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import gradio as gr
import torch
from yolov6 import YOLOV6
# Images
torch.hub.download_url_to_file('https://raw.githubusercontent.com/kadirnar/dethub/main/data/images/highway.jpg', 'highway.jpg')
torch.hub.download_url_to_file('https://user-images.githubusercontent.com/34196005/142742872-1fefcc4d-d7e6-4c43-bbb7-6b5982f7e4ba.jpg', 'highway1.jpg')
def yolov6_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,
):
"""
YOLOv6 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 = YOLOV6(model_path, device="cpu", hf_model=True)
model.conf_thres = conf_threshold
model.iou_thresh = iou_threshold
model.save_img = True
model.font_path = "Arial.ttf"
pred = model.predict(source=image, img_size=image_size, yaml="coco.yaml")
return pred
inputs = [
gr.inputs.Image(type="filepath", label="Input Image"),
gr.inputs.Dropdown(
label="Model",
choices=[
"kadirnar/yolov6n-v3.0",
"kadirnar/yolov6s-v3.0",
"kadirnar/yolov6m-v3.0",
"kadirnar/yolov6l-v3.0",
"kadirnar/yolov6s6-v3.0",
"kadirnar/yolov6m6-v3.0",
"kadirnar/yolov6l6-v3.0",
],
default="kadirnar/yolov6s-v3.0",
),
gr.inputs.Slider(minimum=320, maximum=1280, default=1280, 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 = "YOLOv6: a single-stage object detection framework dedicated to industrial applications."
examples = [['highway1.jpg', 'kadirnar/yolov6m6-v3.0', 1280, 0.25, 0.45],['highway.jpg', 'kadirnar/yolov6s6-v3.0', 1280, 0.25, 0.45]]
demo_app = gr.Interface(
fn=yolov6_inference,
inputs=inputs,
outputs=outputs,
title=title,
examples=examples,
cache_examples=True,
theme='huggingface',
)
demo_app.launch(debug=True, enable_queue=True)
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