import math import random import hashlib import logging from enum import Enum import cv2 import numpy as np # from annotator.lama.saicinpainting.evaluation.masks.mask import SegmentationMask from annotator.lama.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)