app.py

import gradio as gr
from detect_strongsort import run
import os
import threading

should_continue = True


def yolov9_inference(model_id, image_size, conf_threshold, iou_threshold, img_path=None, vid_path=None):
    global should_continue
    img_extensions = ['.jpg', '.jpeg', '.png', '.gif']  # Add more image extensions if needed
    vid_extensions = ['.mp4', '.avi', '.mov', '.mkv']  # Add more video extensions if needed

    input_path = None
    if img_path is not None:
        _, img_extension = os.path.splitext(img_path)
        if img_extension.lower() in img_extensions:
            input_path = img_path
    elif vid_path is not None:
        _, vid_extension = os.path.splitext(vid_path)
        if vid_extension.lower() in vid_extensions:
            input_path = vid_path

    output_path = run(yolo_weights=model_id, imgsz=(image_size,image_size), conf_thres=conf_threshold, iou_thres=iou_threshold, source=input_path, device='cpu', strong_sort_weights = "osnet_x0_25_msmt17.pt", hide_conf= True)
    # Assuming output_path is the path to the output file
    _, output_extension = os.path.splitext(output_path)
    if output_extension.lower() in img_extensions:
        output_image = output_path  # Load the image file here
        output_video = None
    elif output_extension.lower() in vid_extensions:
        output_image = None
        output_video = output_path  # Load the video file here

    return output_image, output_video, output_path

def inference(model_id, image_size, conf_threshold, iou_threshold, img_path=None, vid_path=None):
    global should_continue
    should_continue = True
    output_image, output_video, output_path = yolov9_inference(model_id, image_size, conf_threshold, iou_threshold, img_path, vid_path)
    return output_image, output_video, output_path


def stop_processing():
    global should_continue
    should_continue = False 
    return "Stop..."

def app():
    with gr.Blocks():
        with gr.Row():
            with gr.Column():
                gr.HTML("<h2>Input Parameters</h2>")
                img_path = gr.File(label="Image")
                vid_path = gr.File(label="Video")
                model_id = gr.Dropdown(
                    label="Model",
                    choices=[
                        "last_best_model.pt",
                        "best_model-converted.pt"
                    ],
                    value="./last_best_model.pt"
                    
                )
                image_size = gr.Slider(
                    label="Image Size",
                    minimum=320,
                    maximum=1280,
                    step=32,
                    value=640,
                )
                conf_threshold = gr.Slider(
                    label="Confidence Threshold",
                    minimum=0.1,
                    maximum=1.0,
                    step=0.1,
                    value=0.4,
                )
                iou_threshold = gr.Slider(
                    label="IoU Threshold",
                    minimum=0.1,
                    maximum=1.0,
                    step=0.1,
                    value=0.5,
                )
                yolov9_infer = gr.Button(value="Inference")
                stop_button = gr.Button(value="Stop")
            with gr.Column():
                gr.HTML("<h2>Output</h2>")
                output_image = gr.Image(type="numpy",label="Output Image")
                output_video = gr.Video(label="Output Video")
                output_path = gr.Textbox(label="Output path")

        yolov9_infer.click(
            fn=inference,
            inputs=[
                model_id,
                image_size,
                conf_threshold,
                iou_threshold,
                img_path,
                vid_path
            ],
            outputs=[output_image, output_video, output_path],
        )
        stop_button.click(stop_processing)


gradio_app = gr.Blocks()
with gradio_app:
    gr.HTML(
        """
    <h1 style='text-align: center'>
    YOLOv9: Real-time Object Detection
    </h1>
    """)
    css = """
    body {
        background-color: #f0f0f0;
    }
    h1 {
        color: #4CAF50;
    }
    """
    with gr.Row():
        with gr.Column():
            app()

gradio_app.launch(debug=True)