332 lines
10 KiB
Python
332 lines
10 KiB
Python
|
import math
|
||
|
|
import random
|
||
|
|
import warnings
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
import scipy.ndimage
|
||
|
|
|
||
|
|
import torch
|
||
|
|
from torch.autograd import Function
|
||
|
|
from torch.autograd.function import once_differentiable
|
||
|
|
import torch.backends.cudnn as cudnn
|
||
|
|
|
||
|
|
from util.logconf import logging
|
||
|
|
log = logging.getLogger(__name__)
|
||
|
|
# log.setLevel(logging.WARN)
|
||
|
|
# log.setLevel(logging.INFO)
|
||
|
|
log.setLevel(logging.DEBUG)
|
||
|
|
|
||
|
|
def cropToShape(image, new_shape, center_list=None, fill=0.0):
|
||
|
|
# log.debug([image.shape, new_shape, center_list])
|
||
|
|
# assert len(image.shape) == 3, repr(image.shape)
|
||
|
|
|
||
|
|
if center_list is None:
|
||
|
|
center_list = [int(image.shape[i] / 2) for i in range(3)]
|
||
|
|
|
||
|
|
crop_list = []
|
||
|
|
for i in range(0, 3):
|
||
|
|
crop_int = center_list[i]
|
||
|
|
if image.shape[i] > new_shape[i] and crop_int is not None:
|
||
|
|
|
||
|
|
# We can't just do crop_int +/- shape/2 since shape might be odd
|
||
|
|
# and ints round down.
|
||
|
|
start_int = crop_int - int(new_shape[i]/2)
|
||
|
|
end_int = start_int + new_shape[i]
|
||
|
|
crop_list.append(slice(max(0, start_int), end_int))
|
||
|
|
else:
|
||
|
|
crop_list.append(slice(0, image.shape[i]))
|
||
|
|
|
||
|
|
# log.debug([image.shape, crop_list])
|
||
|
|
image = image[crop_list]
|
||
|
|
|
||
|
|
crop_list = []
|
||
|
|
for i in range(0, 3):
|
||
|
|
if image.shape[i] < new_shape[i]:
|
||
|
|
crop_int = int((new_shape[i] - image.shape[i]) / 2)
|
||
|
|
crop_list.append(slice(crop_int, crop_int + image.shape[i]))
|
||
|
|
else:
|
||
|
|
crop_list.append(slice(0, image.shape[i]))
|
||
|
|
|
||
|
|
# log.debug([image.shape, crop_list])
|
||
|
|
new_image = np.zeros(new_shape, dtype=image.dtype)
|
||
|
|
new_image[:] = fill
|
||
|
|
new_image[crop_list] = image
|
||
|
|
|
||
|
|
return new_image
|
||
|
|
|
||
|
|
|
||
|
|
def zoomToShape(image, new_shape, square=True):
|
||
|
|
# assert image.shape[-1] in {1, 3, 4}, repr(image.shape)
|
||
|
|
|
||
|
|
if square and image.shape[0] != image.shape[1]:
|
||
|
|
crop_int = min(image.shape[0], image.shape[1])
|
||
|
|
new_shape = [crop_int, crop_int, image.shape[2]]
|
||
|
|
image = cropToShape(image, new_shape)
|
||
|
|
|
||
|
|
zoom_shape = [new_shape[i] / image.shape[i] for i in range(3)]
|
||
|
|
|
||
|
|
with warnings.catch_warnings():
|
||
|
|
warnings.simplefilter("ignore")
|
||
|
|
image = scipy.ndimage.interpolation.zoom(
|
||
|
|
image, zoom_shape,
|
||
|
|
output=None, order=0, mode='nearest', cval=0.0, prefilter=True)
|
||
|
|
|
||
|
|
return image
|
||
|
|
|
||
|
|
def randomOffset(image_list, offset_rows=0.125, offset_cols=0.125):
|
||
|
|
|
||
|
|
center_list = [int(image_list[0].shape[i] / 2) for i in range(3)]
|
||
|
|
center_list[0] += int(offset_rows * (random.random() - 0.5) * 2)
|
||
|
|
