mirror of https://github.com/open-mmlab/mmocr.git
Tong Gao
GitHub
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# Demo
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This page provides tutorials about running demos. Please click the link below for more information.
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Please refer to [Text Detection](docs/text_det_demo.md) for the tutorial of Text Detection demo.
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<div align="center">
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<img src="resources/demo_text_det_pred.jpg"/><br>
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</div>
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<br>
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<br>
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Please refer to [Text Recognition](docs/text_recog_demo.md) for the tutorial of Text Recognition demo.
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<div align="center">
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<img src="resources/demo_text_recog_pred.jpg" width="200px" alt/><br>
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</div>
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<br>
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<br>
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Please refer to [End2End Demo](docs/ocr_demo.md) for the tutorial of Text Detection and Recognition end-to-end demo.
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<div align="center">
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<img src="resources/demo_ocr_pred.jpg"/><br>
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</div>
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<br>
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<br>
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Please refer to [KIE End2End Demo](docs/kie_demo.md) for the tutorial of KIE end-to-end demo.
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<div align="center">
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<img src="resources/demo_kie_pred.jpeg"/><br>
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</div>
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<br>
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# Copyright (c) OpenMMLab. All rights reserved.
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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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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, batch_mode=True)
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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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# Copyright (c) OpenMMLab. All rights reserved.
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from argparse import ArgumentParser
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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('img', help='Image file.')
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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('out_file', help='Path to save visualized image.')
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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 a single image
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result = model_inference(model, args.img)
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print(f'result: {result}')
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# show the results
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img = model.show_result(
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args.img, result, out_file=args.out_file, show=False)
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if img is None:
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img = mmcv.imread(args.img)
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mmcv.imwrite(img, args.out_file)
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if args.imshow:
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mmcv.imshow(img, 'predicted results')
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if __name__ == '__main__':
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main()
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# Copyright (c) OpenMMLab. All rights reserved.
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from argparse import ArgumentParser
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from mmdet.apis import init_detector
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from mmocr.apis import init_detector
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from mmocr.apis.inference import text_model_inference
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from mmocr.datasets import build_dataset # NOQA
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# Copyright (c) OpenMMLab. All rights reserved.
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from argparse import ArgumentParser
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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.core.visualize import det_recog_show_result
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from mmocr.datasets.pipelines.crop import crop_img
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from mmocr.utils.box_util import stitch_boxes_into_lines
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def det_and_recog_inference(args, det_model, recog_model):
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image_path = args.img
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end2end_res = {'filename': image_path}
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end2end_res['result'] = []
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image = mmcv.imread(image_path)
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det_result = model_inference(det_model, image)
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bboxes = det_result['boundary_result']
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box_imgs = []
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for bbox in bboxes:
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box_res = {}
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box_res['box'] = [round(x) for x in bbox[:-1]]
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box_res['box_score'] = float(bbox[-1])
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box = bbox[:8]
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if len(bbox) > 9:
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min_x = min(bbox[0:-1:2])
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min_y = min(bbox[1:-1:2])
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max_x = max(bbox[0:-1:2])
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max_y = max(bbox[1:-1:2])
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box = [min_x, min_y, max_x, min_y, max_x, max_y, min_x, max_y]
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box_img = crop_img(image, box)
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if args.batch_mode:
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box_imgs.append(box_img)
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else:
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recog_result = model_inference(recog_model, box_img)
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text = recog_result['text']
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text_score = recog_result['score']
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if isinstance(text_score, list):
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text_score = sum(text_score) / max(1, len(text))
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box_res['text'] = text
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box_res['text_score'] = text_score
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end2end_res['result'].append(box_res)
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if args.batch_mode:
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batch_size = args.batch_size
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for chunk_idx in range(len(box_imgs) // batch_size + 1):
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start_idx = chunk_idx * batch_size
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end_idx = (chunk_idx + 1) * batch_size
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chunk_box_imgs = box_imgs[start_idx:end_idx]
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if len(chunk_box_imgs) == 0:
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continue
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recog_results = model_inference(
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recog_model, chunk_box_imgs, batch_mode=True)
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for i, recog_result in enumerate(recog_results):
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text = recog_result['text']
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text_score = recog_result['score']
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if isinstance(text_score, list):
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text_score = sum(text_score) / max(1, len(text))
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end2end_res['result'][start_idx + i]['text'] = text
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end2end_res['result'][start_idx + i]['text_score'] = text_score
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return end2end_res
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def main():
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parser = ArgumentParser()
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parser.add_argument('img', type=str, help='Input Image file.')
