File size: 1,374 Bytes
c2fbc3b 7f7a4fc 73cb701 1810c2f 7f7a4fc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | from typing import Dict, List, Any
import os
import requests
from flask import Flask, Response, request, jsonify
from segment_anything import SamPredictor, sam_model_registry
class EndpointHandler():
def __init__(self, path=""):
# Preload all the elements you are going to need at inference.
model_type = "vit_b"
# prefix = "/opt/ml/model"
print('current working directory', os.getcwd())
model_path = "models/tf_model.h5"
# model_checkpoint_path = os.path.join(prefix, "sam_vit_h_4b8939.pth")
sam = sam_model_registry[model_type](checkpoint=model_path)
self.predictor = SamPredictor(sam)
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
data args:
inputs (:obj: `str` | `PIL.Image` | `np.array`)
kwargs
Return:
A :obj:`list` | `dict`: will be serialized and returned
"""
inputs = data.pop("inputs", data)
image_url = inputs.pop("imageUrl", None)
if not image_url:
return jsonify({"error": "image_url not provided"}), 400
try:
response = requests.get(image_url)
response.raise_for_status()
image = response.content
except requests.RequestException as e:
return jsonify({"error": f"Error downloading image: {str(e)}"}), 500
self.predictor.set_image(image)
image_embedding = self.predictor.get_image_embedding().cpu().numpy().tolist()
return jsonify(image_embedding)
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