center_list[1] += int(offset_cols * (random.random() - 0.5) * 2)
|
||
|
|
center_list[2] = None
|
||
|
|
|
||
|
|
new_list = []
|
||
|
|
for image in image_list:
|
||
|
|
new_image = cropToShape(image, image.shape, center_list)
|
||
|
|
new_list.append(new_image)
|
||
|
|
|
||
|
|
return new_list
|
||
|
|
|
||
|
|
|
||
|
|
def randomZoom(image_list, scale=None, scale_min=0.8, scale_max=1.3):
|
||
|
|
if scale is None:
|
||
|
|
scale = scale_min + (scale_max - scale_min) * random.random()
|
||
|
|
|
||
|
|
new_list = []
|
||
|
|
for image in image_list:
|
||
|
|
# assert image.shape[-1] in {1, 3, 4}, repr(image.shape)
|
||
|
|
|
||
|
|
with warnings.catch_warnings():
|
||
|
|
warnings.simplefilter("ignore")
|
||
|
|
# log.info([image.shape])
|
||
|
|
zimage = scipy.ndimage.interpolation.zoom(
|
||
|
|
image, [scale, scale, 1.0],
|
||
|
|
output=None, order=0, mode='nearest', cval=0.0, prefilter=True)
|
||
|
|
image = cropToShape(zimage, image.shape)
|
||
|
|
|
||
|
|
new_list.append(image)
|
||
|
|
|
||
|
|
return new_list
|
||
|
|
|
||
|
|
|
||
|
|
_randomFlip_transform_list = [
|
||
|
|
# lambda a: np.rot90(a, axes=(0, 1)),
|
||
|
|
# lambda a: np.flip(a, 0),
|
||
|
|
lambda a: np.flip(a, 1),
|
||
|
|
]
|
||
|
|
|
||
|
|
def randomFlip(image_list, transform_bits=None):
|
||
|
|
if transform_bits is None:
|
||
|
|
transform_bits = random.randrange(0, 2 ** len(_randomFlip_transform_list))
|
||
|
|
|
||
|
|
new_list = []
|
||
|
|
for image in image_list:
|
||
|
|
# assert image.shape[-1] in {1, 3, 4}, repr(image.shape)
|
||
|
|
|
||
|
|
for n in range(len(_randomFlip_transform_list)):
|
||
|
|
if transform_bits & 2**n:
|
||
|
|
# prhist(image, 'before')
|
||
|
|
image = _randomFlip_transform_list[n](image)
|
||
|
|
# prhist(image, 'after ')
|
||
|
|
|
||
|
|
new_list.append(image)
|
||
|
|
|
||
|
|
return new_list
|
||
|
|
|
||
|
|
|
||
|
|
def randomSpin(image_list, angle=None, range_tup=None, axes=(0, 1)):
|
||
|
|
if range_tup is None:
|
||
|
|
range_tup = (0, 360)
|
||
|
|
|
||
|
|
if angle is None:
|
||
|
|
angle = range_tup[0] + (range_tup[1] - range_tup[0]) * random.random()
|
||
|
|
|
||
|
|
new_list = []
|
||
|
|
for image in image_list:
|
||
|
|
# assert image.shape[-1] in {1, 3, 4}, repr(image.shape)
|
||
|
|
|
||
|
|
image = scipy.ndimage.interpolation.rotate(
|
||
|
|
image, angle, axes=axes, reshape=False,
|
||
|
|
output=None, order=0, mode='nearest', cval=0.0, prefilter=True)
|
||
|
|
|
||
|
|
new_list.append(image)
|
||
|
|
|
||
|
|
return new_list
|
||
|
|
|
||
|
|
|
||
|
|
def randomNoise(image_list, noise_min=-0.1, noise_max=0.1):
|
||
|
|
noise = np.zeros_like(image_list[0])
|
||
|
|
noise += (noise_max - noise_min) * np.random.random_sample(image_list[0].shape) + noise_min
|
||
|
|
noise *= 5
|
||
|
|
noise = scipy.ndimage.filters.gaussian_filter(noise, 3)
|
||
|
|
# noise += (noise_max - noise_min) * np.random.random_sample(image_hsv.shape) + noise_min
|
||
|
|
|
||
|
|
new_list = []
|
||
|
|
for image_hsv in image_list:
|
||
|
|
image_hsv = image_hsv + noise
|
||
|
|
