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# Copyright (c) Facebook, Inc. and its affiliates.
# Copyright (c) Meta Platforms, Inc. All Rights Reserved
import math
import numbers
import numpy as np
from detectron2.data.transforms.augmentation import Augmentation
from detectron2.data.transforms.transform import (
CropTransform,
ResizeTransform,
TransformList,
)
from PIL import Image
from fvcore.transforms.transform import PadTransform
def mask2box(mask: np.ndarray):
# use naive way
row = np.nonzero(mask.sum(axis=0))[0]
if len(row) == 0:
return None
x1 = row.min()
x2 = row.max()
col = np.nonzero(mask.sum(axis=1))[0]
y1 = col.min()
y2 = col.max()
return x1, y1, x2 + 1 - x1, y2 + 1 - y1
def expand_box(x, y, w, h, expand_ratio=1.0, max_h=None, max_w=None):
cx = x + 0.5 * w
cy = y + 0.5 * h
w = w * expand_ratio
h = h * expand_ratio
box = [cx - 0.5 * w, cy - 0.5 * h, cx + 0.5 * w, cy + 0.5 * h]
if max_h is not None:
box[1] = max(0, box[1])
box[3] = min(max_h - 1, box[3])
if max_w is not None:
box[0] = max(0, box[0])
box[2] = min(max_w - 1, box[2])
box[2] = box[2] - box[0]
box[3] = box[3] - box[1]
return [int(b) for b in box]
class CropImageWithMask(Augmentation):
def __init__(self, expand_ratio=1.0, mode="choice"):
if isinstance(expand_ratio, numbers.Number):
expand_ratio = (expand_ratio, expand_ratio)
self.mode = mode
self.expand_ratio = expand_ratio
if self.mode == "range":
assert len(expand_ratio) == 2 and expand_ratio[0] < expand_ratio[1]
def get_transform(self, image, sem_seg, category_id):
input_size = image.shape[:2]
bin_mask = sem_seg == category_id
x, y, w, h = mask2box(bin_mask)
if self.mode == "choice":
expand_ratio = np.random.choice(self.expand_ratio)
else:
expand_ratio = np.random.uniform(self.expand_ratio[0], self.expand_ratio[1])
x, y, w, h = expand_box(x, y, w, h, expand_ratio, *input_size)
w = max(w, 1)
h = max(h, 1)
return CropTransform(x, y, w, h, input_size[1], input_size[0])
class CropImageWithBox(Augmentation):
def __init__(self, expand_ratio=1.0, mode="choice"):
if isinstance(expand_ratio, numbers.Number):
expand_ratio = (expand_ratio, expand_ratio)
self.mode = mode
self.expand_ratio = expand_ratio
if self.mode == "range":
assert len(expand_ratio) == 2 and expand_ratio[0] < expand_ratio[1]
def get_transform(self, image, boxes):
input_size = image.shape[:2]
x, y, x2, y2 = boxes[0]
w = x2 - x + 1
h = y2 - y + 1
if self.mode == "choice":
expand_ratio = np.random.choice(self.expand_ratio)
else:
expand_ratio = np.random.uniform(self.expand_ratio[0], self.expand_ratio[1])
x, y, w, h = expand_box(x, y, w, h, expand_ratio, *input_size)
w = max(w, 1)
h = max(h, 1)
return CropTransform(x, y, w, h, input_size[1], input_size[0])
class RandomResizedCrop(Augmentation):
def __init__(
self,
size,
scale=(0.08, 1.0),
ratio=(3.0 / 4.0, 4.0 / 3.0),
interpolation=Image.BILINEAR,
):
if isinstance(size, int):
size = (size, size)
else:
assert isinstance(size, (tuple, list)) and len(size) == 2
self.size = size
self.scale = scale
self.ratio = ratio
self.interpolation = interpolation
def get_transform(self, image):
height, width = image.shape[:2]
area = height * width
log_ratio = np.log(np.array(self.ratio))
is_success = False
for _ in range(10):
target_area = area * np.random.uniform(self.scale[0], self.scale[1])
aspect_ratio = np.exp(np.random.uniform(log_ratio[0], log_ratio[1]))
w = int(round(math.sqrt(target_area * aspect_ratio)))
h = int(round(math.sqrt(target_area / aspect_ratio)))
if 0 < w <= width and 0 < h <= height:
i = np.random.randint(0, width - w + 1)
j = np.random.randint(0, height - h + 1)
is_success = True
break
if not is_success:
# Fallback to central crop
in_ratio = float(width) / float(height)
if in_ratio < min(self.ratio):
w = width
h = int(round(w / min(self.ratio)))
elif in_ratio > max(self.ratio):
h = height
w = int(round(h * max(self.ratio)))
else: # whole image
w = width
h = height
i = (width - w) // 2
j = (height - h) // 2
return TransformList(
[
CropTransform(i, j, w, h, width, height),
ResizeTransform(
h, w, self.size[1], self.size[0], interp=self.interpolation
),
]
)
class CenterCrop(Augmentation):
def __init__(self, size, seg_ignore_label):
if isinstance(size, numbers.Number):
size = (int(size), int(size))
elif isinstance(size, (tuple, list)) and len(size) == 1:
size = (size[0], size[0])
self.size = size
self.seg_ignore_label = seg_ignore_label
def get_transform(self, image):
image_height, image_width = image.shape[:2]
crop_height, crop_width = self.size
transforms = []
if crop_width > image_width or crop_height > image_height:
padding_ltrb = [
(crop_width - image_width) // 2 if crop_width > image_width else 0,
(crop_height - image_height) // 2 if crop_height > image_height else 0,
(crop_width - image_width + 1) // 2 if crop_width > image_width else 0,
(crop_height - image_height + 1) // 2
if crop_height > image_height
else 0,
]
transforms.append(
PadTransform(
*padding_ltrb,
orig_w=image_width,
orig_h=image_height,
seg_pad_value=self.seg_ignore_label
)
)
image_width, image_height = (
image_width + padding_ltrb[0] + padding_ltrb[2],
image_height + padding_ltrb[1] + padding_ltrb[3],
)
crop_top = int(round((image_height - crop_height) / 2.0))
crop_left = int(round((image_width - crop_width) / 2.0))
transforms.append(
CropTransform(
crop_left, crop_top, crop_width, crop_height, image_width, image_height
)
)
return TransformList(transforms)
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