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# Install dependencies | |
import os | |
os.system('python -m mim install mmocr') | |
os.system('pip install gradio_client==0.2.7') | |
os.system('python -m mim install "mmcv==2.0.0rc4"') | |
os.system('python -m mim install mmengine==0.7.1') | |
os.system('python -m mim install "mmdet==3.0.0rc5"') | |
os.system('pip install -v -e .') | |
import cv2 | |
import argparse | |
import gradio as gr | |
import numpy as np | |
# MMOCR | |
from mmocr.apis.inferencers import MMOCRInferencer | |
def arg_parse(): | |
parser = argparse.ArgumentParser(description='MMOCR demo for gradio app') | |
parser.add_argument( | |
'--rec_config', | |
type=str, | |
default='configs/textrecog/maerec/maerec_b_union14m.py', | |
help='The recognition config file.') | |
parser.add_argument( | |
'--rec_weight', | |
type=str, | |
default= | |
'maerec_b_union14m.pth', | |
help='The recognition weight file.') | |
parser.add_argument( | |
'--det_config', | |
type=str, | |
default= | |
'configs/textdet/dbnetpp/dbnetpp_resnet50-oclip_fpnc_1200e_icdar2015.py', # noqa, | |
help='The detection config file.') | |
parser.add_argument( | |
'--det_weight', | |
type=str, | |
default='dbnetpp.pth', | |
help='The detection weight file.') | |
parser.add_argument( | |
'--device', | |
type=str, | |
default='cpu', | |
help='The device used for inference.') | |
args = parser.parse_args() | |
return args | |
def run_mmocr(img: np.ndarray, use_detector: bool = True): | |
"""Run MMOCR and SAM | |
Args: | |
img (np.ndarray): Input image | |
use_detector (bool, optional): Whether to use detector. Defaults to | |
True. | |
""" | |
if use_detector: | |
mode = 'det_rec' | |
else: | |
mode = 'rec' | |
# Build MMOCR | |
mmocr_inferencer.mode = mode | |
result = mmocr_inferencer(img, return_vis=True) | |
visualization = result['visualization'][0] | |
result = result['predictions'][0] | |
if mode == 'det_rec': | |
rec_texts = result['rec_texts'] | |
det_polygons = result['det_polygons'] | |
det_results = [] | |
for rec_text, det_polygon in zip(rec_texts, det_polygons): | |
det_polygon = np.array(det_polygon).astype(np.int32).tolist() | |
det_results.append(f'{rec_text}: {det_polygon}') | |
out_results = '\n'.join(det_results) | |
visualization = cv2.cvtColor( | |
np.array(visualization), cv2.COLOR_RGB2BGR) | |
else: | |
rec_text = result['rec_texts'][0] | |
rec_score = result['rec_scores'][0] | |
out_results = f'pred: {rec_text} \n score: {rec_score:.2f}' | |
visualization = None | |
return visualization, out_results | |
if __name__ == '__main__': | |
args = arg_parse() | |
mmocr_inferencer = MMOCRInferencer( | |
args.det_config, | |
args.det_weight, | |
args.rec_config, | |
args.rec_weight, | |
device=args.device) | |
with gr.Blocks() as demo: | |
with gr.Row(): | |
with gr.Column(scale=1): | |
gr.HTML(""" | |
<div style="text-align: center; max-width: 1200px; margin: 20px auto;"> | |
<h1 style="font-weight: 900; font-size: 3rem; margin: 0rem"> | |
MAERec: A MAE-pretrained Scene Text Recognizer | |
</h1> | |
<h3 style="font-weight: 450; font-size: 1rem; margin: 0rem"> | |
[<a href="https://github.com/Mountchicken/Union14M" style="color:green;">Code</a>] | |
</h3> | |
<h2 style="text-align: left; font-weight: 600; font-size: 1rem; margin-top: 0.5rem; margin-bottom: 0.5rem"> | |
MAERec is a scene text recognition model composed of a ViT backbone and a Transformer decoder in auto-regressive | |
style. It shows an outstanding performance in scene text recognition, especially when pre-trained on the | |
Union14M-U through MAE. | |
</h2> | |
<h2 style="text-align: left; font-weight: 600; font-size: 1rem; margin-top: 0.5rem; margin-bottom: 0.5rem"> | |
In this demo, we combine MAERec with DBNet++ to build an | |
end-to-end scene text recognition model. | |
</h2> | |
</div> | |
""") | |
gr.Image('github/maerec.png') | |
with gr.Column(scale=1): | |
input_image = gr.Image(label='Input Image') | |
output_image = gr.Image(label='Output Image') | |
use_detector = gr.Checkbox( | |
label= | |
'Use Scene Text Detector or Not (Disabled for Recognition Only)', | |
default=True) | |
det_results = gr.Textbox(label='Detection Results') | |
mmocr = gr.Button('Run MMOCR') | |
gr.Markdown("## Image Examples") | |
with gr.Row(): | |
gr.Examples( | |
examples=[ | |
'github/author.jpg','github/gradio1.jpeg','github/add1.jpg','github/add2.jpg','github/add3.jpg', | |
'github/Art_Curve_178.jpg','github/add4.jpg','github/add5.jpg','github/add6.jpg', | |
'github/add7.jpg','github/add8.jpg','github/add9.jpg','github/add10.jpg','github/add11.jpg', | |
'github/add12.jpg', | |
'github/cute_168.jpg', 'github/hiercurve_2229.jpg', | |
'github/ic15_52.jpg', 'github/ic15_698.jpg', | |
'github/Art_Curve_352.jpg' | |
], | |
inputs=input_image, | |
) | |
mmocr.click( | |
fn=run_mmocr, | |
inputs=[input_image, use_detector], | |
outputs=[output_image, det_results]) | |
demo.launch(debug=True) | |