64 lines
2.4 KiB
Python
64 lines
2.4 KiB
Python
# -*- 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)
|