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from flask import Flask, request, render_template, jsonify, send_from_directory
import os
import torch
import numpy as np
import cv2
from segment_anything import sam_model_registry, SamPredictor
from werkzeug.utils import secure_filename
import warnings
# Initialisation de Flask
app = Flask(
__name__,
template_folder='templates', # Chemin des fichiers HTML
static_folder='static' # Chemin des fichiers statiques
)
app.config['UPLOAD_FOLDER'] = os.path.join('static', 'uploads')
os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)
# Charger le modèle SAM
MODEL_TYPE = "vit_b"
MODEL_PATH = os.path.join('models', 'sam_vit_b_01ec64.pth')
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print("Chargement du modèle SAM...")
try:
state_dict = torch.load(MODEL_PATH, map_location="cpu", weights_only=True)
except TypeError:
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=UserWarning)
state_dict = torch.load(MODEL_PATH, map_location="cpu")
# Initialiser et charger le modèle
sam = sam_model_registry[MODEL_TYPE]()
sam.load_state_dict(state_dict, strict=False)
sam.to(device=device)
predictor = SamPredictor(sam)
print("Modèle SAM chargé avec succès!")
@app.route('/', methods=['GET', 'POST'])
def index():
if request.method == 'POST':
if 'image' not in request.files:
return "Aucun fichier sélectionné", 400
file = request.files['image']
if file.filename == '':
return "Nom de fichier vide", 400
filename = secure_filename(file.filename)
filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
file.save(filepath)
# Passer le nom du fichier au template pour affichage
return render_template('index.html', uploaded_image=filename)
return render_template('index.html')
@app.route('/uploads/<filename>')
def uploaded_file(filename):
return send_from_directory(app.config['UPLOAD_FOLDER'], filename)
if __name__ == '__main__':
app.run(debug=True, host='0.0.0.0', port=5000)
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