clipseg library and environment in place

ldm/invoke/txt2mask.py

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+'''Makes available the Txt2Mask class, which assists in the automatic
+assignment of masks via text prompt using clipseg.
+
+Here is typical usage:
+    
+    from ldm.invoke.txt2mask import Txt2Mask, SegmentedGrayscale
+    from PIL import Image
+
+    txt2mask = Txt2Mask(self.device)
+    segmented = txt2mask.segment(Image.open('/path/to/img.png'),'a bagel')
+    
+    # this will return a grayscale Image of the segmented data
+    grayscale = segmented.to_grayscale()
+
+    # this will return a semi-transparent image in which the
+    # selected object(s) are opaque and the rest is at various
+    # levels of transparency
+    transparent = segmented.to_transparent()
+
+    # this will return a masked image suitable for use in inpainting:
+    mask = segmented.to_mask(threshold=0.5)
+
+The threshold used in the call to to_mask() selects pixels for use in
+the mask that exceed the indicated confidence threshold. Values range
+from 0.0 to 1.0. The higher the threshold, the more confident the
+algorithm is. In limited testing, I have found that values around 0.5
+work fine.
+'''
+
+import torch
+import numpy as  np
+from models.clipseg import CLIPDensePredT
+from einops import rearrange, repeat
+from PIL import Image
+from torchvision import transforms
+
+CLIP_VERSION = 'ViT-B/16'
+CLIPSEG_WEIGHTS = 'src/clipseg/weights/rd64-uni.pth'
+
+class SegmentedGrayscale(object):
+    def __init__(self, image:Image, heatmap:torch.Tensor):
+        self.heatmap = heatmap
+        self.image = image
+        
+    def to_grayscale(self)->Image:
+        return Image.fromarray(np.uint8(self.heatmap*255))
+
+    def to_mask(self,threshold:float=0.5)->Image:
+        discrete_heatmap = self.heatmap.lt(threshold).int()
+        return Image.fromarray(np.uint8(discrete_heatmap*255),mode='L')
+
+    def to_transparent(self)->Image:
+        transparent_image = self.image.copy()
+        transparent_image.putalpha(self.to_image)
+        return transparent_image
+
+class Txt2Mask(object):
+    '''
+    Create new Txt2Mask object. The optional device argument can be one of
+    'cuda', 'mps' or 'cpu'.
+    '''
+    def __init__(self,device='cpu'):
+        print('>> Initializing clipseg model')
+        self.model = CLIPDensePredT(version=CLIP_VERSION, reduce_dim=64, )
+        self.model.eval()
+        self.model.to(device)
+        self.model.load_state_dict(torch.load(CLIPSEG_WEIGHTS, map_location=torch.device(device)), strict=False)
+
+    @torch.no_grad()
+    def segment(self, image:Image, prompt:str) -> SegmentedGrayscale:
+        '''
+        Given a prompt string such as "a bagel", tries to identify the object in the
+        provided image and returns a SegmentedGrayscale object in which the brighter
+        pixels indicate where the object is inferred to be.
+        '''
+        prompts = [prompt]   # right now we operate on just a single prompt at a time
+
+        transform = transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+            transforms.Resize((image.width, image.height)), # must be multiple of 64...
+        ])
+        img = transform(image).unsqueeze(0)
+        preds = self.model(img.repeat(len(prompts),1,1,1), prompts)[0]
+        heatmap = torch.sigmoid(preds[0][0]).cpu()
+        return SegmentedGrayscale(image, heatmap)
+
+
+
+