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transformations.py
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transformations.py
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#!/usr/bin/python
import numpy as np
from sklearn.externals._pilutil import bytescale
import random
import matplotlib.pyplot as plt
import cv2
import random
def normalize_01(inp: np.ndarray):
mi = np.min(inp)
ma = np.max(inp)
range = np.ptp(inp)
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 transforms 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)
#tar = np.moveaxis(tar, -1, 0)
return inp, tar
def __repr__(self):
return str({self.__class__.__name__: self.__dict__})
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):
pass
def __call__(self, inp: np.ndarray, tar: np.ndarray):
crop_width = 512
crop_height =512
max_x = inp.shape[1] - crop_width
print(max_x)
max_y = inp.shape[2] - crop_height
random.seed(27)
x = random.randint(0, max_x)
y = random.randint(0, max_y)
inp = np.moveaxis(inp, 0, -1)
inp = inp[x: x + crop_width, y: y + crop_height,:]
inp = np.moveaxis(inp, -1, 0)
tar = tar[x: x + crop_width, y: y + crop_height]
return inp, tar
class Resize_Sample:
def __init__(self):
pass
def __call__(self, inp: np.ndarray, tar: np.ndarray):
inp = np.moveaxis(inp, 0, -1)
inp = cv2.resize(inp, (256,256), interpolation = cv2.INTER_NEAREST)
inp = np.moveaxis(inp, -1, 0)
tar = cv2.resize(tar, (256,256), 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
def __repr__(self):
return str({self.__class__.__name__: self.__dict__})
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
def __repr__(self):
return str({self.__class__.__name__: self.__dict__})