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# Copyright (C) 2022-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
# --------------------------------------------------------
# Data augmentation for training stereo and flow
# --------------------------------------------------------
# References
# https://github.com/autonomousvision/unimatch/blob/master/dataloader/stereo/transforms.py
# https://github.com/autonomousvision/unimatch/blob/master/dataloader/flow/transforms.py
import numpy as np
import random
from PIL import Image
import cv2
cv2.setNumThreads(0)
cv2.ocl.setUseOpenCL(False)
import torch
from torchvision.transforms import ColorJitter
import torchvision.transforms.functional as FF
class StereoAugmentor(object):
def __init__(self, crop_size, scale_prob=0.5, scale_xonly=True, lhth=800., lminscale=0.0, lmaxscale=1.0, hminscale=-0.2, hmaxscale=0.4, scale_interp_nearest=True, rightjitterprob=0.5, v_flip_prob=0.5, color_aug_asym=True, color_choice_prob=0.5):
self.crop_size = crop_size
self.scale_prob = scale_prob
self.scale_xonly = scale_xonly
self.lhth = lhth
self.lminscale = lminscale
self.lmaxscale = lmaxscale
self.hminscale = hminscale
self.hmaxscale = hmaxscale
self.scale_interp_nearest = scale_interp_nearest
self.rightjitterprob = rightjitterprob
self.v_flip_prob = v_flip_prob
self.color_aug_asym = color_aug_asym
self.color_choice_prob = color_choice_prob
def _random_scale(self, img1, img2, disp):
ch,cw = self.crop_size
h,w = img1.shape[:2]
if self.scale_prob>0. and np.random.rand()<self.scale_prob:
min_scale, max_scale = (self.lminscale,self.lmaxscale) if min(h,w) < self.lhth else (self.hminscale,self.hmaxscale)
scale_x = 2. ** np.random.uniform(min_scale, max_scale)
scale_x = np.clip(scale_x, (cw+8) / float(w), None)
scale_y = 1.
if not self.scale_xonly:
scale_y = scale_x
scale_y = np.clip(scale_y, (ch+8) / float(h), None)
img1 = cv2.resize(img1, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
img2 = cv2.resize(img2, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
disp = cv2.resize(disp, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR if not self.scale_interp_nearest else cv2.INTER_NEAREST) * scale_x
else: # check if we need to resize to be able to crop
h,w = img1.shape[:2]
clip_scale = (cw+8) / float(w)
if clip_scale>1.:
scale_x = clip_scale
scale_y = scale_x if not self.scale_xonly else 1.0
img1 = cv2.resize(img1, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
img2 = cv2.resize(img2, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
disp = cv2.resize(disp, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR if not self.scale_interp_nearest else cv2.INTER_NEAREST) * scale_x
return img1, img2, disp
def _random_crop(self, img1, img2, disp):
h,w = img1.shape[:2]
ch,cw = self.crop_size
assert ch<=h and cw<=w, (img1.shape, h,w,ch,cw)
offset_x = np.random.randint(w - cw + 1)
offset_y = np.random.randint(h - ch + 1)
img1 = img1[offset_y:offset_y+ch,offset_x:offset_x+cw]
img2 = img2[offset_y:offset_y+ch,offset_x:offset_x+cw]
disp = disp[offset_y:offset_y+ch,offset_x:offset_x+cw]
return img1, img2, disp
def _random_vflip(self, img1, img2, disp):
# vertical flip
if self.v_flip_prob>0 and np.random.rand() < self.v_flip_prob:
img1 = np.copy(np.flipud(img1))
img2 = np.copy(np.flipud(img2))
disp = np.copy(np.flipud(disp))
return img1, img2, disp
def _random_rotate_shift_right(self, img2):
if self.rightjitterprob>0. and np.random.rand()<self.rightjitterprob:
angle, pixel = 0.1, 2
px = np.random.uniform(-pixel, pixel)
ag = np.random.uniform(-angle, angle)
