app.py

import spaces
import gradio as gr
from detect_deepsort import run_deepsort
from detect_strongsort import run_strongsort
from detect import run
import os
import torch
from PIL import Image
import numpy as np
import threading
import cv2

should_continue = True

@spaces.GPU(duration=120)
def yolov9_inference(model_id, img_path=None, vid_path=None, tracking_algorithm = 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
    #assert img_path is not None or vid_path is not None, "Either img_path or vid_path must be provided."
    image_size = 640
    conf_threshold = 0.5
    iou_threshold = 0.5
    input_path = None
    output_path = None
    if img_path is not None:
        # Convert the numpy array to an image
        img = Image.fromarray(img_path)
        img_path = 'output.png'
        # Save the image
        img.save(img_path)
        input_path = img_path
        print(input_path)
        output_path = run(weights=model_id, imgsz=(image_size,image_size), conf_thres=conf_threshold, iou_thres=iou_threshold, source=input_path, device='0', hide_conf= True)
    elif vid_path is not None:
        vid_name = 'output.mp4'

        # Create a VideoCapture object
        cap = cv2.VideoCapture(vid_path)

        # Check if video opened successfully
        if not cap.isOpened():
            print("Error opening video file")

        # Read the video frame by frame
        frames = []
        while cap.isOpened():
            ret, frame = cap.read()
            if ret:
                frames.append(frame)
            else:
                break

        # Release the VideoCapture object
        cap.release()

        # Convert the list of frames to a numpy array
        vid_data = np.array(frames)

        # Create a VideoWriter object
        out = cv2.VideoWriter(vid_name, cv2.VideoWriter_fourcc(*'mp4v'), 30, (frames[0].shape[1], frames[0].shape[0]))

        # Write the frames to the output video file
        for frame in frames:
            out.write(frame)

        # Release the VideoWriter object
        out.release()
        input_path = vid_name
        if tracking_algorithm == 'deep_sort':
            output_path = run_deepsort(weights=model_id, imgsz=(image_size,image_size), conf_thres=conf_threshold, iou_thres=iou_threshold, source=input_path, device='0', draw_trails=True)
        elif tracking_algorithm == 'strong_sort':
            device_strongsort = torch.device('cuda:0')
            output_path = run_strongsort(yolo_weights=model_id, imgsz=(image_size,image_size), conf_thres=conf_threshold, iou_thres=iou_threshold, source=input_path, device=device_strongsort, strong_sort_weights = "osnet_x0_25_msmt17.pt", hide_conf= True)
        else: 
            output_path =  run(weights=model_id, imgsz=(image_size,image_size), conf_thres=conf_threshold, iou_thres=iou_threshold, source=input_path, device='0', 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 app(model_id, img_path, vid_path, tracking_algorithm):
    return yolov9_inference(model_id, img_path, vid_path, tracking_algorithm)

iface = gr.Interface(
    fn=app, 
    inputs=[
        gr.Dropdown(
            label="Model",
            choices=[
                "our-converted.pt",
                "yolov9_e_trained-converted.pt",
                "last_best_model.pt"
            ],
            value="our-converted.pt"
        ),
        gr.Image(label="Image"),
        gr.Video(label="Video"),
        gr.Dropdown(
            label= "Tracking Algorithm",
            choices=[
                "None",
                "deep_sort",
                "strong_sort"
            ],
            value="None"
        )
    ], 
    outputs=[
        gr.Image(type="numpy",label="Output Image"),
        gr.Video(label="Output Video"),
        gr.Textbox(label="Output path")
    ],
    examples=[
        ["last_best_model.pt", "camera1_A_133.png", None, "deep_sort"],
        ["last_best_model.pt", None, "test.mp4", "strong_sort"]
    ],
    title='YOLOv9: Real-time Object Detection',
    description='This is a real-time object detection system using YOLOv9.',
    theme='huggingface'
)

iface.launch(debug=True)