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import torch
import numpy as np
import gradio as gr
from PIL import Image
import time
# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Load optimized YOLOv5s model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True).to(device)
# Performance optimizations
model.conf = 0.5 # Confidence threshold (adjust for speed/accuracy balance)
if device.type == 'cuda':
model.half() # FP16 precision
def process_frame(image):
"""Process single frame with error handling"""
if image is None:
return None
try:
# Convert numpy array to PIL Image
image_pil = Image.fromarray(image)
# Perform inference
with torch.no_grad():
results = model(image_pil)
# Render results
rendered_images = results.render()
return np.array(rendered_images[0]) if rendered_images else image
except Exception as e:
print(f"Processing error: {e}")
return image
with gr.Blocks(title="Real-Time Object Detection") as app:
gr.Markdown("# 🚀 Real-Time Object Detection with Dual Input")
gr.Markdown("Supports live webcam streaming and image uploads")
with gr.Tabs():
with gr.TabItem("📷 Live Camera"):
with gr.Row():
webcam_input = gr.Webcam(label="Live Feed", streaming=True)
live_output = gr.Image(label="Processed Feed", streaming=True)
webcam_input.change(process_frame, webcam_input, live_output)
with gr.TabItem("🖼️ Image Upload"):
with gr.Row():
upload_input = gr.Image(type="numpy", label="Upload Image")
upload_output = gr.Image(label="Detection Result")
upload_input.change(process_frame, upload_input, upload_output)
gr.Markdown("### ⚙️ Performance Settings")
with gr.Accordion("Advanced Settings", open=False):
gr.Slider(minimum=0.1, maximum=0.9, value=0.5,
label="Confidence Threshold", interactive=True)
gr.Checkbox(label="Enable FP16 Acceleration", value=True)
# Configure queue and launch
app.queue(concurrency_count=4, max_size=20).launch(
server_name="0.0.0.0",
server_port=7860,
share=False,
enable_queue=True
) |