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import math
import random
import hashlib
import logging
from enum import Enum
import cv2
import numpy as np
from saicinpainting.evaluation.masks.mask import SegmentationMask
from saicinpainting.utils import LinearRamp
LOGGER = logging.getLogger(__name__)
class DrawMethod(Enum):
LINE = 'line'
CIRCLE = 'circle'
SQUARE = 'square'
def make_random_irregular_mask(shape, max_angle=4, max_len=60, max_width=20, min_times=0, max_times=10,
draw_method=DrawMethod.LINE):
draw_method = DrawMethod(draw_method)
height, width = shape
mask = np.zeros((height, width), np.float32)
times = np.random.randint(min_times, max_times + 1)
for i in range(times):
start_x = np.random.randint(width)
start_y = np.random.randint(height)
for j in range(1 + np.random.randint(5)):
angle = 0.01 + np.random.randint(max_angle)
if i % 2 == 0:
angle = 2 * 3.1415926 - angle
length = 10 + np.random.randint(max_len)
brush_w = 5 + np.random.randint(max_width)
end_x = np.clip((start_x + length * np.sin(angle)).astype(np.int32), 0, width)
end_y = np.clip((start_y + length * np.cos(angle)).astype(np.int32), 0, height)
if draw_method == DrawMethod.LINE:
cv2.line(mask, (start_x, start_y), (end_x, end_y), 1.0, brush_w)
elif draw_method == DrawMethod.CIRCLE:
cv2.circle(mask, (start_x, start_y), radius=brush_w, color=1., thickness=-1)
elif draw_method == DrawMethod.SQUARE:
radius = brush_w // 2
mask[start_y - radius:start_y + radius, start_x - radius:start_x + radius] = 1
start_x, start_y = end_x, end_y
return mask[None, ...]
class RandomIrregularMaskGenerator:
def __init__(self, max_angle=4, max_len=60, max_width=20, min_times=0, max_times=10, ramp_kwargs=None,
draw_method=DrawMethod.LINE):
self.max_angle = max_angle
self.max_len = max_len
self.max_width = max_width
self.min_times = min_times
self.max_times = max_times
self.draw_method = draw_method
self.ramp = LinearRamp(**ramp_kwargs) if ramp_kwargs is not None else None
def __call__(self, img, iter_i=None, raw_image=None):
coef = self.ramp(iter_i) if (self.ramp is not None) and (iter_i is not None) else 1
cur_max_len = int(max(1, self.max_len * coef))
cur_max_width = int(max(1, self.max_width * coef))
cur_max_times = int(self.min_times + 1 + (self.max_times - self.min_times) * coef)
return make_random_irregular_mask(img.shape[1:], max_angle=self.max_angle, max_len=cur_max_len,
max_width=cur_max_width, min_times=self.min_times, max_times=cur_max_times,
draw_method=self.draw_method)
def make_random_rectangle_mask(shape, margin=10, bbox_min_size=30, bbox_max_size=100, min_times=0, max_times=3):
height, width = shape
mask = np.zeros((height, width), np.float32)
bbox_max_size = min(bbox_max_size, height - margin * 2, width - margin * 2)
times = np.random.randint(min_times, max_times + 1)
for i in range(times):
box_width = np.random.randint(bbox_min_size, bbox_max_size)
box_height = np.random.randint(bbox_min_size, bbox_max_size)
start_x = np.random.randint(margin, width - margin - box_width + 1)
start_y = np.random.randint(margin, height - margin - box_height + 1)
mask[start_y:start_y + box_height, start_x:start_x + box_width] = 1
return mask[None, ...]
