mirror of https://github.com/open-mmlab/mmocr.git
52 lines
1.6 KiB
Python
52 lines
1.6 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import base64
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import os
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import mmcv
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import torch
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from ts.torch_handler.base_handler import BaseHandler
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from mmocr.apis import init_detector, model_inference
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from mmocr.datasets.pipelines import * # NOQA
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class MMOCRHandler(BaseHandler):
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threshold = 0.5
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def initialize(self, context):
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properties = context.system_properties
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self.map_location = 'cuda' if torch.cuda.is_available() else 'cpu'
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self.device = torch.device(self.map_location + ':' +
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str(properties.get('gpu_id')) if torch.cuda.
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is_available() else self.map_location)
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self.manifest = context.manifest
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model_dir = properties.get('model_dir')
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serialized_file = self.manifest['model']['serializedFile']
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checkpoint = os.path.join(model_dir, serialized_file)
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self.config_file = os.path.join(model_dir, 'config.py')
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self.model = init_detector(self.config_file, checkpoint, self.device)
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self.initialized = True
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def preprocess(self, data):
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images = []
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for row in data:
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image = row.get('data') or row.get('body')
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if isinstance(image, str):
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image = base64.b64decode(image)
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image = mmcv.imfrombytes(image)
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images.append(image)
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return images
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def inference(self, data, *args, **kwargs):
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results = model_inference(self.model, data)
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return results
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def postprocess(self, data):
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# Format output following the example OCRHandler format
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return data
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