zyliu's picture
release iChatApp
0f90f73
raw
history blame
13.7 kB
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)