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import gradio as gr
import os
os.system("pip -qq install yoloxdetect==0.0.7")
import torch
from yoloxdetect import YoloxDetector
# Images
torch.hub.download_url_to_file('https://tochkanews.ru/wp-content/uploads/2020/09/0.jpg', '1.jpg')
torch.hub.download_url_to_file('https://s.rdrom.ru/1/pubs/4/35893/1906770.jpg', '2.jpg')
torch.hub.download_url_to_file('https://static.mk.ru/upload/entities/2022/04/17/07/articles/detailPicture/5b/39/28/b6/ffb1aa636dd62c30e6ff670f84474f75.jpg', '3.jpg')
def yolox_inference(
image_path: gr.inputs.Image = None,
model_path: gr.inputs.Dropdown = 'kadirnar/yolox_s-v0.1.1',
config_path: gr.inputs.Textbox = 'configs.yolox_s',
image_size: gr.inputs.Slider = 640
):
"""
YOLOX inference function
Args:
image: Input image
model_path: Path to the model
config_path: Path to the config file
image_size: Image size
Returns:
Rendered image
"""
model = YoloxDetector(model_path, config_path=config_path, device="cpu", hf_model=True)
pred = model.predict(image_path=image_path, image_size=image_size)
return pred
inputs = [
gr.inputs.Image(type="filepath", label="Input Image"),
gr.inputs.Dropdown(
label="Model Path",
choices=[
"kadirnar/yolox_s-v0.1.1",
"kadirnar/yolox_m-v0.1.1",
"kadirnar/yolox_tiny-v0.1.1",
],
default="kadirnar/yolox_s-v0.1.1",
),
gr.inputs.Dropdown(
label="Config Path",
choices=[
"configs.yolox_s",
"configs.yolox_m",
"configs.yolox_tiny",
],
default="configs.yolox_s",
),
gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
]
outputs = gr.outputs.Image(type="filepath", label="Output Image")
title = "YOLOX is a high-performance anchor-free YOLO."
examples = [
["1.jpg", "kadirnar/yolox_m-v0.1.1", "configs.yolox_m", 640],
["2.jpg", "kadirnar/yolox_s-v0.1.1", "configs.yolox_s", 640],
["3.jpg", "kadirnar/yolox_tiny-v0.1.1", "configs.yolox_tiny", 640],
]
demo_app = gr.Interface(
fn=yolox_inference,
inputs=inputs,
outputs=outputs,
title=title,
examples=examples,
cache_examples=True,
theme='huggingface',
)
demo_app.launch(debug=True, enable_queue=True) |