|
||
|
|
new_list.append(image_hsv)
|
||
|
|
|
||
|
|
return new_list
|
||
|
|
|
||
|
|
|
||
|
|
def randomHsvShift(image_list, h=None, s=None, v=None,
|
||
|
|
h_min=-0.1, h_max=0.1,
|
||
|
|
s_min=0.5, s_max=2.0,
|
||
|
|
v_min=0.5, v_max=2.0):
|
||
|
|
if h is None:
|
||
|
|
h = h_min + (h_max - h_min) * random.random()
|
||
|
|
if s is None:
|
||
|
|
s = s_min + (s_max - s_min) * random.random()
|
||
|
|
if v is None:
|
||
|
|
v = v_min + (v_max - v_min) * random.random()
|
||
|
|
|
||
|
|
new_list = []
|
||
|
|
for image_hsv in image_list:
|
||
|
|
# assert image_hsv.shape[-1] == 3, repr(image_hsv.shape)
|
||
|
|
|
||
|
|
image_hsv[:,:,0::3] += h
|
||
|
|
image_hsv[:,:,1::3] = image_hsv[:,:,1::3] ** s
|
||
|
|
image_hsv[:,:,2::3] = image_hsv[:,:,2::3] ** v
|
||
|
|
|
||
|
|
new_list.append(image_hsv)
|
||
|
|
|
||
|
|
return clampHsv(new_list)
|
||
|
|
|
||
|
|
|
||
|
|
def clampHsv(image_list):
|
||
|
|
new_list = []
|
||
|
|
for image_hsv in image_list:
|
||
|
|
image_hsv = image_hsv.clone()
|
||
|
|
|
||
|
|
# Hue wraps around
|
||
|
|
image_hsv[:,:,0][image_hsv[:,:,0] > 1] -= 1
|
||
|
|
image_hsv[:,:,0][image_hsv[:,:,0] < 0] += 1
|
||
|
|
|
||
|
|
# Everything else clamps between 0 and 1
|
||
|
|
image_hsv[image_hsv > 1] = 1
|
||
|
|
image_hsv[image_hsv < 0] = 0
|
||
|
|
|
||
|
|
new_list.append(image_hsv)
|
||
|
|
|
||
|
|
return new_list
|
||
|
|
|
||
|
|
|
||
|
|
# def torch_augment(input):
|
||
|
|
# theta = random.random() * math.pi * 2
|
||
|
|
# s = math.sin(theta)
|
||
|
|
# c = math.cos(theta)
|
||
|
|
# c1 = 1 - c
|
||
|
|
# axis_vector = torch.rand(3, device='cpu', dtype=torch.float64)
|
||
|
|
# axis_vector -= 0.5
|
||
|
|
# axis_vector /= axis_vector.abs().sum()
|
||
|
|
# l, m, n = axis_vector
|
||
|
|
#
|
||
|
|
# matrix = torch.tensor([
|
||
|
|
# [l*l*c1 + c, m*l*c1 - n*s, n*l*c1 + m*s, 0],
|
||
|
|
# [l*m*c1 + n*s, m*m*c1 + c, n*m*c1 - l*s, 0],
|
||
|
|
# [l*n*c1 - m*s, m*n*c1 + l*s, n*n*c1 + c, 0],
|
||
|
|
# [0, 0, 0, 1],
|
||
|
|
# ], device=input.device, dtype=torch.float32)
|
||
|
|
#
|
||
|
|
# return th_affine3d(input, matrix)
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
# following from https://github.com/ncullen93/torchsample/blob/master/torchsample/utils.py
|
||
|
|
# MIT licensed
|
||
|
|
|
||
|
|
# def th_affine3d(input, matrix):
|
||
|
|
# """
|
||
|
|
# 3D Affine image transform on torch.Tensor
|
||
|
|
# """
|
||
|
|
# A = matrix[:3,:3]
|
||
|
|
# b = matrix[:3,3]
|
||
|
|
#
|
||
|
|
# # make a meshgrid of normal coordinates
|
||
|
|
# coords = th_iterproduct(input.size(-3), input.size(-2), input.size(-1), dtype=torch.float32)
|
||
|
|
#
|
||
|
|
# # shift the coordinates so center is the origin
|
||
|
|
# coords[:,0] = coords[:,0] - (input.size(-3) / 2. - 0.5)
|
||
|
|
# coords[:,1] = coords[:,1] - (input.size(-2) / 2. - 0.5)
|
||
|
|
# coords[:,2] = coords[:,2] - (input.size(-1) / 2. - 0.5)
|
||
|
|
#
|
||
|
|
# # apply the coordinate transformation
|
||
|
|
# new_coords = coords.mm(A.t().contiguous()) + b.expand_as(coords)
|
