59 lines
2.1 KiB
Python
59 lines
2.1 KiB
Python
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#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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import torch
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from ..models.clipseg import CLIPSegForImageSegmentation
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from ..utils import is_vision_available, requires_backends
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from .base import PipelineTool
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if is_vision_available():
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from PIL import Image
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class ImageSegmentationTool(PipelineTool):
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description = (
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"This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image. "
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"It takes two arguments named `image` which should be the original image, and `label` which should be a text "
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"describing the elements what should be identified in the segmentation mask. The tool returns the mask."
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)
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default_checkpoint = "CIDAS/clipseg-rd64-refined"
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name = "image_segmenter"
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model_class = CLIPSegForImageSegmentation
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inputs = ["image", "text"]
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outputs = ["image"]
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def __init__(self, *args, **kwargs):
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requires_backends(self, ["vision"])
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super().__init__(*args, **kwargs)
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def encode(self, image: "Image", label: str):
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return self.pre_processor(text=[label], images=[image], padding=True, return_tensors="pt")
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def forward(self, inputs):
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with torch.no_grad():
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logits = self.model(**inputs).logits
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return logits
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def decode(self, outputs):
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array = outputs.cpu().detach().numpy()
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array[array <= 0] = 0
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array[array > 0] = 1
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return Image.fromarray((array * 255).astype(np.uint8))
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