tarinmodel4 / app.py
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import cv2
import torch
import gradio as gr
import numpy as np
from ultralytics import YOLO
# Load YOLOv8 model
model = YOLO('./data/best.pt') # Path to your model
# 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)
# Create an empty list to store processed frames
processed_frames = []
while True:
# Read a frame from the video
ret, frame = input_video.read()
if not ret:
break # End of video
# Perform inference on the frame
results = model(frame)
# The results object contains annotations for the frame
annotated_frame = results[0].plot() # Plot the frame with bounding boxes
# Convert the annotated frame to RGB format
annotated_frame_rgb = cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB)
# Append the frame to the list
processed_frames.append(annotated_frame_rgb)
# Release resources
input_video.release()
# Return the processed frames as an output video in Gradio
return processed_frames
# Create a Gradio interface for video upload
iface = gr.Interface(fn=process_video,
inputs=gr.Video(label="Upload Video"), # Updated line
outputs=gr.Video(), # This will display the output video directly
title="YOLOv8 Object Detection on Video",
description="Upload a video for object detection using YOLOv8")
# Launch the interface
iface.launch()