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import gradio as gr
import torch
import cv2
from ultralytics import YOLO
# Safe load method to handle custom YOLO class during deserialization
def safe_load_yolo_model(path):
# Add necessary safe globals to allow the detection model class during loading
torch.serialization.add_safe_globals([torch, 'ultralytics.nn.tasks.DetectionModel'])
return YOLO(path)
# Load YOLO models
model_yolo11 = safe_load_yolo_model('./data/yolo11n.pt')
model_best = safe_load_yolo_model('./data/best.pt')
def process_video(video):
# Read video input
cap = cv2.VideoCapture(video.name)
fps = cap.get(cv2.CAP_PROP_FPS)
frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
# Create a VideoWriter object to save the output video
fourcc = cv2.VideoWriter_fourcc(*'mp4v') # Codec for .mp4
out = cv2.VideoWriter('output_video.mp4', fourcc, fps, (frame_width, frame_height))
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
# Use both YOLO models for detection
results_yolo11 = model_yolo11(frame)
results_best = model_best(frame)
# Combine the results from both models
# For simplicity, we will overlay bounding boxes and labels from both models
for result in results_yolo11:
boxes = result.boxes
for box in boxes:
x1, y1, x2, y2 = map(int, box.xyxy[0].tolist())
cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
label = f"YOLOv11: {box.cls[0]} - {box.conf[0]:.2f}"
cv2.putText(frame, label, (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
for result in results_best:
boxes = result.boxes
for box in boxes:
x1, y1, x2, y2 = map(int, box.xyxy[0].tolist())
cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), 2)
label = f"Best: {box.cls[0]} - {box.conf[0]:.2f}"
cv2.putText(frame, label, (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 0, 0), 2)
# Write the processed frame to the output video
out.write(frame)
cap.release()
out.release()
return 'output_video.mp4'
# Gradio interface
iface = gr.Interface(fn=process_video, inputs=gr.Video(), outputs=gr.Video(), live=True)
# Launch the app
iface.launch()
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