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# Scene Text Recognition Model Hub
# Copyright 2022 Darwin Bautista
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import glob
import torch
from torchvision import transforms as T
import gradio as gr
class App:
title = 'Scene Text Recognition with<br/>Permuted Autoregressive Sequence Models'
models = ['parseq', 'parseq_tiny', 'abinet', 'crnn', 'trba', 'vitstr']
def __init__(self):
self._model_cache = {}
self._preprocess = T.Compose([
T.Resize((32, 128), T.InterpolationMode.BICUBIC),
T.ToTensor(),
T.Normalize(0.5, 0.5)
])
def _get_model(self, name):
if name in self._model_cache:
return self._model_cache[name]
model = torch.hub.load('baudm/parseq', name, pretrained=True).eval()
model.freeze()
self._model_cache[name] = model
return model
def __call__(self, model_name, image):
if image is None:
return '', []
model = self._get_model(model_name)
image = self._preprocess(image.convert('RGB')).unsqueeze(0)
# Greedy decoding
pred = model(image).softmax(-1)
label, _ = model.tokenizer.decode(pred)
raw_label, raw_confidence = model.tokenizer.decode(pred, raw=True)
# Format confidence values
max_len = 25 if model_name == 'crnn' else len(label[0]) + 1
conf = list(map('{:0.1f}'.format, raw_confidence[0][:max_len].tolist()))
return label[0], [raw_label[0][:max_len], conf]
def main():
app = App()
with gr.Blocks(analytics_enabled=False, title=app.title.replace('<br/>', ' ')) as demo:
gr.Markdown(f"""
<div align="center">
# {app.title}
[](https://github.com/baudm/parseq)
</div>
To use this interactive demo for PARSeq and reproduced models:
1. Select which model you want to use.
2. Upload your own image, choose from the examples below, or draw on the canvas.
3. Click **Read Image** or **Read Drawing**.
*NOTE*: None of these models were trained on handwritten text datasets.
""")
model_name = gr.Radio(app.models, value=app.models[0], label='The STR model to use')
with gr.Row():
with gr.Column():
image_upload = gr.Image(type='pil', source='upload', label='Image')
read_upload = gr.Button('Read Image')
with gr.Column():
image_canvas = gr.Image(type='pil', source='canvas', label='Drawing')
read_canvas = gr.Button('Read Drawing')
output = gr.Textbox(max_lines=1, label='Model output')
raw_output = gr.Dataframe(row_count=2, col_count=0, label='Raw output with confidence values (interval: [0, 1], [B]: BOS or BLANK token, [E]: EOS token)')
gr.Examples(glob.glob('demo_images/*.*'), inputs=image_upload)
read_upload.click(app, inputs=[model_name, image_upload], outputs=[output, raw_output])
read_canvas.click(app, inputs=[model_name, image_canvas], outputs=[output, raw_output])
demo.launch()
if __name__ == '__main__':
main()
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