usrbinenv python codingutf8 2023 the huggingface inc team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache orglicenseslicense2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license usr bin env python coding utf 8 2023 the huggingface inc team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license
import numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
    from PIL import Image


class ImageSegmentationTool(PipelineTool):
    description = (
        "This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image. "
        "It takes two arguments named `image` which should be the original image, and `label` which should be a text "
        "describing the elements what should be identified in the segmentation mask. The tool returns the mask."
    )
    default_checkpoint = "CIDAS/clipseg-rd64-refined"
    name = "image_segmenter"
    model_class = CLIPSegForImageSegmentation

    inputs = ["image", "text"]
    outputs = ["image"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, ["vision"])
        super().__init__(*args, **kwargs)

    def encode(self, image: "Image", label: str):
        return self.pre_processor(text=[label], images=[image], padding=True, return_tensors="pt")

    def forward(self, inputs):
        with torch.no_grad():
            logits = self.model(**inputs).logits
        return logits

    def decode(self, outputs):
        array = outputs.cpu().detach().numpy()
        array[array <= 0] = 0
        array[array > 0] = 1
        return Image.fromarray((array * 255).astype(np.uint8))