image_center = (np.random.uniform(0, img2.shape[0]), np.random.uniform(0, img2.shape[1]) )
rot_mat = cv2.getRotationMatrix2D(image_center, ag, 1.0)
img2 = cv2.warpAffine(img2, rot_mat, img2.shape[1::-1], flags=cv2.INTER_LINEAR)
trans_mat = np.float32([[1, 0, 0], [0, 1, px]])
img2 = cv2.warpAffine(img2, trans_mat, img2.shape[1::-1], flags=cv2.INTER_LINEAR)
return img2
def _random_color_contrast(self, img1, img2):
if np.random.random() < 0.5:
contrast_factor = np.random.uniform(0.8, 1.2)
img1 = FF.adjust_contrast(img1, contrast_factor)
if self.color_aug_asym and np.random.random() < 0.5: contrast_factor = np.random.uniform(0.8, 1.2)
img2 = FF.adjust_contrast(img2, contrast_factor)
return img1, img2
def _random_color_gamma(self, img1, img2):
if np.random.random() < 0.5:
gamma = np.random.uniform(0.7, 1.5)
img1 = FF.adjust_gamma(img1, gamma)
if self.color_aug_asym and np.random.random() < 0.5: gamma = np.random.uniform(0.7, 1.5)
img2 = FF.adjust_gamma(img2, gamma)
return img1, img2
def _random_color_brightness(self, img1, img2):
if np.random.random() < 0.5:
brightness = np.random.uniform(0.5, 2.0)
img1 = FF.adjust_brightness(img1, brightness)
if self.color_aug_asym and np.random.random() < 0.5: brightness = np.random.uniform(0.5, 2.0)
img2 = FF.adjust_brightness(img2, brightness)
return img1, img2
def _random_color_hue(self, img1, img2):
if np.random.random() < 0.5:
hue = np.random.uniform(-0.1, 0.1)
img1 = FF.adjust_hue(img1, hue)
if self.color_aug_asym and np.random.random() < 0.5: hue = np.random.uniform(-0.1, 0.1)
img2 = FF.adjust_hue(img2, hue)
return img1, img2
def _random_color_saturation(self, img1, img2):
if np.random.random() < 0.5:
saturation = np.random.uniform(0.8, 1.2)
img1 = FF.adjust_saturation(img1, saturation)
if self.color_aug_asym and np.random.random() < 0.5: saturation = np.random.uniform(-0.8,1.2)
img2 = FF.adjust_saturation(img2, saturation)
return img1, img2
def _random_color(self, img1, img2):
trfs = [self._random_color_contrast,self._random_color_gamma,self._random_color_brightness,self._random_color_hue,self._random_color_saturation]
img1 = Image.fromarray(img1.astype('uint8'))
img2 = Image.fromarray(img2.astype('uint8'))
if np.random.random() < self.color_choice_prob:
# A single transform
t = random.choice(trfs)
img1, img2 = t(img1, img2)
else:
# Combination of trfs
# Random order
random.shuffle(trfs)
for t in trfs:
img1, img2 = t(img1, img2)
img1 = np.array(img1).astype(np.float32)
img2 = np.array(img2).astype(np.float32)
return img1, img2
def __call__(self, img1, img2, disp, dataset_name):
img1, img2, disp = self._random_scale(img1, img2, disp)
img1, img2, disp = self._random_crop(img1, img2, disp)
img1, img2, disp = self._random_vflip(img1, img2, disp)
img2 = self._random_rotate_shift_right(img2)
img1, img2 = self._random_color(img1, img2)
return img1, img2, disp
class FlowAugmentor:
def __init__(self, crop_size, min_scale=-0.2, max_scale=0.5, spatial_aug_prob=0.8, stretch_prob=0.8, max_stretch=0.2, h_flip_prob=0.5, v_flip_prob=0.1, asymmetric_color_aug_prob=0.2):
# spatial augmentation params
self.crop_size = crop_size
self.min_scale = min_scale
self.max_scale = max_scale
self.spatial_aug_prob = spatial_aug_prob
self.stretch_prob = stretch_prob
self.max_stretch = max_stretch
# flip augmentation params
self.h_flip_prob = h_flip_prob
self.v_flip_prob = v_flip_prob
# photometric augmentation params
self.photo_aug = ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.5 / 3.14)
self.asymmetric_color_aug_prob = asymmetric_color_aug_prob
def color_transform(self, img1, img2):
""" Photometric augmentation """
