686 lines
No EOL
27 KiB
Python
686 lines
No EOL
27 KiB
Python
import os
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import sys
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import requests
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import streamlit as st
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import numpy as np
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import matplotlib.pyplot as plt
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from PIL import Image
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import io
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import base64
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import pickle
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# Configuration de l'API
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# prod docker
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API_BASE_URL = os.getenv("MULTISEG_API_BASE_URL", "http://127.0.0.1:8080/api")
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# test local
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# API_BASE_URL = os.getenv("MULTISEG_API_BASE_URL", "http://127.0.0.1:8000") # ← Port 8000 au lieu de 8080
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# Import des fonctions utils pour l'affichage local
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sys.path.append('/app')
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# APRÈS (pour test local)
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sys.path.append('.')
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from utils.utils import colorize_mask
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# Configuration de la page
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st.set_page_config(
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page_title="Multi-Class Segmentation",
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page_icon="🚗",
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layout="wide"
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)
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# CSS personnalisé
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st.markdown("""
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<style>
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.main-header {
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background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
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padding: 2rem;
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border-radius: 10px;
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margin-bottom: 2rem;
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color: white;
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text-align: center;
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}
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.metric-card {
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background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%);
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padding: 1rem;
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border-radius: 8px;
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border-left: 4px solid #667eea;
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margin: 0.5rem 0;
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color: #2c3e50;
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font-weight: 500;
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}
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.prediction-stats {
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background: #e8f4fd;
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padding: 1rem;
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border-radius: 8px;
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margin: 1rem 0;
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}
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</style>
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""", unsafe_allow_html=True)
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# En-tête principal
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st.markdown("""
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<div class="main-header">
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<h1>🚗 Segmentation Multi-Classes</h1>
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<p>Système de segmentation sémantique pour véhicules autonomes</p>
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</div>
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""", unsafe_allow_html=True)
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# Initialisation des états
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if "prediction_result" not in st.session_state:
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st.session_state.prediction_result = None
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if "selected_image" not in st.session_state:
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st.session_state.selected_image = None
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if "uploaded_file" not in st.session_state:
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st.session_state.uploaded_file = None
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def reset_state():
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"""Reset des états de session"""
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st.session_state.prediction_result = None
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st.session_state.selected_image = None
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st.session_state.uploaded_file = None
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def get_model_info():
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"""Récupère les informations du modèle"""
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try:
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response = requests.get(f"{API_BASE_URL}/models")
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if response.status_code == 200:
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return response.json()
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else:
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st.error(f"Erreur lors de la récupération des infos modèle: {response.status_code}")
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return None
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except Exception as e:
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st.error(f"Erreur de connexion à l'API: {e}")
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return None
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def get_sample_images():
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"""Récupère la liste des images d'exemple"""
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try:
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response = requests.get(f"{API_BASE_URL}/sample-images")
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if response.status_code == 200:
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return response.json()
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else:
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st.error(f"Erreur lors de la récupération des images: {response.status_code}")
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return None
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except Exception as e:
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st.error(f"Erreur de connexion à l'API: {e}")
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return None
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def predict_sample_image(filename):
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"""Lance une prédiction sur une image d'exemple"""
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try:
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# CORRECTION: Envoyer en JSON au lieu de form data
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data = {"filename": filename}
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response = requests.post(
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f"{API_BASE_URL}/predict-sample",
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json=data, # ← Changé de 'data=' à 'json='
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headers={"Content-Type": "application/json"} # ← Ajout du header explicite
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)
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if response.status_code == 200:
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return response.json()
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else:
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st.error(f"Erreur API: {response.status_code} - {response.text}")
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return None
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except Exception as e:
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st.error(f"Erreur lors de la prédiction: {e}")
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return None
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def predict_uploaded_image(uploaded_file):
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"""Lance une prédiction sur une image uploadée"""
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try:
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files = {"file": (uploaded_file.name, uploaded_file.getvalue(), uploaded_file.type)}
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response = requests.post(f"{API_BASE_URL}/predict-upload", files=files)
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if response.status_code == 200:
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return response.json()
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else:
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st.error(f"Erreur API: {response.status_code} - {response.text}")
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return None
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except Exception as e:
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st.error(f"Erreur lors de la prédiction: {e}")
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return None
