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import gc
import numpy as np
import PIL.Image
import torch
from controlnet_aux import (CannyDetector, ContentShuffleDetector, HEDdetector,
LineartAnimeDetector, LineartDetector,
MidasDetector, MLSDdetector, NormalBaeDetector,
OpenposeDetector, PidiNetDetector)
from controlnet_aux.util import HWC3
from cv_utils import resize_image
from depth_estimator import DepthEstimator
from image_segmentor import ImageSegmentor
class Preprocessor:
MODEL_ID = 'lllyasviel/Annotators'
def __init__(self):
self.model = None
self.name = ''
def load(self, name: str) -> None:
if name == self.name:
return
if name == 'HED':
self.model = HEDdetector.from_pretrained(self.MODEL_ID)
elif name == 'Midas':
self.model = MidasDetector.from_pretrained(self.MODEL_ID)
elif name == 'MLSD':
self.model = MLSDdetector.from_pretrained(self.MODEL_ID)
elif name == 'Openpose':
self.model = OpenposeDetector.from_pretrained(self.MODEL_ID)
elif name == 'PidiNet':
self.model = PidiNetDetector.from_pretrained(self.MODEL_ID)
elif name == 'NormalBae':
self.model = NormalBaeDetector.from_pretrained(self.MODEL_ID)
elif name == 'Lineart':
self.model = LineartDetector.from_pretrained(self.MODEL_ID)
elif name == 'LineartAnime':
self.model = LineartAnimeDetector.from_pretrained(self.MODEL_ID)
elif name == 'Canny':
self.model = CannyDetector()
elif name == 'ContentShuffle':
self.model = ContentShuffleDetector()
elif name == 'DPT':
self.model = DepthEstimator()
elif name == 'UPerNet':
self.model = ImageSegmentor()
else:
raise ValueError
torch.cuda.empty_cache()
gc.collect()
self.name = name
def __call__(self, image: PIL.Image.Image, **kwargs) -> PIL.Image.Image:
if self.name == 'Canny':
if 'detect_resolution' in kwargs:
detect_resolution = kwargs.pop('detect_resolution')
image = np.array(image)
image = HWC3(image)
image = resize_image(image, resolution=detect_resolution)
image = self.model(image, **kwargs)
return PIL.Image.fromarray(image)
elif self.name == 'Midas':
detect_resolution = kwargs.pop('detect_resolution', 512)
image_resolution = kwargs.pop('image_resolution', 512)
image = np.array(image)
image = HWC3(image)
image = resize_image(image, resolution=detect_resolution)
image = self.model(image, **kwargs)
image = HWC3(image)
image = resize_image(image, resolution=image_resolution)
return PIL.Image.fromarray(image)
else:
return self.model(image, **kwargs)
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