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import math
import random
import hashlib
import logging
from enum import Enum
import cv2
import numpy as np
from utils.data_utils import LinearRamp
from metrics.evaluation.masks.mask import SegmentationMask
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, shape, 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(shape, 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, shape, 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(shape, 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)
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, shape, iter_i=None):
return make_random_superres_mask(shape, **self.kwargs)
class MixedMaskGenerator:
def __init__(self, irregular_proba=1/3, hole_range=[0,0,0.7], 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 = []
self.hole_range = hole_range
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 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))
self.probas = np.array(self.probas, dtype='float32')
self.probas /= self.probas.sum()
self.invert_proba = invert_proba
def __call__(self, shape, iter_i=None, raw_image=None):
kind = np.random.choice(len(self.probas), p=self.probas)
gen = self.gens[kind]
result = gen(shape, iter_i=iter_i, raw_image=raw_image)
if self.invert_proba > 0 and random.random() < self.invert_proba:
result = 1 - result
if np.mean(result) <= self.hole_range[0] or np.mean(result) >= self.hole_range[1]:
return self.__call__(shape, iter_i=iter_i, raw_image=raw_image)
else:
return result
class RandomSegmentationMaskGenerator:
def __init__(self, **kwargs):
self.kwargs = kwargs
self.impl = SegmentationMask(**self.kwargs)
def __call__(self, img, iter_i=None, raw_image=None, hole_range=[0.0, 0.3]):
masks = self.impl.get_masks(img)
fil_masks = []
for cur_mask in masks:
if len(np.unique(cur_mask)) == 0 or cur_mask.mean() > hole_range[1]:
continue
fil_masks.append(cur_mask)
mask_index = np.random.choice(len(fil_masks),
size=1,
replace=False)
mask = fil_masks[mask_index]
return mask
class SegMaskGenerator:
def __init__(self, hole_range=[0.1, 0.2], segm_kwargs=None):
if segm_kwargs is None:
segm_kwargs = {}
self.gen = RandomSegmentationMaskGenerator(**segm_kwargs)
self.hole_range = hole_range
def __call__(self, img, iter_i=None, raw_image=None):
result = self.gen(img=img, iter_i=iter_i, raw_image=raw_image, hole_range=self.hole_range)
return result
class FGSegmentationMaskGenerator:
def __init__(self, **kwargs):
self.kwargs = kwargs
self.impl = SegmentationMask(**self.kwargs)
def __call__(self, img, iter_i=None, raw_image=None, hole_range=[0.0, 0.3]):
masks = self.impl.get_masks(img)
mask = masks[0]
for cur_mask in masks:
if len(np.unique(cur_mask)) == 0 or cur_mask.mean() > hole_range[1]:
continue
mask += cur_mask
mask = mask > 0
return mask
class SegBGMaskGenerator:
def __init__(self, hole_range=[0.1, 0.2], segm_kwargs=None):
if segm_kwargs is None:
segm_kwargs = {}
self.gen = FGSegmentationMaskGenerator(**segm_kwargs)
self.hole_range = hole_range
self.cfg = {
'irregular_proba': 1,
'hole_range': [0.0, 1.0],
'irregular_kwargs': {
'max_angle': 4,
'max_len': 250,
'max_width': 150,
'max_times': 3,
'min_times': 1,
},
'box_proba': 0,
'box_kwargs': {
'margin': 10,
'bbox_min_size': 30,
'bbox_max_size': 150,
'max_times': 4,
'min_times': 1,
}
}
self.bg_mask_gen = MixedMaskGenerator(**self.cfg)
def __call__(self, img, iter_i=None, raw_image=None):
shape = img.shape[:2]
mask_fg = self.gen(img=img, iter_i=iter_i, raw_image=raw_image, hole_range=self.hole_range)
bg_ratio = 1 - np.mean(mask_fg)
result = self.bg_mask_gen(shape, iter_i=iter_i, raw_image=raw_image)
result = result - mask_fg
if np.mean(result) <= self.hole_range[0]*bg_ratio or np.mean(result) >= self.hole_range[1]*bg_ratio:
return self.__call__(shape, iter_i=iter_i, raw_image=raw_image)
return result
def get_mask_generator(kind, cfg=None):
if kind is None:
kind = "mixed"
if cfg is None:
cfg = {
'irregular_proba': 1,
'hole_range': [0.0, 0.7],
'irregular_kwargs': {
'max_angle': 4,
'max_len': 200,
'max_width': 100,
'max_times': 5,
'min_times': 1,
},
'box_proba': 1,
'box_kwargs': {
'margin': 10,
'bbox_min_size': 30,
'bbox_max_size': 150,
'max_times': 4,
'min_times': 1,
},
'segm_proba': 0,}
if kind == "mixed":
cl = MixedMaskGenerator
elif kind =="segmentation":
cl = SegBGMaskGenerator
else:
raise NotImplementedError(f"No such generator kind = {kind}")
return cl(**cfg) |