mirror of
https://github.com/invoke-ai/InvokeAI
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129 lines
5.1 KiB
Python
129 lines
5.1 KiB
Python
'''Makes available the Txt2Mask class, which assists in the automatic
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assignment of masks via text prompt using clipseg.
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Here is typical usage:
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from ldm.invoke.txt2mask import Txt2Mask, SegmentedGrayscale
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from PIL import Image
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txt2mask = Txt2Mask(self.device)
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segmented = txt2mask.segment(Image.open('/path/to/img.png'),'a bagel')
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# this will return a grayscale Image of the segmented data
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grayscale = segmented.to_grayscale()
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# this will return a semi-transparent image in which the
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# selected object(s) are opaque and the rest is at various
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# levels of transparency
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transparent = segmented.to_transparent()
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# this will return a masked image suitable for use in inpainting:
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mask = segmented.to_mask(threshold=0.5)
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The threshold used in the call to to_mask() selects pixels for use in
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the mask that exceed the indicated confidence threshold. Values range
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from 0.0 to 1.0. The higher the threshold, the more confident the
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algorithm is. In limited testing, I have found that values around 0.5
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work fine.
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'''
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import torch
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import numpy as np
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from transformers import AutoProcessor, CLIPSegForImageSegmentation
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from PIL import Image, ImageOps
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from torchvision import transforms
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from ldm.invoke.globals import global_cache_dir
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CLIPSEG_MODEL = 'CIDAS/clipseg-rd64-refined'
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CLIPSEG_SIZE = 352
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class SegmentedGrayscale(object):
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def __init__(self, image:Image, heatmap:torch.Tensor):
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self.heatmap = heatmap
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self.image = image
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def to_grayscale(self,invert:bool=False)->Image:
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return self._rescale(Image.fromarray(np.uint8(255 - self.heatmap * 255 if invert else self.heatmap * 255)))
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def to_mask(self,threshold:float=0.5)->Image:
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discrete_heatmap = self.heatmap.lt(threshold).int()
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return self._rescale(Image.fromarray(np.uint8(discrete_heatmap*255),mode='L'))
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def to_transparent(self,invert:bool=False)->Image:
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transparent_image = self.image.copy()
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# For img2img, we want the selected regions to be transparent,
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# but to_grayscale() returns the opposite. Thus invert.
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gs = self.to_grayscale(not invert)
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transparent_image.putalpha(gs)
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return transparent_image
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# unscales and uncrops the 352x352 heatmap so that it matches the image again
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def _rescale(self, heatmap:Image)->Image:
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size = self.image.width if (self.image.width > self.image.height) else self.image.height
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resized_image = heatmap.resize(
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(size,size),
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resample=Image.Resampling.LANCZOS
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)
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return resized_image.crop((0,0,self.image.width,self.image.height))
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class Txt2Mask(object):
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'''
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Create new Txt2Mask object. The optional device argument can be one of
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'cuda', 'mps' or 'cpu'.
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'''
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def __init__(self,device='cpu',refined=False):
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print('>> Initializing clipseg model for text to mask inference')
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# BUG: we are not doing anything with the device option at this time
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self.device = device
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self.processor = AutoProcessor.from_pretrained(CLIPSEG_MODEL,
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cache_dir=global_cache_dir('hub')
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)
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self.model = CLIPSegForImageSegmentation.from_pretrained(CLIPSEG_MODEL,
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cache_dir=global_cache_dir('hub')
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)
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@torch.no_grad()
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def segment(self, image, prompt:str) -> SegmentedGrayscale:
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'''
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Given a prompt string such as "a bagel", tries to identify the object in the
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provided image and returns a SegmentedGrayscale object in which the brighter
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pixels indicate where the object is inferred to be.
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'''
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transform = transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
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transforms.Resize((CLIPSEG_SIZE, CLIPSEG_SIZE)), # must be multiple of 64...
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])
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if type(image) is str:
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image = Image.open(image).convert('RGB')
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image = ImageOps.exif_transpose(image)
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img = self._scale_and_crop(image)
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img = transform(img).unsqueeze(0)
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inputs = self.processor(text=[prompt],
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images=[image],
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padding=True,
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return_tensors='pt')
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outputs = self.model(**inputs)
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heatmap = torch.sigmoid(outputs.logits)
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return SegmentedGrayscale(image, heatmap)
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def _scale_and_crop(self, image:Image)->Image:
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scaled_image = Image.new('RGB',(CLIPSEG_SIZE,CLIPSEG_SIZE))
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if image.width > image.height: # width is constraint
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scale = CLIPSEG_SIZE / image.width
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else:
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scale = CLIPSEG_SIZE / image.height
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scaled_image.paste(
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image.resize(
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(int(scale * image.width),
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int(scale * image.height)
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),
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resample=Image.Resampling.LANCZOS
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),box=(0,0)
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)
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return scaled_image
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