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parser.add_argument(
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'out_file', type=str, help='Output file name of the visualized image.')
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parser.add_argument(
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'--det-config',
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type=str,
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default='./configs/textdet/psenet/'
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'psenet_r50_fpnf_600e_icdar2015.py',
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help='Text detection config file.')
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parser.add_argument(
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'--det-ckpt',
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type=str,
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default='https://download.openmmlab.com/'
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'mmocr/textdet/psenet/'
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'psenet_r50_fpnf_600e_icdar2015_pretrain-eefd8fe6.pth',
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help='Text detection checkpint file (local or url).')
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parser.add_argument(
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'--recog-config',
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type=str,
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default='./configs/textrecog/sar/'
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'sar_r31_parallel_decoder_academic.py',
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help='Text recognition config file.')
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parser.add_argument(
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'--recog-ckpt',
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type=str,
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default='https://download.openmmlab.com/'
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'mmocr/textrecog/sar/'
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'sar_r31_parallel_decoder_academic-dba3a4a3.pth',
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help='Text recognition checkpint file (local or url).')
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parser.add_argument(
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'--batch-mode',
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action='store_true',
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help='Whether use batch mode for text recognition.')
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parser.add_argument(
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'--batch-size',
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type=int,
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default=4,
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help='Batch size for text recognition inference '
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'if batch_mode is True above.')
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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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parser.add_argument(
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'--ocr-in-lines',
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action='store_true',
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help='Whether group ocr results in lines.')
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args = parser.parse_args()
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if args.device == 'cpu':
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args.device = None
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# build detect model
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detect_model = init_detector(
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args.det_config, args.det_ckpt, device=args.device)
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if hasattr(detect_model, 'module'):
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detect_model = detect_model.module
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if detect_model.cfg.data.test['type'] == 'ConcatDataset':
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detect_model.cfg.data.test.pipeline = \
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detect_model.cfg.data.test['datasets'][0].pipeline
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# build recog model
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recog_model = init_detector(
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args.recog_config, args.recog_ckpt, device=args.device)
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if hasattr(recog_model, 'module'):
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recog_model = recog_model.module
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if recog_model.cfg.data.test['type'] == 'ConcatDataset':
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recog_model.cfg.data.test.pipeline = \
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recog_model.cfg.data.test['datasets'][0].pipeline
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det_recog_result = det_and_recog_inference(args, detect_model, recog_model)
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print(f'result: {det_recog_result}')
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mmcv.dump(
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det_recog_result,
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args.out_file + '.json',
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ensure_ascii=False,
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indent=4)
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if args.ocr_in_lines:
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res = det_recog_result['result']
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res = stitch_boxes_into_lines(res, 10, 0.5)
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det_recog_result['result'] = res
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mmcv.dump(
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det_recog_result,
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args.out_file + '.line.json',
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ensure_ascii=False,
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indent=4)
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img = det_recog_show_result(args.img, det_recog_result)
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mmcv.imwrite(img, args.out_file)
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if args.imshow:
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mmcv.imshow(img, 'predicted results')
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if __name__ == '__main__':
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main()
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@ -3,9 +3,8 @@ import argparse
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import cv2
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import torch
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from mmdet.apis import init_detector
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from mmocr.apis import model_inference
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from mmocr.apis import init_detector, 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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