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94 lines
3.1 KiB
Python
94 lines
3.1 KiB
Python
#
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# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import os
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import sys
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sys.path.insert(
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0,
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os.path.abspath(
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os.path.join(
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os.path.dirname(
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os.path.abspath(__file__)),
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'../../')))
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from deepdoc.vision.seeit import draw_box
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from deepdoc.vision import OCR, init_in_out
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import argparse
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import numpy as np
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import trio
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# os.environ['CUDA_VISIBLE_DEVICES'] = '0,2' #2 gpus, uncontinuous
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os.environ['CUDA_VISIBLE_DEVICES'] = '0' #1 gpu
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# os.environ['CUDA_VISIBLE_DEVICES'] = '' #cpu
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def main(args):
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import torch.cuda
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cuda_devices = torch.cuda.device_count()
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limiter = [trio.CapacityLimiter(1) for _ in range(cuda_devices)] if cuda_devices > 1 else None
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ocr = OCR()
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images, outputs = init_in_out(args)
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def __ocr(i, id, img):
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print("Task {} start".format(i))
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bxs = ocr(np.array(img), id)
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bxs = [(line[0], line[1][0]) for line in bxs]
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bxs = [{
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"text": t,
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"bbox": [b[0][0], b[0][1], b[1][0], b[-1][1]],
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"type": "ocr",
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"score": 1} for b, t in bxs if b[0][0] <= b[1][0] and b[0][1] <= b[-1][1]]
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img = draw_box(images[i], bxs, ["ocr"], 1.)
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img.save(outputs[i], quality=95)
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with open(outputs[i] + ".txt", "w+", encoding='utf-8') as f:
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f.write("\n".join([o["text"] for o in bxs]))
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print("Task {} done".format(i))
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async def __ocr_thread(i, id, img, limiter = None):
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if limiter:
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async with limiter:
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print("Task {} use device {}".format(i, id))
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await trio.to_thread.run_sync(lambda: __ocr(i, id, img))
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else:
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__ocr(i, id, img)
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async def __ocr_launcher():
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if cuda_devices > 1:
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async with trio.open_nursery() as nursery:
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for i, img in enumerate(images):
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nursery.start_soon(__ocr_thread, i, i % cuda_devices, img, limiter[i % cuda_devices])
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await trio.sleep(0.1)
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else:
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for i, img in enumerate(images):
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await __ocr_thread(i, 0, img)
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trio.run(__ocr_launcher)
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print("OCR tasks are all done")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument('--inputs',
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help="Directory where to store images or PDFs, or a file path to a single image or PDF",
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required=True)
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parser.add_argument('--output_dir', help="Directory where to store the output images. Default: './ocr_outputs'",
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default="./ocr_outputs")
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args = parser.parse_args()
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main(args)
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