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import gradio as gr
from ultralytics import YOLO
import torch
model_id = "mosesb/best-comic-panel-detection"
model = YOLO("best.pt")
def detect_panels(pil_image, conf_threshold, iou_threshold):
"""
Takes a PIL image and thresholds, runs YOLOv12 object detection,
and returns the annotated image with bounding boxes.
"""
# Run inference on the image with the specified thresholds
results = model.predict(pil_image, conf=conf_threshold, iou=iou_threshold, verbose=False)
annotated_image = results[0].plot()
# Gradio's gr.Image component expects an RGB image. The .plot() method
# returns a BGR image, so we convert it.
annotated_image_rgb = annotated_image[..., ::-1]
return annotated_image_rgb
# --- Gradio Interface ---
title = "YOLOv12 Comic Panel Detection"
description = """
This demo showcases a **YOLOv12 object detection model** that has been fine-tuned to detect panels in comic book pages.
Upload an image of a comic page, and the model will draw bounding boxes around each detected panel.
This can be a useful first step for downstream tasks like Optical Character Recognition (OCR) or character analysis within comics.
"""
article = f"""
<div style='text-align: center;'>
<p style='text-align: center'>Model loaded from <a href='https://huggingface.co/{model_id}' target='_blank'>{model_id}</a></p>
<p style='text-align: center'>For more details on the training process, check out the project repository: <a href='https://github.com/mosesab/YOLOV12-Comic-Panel-Detection/blob/main/comic-boundary-detection.ipynb' target='_blank'>Comic Boundary Detection</a></p>
<p>If you like this demo, consider leaving a star on the <a href='https://github.com/mosesab/YOLOV12-Comic-Panel-Detection' target='_blank'>Github repo</a> or a like on the <a href='https://huggingface.co/{model_id}' target='_blank'>Hugging Face model</a>. It helps me know people are interested and motivates further development.</p>
</div>
"""
# Define the input components for the Gradio interface
inputs = [
gr.Image(type="pil", label="Upload Comic Page Image"),
gr.Slider(
minimum=0.0,
maximum=1.0,
value=0.25, # The default confidence threshold in ultralytics
step=0.05,
label="Confidence Threshold",
info="Filters detections. Only boxes with confidence above this value will be shown."
),
gr.Slider(
minimum=0.0,
maximum=1.0,
value=0.7, # The default IoU threshold in ultralytics
step=0.05,
label="IoU Threshold",
info="Controls merging of overlapping boxes. Higher values allow more overlap."
)
]
examples = [
["aura_farmer_1.jpg", 0.25, 0.7],
["aura_farmer_2.jpg", 0.25, 0.7],
["aura_farmer_3.jpg", 0.25, 0.7],
["aura_farmer_4.jpg", 0.25, 0.7],
]
gr.Interface(
fn=detect_panels,
inputs=inputs,
outputs=gr.Image(type="pil", label="Detected Panels"),
title=title,
description=description,
article=article,
examples=examples,
allow_flagging="auto"
).launch() |