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Zero
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# -*- coding: utf-8 -*-
# Copyright (c) Alibaba, Inc. and its affiliates.
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 dict_to_yaml
@ANNOTATORS.register_class()
class CannyAnnotator(BaseAnnotator, metaclass=ABCMeta):
para_dict = {}
def __init__(self, cfg, logger=None):
super().__init__(cfg, logger=logger)
self.low_threshold = cfg.get('LOW_THRESHOLD', 100)
self.high_threshold = cfg.get('HIGH_THRESHOLD', 200)
self.random_cfg = cfg.get('RANDOM_CFG', None)
def forward(self, image):
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.copy()
else:
raise f'Unsurpport datatype{type(image)}, only surpport np.ndarray, torch.Tensor, Pillow Image.'
assert len(image.shape) < 4
if self.random_cfg is None:
image = cv2.Canny(image, self.low_threshold, self.high_threshold)
else:
proba = self.random_cfg.get('PROBA', 1.0)
if np.random.random() < proba:
min_low_threshold = self.random_cfg.get(
'MIN_LOW_THRESHOLD', 50)
max_low_threshold = self.random_cfg.get(
'MAX_LOW_THRESHOLD', 100)
min_high_threshold = self.random_cfg.get(
'MIN_HIGH_THRESHOLD', 200)
max_high_threshold = self.random_cfg.get(
'MAX_HIGH_THRESHOLD', 350)
low_th = np.random.randint(min_low_threshold,
max_low_threshold)
high_th = np.random.randint(min_high_threshold,
max_high_threshold)
else:
low_th, high_th = self.low_threshold, self.high_threshold
image = cv2.Canny(image, low_th, high_th)
return image[..., None].repeat(3, 2)
@staticmethod
def get_config_template():
return dict_to_yaml('ANNOTATORS',
__class__.__name__,
CannyAnnotator.para_dict,
set_name=True)
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