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import gradio as gr
import os
import torch
import json
import yoloxdetect2.helpers as yoloxdetect
#model = yoloxdetect.YoloxDetector2('./dataset/yolox_s.pth', 'configs.yolox_s', device="cpu", hf_model=True)
model = yoloxdetect.YoloxDetector2('kadirnar/yolox_s-v0.1.1', 'configs.yolox_s', device="cpu", hf_model=True)
image_size = 640
def yolox_inference(
image_path: gr.inputs.Image = None,
):
"""
YOLOX inference function
Args:
image: Input image
Returns:
Rendered image
"""
pred2 = []
if image_path is not None :
print(image_path)
model.torchyolo = True
pred2 = model.predict(image_path=image_path, image_size=image_size)
tensor = {
"tensorflow": [
]
}
if pred2 is not None:
for i, element in enumerate(pred2[0]):
object = {}
itemclass = round(pred2[2][i].item())
object["classe"] = itemclass
object["nome"] = pred2[3][itemclass]
object["score"] = pred2[1][i].item()
object["x"] = element[0].item()
object["y"] = element[1].item()
object["w"] = element[2].item()
object["h"] = element[3].item()
tensor["tensorflow"].append(object)
text = json.dumps(tensor)
return text
inputs = [
gr.inputs.Image(type="pil", label="Input Image"),
]
outputs = gr.outputs.Image(type="filepath", label="Output Image")
title = "SIMULADOR PARA RECONHECIMENTO DE IMAGEM"
examples = [
["small-vehicles1.jpeg"],
["zidane.jpg"],
["dog.jpg"],
]
demo_app = gr.Interface(
fn=yolox_inference,
inputs=inputs,
outputs=["text"],
title=title,
examples=examples,
cache_examples=True,
live=True,
)
demo_app.launch(debug=True, server_name="192.168.0.153", server_port=8080, enable_queue=True)
#demo_app.launch(debug=True, server_port=8083, enable_queue=True) |