sneed

demo copy.py

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+import glob
+import gradio as gr
+from demo import get_model, preprocess, postprocess, load
+from utils import Config, Logger, CharsetMapper
+
+config = Config('configs/train_iternet.yaml')
+config.model_vision_checkpoint = None
+model = get_model(config)
+model = load(model, 'workdir/train-iternet/best-train-iternet.pth')
+charset = CharsetMapper(filename=config.dataset_charset_path, max_length=config.dataset_max_length + 1)
+
+def process_image(image):
+  img = image.convert('RGB')
+  img = preprocess(img, config.dataset_image_width, config.dataset_image_height)
+  res = model(img)
+  return postprocess(res, charset, 'alignment')[0][0]
+
+title = "Interactive demo: ABINet"
+description = "Demo for ABINet, ABINet uses a vision model and an explicit language model to recognize text in the wild, which are trained in end-to-end way. The language model (BCN) achieves bidirectional language representation in simulating cloze test, additionally utilizing iterative correction strategy. To use it, simply upload a (single-text line) image or use one of the example images below and click 'submit'. Results will show up in a few seconds."
+article = "<p style='text-align: center'><a href='https://arxiv.org/pdf/2103.06495.pdf'>Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition</a> | <a href='https://github.com/FangShancheng/ABINet'>Github Repo</a></p>"
+
+iface = gr.Interface(fn=process_image, 
+                     inputs=gr.inputs.Image(type="pil"), 
+                     outputs=gr.outputs.Textbox(),
+                     title=title,
+                     description=description,
+                     article=article,
+                     examples=glob.glob('figs/test/*.png'))
+
+iface.launch(debug=True, share=True,enable_queue=True)