43 lines
1.2 KiB
Python
43 lines
1.2 KiB
Python
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# Copyright (C) 2020-2022 Intel Corporation
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# Copyright (C) CVAT.ai Corporation
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#
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# SPDX-License-Identifier: MIT
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import json
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import base64
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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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from model_handler import ModelHandler
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def init_context(context):
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context.logger.info("Init context... 0%")
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model = ModelHandler()
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context.user_data.model = model
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context.logger.info("Init context...100%")
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def handler(context, event):
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context.logger.info("call handler")
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data = event.body
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pos_points = data["pos_points"]
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neg_points = data["neg_points"]
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obj_bbox = data.get("obj_bbox", None)
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threshold = data.get("threshold", 0.8)
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buf = io.BytesIO(base64.b64decode(data["image"]))
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image = Image.open(buf)
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if obj_bbox is None:
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x, y = np.split(np.transpose(np.array(neg_points)), 2)
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obj_bbox = [np.min(x), np.min(y), np.max(x), np.max(y)]
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neg_points = []
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mask = context.user_data.model.handle(image, obj_bbox, pos_points, neg_points, threshold)
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return context.Response(
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body=json.dumps({ 'mask': mask.tolist() }),
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headers={},
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content_type='application/json',
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status_code=200
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)
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