[Enhancement] Update demo/ (#454)

* update demo

* rm demo/readme.md

demo/README.md

@@ -1,40 +0,0 @@
-# Demo
-
-This page provides tutorials about running demos. Please click the link below for more information.
-
-Please refer to [Text Detection](docs/text_det_demo.md) for the tutorial of Text Detection demo.
-
-<div align="center">
-    <img src="resources/demo_text_det_pred.jpg"/><br>
-
-</div>
-<br>
-<br>
-
-Please refer to [Text Recognition](docs/text_recog_demo.md) for the tutorial of Text Recognition demo.
-
-<div align="center">
-    <img src="resources/demo_text_recog_pred.jpg" width="200px" alt/><br>
-
-</div>
-<br>
-<br>
-
-Please refer to [End2End Demo](docs/ocr_demo.md) for the tutorial of Text Detection and Recognition end-to-end demo.
-
-<div align="center">
-    <img src="resources/demo_ocr_pred.jpg"/><br>
-
-</div>
-<br>
-<br>
-
-Please refer to [KIE End2End Demo](docs/kie_demo.md) for the tutorial of KIE end-to-end demo.
-
-<div align="center">
-    <img src="resources/demo_kie_pred.jpeg"/><br>
-
-</div>
-
-
-<br>

demo/batch_image_demo.py

@@ -1,54 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from argparse import ArgumentParser
-from pathlib import Path
-
-import mmcv
-from mmdet.apis import init_detector
-
-from mmocr.apis.inference import model_inference
-from mmocr.datasets import build_dataset  # noqa: F401
-from mmocr.models import build_detector  # noqa: F401
-
-
-def main():
-    parser = ArgumentParser()
-    parser.add_argument('config', help='Config file.')
-    parser.add_argument('checkpoint', help='Checkpoint file.')
-    parser.add_argument('save_path', help='Folder to save visualized images.')
-    parser.add_argument(
-        '--images',
-        nargs='+',
-        help='Image files to be predicted with batch mode, '
-        'separated by space, like "image_1.jpg image2.jpg".')
-    parser.add_argument(
-        '--device', default='cuda:0', help='Device used for inference.')
-    parser.add_argument(
-        '--imshow',
-        action='store_true',
-        help='Whether show image with OpenCV.')
-    args = parser.parse_args()
-
-    # build the model from a config file and a checkpoint file
-    model = init_detector(args.config, args.checkpoint, device=args.device)
-    if model.cfg.data.test['type'] == 'ConcatDataset':
-        model.cfg.data.test.pipeline = model.cfg.data.test['datasets'][
-            0].pipeline
-
-    # test multiple images
-    results = model_inference(model, args.images, batch_mode=True)
-    print(f'results: {results}')
-
-    save_path = Path(args.save_path)
-    for img_path, result in zip(args.images, results):
-
-        out_file = save_path / f'result_{Path(img_path).stem}.png'
-
-        # show the results
-        img = model.show_result(
-            img_path, result, out_file=str(out_file), show=False)
-        if args.imshow:
-            mmcv.imshow(img, f'predicted results ({img_path})')
-
-
-if __name__ == '__main__':
-    main()

