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import gradio as gr
import os
#os.system("pip -qq install yoloxdetect==0.0.7")
os.system("pip -qq install yoloxdetect")
import torch
import json
import yoloxdetect2.helpers as yoloxdetectow
#from yoloxdetect import YoloxDetector
# Images
torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg', 'zidane.jpg')
torch.hub.download_url_to_file('https://raw.githubusercontent.com/obss/sahi/main/tests/data/small-vehicles1.jpeg', 'small-vehicles1.jpeg')
torch.hub.download_url_to_file('https://raw.githubusercontent.com/Megvii-BaseDetection/YOLOX/main/assets/dog.jpg', 'dog.jpg')
model = yoloxdetectow.YoloxDetector2('kadirnar/yolox_s-v0.1.1', 'configs.yolox_s', device="cpu", hf_model=True)
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)
pred2 = []
if model :
model.torchyolo = True
pred2 = model.predict(image_path=image_path, image_size=image_size)
#text = "Ola"
#print (vars(model))
#print (pred2[0])
#print (pred2[1])
#print (pred2[2])
tensor = {
"tensorflow": [
]
}
if pred2 is not None:
#print (pred2[3])
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)
#print(tensor)
text = json.dumps(tensor)
return text
inputs = [
gr.inputs.Image(type="filepath", label="Input Image"),
gr.inputs.Textbox(lines=1, label="Model Path", default="kadirnar/yolox_s-v0.1.1"),
gr.inputs.Textbox(lines=1, label="Config Path", 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 = "SIMULADOR PARA RECONHECIMENTO DE IMAGEM"
examples = [
["small-vehicles1.jpeg", "kadirnar/yolox_m-v0.1.1", "configs.yolox_m", 640],
["zidane.jpg", "kadirnar/yolox_s-v0.1.1", "configs.yolox_s", 640],
["dog.jpg", "kadirnar/yolox_tiny-v0.1.1", "configs.yolox_tiny", 640],
]
demo_app = gr.Interface(
fn=yolox_inference,
inputs=inputs,
outputs=["text"],
title=title,
examples=examples,
cache_examples=True,
live=True,
theme='huggingface',
)
demo_app.launch(debug=True, enable_queue=True) |