app.py

import cv2
import torch
import numpy as np
import gradio as gr
import time
import os
from pathlib import Path
import onnxruntime as ort

# Set device for ONNX Runtime
providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if torch.cuda.is_available() else ['CPUExecutionProvider']
session = ort.InferenceSession("models/yolov5n.onnx", providers=providers)

# Load model class names
class_names = ["person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light"]  # Modify based on model

# Generate random colors for classes
np.random.seed(42)
colors = np.random.uniform(0, 255, size=(len(class_names), 3))

def preprocess(image):
    image = cv2.resize(image, (640, 640))
    image = image.transpose((2, 0, 1)) / 255.0  # Normalize
    image = np.expand_dims(image, axis=0).astype(np.float32)
    return image

def detect_objects(image):
    start_time = time.time()
    image_input = preprocess(image)
    outputs = session.run(None, {session.get_inputs()[0].name: image_input})
    detections = outputs[0][0]
    output_image = image.copy()
    
    for det in detections:
        x1, y1, x2, y2, conf, cls = map(int, det[:6])
        if conf > 0.6:  # Confidence threshold
            color = colors[cls].tolist()
            cv2.rectangle(output_image, (x1, y1), (x2, y2), color, 2)
            label = f"{class_names[cls]} {conf:.2f}"
            cv2.putText(output_image, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)
    
    fps = 1 / (time.time() - start_time)
    cv2.putText(output_image, f"FPS: {fps:.2f}", (20, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
    return output_image

def real_time_detection():
    cap = cv2.VideoCapture(0)
    cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)
    cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)
    cap.set(cv2.CAP_PROP_FPS, 60)
    
    while cap.isOpened():
        start_time = time.time()
        ret, frame = cap.read()
        if not ret:
            break
        output_frame = detect_objects(frame)
        cv2.imshow("Real-Time Object Detection", output_frame)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
        print(f"FPS: {1 / (time.time() - start_time):.2f}")
    cap.release()
    cv2.destroyAllWindows()

with gr.Blocks(title="YOLOv5 Real-Time Object Detection") as demo:
    gr.Markdown("""
    # Real-Time Object Detection with YOLOv5
    **Upload an image or run real-time detection**
    """)
    
    with gr.Row():
        with gr.Column():
            input_image = gr.Image(label="Upload Image", type="numpy")
            detect_button = gr.Button("Detect Objects")
            start_rt_button = gr.Button("Start Real-Time Detection")
        
        with gr.Column():
            output_image = gr.Image(label="Detection Results", type="numpy")
    
    detect_button.click(detect_objects, inputs=input_image, outputs=output_image)
    start_rt_button.click(lambda: real_time_detection(), None, None)

demo.launch()