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import cv2
import torch
import gradio as gr
from ultralytics import YOLO
import numpy as np
# Load YOLOv5 model (assuming weights are already downloaded)
model = YOLO("yolov5s.pt") # You can change to 'yolov5m.pt' or 'yolov5l.pt' for better accuracy
def detect_objects_image(image):
results = model(image)
result_img = results[0].plot() # Render image with bounding boxes
return result_img
# Video detection function
def detect_objects_video():
cap = cv2.VideoCapture(0) # Capture from default webcam
cap.set(cv2.CAP_PROP_FPS, 30) # Set FPS
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
results = model(frame)
result_img = results[0].plot()
_, buffer = cv2.imencode(".jpg", result_img)
yield buffer.tobytes()
cap.release()
def start_video():
return gr.Video(update=detect_objects_video, streaming=True)
# Gradio UI
with gr.Blocks() as demo:
gr.Markdown("## Live Object Detection with YOLOv5")
with gr.Row():
img_input = gr.Image(type="numpy")
img_output = gr.Image()
img_button = gr.Button("Detect Objects in Image")
img_button.click(detect_objects_image, inputs=img_input, outputs=img_output)
with gr.Row():
video_button = gr.Button("Start Live Video Detection")
video_output = gr.Video()
video_button.click(start_video, outputs=video_output)
demo.launch()
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