * self.noise_std
inp_tensor += noise
inp_tensor = torch.clamp(inp_tensor, 0, 1)
inp = inp_tensor.numpy()
tar = tar_tensor.numpy()
return inp, tar
class RandomFlip:
def __init__(self):
pass
def __call__(self, inp: np.ndarray, tar: np.ndarray):
rand = random.choice([0, 1])
if rand == 1:
inp = np.moveaxis(inp, 0, -1)
inp = cv2.flip(inp, 1)
inp = np.moveaxis(inp, -1, 0)
tar = np.ndarray.copy(np.fliplr(tar))
rand = random.choice([0, 1])
if rand == 1:
inp = np.moveaxis(inp, 0, -1)
inp = cv2.flip(inp, 0)
inp = np.moveaxis(inp, -1, 0)
tar = np.ndarray.copy(np.flipud(tar))
rand = random.choice([0, 1])
if rand == 1:
inp = np.ndarray.copy(np.rot90(inp, k=1, axes=(1, 2)))
tar = np.ndarray.copy(np.rot90(tar, k=1, axes=(0, 1)))
return inp, tar
def __repr__(self):
return str({self.__class__.__name__: self.__dict__})
class RandomCrop:
def __init__(self, crop_size):
self.crop_size = crop_size
pass
def __call__(self, inp: np.ndarray, tar: np.ndarray):
max_x = inp.shape[1] - self.crop_size
max_y = inp.shape[2] - self.crop_size
x = random.randint(0, max_x)
y = random.randint(0, max_y)
inp = np.moveaxis(inp, 0, -1)
inp = inp[x: x + self.crop_size, y: y + self.crop_size,:]
inp = np.moveaxis(inp, -1, 0)
tar = tar[x: x + self.crop_size, y: y + self.crop_size]
return inp, tar
class Resize:
def __init__(self, img_size):
self.img_size = img_size
pass
def __call__(self, inp: np.ndarray, tar: np.ndarray):
inp = np.moveaxis(inp, 0, -1)
inp = cv2.resize(inp, (self.img_size,self.img_size), interpolation = cv2.INTER_NEAREST)
inp = np.moveaxis(inp, -1, 0)
tar = cv2.resize(tar, (self.img_size,self.img_size), interpolation = cv2.INTER_NEAREST)
return inp, tar
class Normalize01:
"""Squash image input to the value range [0, 1] (no clipping)"""
def __init__(self):
pass
def __call__(self, inp, tar):
inp = normalize_01(inp)
return inp, tar
class Normalize:
"""Normalize based on mean and standard deviation."""
def __init__(self,
mean: float,
std: float,
transform_input=True,
transform_target=False
):
self.transform_input = transform_input
self.transform_target = transform_target
self.mean = mean
self.std = std
def __call__(self, inp, tar):
inp = normalize(inp)
return inp, tar