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https://github.com/invoke-ai/InvokeAI
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123 lines
4.5 KiB
Python
123 lines
4.5 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 clipseg import CLIPDensePredT
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from einops import rearrange, repeat
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from PIL import Image
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from torchvision import transforms
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CLIP_VERSION = 'ViT-B/16'
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CLIPSEG_WEIGHTS = 'src/clipseg/weights/rd64-uni.pth'
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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)->Image:
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return self._rescale(Image.fromarray(np.uint8(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)->Image:
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transparent_image = self.image.copy()
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transparent_image.putalpha(self.to_grayscale())
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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'):
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print('>> Initializing clipseg model for text to mask inference')
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self.device = device
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self.model = CLIPDensePredT(version=CLIP_VERSION, reduce_dim=64, )
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self.model.eval()
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# initially we keep everything in cpu to conserve space
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self.model.to('cpu')
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self.model.load_state_dict(torch.load(CLIPSEG_WEIGHTS, map_location=torch.device('cpu')), strict=False)
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@torch.no_grad()
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def segment(self, image: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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self._to_device(self.device)
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prompts = [prompt] # right now we operate on just a single prompt at a time
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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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img = self._scale_and_crop(image)
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img = transform(img).unsqueeze(0)
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preds = self.model(img.repeat(len(prompts),1,1,1), prompts)[0]
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heatmap = torch.sigmoid(preds[0][0]).cpu()
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self._to_device('cpu')
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return SegmentedGrayscale(image, heatmap)
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def _to_device(self, device):
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self.model.to(device)
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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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