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import gradio as gr
import torch
from sahi.prediction import ObjectPrediction
from sahi.utils.cv import visualize_object_predictions, read_image
from ultralyticsplus import YOLO, render_result
# Images
torch.hub.download_url_to_file('https://huggingface.co/spaces/foduucom/table-extraction-yolov8/resolve/main/test/table1.jpg', 'document1.jpg')
torch.hub.download_url_to_file('https://huggingface.co/spaces/foduucom/table-extraction-yolov8/resolve/main/test/table2.jpg', 'document2.jpg')
torch.hub.download_url_to_file('https://huggingface.co/spaces/foduucom/table-extraction-yolov8/resolve/main/test/table3.jpg', 'document3.jpg')
def yolov8_inference(
image: gr.inputs.Image = None,
model_path: gr.inputs.Dropdown = None,
image_size: gr.inputs.Slider = 640,
conf_threshold: gr.inputs.Slider = 0.25,
iou_threshold: gr.inputs.Slider = 0.45,
):
"""
YOLOv8 inference function
Args:
image: Input image
model_path: Path to the model
image_size: Image size
conf_threshold: Confidence threshold
iou_threshold: IOU threshold
Returns:
Rendered image
"""
model = YOLO(model_path)
model.overrides['conf'] = conf_threshold
model.overrides['iou']= iou_threshold
model.overrides['agnostic_nms'] = False # NMS class-agnostic
model.overrides['max_det'] = 1000
image = read_image(image)
results = model.predict(image)
render = render_result(model=model, image=image, result=results[0])
return render
inputs = [
gr.inputs.Image(type="filepath", label="Input Image"),
gr.inputs.Dropdown(["foduucom/table-detection-and-extraction"],
default="foduucom/table-detection-and-extraction", label="Model"),
gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
]
outputs = gr.outputs.Image(type="filepath", label="Output Image")
title = "YoloTableExtract: Efficient Table Detection"
description = "🔍 YoloTableExtract is a powerful space that utilizes YOLOv8s for accurate table detection and extraction. Whether tables are bordered or borderless, this space can effectively identify and extract them from images. For further assistance and support related to documentation or data-related issues, feel free to contact [email protected]. If you find this space helpful, please show your appreciation by liking it. ❤️👍🏼"
examples = [['document1.jpg', 'foduucom/table-detection-and-extraction', 640, 0.25, 0.45], ['document2.jpg', 'foduucom/table-detection-and-extraction', 640, 0.25, 0.45], ['document3.jpg', 'foduucom/table-detection-and-extraction', 1280, 0.25, 0.45]]
demo_app = gr.Interface(
fn=yolov8_inference,
inputs=inputs,
outputs=outputs,
title=title,
description=description,
examples=examples,
cache_examples=True,
theme='huggingface',
)
demo_app.launch(debug=True, enable_queue=True) |