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from ultralytics import YOLO
import os
# Define the correct path to config.yaml (in the root directory)
config_path = './config.yaml' # Adjust based on the actual path to your config.yaml
# Load YOLO model
model = YOLO("yolo11n.yaml") # You can choose a different model type like yolo5n, yolo6n, etc.
# Train the model
results = model.train(data=config_path, epochs=1)
# Define the save directory
save_dir = './runs/detect/train/weights'
# Create directory if it doesn't exist
if not os.path.exists(save_dir):
os.makedirs(save_dir)
# Save the model
model.save(os.path.join(save_dir, 'best.pt'))
# Print confirmation
print("Model saved to:", os.path.join(save_dir, 'best.pt'))
from ultralytics import YOLO
import gradio as gr
import cv2
import os
import tempfile
# Load the trained YOLO model
model = YOLO("./runs/detect/train/weights/best.pt") # Path to your trained model
def process_video(video_path):
"""
Process the input video using the YOLO model and save the output with bounding boxes.
Returns the path to the output video.
"""
# Create a temporary file for the output video
output_path = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False).name
# Open the input video
cap = cv2.VideoCapture(video_path)
if not cap.isOpened():
raise ValueError("Error opening video file")
# Get video properties
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(cap.get(cv2.CAP_PROP_FPS))
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
# Define the codec and create VideoWriter object
fourcc = cv2.VideoWriter_fourcc(*'mp4v') # Use 'mp4v' for MP4 format
out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
# Process each frame
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
# Perform YOLO inference on the frame
results = model(frame)
# Draw bounding boxes and labels on the frame
annotated_frame = results[0].plot() # Ultralytics provides a plot method to draw boxes
# Write the annotated frame to the output video
out.write(annotated_frame)
# Release resources
cap.release()
out.release()
cv2.destroyAllWindows()
return output_path
def gradio_interface(video):
"""
Gradio interface function to handle video input and return the processed video.
"""
if video is None:
return "Please upload a video file."
try:
# Process the video and get the output path
output_video_path = process_video(video)
# Return the output video for Gradio to display
return output_video_path
except Exception as e:
return f"Error processing video: {str(e)}"
# Create Gradio interface
iface = gr.Interface(
fn=gradio_interface,
inputs=gr.Video(label="Upload Video"),
outputs=gr.Video(label="Processed Video with Detections"),
title="YOLOv11 Object Detection on Video",
description="Upload a video to run object detection using a trained YOLOv11 model."
)
# Launch the Gradio interface
iface.launch()
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