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
import seaborn as sns
from PIL import Image
import cv2
import numpy as np
import matplotlib.pyplot as plt
import threading

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

        output_path, df, frame_counts_df = 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, hide_labels = 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, df, frame_counts_df = 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, df, frame_counts_df = 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, hide_labels = True)
        else: 
            output_path, df, frame_counts_df =  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, hide_labels = True)
        # Assuming output_path is the path to the output file
    _, output_extension = os.path.splitext(output_path)
    palette = {"Bus": "red", "Bike": "blue", "Car": "green", "Pedestrian": "yellow", "Truck": "purple"}
    if output_extension.lower() in img_extensions:
        output_image = output_path  # Load the image file here
        output_video = None
        plt.style.use("ggplot")
        fig, ax = plt.subplots(figsize=(10, 6))
        #for label in labels:
            #df_label = frame_counts_df[frame_counts_df['label'] == label]
        sns.barplot(ax = ax, data = df, x = 'label', y = 'count', palette=palette, hue = 'label', legend = False)

        # Customizations
        ax.set_title('Count of Labels', fontsize=20)
        ax.set_xlabel('Label', fontsize=15)
        ax.set_ylabel('Count', fontsize=15)
        ax.tick_params(axis='x', rotation=45)  # Rotate x-axis labels for better readability
        sns.despine()  # Remove the top and right spines from plot
        #ax.legend()
        ax.grid(True)
        #ax.set_facecolor('#D3D3D3')
    elif output_extension.lower() in vid_extensions:
        output_video = output_path  # Load the video file here
        output_image = None
        plt.style.use("ggplot")
        fig, ax = plt.subplots(figsize=(10, 6))
        #for label in labels:
            #df_label = frame_counts_df[frame_counts_df['label'] == label]
        sns.lineplot(ax = ax, data = frame_counts_df,  x = 'frame', y = 'count', hue = 'label', palette = palette)

        ax.set_xlabel('Frame')
        ax.set_ylabel('Count')
        ax.set_title('Count of Labels over Frames')
        ax.legend()
        ax.grid(True)
        ax.set_facecolor('#D3D3D3')
    return output_image, output_video, fig

def app():
    with gr.Blocks(title="YOLOv9: Real-time Object Detection", css=".gradio-container {background:lightyellow;}"):
        with gr.Row():
            with gr.Column():
                gr.HTML("<h2>Input Parameters</h2>")
                img_path = gr.Image(label="Image", height = 370, width = 600)
                vid_path = gr.Video(label="Video", height = 370, width = 600)
                model_id = gr.Dropdown(
                    label="Model",
                    choices=[
                        "Our_Model.pt",
                        "yolov9_e_trained.pt"
                    ],
                    value="Our_Model.pt"
                )
                tracking_algorithm = gr.Dropdown(
                    label= "Tracking Algorithm",
                    choices=[
                        "None",
                        "deep_sort",
                        "strong_sort"
                    ],
                    value="None"
                )
                yolov9_infer = gr.Button(value="Inference")
                gr.Examples(['./img_examples/Exam_1.png','./img_examples/Exam_2.png','./img_examples/Exam_3.png','./img_examples/Exam_4.png','./img_examples/Exam_5.png'], inputs=img_path,label = "Image Example", cache_examples = False)
                gr.Examples(['./video_examples/video_1.mp4', './video_examples/video_2.mp4','./video_examples/video_3.mp4','./video_examples/video_4.mp4','./video_examples/video_5.mp4'], inputs=vid_path, label = "Video Example", cache_examples = False)
            with gr.Column():
                gr.HTML("<h2>Output</h2>")
                output_image = gr.Image(type="numpy",label="Output")
                #df = gr.BarPlot(show_label=False, x="label", y="counts", x_title="Labels", y_title="Counts", vertical=False)
                output_video = gr.Video(label="Output") 
                #frame_counts_df = gr.LinePlot(show_label=False, x="frame", y="count", x_title="Frame", y_title="Counts", color="label")
                fig = gr.Plot(label = "Plot")
                #output_path = gr.Textbox(label="Output path")
                

        yolov9_infer.click(
            fn=yolov9_inference,
            inputs=[
                model_id,
                img_path,
                vid_path,
                tracking_algorithm
            ],
            outputs=[output_image, output_video, fig],
        )


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)