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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 yoloxdetect | |
| #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 = yoloxdetect.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, server_name="192.168.0.153", server_port=8080, enable_queue=True) | |
| #demo_app.launch(debug=True, server_port=8083, enable_queue=True) |