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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', 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", | |
"kadirnar/yolov7-v0.1", | |
], | |
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) | |