app.py

import gradio as gr
from ultralytics import YOLO
import cv2
import numpy as np
# charger le modèle
model = YOLO("yolov8n-seg.pt")

# Fonction pour la détection sur image
def detect_objects_image(img):
    results = model(img)  # Détection
    annotated_frame = results[0].plot()  # Annoter les résultats
    return annotated_frame

import tempfile
# Fonction pour la détection sur vidéo
def detect_objects_video(video):
    # Si l'entrée est une chaîne, utiliser telle quelle. Sinon, utiliser .name (cas Gradio)
    video_path = video.name if hasattr(video, 'name') else video

    temp_output = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)
    cap = cv2.VideoCapture(video_path)

    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
    fps = cap.get(cv2.CAP_PROP_FPS)

    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
    out = cv2.VideoWriter(temp_output.name, fourcc, fps, (width, height))

    while True:
        ret, frame = cap.read()
        if not ret:
            break

        results = model(frame)
        annotated_frame = results[0].plot()
        out.write(annotated_frame)

    cap.release()
    out.release()

    return temp_output.name



demo = gr.Blocks(theme='NoCrypt/miku')

#Interface Gradio
image_input = gr.Image(type='numpy',label="Image à analyser")
image_output = gr.Image(type = 'numpy', label="Image annotée")

video_input = gr.Video(label="Video à analyser")
video_output = gr.Video(label="Video annotée")

interface1 = gr.Interface(fn=detect_objects_image, inputs=image_input, outputs=image_output, title="Détection sur Image")

interface2 = gr.Interface(fn=detect_objects_video, inputs=video_input, outputs=video_output, title="Détection sur Video")

with demo:
  gr.TabbedInterface([interface1, interface2], ['image detection', 'video detection'])

demo.launch()