demo/image_demo.py

@@ -1,49 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from argparse import ArgumentParser
-
-import mmcv
-from mmdet.apis import init_detector
-
-from mmocr.apis.inference import model_inference
-from mmocr.datasets import build_dataset  # noqa: F401
-from mmocr.models import build_detector  # noqa: F401
-
-
-def main():
-    parser = ArgumentParser()
-    parser.add_argument('img', help='Image file.')
-    parser.add_argument('config', help='Config file.')
-    parser.add_argument('checkpoint', help='Checkpoint file.')
-    parser.add_argument('out_file', help='Path to save visualized image.')
-    parser.add_argument(
-        '--device', default='cuda:0', help='Device used for inference.')
-    parser.add_argument(
-        '--imshow',
-        action='store_true',
-        help='Whether show image with OpenCV.')
-    args = parser.parse_args()
-
-    # build the model from a config file and a checkpoint file
-    model = init_detector(args.config, args.checkpoint, device=args.device)
-    if model.cfg.data.test['type'] == 'ConcatDataset':
-        model.cfg.data.test.pipeline = model.cfg.data.test['datasets'][
-            0].pipeline
-
-    # test a single image
-    result = model_inference(model, args.img)
-    print(f'result: {result}')
-
-    # show the results
-    img = model.show_result(
-        args.img, result, out_file=args.out_file, show=False)
-
-    if img is None:
-        img = mmcv.imread(args.img)
-
-    mmcv.imwrite(img, args.out_file)
-    if args.imshow:
-        mmcv.imshow(img, 'predicted results')
-
-
-if __name__ == '__main__':
-    main()

demo/ner_demo.py

@@ -1,8 +1,7 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from argparse import ArgumentParser
 
-from mmdet.apis import init_detector
+from mmocr.apis import init_detector
-
 from mmocr.apis.inference import text_model_inference
 