class RandomRectangleMaskGenerator:
def __init__(self, margin=10, bbox_min_size=30, bbox_max_size=100, min_times=0, max_times=3, ramp_kwargs=None):
self.margin = margin
self.bbox_min_size = bbox_min_size
self.bbox_max_size = bbox_max_size
self.min_times = min_times
self.max_times = max_times
self.ramp = LinearRamp(**ramp_kwargs) if ramp_kwargs is not None else None
def __call__(self, img, iter_i=None, raw_image=None):
coef = self.ramp(iter_i) if (self.ramp is not None) and (iter_i is not None) else 1
cur_bbox_max_size = int(self.bbox_min_size + 1 + (self.bbox_max_size - self.bbox_min_size) * coef)
cur_max_times = int(self.min_times + (self.max_times - self.min_times) * coef)
return make_random_rectangle_mask(img.shape[1:], margin=self.margin, bbox_min_size=self.bbox_min_size,
bbox_max_size=cur_bbox_max_size, min_times=self.min_times,
max_times=cur_max_times)
class RandomSegmentationMaskGenerator:
def __init__(self, **kwargs):
self.impl = None # will be instantiated in first call (effectively in subprocess)
self.kwargs = kwargs
def __call__(self, img, iter_i=None, raw_image=None):
if self.impl is None:
self.impl = SegmentationMask(**self.kwargs)
masks = self.impl.get_masks(np.transpose(img, (1, 2, 0)))
masks = [m for m in masks if len(np.unique(m)) > 1]
return np.random.choice(masks)
def make_random_superres_mask(shape, min_step=2, max_step=4, min_width=1, max_width=3):
height, width = shape
mask = np.zeros((height, width), np.float32)
step_x = np.random.randint(min_step, max_step + 1)
width_x = np.random.randint(min_width, min(step_x, max_width + 1))
offset_x = np.random.randint(0, step_x)
step_y = np.random.randint(min_step, max_step + 1)
width_y = np.random.randint(min_width, min(step_y, max_width + 1))
offset_y = np.random.randint(0, step_y)
for dy in range(width_y):
mask[offset_y + dy::step_y] = 1
for dx in range(width_x):
mask[:, offset_x + dx::step_x] = 1
return mask[None, ...]
class RandomSuperresMaskGenerator:
def __init__(self, **kwargs):
self.kwargs = kwargs
def __call__(self, img, iter_i=None):
return make_random_superres_mask(img.shape[1:], **self.kwargs)
class DumbAreaMaskGenerator:
min_ratio = 0.1
max_ratio = 0.35
default_ratio = 0.225
def __init__(self, is_training):
#Parameters:
# is_training(bool): If true - random rectangular mask, if false - central square mask
self.is_training = is_training
def _random_vector(self, dimension):
if self.is_training:
lower_limit = math.sqrt(self.min_ratio)
upper_limit = math.sqrt(self.max_ratio)
mask_side = round((random.random() * (upper_limit - lower_limit) + lower_limit) * dimension)
u = random.randint(0, dimension-mask_side-1)
v = u+mask_side
else:
margin = (math.sqrt(self.default_ratio) / 2) * dimension
u = round(dimension/2 - margin)
v = round(dimension/2 + margin)
return u, v
def __call__(self, img, iter_i=None, raw_image=None):
c, height, width = img.shape
mask = np.zeros((height, width), np.float32)
x1, x2 = self._random_vector(width)
y1, y2 = self._random_vector(height)
mask[x1:x2, y1:y2] = 1
return mask[None, ...]
class OutpaintingMaskGenerator:
def __init__(self, min_padding_percent:float=0.04, max_padding_percent:int=0.25, left_padding_prob:float=0.5, top_padding_prob:float=0.5,
right_padding_prob:float=0.5, bottom_padding_prob:float=0.5, is_fixed_randomness:bool=False):
"""
is_fixed_randomness - get identical paddings for the same image if args are the same
"""
self.min_padding_percent = min_padding_percent
self.max_padding_percent = max_padding_percent
self.probs = [left_padding_prob, top_padding_prob, right_padding_prob, bottom_padding_prob]
self.is_fixed_randomness = is_fixed_randomness
assert self.min_padding_percent <= self.max_padding_percent
assert self.max_padding_percent > 0
assert len([x for x in [self.min_padding_percent, self.max_padding_percent] if (x>=0 and x<=1)]) == 2, f"Padding percentage should be in [0,1]"
assert sum(self.probs) > 0, f"At least one of the padding probs should be greater than 0 - {self.probs}"