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def display_segmentation_plots(fig):
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"""Affiche chaque subplot séparément dans des colonnes"""
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if fig is None:
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st.warning("Aucune figure de segmentation disponible")
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return
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axes = fig.get_axes()
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num_plots = len(axes)
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st.markdown("### 🎨 Résultat de la Segmentation")
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# Créer les colonnes selon le nombre de subplots
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if num_plots <= 4:
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cols = st.columns(num_plots)
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else:
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cols = st.columns(4) # Maximum 4 colonnes
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for i, ax in enumerate(axes):
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col_idx = i % len(cols)
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with cols[col_idx]:
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# Récupérer le titre existant
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title = ax.get_title()
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# st.markdown(f"**{title}:**")
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# Créer une figure individuelle avec ce subplot
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individual_fig, individual_ax = plt.subplots(figsize=(6, 6))
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# Copier toutes les images de l'axe original
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for image in ax.get_images():
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individual_ax.imshow(
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image.get_array(),
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cmap=image.get_cmap() if image.get_cmap() else None,
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alpha=image.get_alpha() if image.get_alpha() else None,
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vmin=image.get_clim()[0] if image.get_clim() else None,
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vmax=image.get_clim()[1] if image.get_clim() else None
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)
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# Copier les propriétés de l'axe
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individual_ax.set_title(title)
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individual_ax.axis('off')
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# Ajuster la mise en page
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individual_fig.tight_layout()
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# Afficher dans Streamlit
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st.pyplot(individual_fig)
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# Libérer la mémoire
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plt.close(individual_fig)
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# Fermer la figure originale pour libérer la mémoire
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plt.close(fig)
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def display_prediction_stats_with_chart(stats, model_info):
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"""Affiche les statistiques avec un graphique en barres"""
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st.markdown("### 📊 Statistiques de Segmentation")
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if not stats:
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st.warning("Aucune statistique disponible")
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return
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# Ratio 1:2 pour donner plus de place au graphique
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col1, col2 = st.columns([1, 2])
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# Colonne 1: Cartes de statistiques compactes avec carrés colorés
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with col1:
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st.markdown("**Détails par classe:**")
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# Récupérer les couleurs des classes pour les carrés
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class_colors_dict = {}
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if model_info and 'class_colors' in model_info and 'class_names' in model_info:
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model_class_names = model_info['class_names']
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model_class_colors = model_info['class_colors']
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for class_name in stats.keys():
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try:
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idx = model_class_names.index(class_name)
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color = model_class_colors[idx]
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# Convertir en hex pour CSS
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color_hex = f"#{color[0]:02x}{color[1]:02x}{color[2]:02x}"
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class_colors_dict[class_name] = color_hex
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except (ValueError, IndexError):
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# Couleur par défaut si pas trouvée
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class_colors_dict[class_name] = "#808080"
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for class_name, data in stats.items():
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# Récupérer la couleur pour cette classe
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color_hex = class_colors_dict.get(class_name, "#808080")
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# ✨ NOUVEAU: Carré coloré + nom de classe
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st.markdown(f"""
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<div style="
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background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%);
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padding: 0.5rem 0.8rem;
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border-radius: 6px;
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border-left: 3px solid #667eea;
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margin: 0.3rem 0;
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color: #2c3e50;
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font-weight: 500;
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font-size: 0.85rem;
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line-height: 1.3;
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">
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<div style="
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font-weight: 600;
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margin-bottom: 0.2rem;
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display: flex;
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align-items: center;
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gap: 0.5rem;
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">
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<div style="
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width: 12px;
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height: 12px;
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background-color: {color_hex};
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border-radius: 2px;
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border: 1px solid rgba(0,0,0,0.2);
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flex-shrink: 0;
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"></div>
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<span>{class_name}</span>
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</div>
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<div style="font-size: 0.8rem; color: #555;">
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🔢 {data['pixels']:,} pixels | 📊 {data['percentage']}%
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</div>
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</div>
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""", unsafe_allow_html=True)
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# Colonne 2: Graphique en barres (reste identique)
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with col2:
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st.markdown("**Répartition visuelle:**")
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# Préparer les données pour le graphique
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class_names = list(stats.keys())
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percentages = [data['percentage'] for data in stats.values()]
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# Récupérer les couleurs des classes depuis model_info
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colors = []
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if model_info and 'class_colors' in model_info and 'class_names' in model_info:
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model_class_names = model_info['class_names']
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model_class_colors = model_info['class_colors']
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for class_name in class_names:
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# Trouver l'index de cette classe dans le modèle
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try:
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idx = model_class_names.index(class_name)
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color = model_class_colors[idx]
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# Convertir RGB en format matplotlib
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colors.append([c/255.0 for c in color])
