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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import random
import cv2
import numpy as np
from PIL import Image
from shapely import affinity
from shapely.geometry import Polygon
from torchvision.transforms import functional as F
class Compose(object):
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, image, target):
for t in self.transforms:
image, target = t(image, target)
return image, target
def __repr__(self):
format_string = self.__class__.__name__ + "("
for t in self.transforms:
format_string += "\n"
format_string += " {0}".format(t)
format_string += "\n)"
return format_string
class Resize(object):
def __init__(self, min_size, max_size, strict_resize):
self.min_size = min_size
self.max_size = max_size
self.strict_resize = strict_resize
# modified from torchvision to add support for max size
def get_size(self, image_size):
w, h = image_size
if isinstance(self.min_size, tuple):
if len(self.min_size) == 1:
size = self.min_size[0]
else:
random_size_index = random.randint(0, len(self.min_size) - 1)
size = self.min_size[random_size_index]
else:
size = self.min_size
max_size = self.max_size
if max_size is not None:
min_original_size = float(min((w, h)))
max_original_size = float(max((w, h)))
if max_original_size / min_original_size * size > max_size:
size = int(round(max_size * min_original_size / max_original_size))
if (w <= h and w == size) or (h <= w and h == size):
if self.strict_resize:
h = h if h % 32 == 0 else (h // 32) * 32
w = w if w % 32 == 0 else (w // 32) * 32
return (h, w)
if w < h:
ow = size
oh = int(size * h / w)
else:
oh = size
ow = int(size * w / h)
if self.strict_resize:
oh = oh if oh % 32 == 0 else (oh // 32) * 32
ow = ow if ow % 32 == 0 else (ow // 32) * 32
return (oh, ow)
def __call__(self, image, target):
size = self.get_size(image.size)
image = F.resize(image, size)
if target is not None:
target = target.resize(image.size)
return image, target
class RandomCrop(object):
def __init__(self, prob, crop_min_size=500, crop_max_size=1000, max_trys=50):
self.min_size = crop_min_size
self.max_size = crop_max_size
self.max_trys = max_trys
self.prob = prob
def __call__(self, image, target):
if random.random() < self.prob:
im = np.array(image)
w, h = image.size
h_array = np.zeros((h), dtype=np.int32)
w_array = np.zeros((w), dtype=np.int32)
boxes = target.bbox.numpy()
if len(boxes) == 0:
return image, target
for box in boxes:
box = np.round(box, decimals=0).astype(np.int32)
minx = box[0]
maxx = box[2]
w_array[minx:maxx] = 1
miny = box[1]
maxy = box[3]
h_array[miny:maxy] = 1
h_axis = np.where(h_array == 0)[0]
w_axis = np.where(w_array == 0)[0]
if len(h_axis) == 0 or len(w_axis) == 0:
return image, target
for _ in range(self.max_trys):
xx = np.random.choice(w_axis, size=2)
xmin = min(xx)
xmax = max(xx)
x_size = xmax - xmin
if x_size > self.max_size or x_size < self.min_size:
continue
yy = np.random.choice(h_axis, size=2)
ymin = min(yy)
ymax = max(yy)
y_size = ymax - ymin
if y_size > self.max_size or y_size < self.min_size:
continue
box_in_area = (
(boxes[:, 0] >= xmin)
& (boxes[:, 1] >= ymin)
& (boxes[:, 2] <= xmax)
& (boxes[:, 3] <= ymax)
)
if len(np.where(box_in_area)[0]) == 0:
continue
im = im[ymin:ymax, xmin:xmax]
target = target.crop([xmin, ymin, xmax, ymax])
return Image.fromarray(im), target
return image, target
else:
return image, target
# class RandomCropFixSize(object):
# def __init__(self, prob, crop_size=512, max_trys=50):
# self.crop_size = crop_size
# self.max_trys = max_trys
# self.prob = prob
# def __call__(self, image, target):
# if random.random() < self.prob:
# im = np.array(image)
# w, h = image.size
# h_array = np.zeros((h), dtype=np.int32)
# w_array = np.zeros((w), dtype=np.int32)
# boxes = target.bbox.numpy()
# if len(boxes) == 0:
# return image, target
# for box in boxes:
# box = np.round(box, decimals=0).astype(np.int32)
# minx = box[0]
# maxx = box[2]
# w_array[minx:maxx] = 1
# miny = box[1]
# maxy = box[3]
# h_array[miny:maxy] = 1
# h_axis = np.where(h_array == 0)[0]
# w_axis = np.where(w_array == 0)[0]
# if len(h_axis) == 0 or len(w_axis) == 0:
# return image, target
# for _ in range(self.max_trys):
# xx = np.random.choice(w_axis, size=2)
# xmin = min(xx)
# xmax = max(xx)
# x_size = xmax - xmin
# if x_size > self.max_size or x_size < self.min_size:
# continue
# yy = np.random.choice(h_axis, size=2)
# ymin = min(yy)
# ymax = max(yy)
# y_size = ymax - ymin
# if y_size > self.max_size or y_size < self.min_size:
# continue
# box_in_area = (
# (boxes[:, 0] >= xmin)
# & (boxes[:, 1] >= ymin)
# & (boxes[:, 2] <= xmax)
# & (boxes[:, 3] <= ymax)
# )
# if len(np.where(box_in_area)[0]) == 0:
# continue
# im = im[ymin:ymax, xmin:xmax]
# target = target.crop([xmin, ymin, xmax, ymax])
# return Image.fromarray(im), target
# return image, target
# else:
# return image, target
class RandomHorizontalFlip(object):
