46 lines
1.7 KiB
Python
46 lines
1.7 KiB
Python
|
import gradio as gr
|
||
|
|
from transformers import AutoImageProcessor, TableTransformerForObjectDetection
|
||
|
|
import torch
|
||
|
|
from PIL import Image
|
||
|
|
|
||
|
|
def inference(img):
|
||
|
|
pretrained_model_path = "table-transformer-detection"
|
||
|
|
|
||
|
|
image = img.convert("RGB")
|
||
|
|
|
||
|
|
image_processor = AutoImageProcessor.from_pretrained(pretrained_model_path)
|
||
|
|
model = TableTransformerForObjectDetection.from_pretrained(pretrained_model_path)
|
||
|
|
|
||
|
|
inputs = image_processor(images=image, return_tensors="pt")
|
||
|
|
outputs = model(**inputs)
|
||
|
|
|
||
|
|
# convert outputs (bounding boxes and class logits) to COCO API
|
||
|
|
target_sizes = torch.tensor([image.size[::-1]])
|
||
|
|
results = image_processor.post_process_object_detection(outputs, threshold=0.9, target_sizes=target_sizes)[
|
||
|
|
0
|
||
|
|
]
|
||
|
|
|
||
|
|
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
|
||
|
|
box = [round(i, 2) for i in box.tolist()]
|
||
|
|
return (
|
||
|
|
f"Detected {model.config.id2label[label.item()]} with confidence "
|
||
|
|
f"{round(score.item(), 3)} at location {box}"
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
title = "object detection:table-transformer-detection"
|
||
|
|
description = "Gradio Demo for table-transformer-detection. To use it, simply upload your image, or click one of the examples to load them."
|
||
|
|
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>"
|
||
|
|
examples=[['example_pdf.png']]
|
||
|
|
|
||
|
|
|
||
|
|
demo = gr.Interface(
|
||
|
|
fn=inference,
|
||
|
|
inputs=[gr.inputs.Image(type="pil")],
|
||
|
|
outputs=gr.outputs.Textbox(),
|
||
|
|
title=title,
|
||
|
|
description=description,
|
||
|
|
article=article,
|
||
|
|
examples=examples)
|
||
|
|
|
||
|
|
demo.launch()
|