 from mmocr.datasets import build_dataset  # NOQA

demo/ocr_image_demo.py

@@ -1,167 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from argparse import ArgumentParser
-
-import mmcv
-from mmdet.apis import init_detector
-
-from mmocr.apis.inference import model_inference
-from mmocr.core.visualize import det_recog_show_result
-from mmocr.datasets.pipelines.crop import crop_img
-from mmocr.utils.box_util import stitch_boxes_into_lines
-
-
-def det_and_recog_inference(args, det_model, recog_model):
-    image_path = args.img
-    end2end_res = {'filename': image_path}
-    end2end_res['result'] = []
-
-    image = mmcv.imread(image_path)
-    det_result = model_inference(det_model, image)
-    bboxes = det_result['boundary_result']
-
-    box_imgs = []
-    for bbox in bboxes:
-        box_res = {}
-        box_res['box'] = [round(x) for x in bbox[:-1]]
-        box_res['box_score'] = float(bbox[-1])
-        box = bbox[:8]
-        if len(bbox) > 9:
-            min_x = min(bbox[0:-1:2])
-            min_y = min(bbox[1:-1:2])
-            max_x = max(bbox[0:-1:2])
-            max_y = max(bbox[1:-1:2])
-            box = [min_x, min_y, max_x, min_y, max_x, max_y, min_x, max_y]
-        box_img = crop_img(image, box)
-        if args.batch_mode:
-            box_imgs.append(box_img)
-        else:
-            recog_result = model_inference(recog_model, box_img)
-            text = recog_result['text']
-            text_score = recog_result['score']
-            if isinstance(text_score, list):
-                text_score = sum(text_score) / max(1, len(text))
-            box_res['text'] = text
-            box_res['text_score'] = text_score
-
-        end2end_res['result'].append(box_res)
-
-    if args.batch_mode:
-        batch_size = args.batch_size
-        for chunk_idx in range(len(box_imgs) // batch_size + 1):
-            start_idx = chunk_idx * batch_size
-            end_idx = (chunk_idx + 1) * batch_size
-            chunk_box_imgs = box_imgs[start_idx:end_idx]
-            if len(chunk_box_imgs) == 0:
-                continue
-            recog_results = model_inference(
-                recog_model, chunk_box_imgs, batch_mode=True)
-            for i, recog_result in enumerate(recog_results):
-                text = recog_result['text']
-                text_score = recog_result['score']
-                if isinstance(text_score, list):
-                    text_score = sum(text_score) / max(1, len(text))
-                end2end_res['result'][start_idx + i]['text'] = text
-                end2end_res['result'][start_idx + i]['text_score'] = text_score
-
-    return end2end_res
-
-
-def main():
-    parser = ArgumentParser()
-    parser.add_argument('img', type=str, help='Input Image file.')
-    parser.add_argument(
-        'out_file', type=str, help='Output file name of the visualized image.')
-    parser.add_argument(
-        '--det-config',
-        type=str,
-        default='./configs/textdet/psenet/'
-        'psenet_r50_fpnf_600e_icdar2015.py',
-        help='Text detection config file.')
-    parser.add_argument(
-        '--det-ckpt',
-        type=str,
-        default='https://download.openmmlab.com/'
-        'mmocr/textdet/psenet/'
-        'psenet_r50_fpnf_600e_icdar2015_pretrain-eefd8fe6.pth',
-        help='Text detection checkpint file (local or url).')
-    parser.add_argument(
-        '--recog-config',
-        type=str,
-        default='./configs/textrecog/sar/'
-        'sar_r31_parallel_decoder_academic.py',
-        help='Text recognition config file.')
-    parser.add_argument(
-        '--recog-ckpt',
-        type=str,
-        default='https://download.openmmlab.com/'
-        'mmocr/textrecog/sar/'
-        'sar_r31_parallel_decoder_academic-dba3a4a3.pth',
-        help='Text recognition checkpint file (local or url).')
-    parser.add_argument(
-        '--batch-mode',
-        action='store_true',
-        help='Whether use batch mode for text recognition.')
-    parser.add_argument(
-        '--batch-size',
-        type=int,
-        default=4,
-        help='Batch size for text recognition inference '
-        'if batch_mode is True above.')
-    parser.add_argument(
-        '--device', default='cuda:0', help='Device used for inference.')
-    parser.add_argument(
-        '--imshow',
-        action='store_true',
-        help='Whether show image with OpenCV.')
-    parser.add_argument(
-        '--ocr-in-lines',
-        action='store_true',
-        help='Whether group ocr results in lines.')
-    args = parser.parse_args()
-
-    if args.device == 'cpu':
-        args.device = None
-    # build detect model
-    detect_model = init_detector(
-        args.det_config, args.det_ckpt, device=args.device)
-    if hasattr(detect_model, 'module'):
-        detect_model = detect_model.module
-    if detect_model.cfg.data.test['type'] == 'ConcatDataset':
-        detect_model.cfg.data.test.pipeline = \
-            detect_model.cfg.data.test['datasets'][0].pipeline
-
-    # build recog model
-    recog_model = init_detector(
-        args.recog_config, args.recog_ckpt, device=args.device)
-    if hasattr(recog_model, 'module'):
-        recog_model = recog_model.module
-    if recog_model.cfg.data.test['type'] == 'ConcatDataset':
-        recog_model.cfg.data.test.pipeline = \
-            recog_model.cfg.data.test['datasets'][0].pipeline
-
-    det_recog_result = det_and_recog_inference(args, detect_model, recog_model)
-    print(f'result: {det_recog_result}')
-    mmcv.dump(
-        det_recog_result,
-        args.out_file + '.json',
-        ensure_ascii=False,
-        indent=4)
-
-    if args.ocr_in_lines:
-        res = det_recog_result['result']
-        res = stitch_boxes_into_lines(res, 10, 0.5)
-        det_recog_result['result'] = res
-        mmcv.dump(
-            det_recog_result,
-            args.out_file + '.line.json',
-            ensure_ascii=False,
-            indent=4)
-
-    img = det_recog_show_result(args.img, det_recog_result)
-    mmcv.imwrite(img, args.out_file)
-    if args.imshow:
-        mmcv.imshow(img, 'predicted results')
-
-
-if __name__ == '__main__':
-    main()

demo/webcam_demo.py

@@ -3,9 +3,8 @@ import argparse
 
 import cv2
 import torch
-from mmdet.apis import init_detector
 
-from mmocr.apis import model_inference
+from mmocr.apis import init_detector, model_inference
 from mmocr.datasets import build_dataset  # noqa: F401
 from mmocr.models import build_detector  # noqa: F401