96 lines
3.3 KiB
Python
96 lines
3.3 KiB
Python
# -*- coding: utf-8 -*-
|
|
# Copyright (c) Alibaba, Inc. and its affiliates.
|
|
import random
|
|
from abc import ABCMeta
|
|
|
|
import cv2
|
|
import numpy as np
|
|
import torch
|
|
from PIL import Image
|
|
|
|
from scepter.modules.annotator.base_annotator import BaseAnnotator
|
|
from scepter.modules.annotator.registry import ANNOTATORS
|
|
from scepter.modules.utils.config import Config, dict_to_yaml
|
|
|
|
|
|
@ANNOTATORS.register_class()
|
|
class RegionCanvasAnnotator(BaseAnnotator, metaclass=ABCMeta):
|
|
para_dict = {}
|
|
|
|
def __init__(self, cfg, logger=None):
|
|
super().__init__(cfg, logger=logger)
|
|
self.scale_range = cfg.get('SCALE_RANGE', [0.75, 1.0])
|
|
self.canvas_value = cfg.get('CANVAS_VALUE', 255)
|
|
self.use_resize = cfg.get('USE_RESIZE', True)
|
|
self.use_canvas = cfg.get('USE_CANVAS', True)
|
|
self.use_aug = cfg.get('USE_AUG', False)
|
|
if self.use_aug:
|
|
mask_aug_dict = {'NAME': 'MaskAugAnnotator'}
|
|
mask_aug_cfg = Config(cfg_dict=mask_aug_dict, load=False)
|
|
self.mask_aug_anno = ANNOTATORS.build(mask_aug_cfg)
|
|
|
|
|
|
def forward(self,
|
|
image,
|
|
mask,
|
|
mask_cfg=None):
|
|
if isinstance(image, Image.Image):
|
|
image = np.array(image)
|
|
elif isinstance(image, torch.Tensor):
|
|
image = image.detach().cpu().numpy()
|
|
elif isinstance(image, np.ndarray):
|
|
image = image
|
|
else:
|
|
raise f'Unsurpport datatype{type(image)}, only surpport np.ndarray, torch.Tensor, Pillow Image.'
|
|
|
|
mask = np.array(mask).astype(np.uint8)
|
|
image_h, image_w = image.shape[:2]
|
|
|
|
if self.use_aug:
|
|
mask = self.mask_aug_anno(mask, mask_cfg)
|
|
|
|
# get region with white bg
|
|
image[np.array(mask) == 0] = self.canvas_value
|
|
x, y, w, h = cv2.boundingRect(mask)
|
|
region_crop = image[y:y + h, x:x + w]
|
|
|
|
if self.use_resize:
|
|
# resize region
|
|
scale_min, scale_max = self.scale_range
|
|
scale_factor = random.uniform(scale_min, scale_max)
|
|
new_w, new_h = int(image_w * scale_factor), int(image_h * scale_factor)
|
|
obj_scale_factor = min(new_w/w, new_h/h)
|
|
|
|
new_w = int(w * obj_scale_factor)
|
|
new_h = int(h * obj_scale_factor)
|
|
region_crop_resized = cv2.resize(region_crop, (new_w, new_h), interpolation=cv2.INTER_AREA)
|
|
else:
|
|
region_crop_resized = region_crop
|
|
|
|
if self.use_canvas:
|
|
# plot region into canvas
|
|
new_canvas = np.ones_like(image) * self.canvas_value
|
|
max_x = max(0, image_w - new_w)
|
|
max_y = max(0, image_h - new_h)
|
|
new_x = random.randint(0, max_x)
|
|
new_y = random.randint(0, max_y)
|
|
|
|
new_canvas[new_y:new_y + new_h, new_x:new_x + new_w] = region_crop_resized
|
|
else:
|
|
new_canvas = region_crop_resized
|
|
return new_canvas
|
|
|
|
@staticmethod
|
|
def get_config_template():
|
|
return dict_to_yaml('ANNOTATORS',
|
|
__class__.__name__,
|
|
RegionCanvasAnnotator.para_dict,
|
|
set_name=True)
|
|
|
|
|
|
@ANNOTATORS.register_class()
|
|
class RegionCanvasCropAnnotator(RegionCanvasAnnotator):
|
|
def __init__(self, cfg, logger=None):
|
|
super().__init__(cfg, logger=logger)
|
|
self.use_resize, self.use_canvas = False, False
|