def __init__(self, prob=0.5):
self.prob = prob
def __call__(self, image, target):
if random.random() < self.prob:
image = F.hflip(image)
target = target.transpose(0)
return image, target
class ToTensor(object):
def __call__(self, image, target):
return F.to_tensor(image), target
class Normalize(object):
def __init__(self, mean, std, to_bgr255=True):
self.mean = mean
self.std = std
self.to_bgr255 = to_bgr255
def __call__(self, image, target):
if self.to_bgr255:
image = image[[2, 1, 0]] * 255
image = F.normalize(image, mean=self.mean, std=self.std)
return image, target
class RandomBrightness(object):
def __init__(self, prob=0.5):
self.prob = prob
def __call__(self, image, target):
if random.random() < self.prob:
brightness_factor = random.uniform(0.5, 2)
image = F.adjust_brightness(image, brightness_factor)
return image, target
class RandomContrast(object):
def __init__(self, prob=0.5):
self.prob = prob
def __call__(self, image, target):
if random.random() < self.prob:
contrast_factor = random.uniform(0.5, 2)
image = F.adjust_contrast(image, contrast_factor)
return image, target
class RandomHue(object):
def __init__(self, prob=0.5):
self.prob = prob
def __call__(self, image, target):
if random.random() < self.prob:
hue_factor = random.uniform(-0.25, 0.25)
image = F.adjust_hue(image, hue_factor)
return image, target
class RandomSaturation(object):
def __init__(self, prob=0.5):
self.prob = prob
def __call__(self, image, target):
if random.random() < self.prob:
saturation_factor = random.uniform(0.5, 2)
image = F.adjust_saturation(image, saturation_factor)
return image, target
class RandomGamma(object):
def __init__(self, prob=0.5):
self.prob = prob
def __call__(self, image, target):
if random.random() < self.prob:
gamma_factor = random.uniform(0.5, 2)
image = F.adjust_gamma(image, gamma_factor)
return image, target
class RandomRotate(object):
def __init__(self, prob, max_theta=30, fix_rotate=False):
self.prob = prob
self.max_theta = max_theta
self.fix_rotate = fix_rotate
def __call__(self, image, target):
if random.random() < self.prob and target is not None:
# try:
if self.fix_rotate:
delta = 30
else:
delta = random.uniform(-1 * self.max_theta, self.max_theta)
width, height = image.size
## get the minimal rect to cover the rotated image
img_box = [[[0, 0], [width, 0], [width, height], [0, height]]]
rotated_img_box = _quad2minrect(
_rotate_polygons(img_box, delta, (width / 2, height / 2))
)
r_height = int(
max(rotated_img_box[0][3], rotated_img_box[0][1])
- min(rotated_img_box[0][3], rotated_img_box[0][1])
)
r_width = int(
max(rotated_img_box[0][2], rotated_img_box[0][0])
- min(rotated_img_box[0][2], rotated_img_box[0][0])
)
r_height = max(r_height, height + 1)
r_width = max(r_width, width + 1)
## padding im
im_padding = np.zeros((r_height, r_width, 3))
start_h, start_w = (
int((r_height - height) / 2.0),
int((r_width - width) / 2.0),
)
end_h, end_w = start_h + height, start_w + width
im_padding[start_h:end_h, start_w:end_w, :] = image
M = cv2.getRotationMatrix2D((r_width / 2, r_height / 2), delta, 1)
im = cv2.warpAffine(im_padding, M, (r_width, r_height))
im = Image.fromarray(im.astype(np.uint8))
target = target.rotate(
-delta, (r_width / 2, r_height / 2), start_h, start_w
)
return im, target
# except:
# return image, target
else:
return image, target
def _quad2minrect(boxes):
## trans a quad(N*4) to a rectangle(N*4) which has miniual area to cover it
return np.hstack(
(
boxes[:, ::2].min(axis=1).reshape((-1, 1)),
boxes[:, 1::2].min(axis=1).reshape((-1, 1)),
boxes[:, ::2].max(axis=1).reshape((-1, 1)),
boxes[:, 1::2].max(axis=1).reshape((-1, 1)),
)
)
def _boxlist2quads(boxlist):
res = np.zeros((len(boxlist), 8))
for i, box in enumerate(boxlist):
# print(box)
res[i] = np.array(
[
box[0][0],
box[0][1],
box[1][0],
box[1][1],
box[2][0],
box[2][1],
box[3][0],
box[3][1],
]
)
return res
def _rotate_polygons(polygons, angle, r_c):
## polygons: N*8
## r_x: rotate center x
## r_y: rotate center y
## angle: -15~15
rotate_boxes_list = []
for poly in polygons:
box = Polygon(poly)
rbox = affinity.rotate(box, angle, r_c)
if len(list(rbox.exterior.coords)) < 5:
print("img_box_ori:", poly)
print("img_box_rotated:", rbox)
# assert(len(list(rbox.exterior.coords))>=5)
rotate_boxes_list.append(rbox.boundary.coords[:-1])
res = _boxlist2quads(rotate_boxes_list)
return res
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