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import gradio as gr
import torch
import yolov7
# Images
torch.hub.download_url_to_file('https://github.com/Michael-OvO/Burn-Detection-Classification/blob/main/inference/images/1st_degree_1.jpg', '1st_degree_1.jpg')
torch.hub.download_url_to_file('https://github.com/Michael-OvO/Burn-Detection-Classification/blob/main/inference/images/3rd_degree_1.jpg', '3rd_degree_1.jpg')
def yolov7_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,
):
"""
YOLOv7 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 = torch.hub.load('kadirnar/yolov7-v0.1', 'custom', path='skin_burn.pt', source='local', hf_model=True, device="cpu")
model.conf = conf_threshold
model.iou = iou_threshold
results = model([image], size=image_size)
return results.render()[0]
inputs = [
gr.inputs.Image(type="pil", label="Input Image"),
gr.inputs.Dropdown(
choices=[
"skin_burn",
"Other"
],
default="skin_burn",
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 = "Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors"
demo_app = gr.Interface(
fn=yolov7_inference,
inputs=inputs,
outputs=outputs,
title=title,
theme='huggingface',
)
demo_app.launch(debug=True, enable_queue=True)
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