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)