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import gradio as gr
import torch
from ultralytics import YOLO
import cv2
import tempfile
# Load the trained YOLOv8 model
model = YOLO('best.pt')
def predict(image):
results = model(image)
# You might want to process results to return bounding boxes, class labels, etc.
annotated_image = results[0].plot() # plot the results on the image
return annotated_image
def predict_video(video):
# Read the video file
cap = cv2.VideoCapture(video)
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))
# Create a temporary file to save the output video
out_file = tempfile.NamedTemporaryFile(delete=False, suffix='.mp4')
out_path = out_file.name
# Define the codec and create VideoWriter object
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter(out_path, fourcc, fps, (width, height))
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
results = model(frame)
annotated_frame = results[0].plot() # plot the results on the frame
out.write(annotated_frame)
cap.release()
out.release()
return out_path
# Create Gradio interface
interface = gr.Interface(
fn=lambda img, vid: (predict(img), predict_video(vid)),
inputs=[
gr.inputs.Image(type="numpy", label="Input Image"),
gr.inputs.Video(label="Input Video")
],
outputs=[
gr.outputs.Image(type="numpy", label="Output Image"),
gr.outputs.Video(label="Output Video")
],
title="YOLOv8 Object Detection",
description="Upload an image or a video and get the object detection results using a YOLOv8 model."
)
if __name__ == "__main__":
interface.launch()
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