89 lines
2.6 KiB
Python
89 lines
2.6 KiB
Python
import requests
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from PIL import Image
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import io
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import numpy as np
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import folder_paths
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class SAM_Embedding:
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def __init__(self):
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self.output_dir = folder_paths.get_output_directory()
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",),
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"embedding_id": ("STRING", {
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"multiline": False,
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"default": "embedding"
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}),
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},
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"optional": {
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"predictor": ("SAMPREDICTOR",)
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}
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}
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RETURN_TYPES = ("EMBEDDINGS", )
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RETURN_NAMES = ("embeddings", )
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FUNCTION = "segment"
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CATEGORY = "image"
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def segment(self, image, embedding_id, predictor=None):
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# Convert PyTorch tensor to numpy array
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image_np = (image[0].numpy() * 255).astype(np.uint8)
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if predictor != None:
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predictor.set_image(image_np)
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emb = predictor.get_image_embedding().cpu().numpy()
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output = {
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"image_embedding": emb,
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"shape": emb.shape,
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"input_size": predictor.input_size
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}
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else:
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# Convert numpy array to PIL Image
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img = Image.fromarray(image_np)
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# Create an in-memory bytes buffer
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img_byte_arr = io.BytesIO()
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# Save the PIL Image to the bytes buffer in PNG format
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img.save(img_byte_arr, format='PNG')
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# Get the bytes value of the buffer
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img_byte_arr = img_byte_arr.getvalue()
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# Create a dictionary with the image bytes
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files = {'image': ('image.png', img_byte_arr)}
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# Send the POST request
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response = requests.post('https://avatechgg--segment-anything-entrypoint.modal.run', files=files)
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# Check if the request was successful
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if response.status_code == 200:
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# Parse the JSON response
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output = response.json()
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else:
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print(f"Request failed with status code {response.status_code}")
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output = None
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# sam = sam_model_registry[model_type](checkpoint=ckpt)
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# predictor = SamPredictor(sam)
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# masks = predictor.set_torch_image
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# masks = predictor.predict
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# print(output)
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np.save(f"{self.output_dir}/{embedding_id}.npy", output["image_embedding"])
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return (output, )
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NODE_CLASS_MAPPINGS = {
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"SAM_Embedding": SAM_Embedding
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"SAM_Embedding": "SAM Embedding"
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}
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