46 lines
1.5 KiB
Python
46 lines
1.5 KiB
Python
from transformers import pipeline as hf_pipeline
|
|
from PIL import Image
|
|
import numpy as np
|
|
import torch
|
|
|
|
image_segmentation_model_name_list = [
|
|
"mattmdjaga/segformer_b2_clothes",
|
|
"nvidia/segformer-b1-finetuned-cityscapes-1024-1024",
|
|
]
|
|
|
|
|
|
class ImageSegmentationPipeline:
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"image": ("IMAGE",),
|
|
"category_name": ("STRING", {"default": ""}),
|
|
"model_name": (image_segmentation_model_name_list, {"default": image_segmentation_model_name_list[0]}),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE",)
|
|
RETURN_NAMES = ("mask",)
|
|
FUNCTION = "image_segmentation_pipeline"
|
|
CATEGORY = "Transformers/ComputerVision/ImageSegmentation"
|
|
|
|
def image_segmentation_pipeline(self, image, category_name, model_name):
|
|
img = 255.0 * image[0].cpu().numpy()
|
|
pil_image = Image.fromarray(np.clip(img, 0, 255).astype(np.uint8))
|
|
|
|
pipe = hf_pipeline(task="image-segmentation", model=model_name)
|
|
result = pipe(pil_image)
|
|
|
|
for item in result:
|
|
if item["label"] == category_name:
|
|
mask = item["mask"]
|
|
arr = np.array(mask).astype(np.float32) / 255.0
|
|
if len(arr.shape) == 2:
|
|
arr = np.stack([arr, arr, arr], axis=-1)
|
|
return (torch.from_numpy(arr)[None,],)
|
|
|
|
h, w = pil_image.size[1], pil_image.size[0]
|
|
empty = torch.zeros(1, h, w, 3)
|
|
return (empty,)
|