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# Import necessary libraries
import gradio as gr # Gradio for creating the web interface
import cv2 # OpenCV for image processing
from huggingface_hub import hf_hub_download # Download models from Hugging Face Hub
from gradio_webrtc import WebRTC # WebRTC integration for streaming webcam feeds in Gradio
from twilio.rest import Client # Twilio client for managing ICE servers for WebRTC
import os # OS module for environment variable access
from inference import YOLOv10 # Custom YOLOv10 inference class
# Download YOLOv10 model file from Hugging Face Hub
model_file = hf_hub_download(
repo_id="onnx-community/yolov10n", # Repository containing the YOLOv10 model
filename="onnx/model.onnx" # Model file to download
)
# Initialize the YOLOv10 model
model = YOLOv10(model_file)
# Retrieve Twilio account credentials from environment variables
account_sid = os.environ.get("TWILIO_ACCOUNT_SID") # Twilio Account SID
auth_token = os.environ.get("TWILIO_AUTH_TOKEN") # Twilio Auth Token
# Check if Twilio credentials are available
if account_sid and auth_token:
# Initialize Twilio client with credentials
client = Client(account_sid, auth_token)
# Create a Twilio token for ICE server configuration
token = client.tokens.create()
# Configure WebRTC to use Twilio ICE servers for better connection reliability
rtc_configuration = {
"iceServers": token.ice_servers, # Use Twilio ICE servers
"iceTransportPolicy": "relay", # Relay policy to improve NAT traversal
}
else:
# Use default WebRTC configuration if Twilio credentials are not available
rtc_configuration = None
# Function to perform object detection
def detection(image, conf_threshold=0.3):
# Resize the input image to match the model's expected dimensions
image = cv2.resize(image, (model.input_width, model.input_height))
# Perform object detection and return the processed image
new_image = model.detect_objects(image, conf_threshold)
return cv2.resize(new_image, (500, 500)) # Resize output image for display
# Define custom CSS for Gradio interface layout
css = """.my-group {max-width: 600px !important; max-height: 600 !important;}
.my-column {display: flex !important; justify-content: center !important; align-items: center !important};"""
# Create a Gradio interface with custom blocks
with gr.Blocks(css=css) as demo:
# Add a title to the Gradio interface
gr.HTML(
"""
<h1 style='text-align: center'>
YOLOv10 Webcam Stream
</h1>
"""
)
# Add links to the arXiv paper and GitHub repository for YOLOv10
gr.HTML(
"""
<h3 style='text-align: center'>
<a href='https://arxiv.org/abs/2405.14458' target='_blank'>arXiv</a> | <a href='https://github.com/THU-MIG/yolov10' target='_blank'>github</a>
</h3>
"""
)
# Define a column layout for the interface
with gr.Column(elem_classes=["my-column"]):
with gr.Group(elem_classes=["my-group"]):
# Add a WebRTC component for webcam streaming
image = WebRTC(label="Stream", rtc_configuration=rtc_configuration)
# Add a slider to adjust the confidence threshold for object detection
conf_threshold = gr.Slider(
label="Confidence Threshold", # Label for the slider
minimum=0.0, # Minimum slider value
maximum=1.0, # Maximum slider value
step=0.05, # Step size for slider
value=0.30, # Default slider value
)
# Stream webcam frames through the detection function
image.stream(
fn=detection, # Detection function to process frames
inputs=[image, conf_threshold], # Inputs: webcam stream and confidence threshold
outputs=[image], # Outputs: processed frames
time_limit=10 # Limit each detection to 10 seconds
)
# Launch the Gradio app when the script is run directly
if __name__ == "__main__":
demo.launch()
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