60 lines
2.2 KiB
Python
60 lines
2.2 KiB
Python
import gradio as gr
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from transformers import AutoImageProcessor, TableTransformerForObjectDetection
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import torch
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from PIL import Image
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def inference(img):
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pretrained_model_path = "table-transformer-detection"
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image = img.convert("RGB")
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image_processor = AutoImageProcessor.from_pretrained(pretrained_model_path)
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model = TableTransformerForObjectDetection.from_pretrained(pretrained_model_path)
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inputs = image_processor(images=image, return_tensors="pt")
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outputs = model(**inputs)
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# convert outputs (bounding boxes and class logits) to COCO API
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target_sizes = torch.tensor([image.size[::-1]])
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results = image_processor.post_process_object_detection(outputs, threshold=0.9, target_sizes=target_sizes)[
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0
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]
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for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
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box = [round(i, 2) for i in box.tolist()]
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return (
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f"Detected {model.config.id2label[label.item()]} with confidence "
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f"{round(score.item(), 3)} at location {box}"
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)
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#title = "#object detection:table-transformer-detection"
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#description = "Gradio Demo for table-transformer-detection. To use it, simply upload your image, or click one of the examples to load them."
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article = "<p style='text-align: center'><a href='https://github.com/bryandlee/animegan2-pytorch' target='_blank'>Github Repo Pytorch</a></p> <center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_animegan' alt='visitor badge'></center></p>"
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examples=[['example_pdf.png']]
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with gr.Blocks() as demo:
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gr.Markdown(
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"""
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# object detection:table-transformer-detection
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Gradio Demo for table-transformer-detection. To use it, simply upload your image, or click one of the examples to load them.
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""")
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with gr.Row():
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image_input = gr.Image(type="pil")
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image_output = gr.Textbox()
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image_button = gr.Button("上传")
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image_button.click(inference, inputs=image_input, outputs=image_output)
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gr.Examples(examples,inputs=image_input)
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# demo = gr.Interface(
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# fn=inference,
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# inputs=[gr.inputs.Image(type="pil")],
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# outputs=gr.outputs.Textbox(),
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# title=title,
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# description=description,
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# article=article,
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# examples=examples)
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demo.launch() |