||
|
|
#
|
||
|
|
# # shift the coordinates back so origin is origin
|
||
|
|
# new_coords[:,0] = new_coords[:,0] + (input.size(-3) / 2. - 0.5)
|
||
|
|
# new_coords[:,1] = new_coords[:,1] + (input.size(-2) / 2. - 0.5)
|
||
|
|
# new_coords[:,2] = new_coords[:,2] + (input.size(-1) / 2. - 0.5)
|
||
|
|
#
|
||
|
|
# # map new coordinates using bilinear interpolation
|
||
|
|
# input_transformed = th_trilinear_interp3d(input, new_coords)
|
||
|
|
#
|
||
|
|
# return input_transformed
|
||
|
|
#
|
||
|
|
#
|
||
|
|
# def th_trilinear_interp3d(input, coords):
|
||
|
|
# """
|
||
|
|
# trilinear interpolation of 3D torch.Tensor image
|
||
|
|
# """
|
||
|
|
# # take clamp then floor/ceil of x coords
|
||
|
|
# x = torch.clamp(coords[:,0], 0, input.size(-3)-2)
|
||
|
|
# x0 = x.floor()
|
||
|
|
# x1 = x0 + 1
|
||
|
|
# # take clamp then floor/ceil of y coords
|
||
|
|
# y = torch.clamp(coords[:,1], 0, input.size(-2)-2)
|
||
|
|
# y0 = y.floor()
|
||
|
|
# y1 = y0 + 1
|
||
|
|
# # take clamp then floor/ceil of z coords
|
||
|
|
# z = torch.clamp(coords[:,2], 0, input.size(-1)-2)
|
||
|
|
# z0 = z.floor()
|
||
|
|
# z1 = z0 + 1
|
||
|
|
#
|
||
|
|
# stride = torch.tensor(input.stride()[-3:], dtype=torch.int64, device=input.device)
|
||
|
|
# x0_ix = x0.mul(stride[0]).long()
|
||
|
|
# x1_ix = x1.mul(stride[0]).long()
|
||
|
|
# y0_ix = y0.mul(stride[1]).long()
|
||
|
|
# y1_ix = y1.mul(stride[1]).long()
|
||
|
|
# z0_ix = z0.mul(stride[2]).long()
|
||
|
|
# z1_ix = z1.mul(stride[2]).long()
|
||
|
|
#
|
||
|
|
# # input_flat = th_flatten(input)
|
||
|
|
# input_flat = x.contiguous().view(x[0], x[1], -1)
|
||
|
|
#
|
||
|
|
# vals_000 = input_flat[:, :, x0_ix+y0_ix+z0_ix]
|
||
|
|
# vals_001 = input_flat[:, :, x0_ix+y0_ix+z1_ix]
|
||
|
|
# vals_010 = input_flat[:, :, x0_ix+y1_ix+z0_ix]
|
||
|
|
# vals_011 = input_flat[:, :, x0_ix+y1_ix+z1_ix]
|
||
|
|
# vals_100 = input_flat[:, :, x1_ix+y0_ix+z0_ix]
|
||
|
|
# vals_101 = input_flat[:, :, x1_ix+y0_ix+z1_ix]
|
||
|
|
# vals_110 = input_flat[:, :, x1_ix+y1_ix+z0_ix]
|
||
|
|
# vals_111 = input_flat[:, :, x1_ix+y1_ix+z1_ix]
|
||
|
|
#
|
||
|
|
# xd = x - x0
|
||
|
|
# yd = y - y0
|
||
|
|
# zd = z - z0
|
||
|
|
# xm1 = 1 - xd
|
||
|
|
# ym1 = 1 - yd
|
||
|
|
# zm1 = 1 - zd
|
||
|
|
#
|
||
|
|
# x_mapped = (
|
||
|
|
# vals_000.mul(xm1).mul(ym1).mul(zm1) +
|
||
|
|
# vals_001.mul(xm1).mul(ym1).mul(zd) +
|
||
|
|
# vals_010.mul(xm1).mul(yd).mul(zm1) +
|
||
|
|
# vals_011.mul(xm1).mul(yd).mul(zd) +
|
||
|
|
# vals_100.mul(xd).mul(ym1).mul(zm1) +
|
||
|
|
# vals_101.mul(xd).mul(ym1).mul(zd) +
|
||
|
|
# vals_110.mul(xd).mul(yd).mul(zm1) +
|
||
|
|
# vals_111.mul(xd).mul(yd).mul(zd)
|
||
|
|
# )
|
||
|
|
#
|
||
|
|
# return x_mapped.view_as(input)
|
||
|
|
#
|
||
|
|
# def th_iterproduct(*args, dtype=None):
|
||
|
|
# return torch.from_numpy(np.indices(args).reshape((len(args),-1)).T)
|
||
|
|
#
|
||
|
|
# def th_flatten(x):
|
||
|
|
# """Flatten tensor"""
|
||
|
|
# return x.contiguous().view(x[0], x[1], -1)
|