assert len([x for x in self.probs if (x >= 0) and (x <= 1)]) == 4, f"At least one of padding probs is not in [0,1] - {self.probs}"
if len([x for x in self.probs if x > 0]) == 1:
LOGGER.warning(f"Only one padding prob is greater than zero - {self.probs}. That means that the outpainting masks will be always on the same side")
def apply_padding(self, mask, coord):
mask[int(coord[0][0]*self.img_h):int(coord[1][0]*self.img_h),
int(coord[0][1]*self.img_w):int(coord[1][1]*self.img_w)] = 1
return mask
def get_padding(self, size):
n1 = int(self.min_padding_percent*size)
n2 = int(self.max_padding_percent*size)
return self.rnd.randint(n1, n2) / size
@staticmethod
def _img2rs(img):
arr = np.ascontiguousarray(img.astype(np.uint8))
str_hash = hashlib.sha1(arr).hexdigest()
res = hash(str_hash)%(2**32)
return res
def __call__(self, img, iter_i=None, raw_image=None):
c, self.img_h, self.img_w = img.shape
mask = np.zeros((self.img_h, self.img_w), np.float32)
at_least_one_mask_applied = False
if self.is_fixed_randomness:
assert raw_image is not None, f"Cant calculate hash on raw_image=None"
rs = self._img2rs(raw_image)
self.rnd = np.random.RandomState(rs)
else:
self.rnd = np.random
coords = [[
(0,0),
(1,self.get_padding(size=self.img_h))
],
[
(0,0),
(self.get_padding(size=self.img_w),1)
],
[
(0,1-self.get_padding(size=self.img_h)),
(1,1)
],
[
(1-self.get_padding(size=self.img_w),0),
(1,1)
]]
for pp, coord in zip(self.probs, coords):
if self.rnd.random() < pp:
at_least_one_mask_applied = True
mask = self.apply_padding(mask=mask, coord=coord)
if not at_least_one_mask_applied:
idx = self.rnd.choice(range(len(coords)), p=np.array(self.probs)/sum(self.probs))
mask = self.apply_padding(mask=mask, coord=coords[idx])
return mask[None, ...]
class MixedMaskGenerator:
def __init__(self, irregular_proba=1/3, irregular_kwargs=None,
box_proba=1/3, box_kwargs=None,
segm_proba=1/3, segm_kwargs=None,
squares_proba=0, squares_kwargs=None,
superres_proba=0, superres_kwargs=None,
outpainting_proba=0, outpainting_kwargs=None,
invert_proba=0):
self.probas = []
self.gens = []
if irregular_proba > 0:
self.probas.append(irregular_proba)
if irregular_kwargs is None:
irregular_kwargs = {}
else:
irregular_kwargs = dict(irregular_kwargs)
irregular_kwargs['draw_method'] = DrawMethod.LINE
self.gens.append(RandomIrregularMaskGenerator(**irregular_kwargs))
if box_proba > 0:
self.probas.append(box_proba)
if box_kwargs is None:
box_kwargs = {}
self.gens.append(RandomRectangleMaskGenerator(**box_kwargs))
if segm_proba > 0:
self.probas.append(segm_proba)
if segm_kwargs is None:
segm_kwargs = {}
self.gens.append(RandomSegmentationMaskGenerator(**segm_kwargs))
if squares_proba > 0:
self.probas.append(squares_proba)
if squares_kwargs is None:
squares_kwargs = {}
else:
squares_kwargs = dict(squares_kwargs)
squares_kwargs['draw_method'] = DrawMethod.SQUARE
self.gens.append(RandomIrregularMaskGenerator(**squares_kwargs))
if superres_proba > 0:
self.probas.append(superres_proba)
if superres_kwargs is None:
superres_kwargs = {}
self.gens.append(RandomSuperresMaskGenerator(**superres_kwargs))
if outpainting_proba > 0:
self.probas.append(outpainting_proba)
if outpainting_kwargs is None:
outpainting_kwargs = {}
self.gens.append(OutpaintingMaskGenerator(**outpainting_kwargs))
self.probas = np.array(self.probas, dtype='float32')
self.probas /= self.probas.sum()
self.invert_proba = invert_proba
def __call__(self, img, iter_i=None, raw_image=None):
kind = np.random.choice(len(self.probas), p=self.probas)
gen = self.gens[kind]
result = gen(img, iter_i=iter_i, raw_image=raw_image)
if self.invert_proba > 0 and random.random() < self.invert_proba:
result = 1 - result
return result
def get_mask_generator(kind, kwargs):
if kind is None:
kind = "mixed"
if kwargs is None:
kwargs = {}
if kind == "mixed":
cl = MixedMaskGenerator
elif kind == "outpainting":
cl = OutpaintingMaskGenerator
elif kind == "dumb":
cl = DumbAreaMaskGenerator
else:
raise NotImplementedError(f"No such generator kind = {kind}")
return cl(**kwargs)