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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import random
from PIL import Image
import cv2
cv2.setNumThreads(0)
cv2.ocl.setUseOpenCL(False)
from torchvision.transforms import ColorJitter, functional, Compose
class AdjustGamma(object):
def __init__(self, gamma_min, gamma_max, gain_min=1.0, gain_max=1.0):
self.gamma_min, self.gamma_max, self.gain_min, self.gain_max = (
gamma_min,
gamma_max,
gain_min,
gain_max,
)
def __call__(self, sample):
gain = random.uniform(self.gain_min, self.gain_max)
gamma = random.uniform(self.gamma_min, self.gamma_max)
return functional.adjust_gamma(sample, gamma, gain)
def __repr__(self):
return f"Adjust Gamma {self.gamma_min}, ({self.gamma_max}) and Gain ({self.gain_min}, {self.gain_max})"
class SequenceDispFlowAugmentor:
def __init__(
self,
crop_size,
min_scale=-0.2,
max_scale=0.5,
do_flip=True,
yjitter=False,
saturation_range=[0.6, 1.4],
gamma=[1, 1, 1, 1],
):
# spatial augmentation params
self.crop_size = crop_size
self.min_scale = min_scale
self.max_scale = max_scale
self.spatial_aug_prob = 1.0
self.stretch_prob = 0.8
self.max_stretch = 0.2
# flip augmentation params
self.yjitter = yjitter
self.do_flip = do_flip
self.h_flip_prob = 0.5
self.v_flip_prob = 0.1
# photometric augmentation params
self.photo_aug = Compose(
[
ColorJitter(
brightness=0.4,
contrast=0.4,
saturation=saturation_range,
hue=0.5 / 3.14,
),
AdjustGamma(*gamma),
]
)
self.asymmetric_color_aug_prob = 0.2
self.eraser_aug_prob = 0.5
def color_transform(self, seq):
"""Photometric augmentation"""
# asymmetric
if np.random.rand() < self.asymmetric_color_aug_prob:
for i in range(len(seq)):
for cam in (0, 1):
seq[i][cam] = np.array(
self.photo_aug(Image.fromarray(seq[i][cam])), dtype=np.uint8
)
# symmetric
else:
image_stack = np.concatenate(
[seq[i][cam] for i in range(len(seq)) for cam in (0, 1)], axis=0
)
image_stack = np.array(
self.photo_aug(Image.fromarray(image_stack)), dtype=np.uint8
)
split = np.split(image_stack, len(seq) * 2, axis=0)
for i in range(len(seq)):
seq[i][0] = split[2 * i]
seq[i][1] = split[2 * i + 1]
return seq
def eraser_transform(self, seq, bounds=[50, 100]):
"""Occlusion augmentation"""
ht, wd = seq[0][0].shape[:2]
for i in range(len(seq)):
for cam in (0, 1):
if np.random.rand() < self.eraser_aug_prob:
mean_color = np.mean(seq[0][0].reshape(-1, 3), axis=0)
for _ in range(np.random.randint(1, 3)):
x0 = np.random.randint(0, wd)
y0 = np.random.randint(0, ht)
dx = np.random.randint(bounds[0], bounds[1])
dy = np.random.randint(bounds[0], bounds[1])
seq[i][cam][y0 : y0 + dy, x0 : x0 + dx, :] = mean_color
return seq
def spatial_transform(self, img, disp):
# randomly sample scale
ht, wd = img[0][0].shape[:2]
min_scale = np.maximum(
(self.crop_size[0] + 8) / float(ht), (self.crop_size[1] + 8) / float(wd)
)
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, min_scale, None)
scale_y = np.clip(scale_y, min_scale, None)
if np.random.rand() < self.spatial_aug_prob:
# rescale the images
for i in range(len(img)):
for cam in (0, 1):
img[i][cam] = cv2.resize(
img[i][cam],
None,
fx=scale_x,
fy=scale_y,
interpolation=cv2.INTER_LINEAR,
)
if len(disp[i]) > 0:
disp[i][cam] = cv2.resize(
disp[i][cam],
None,
fx=scale_x,
fy=scale_y,
interpolation=cv2.INTER_LINEAR,
)
disp[i][cam] = disp[i][cam] * [scale_x, scale_y]
if self.yjitter:
y0 = np.random.randint(2, img[0][0].shape[0] - self.crop_size[0] - 2)
x0 = np.random.randint(2, img[0][0].shape[1] - self.crop_size[1] - 2)
for i in range(len(img)):
y1 = y0 + np.random.randint(-2, 2 + 1)
img[i][0] = img[i][0][
y0 : y0 + self.crop_size[0], x0 : x0 + self.crop_size[1]
]
img[i][1] = img[i][1][
y1 : y1 + self.crop_size[0], x0 : x0 + self.crop_size[1]
]
if len(disp[i]) > 0:
disp[i][0] = disp[i][0][
y0 : y0 + self.crop_size[0], x0 : x0 + self.crop_size[1]
]
disp[i][1] = disp[i][1][
y1 : y1 + self.crop_size[0], x0 : x0 + self.crop_size[1]
]
else:
y0 = np.random.randint(0, img[0][0].shape[0] - self.crop_size[0])
x0 = np.random.randint(0, img[0][0].shape[1] - self.crop_size[1])
for i in range(len(img)):
for cam in (0, 1):
img[i][cam] = img[i][cam][
y0 : y0 + self.crop_size[0], x0 : x0 + self.crop_size[1]
]
if len(disp[i]) > 0:
disp[i][cam] = disp[i][cam][
y0 : y0 + self.crop_size[0], x0 : x0 + self.crop_size[1]
]
return img, disp
def __call__(self, img, disp):
img = self.color_transform(img)
img = self.eraser_transform(img)
img, disp = self.spatial_transform(img, disp)
for i in range(len(img)):
for cam in (0, 1):
img[i][cam] = np.ascontiguousarray(img[i][cam])
if len(disp[i]) > 0:
disp[i][cam] = np.ascontiguousarray(disp[i][cam])
return img, disp
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