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import cv2 | |
import torch | |
import gradio as gr | |
import numpy as np | |
from ultralytics import YOLO | |
# Load YOLOv8 model and set device (GPU if available) | |
device = "cuda" if torch.cuda.is_available() else "cpu" | |
model = YOLO('./data/best.pt') # Path to your model | |
model.to(device) | |
# Define the function that processes the uploaded video | |
def process_video(video): | |
# video is now the file path string, not a file object | |
input_video = cv2.VideoCapture(video) # Directly pass the path to cv2.VideoCapture | |
# Get frame width, height, and fps from input video | |
frame_width = int(input_video.get(cv2.CAP_PROP_FRAME_WIDTH)) | |
frame_height = int(input_video.get(cv2.CAP_PROP_FRAME_HEIGHT)) | |
fps = input_video.get(cv2.CAP_PROP_FPS) | |
# Resize to reduce computation (optional) | |
new_width, new_height = 640, 480 # Resize to 640x480 resolution | |
frame_width, frame_height = new_width, new_height | |
while True: | |
# Read a frame from the video | |
ret, frame = input_video.read() | |
if not ret: | |
break # End of video | |
# Resize the frame to reduce computational load | |
frame = cv2.resize(frame, (new_width, new_height)) | |
# Perform inference on the frame | |
results = model(frame) # Automatically uses GPU if available | |
# Check if any object was detected | |
if len(results[0].boxes) > 0: # If there are detected objects | |
# Annotate the frame with bounding boxes | |
annotated_frame = results[0].plot() # Plot the frame with bounding boxes | |
# Convert the annotated frame to RGB format for displaying | |
annotated_frame_rgb = cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB) | |
# Display the frame with detections | |
cv2.imshow("Detected Frame", annotated_frame_rgb) | |
# Wait for a key press (optional: press 'q' to quit early) | |
if cv2.waitKey(1) & 0xFF == ord('q'): | |
break | |
# Release resources | |
input_video.release() | |
cv2.destroyAllWindows() | |
return "Video processing complete!" | |
# Create a Gradio interface for video upload | |
iface = gr.Interface(fn=process_video, | |
inputs=gr.Video(label="Upload Video"), # Updated line | |
outputs=gr.Textbox(label="Processing Status"), # Output text showing processing status | |
title="YOLOv8 Object Detection - Real-Time Display", | |
description="Upload a video for object detection using YOLOv8. The frames with detections will be shown in real-time.") | |
# Launch the interface | |
iface.launch() | |