Aalaa/Visual_Pollution_detection

1import gradio as gr2import yolov73import subprocess4import tempfile5import time6from pathlib import Path7import uuid8import cv29import gradio as gr10 11 12    13def image_fn(14    image: gr.inputs.Image = None,15    model_path: gr.inputs.Dropdown = None,16    image_size: gr.inputs.Slider = 640,17    conf_threshold: gr.inputs.Slider = 0.25,18    iou_threshold: gr.inputs.Slider = 0.45,19):20    """21    YOLOv7 inference function22    Args:23        image: Input image24        model_path: Path to the model25        image_size: Image size26        conf_threshold: Confidence threshold27        iou_threshold: IOU threshold28    Returns:29        Rendered image30    """31 32    model = yolov7.load(model_path, device="cpu", hf_model=True, trace=False)33    model.conf = conf_threshold34    model.iou = iou_threshold35    results = model([image], size=image_size)36    return results.render()[0]37  38  39        40 41image_interface = gr.Interface(42    fn=image_fn,43    inputs=[44    gr.inputs.Image(type="pil", label="Input Image"),45    gr.inputs.Dropdown(46        choices=[47            "Aalaa/Yolov7_Visual_Pollution_Detection",48        ],49        default="Aalaa/Yolov7_Visual_Pollution_Detection",50        label="Model",51    )52    #gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size")53    #gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),54    #gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold")55],56    outputs=gr.outputs.Image(type="filepath", label="Output Image"),57    58    examples=[['image1.jpg', 'Aalaa/Yolov7_Visual_Pollution_Detection', 640, 0.25, 0.45]],59    cache_examples=True,60    theme='huggingface',61)62 63 64 65if __name__ == "__main__":66    gr.TabbedInterface(67        [image_interface],68        ["Run on Images"],69    ).launch()