# asymmetric
if np.random.rand() < self.asymmetric_color_aug_prob:
img1 = np.array(self.photo_aug(Image.fromarray(img1)), dtype=np.uint8)
img2 = np.array(self.photo_aug(Image.fromarray(img2)), dtype=np.uint8)
# symmetric
else:
image_stack = np.concatenate([img1, img2], axis=0)
image_stack = np.array(self.photo_aug(Image.fromarray(image_stack)), dtype=np.uint8)
img1, img2 = np.split(image_stack, 2, axis=0)
return img1, img2
def _resize_flow(self, flow, scale_x, scale_y, factor=1.0):
if np.all(np.isfinite(flow)):
flow = cv2.resize(flow, None, fx=scale_x/factor, fy=scale_y/factor, interpolation=cv2.INTER_LINEAR)
flow = flow * [scale_x, scale_y]
else: # sparse version
fx, fy = scale_x, scale_y
ht, wd = flow.shape[:2]
coords = np.meshgrid(np.arange(wd), np.arange(ht))
coords = np.stack(coords, axis=-1)
coords = coords.reshape(-1, 2).astype(np.float32)
flow = flow.reshape(-1, 2).astype(np.float32)
valid = np.isfinite(flow[:,0])
coords0 = coords[valid]
flow0 = flow[valid]
ht1 = int(round(ht * fy/factor))
wd1 = int(round(wd * fx/factor))
rescale = np.expand_dims(np.array([fx, fy]), axis=0)
coords1 = coords0 * rescale / factor
flow1 = flow0 * rescale
xx = np.round(coords1[:, 0]).astype(np.int32)
yy = np.round(coords1[:, 1]).astype(np.int32)
v = (xx > 0) & (xx < wd1) & (yy > 0) & (yy < ht1)
xx = xx[v]
yy = yy[v]
flow1 = flow1[v]
flow = np.inf * np.ones([ht1, wd1, 2], dtype=np.float32) # invalid value every where, before we fill it with the correct ones
flow[yy, xx] = flow1
return flow
def spatial_transform(self, img1, img2, flow, dname):
if np.random.rand() < self.spatial_aug_prob:
# randomly sample scale
ht, wd = img1.shape[:2]
clip_min_scale = np.maximum(
(self.crop_size[0] + 8) / float(ht),
(self.crop_size[1] + 8) / float(wd))
min_scale, max_scale = self.min_scale, self.max_scale
scale = 2 ** np.random.uniform(self.min_scale, self.max_scale)
scale_x = scale
scale_y = scale
if np.random.rand() < self.stretch_prob:
scale_x *= 2 ** np.random.uniform(-self.max_stretch, self.max_stretch)
scale_y *= 2 ** np.random.uniform(-self.max_stretch, self.max_stretch)
scale_x = np.clip(scale_x, clip_min_scale, None)
scale_y = np.clip(scale_y, clip_min_scale, None)
# rescale the images
img1 = cv2.resize(img1, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
img2 = cv2.resize(img2, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR)
flow = self._resize_flow(flow, scale_x, scale_y, factor=2.0 if dname=='Spring' else 1.0)
elif dname=="Spring":
flow = self._resize_flow(flow, 1.0, 1.0, factor=2.0)
if self.h_flip_prob>0. and np.random.rand() < self.h_flip_prob: # h-flip
img1 = img1[:, ::-1]
img2 = img2[:, ::-1]
flow = flow[:, ::-1] * [-1.0, 1.0]
if self.v_flip_prob>0. and np.random.rand() < self.v_flip_prob: # v-flip
img1 = img1[::-1, :]
img2 = img2[::-1, :]
flow = flow[::-1, :] * [1.0, -1.0]
# In case no cropping
if img1.shape[0] - self.crop_size[0] > 0:
y0 = np.random.randint(0, img1.shape[0] - self.crop_size[0])
else:
y0 = 0
if img1.shape[1] - self.crop_size[1] > 0:
x0 = np.random.randint(0, img1.shape[1] - self.crop_size[1])
else:
x0 = 0
img1 = img1[y0:y0 + self.crop_size[0], x0:x0 + self.crop_size[1]]
img2 = img2[y0:y0 + self.crop_size[0], x0:x0 + self.crop_size[1]]
flow = flow[y0:y0 + self.crop_size[0], x0:x0 + self.crop_size[1]]
return img1, img2, flow
def __call__(self, img1, img2, flow, dname):
img1, img2, flow = self.spatial_transform(img1, img2, flow, dname)
img1, img2 = self.color_transform(img1, img2)
img1 = np.ascontiguousarray(img1)
img2 = np.ascontiguousarray(img2)
flow = np.ascontiguousarray(flow)
return img1, img2, flow |