mirror of
https://github.com/open-mmlab/mmocr.git
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18c54afbdc
* Add support for numpy arrays in model_inference * Add test for numpy ndarray inference * Fix linting problems * Add support for batch inference * Add batch inference demo script * Fix comment * Test batch inference with paths and arrays * lint code * Update model_inference docstring * Refactor model inference tests * Change inference function to make text detectors and recognizers use the same input data types * Change single state text detector model to support batch inference * Lint code * simplify inference tests * Remove psenet from batch inference test cases to prevent the pytest being killed * Update batch_image_demo.py * fix bug when test with dataset fix bug when test with dataset, for example, `./tools/dist_test.sh configs/textrecog/sar/sar_r31_parallel_decoder_academic.py <checkpoint> 1 --eval acc` Co-authored-by: Hongbin Sun <hongbin306@gmail.com>
56 lines
1.8 KiB
Python
56 lines
1.8 KiB
Python
from argparse import ArgumentParser
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from pathlib import Path
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import mmcv
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from mmdet.apis import init_detector
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from mmocr.apis.inference import model_inference
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from mmocr.datasets import build_dataset # noqa: F401
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from mmocr.models import build_detector # noqa: F401
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def main():
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parser = ArgumentParser()
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parser.add_argument('config', help='Config file.')
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parser.add_argument('checkpoint', help='Checkpoint file.')
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parser.add_argument('save_path', help='Folder to save visualized images.')
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parser.add_argument(
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'--images',
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nargs='+',
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help='Image files to be predicted with batch mode, '
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'separated by space, like "image_1.jpg image2.jpg". '
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'If algorithm use augmentation test, only one '
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'image file can be given.')
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parser.add_argument(
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'--device', default='cuda:0', help='Device used for inference.')
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parser.add_argument(
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'--imshow',
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action='store_true',
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help='Whether show image with OpenCV.')
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args = parser.parse_args()
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# build the model from a config file and a checkpoint file
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model = init_detector(args.config, args.checkpoint, device=args.device)
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if model.cfg.data.test['type'] == 'ConcatDataset':
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model.cfg.data.test.pipeline = model.cfg.data.test['datasets'][
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0].pipeline
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# test multiple images
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results = model_inference(model, args.images)
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print(f'results: {results}')
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save_path = Path(args.save_path)
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for img_path, result in zip(args.images, results):
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out_file = save_path / f'result_{Path(img_path).stem}.png'
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# show the results
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img = model.show_result(
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img_path, result, out_file=str(out_file), show=False)
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if args.imshow:
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mmcv.imshow(img, f'predicted results ({img_path})')
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if __name__ == '__main__':
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main()
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