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except (ValueError, IndexError):
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# Couleur par défaut si pas trouvée
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colors.append([0.5, 0.5, 0.5])
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else:
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# Couleurs par défaut si pas d'info du modèle
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colors = plt.cm.Set3(np.linspace(0, 1, len(class_names)))
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# Créer un graphique plus grand et mieux adapté
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fig, ax = plt.subplots(figsize=(10, 6)) # Plus large
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bars = ax.bar(class_names, percentages, color=colors, alpha=0.8, edgecolor='white', linewidth=1)
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# Personnalisation du graphique améliorée
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ax.set_xlabel('Classes de Segmentation', fontsize=11, fontweight='bold')
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ax.set_ylabel('Pourcentage (%)', fontsize=11, fontweight='bold')
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ax.set_title('Répartition des Classes Détectées', fontsize=13, fontweight='bold', pad=20)
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# Meilleure gestion des labels selon le nombre de classes
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if len(class_names) <= 4:
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plt.xticks(rotation=0, ha='center', fontsize=10)
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elif len(class_names) <= 6:
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plt.xticks(rotation=30, ha='right', fontsize=9)
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else:
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plt.xticks(rotation=45, ha='right', fontsize=8)
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plt.yticks(fontsize=10)
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# Valeurs sur les barres avec meilleur positionnement
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for bar, percentage in zip(bars, percentages):
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height = bar.get_height()
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# Adapter la position du texte selon la hauteur de la barre
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if height < max(percentages) * 0.1:
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# Si la barre est très petite, mettre le texte au-dessus
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va = 'bottom'
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y_pos = height + max(percentages) * 0.02
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else:
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# Sinon, centrer dans la barre
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va = 'center'
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y_pos = height / 2
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ax.text(bar.get_x() + bar.get_width()/2., y_pos,
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f'{percentage:.1f}%',
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ha='center', va=va, fontsize=9, fontweight='bold',
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color='white' if va == 'center' else 'black',
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bbox=dict(boxstyle='round,pad=0.2', facecolor='black', alpha=0.7) if va == 'center' else None)
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# Améliorer la mise en page et le style
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plt.tight_layout()
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# Grille plus subtile
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ax.grid(axis='y', alpha=0.2, linestyle='--')
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ax.set_axisbelow(True)
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# Limites et style des axes
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ax.set_ylim(0, max(percentages) * 1.15) # Plus d'espace au-dessus
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ax.spines['top'].set_visible(False)
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ax.spines['right'].set_visible(False)
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ax.spines['left'].set_color('#666')
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ax.spines['bottom'].set_color('#666')
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# Couleur de fond
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fig.patch.set_facecolor('white')
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ax.set_facecolor('#fafafa')
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# Afficher dans Streamlit
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st.pyplot(fig)
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plt.close(fig)
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def display_class_legend(model_info):
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"""Affiche la légende des classes de segmentation"""
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if model_info and 'class_colors' in model_info:
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st.markdown("**Légende des classes:**")
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class_names = model_info.get('class_names', [])
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class_colors = model_info.get('class_colors', [])
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# Créer une légende colorée
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legend_cols = st.columns(len(class_names))
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for i, (name, color) in enumerate(zip(class_names, class_colors)):
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with legend_cols[i]:
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color_hex = f"#{color[0]:02x}{color[1]:02x}{color[2]:02x}"
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st.markdown(f"""
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<div style="
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background-color: {color_hex};
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padding: 10px;
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border-radius: 5px;
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text-align: center;
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color: white;
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font-weight: bold;
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text-shadow: 1px 1px 2px rgba(0,0,0,0.7);
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margin: 2px;
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">
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{name}
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</div>
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""", unsafe_allow_html=True)
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# # Sidebar avec informations du modèle
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# with st.sidebar:
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# st.markdown("## 🔧 Informations du Modèle")
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# model_info = get_model_info()
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# if model_info:
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# st.success("✅ Modèle chargé")
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# st.write(f"**Nom:** {model_info.get('model_name', 'N/A')}")
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# st.write(f"**Encoder:** {model_info.get('encoder', 'N/A')}")
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# st.write(f"**Classes:** {model_info.get('num_classes', 'N/A')}")
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# st.write(f"**Taille d'entrée:** {model_info.get('input_size', 'N/A')}")
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# st.write(f"**Run ID:** {model_info.get('run_id', 'N/A')[:8]}...")
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# else:
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# st.error("❌ Modèle non disponible")
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# st.markdown("---")
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# st.markdown("## 🎯 Classes Segmentées")
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# if model_info and 'class_names' in model_info:
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# for i, class_name in enumerate(model_info['class_names']):
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# st.write(f"{i}. {class_name}")
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# Sidebar avec informations du modèle
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with st.sidebar:
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st.markdown("## 🔧 Informations du Modèle")
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model_info = get_model_info()
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if model_info:
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st.success("✅ Modèle chargé")
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st.write(f"**Nom:** {model_info.get('model_name', 'N/A')}")
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st.write(f"**Encoder:** {model_info.get('encoder', 'N/A')}")
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st.write(f"**Classes:** {model_info.get('num_classes', 'N/A')}")
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st.write(f"**Taille d'entrée:** {model_info.get('input_size', 'N/A')}")
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st.write(f"**Run ID:** {model_info.get('run_id', 'N/A')[:8]}...")
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else:
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st.error("❌ Modèle non disponible")
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st.markdown("---")
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st.markdown("## 🎯 Classes Segmentées")
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# ✨ NOUVEAU: Affichage avec carrés colorés
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if model_info and 'class_names' in model_info and 'class_colors' in model_info:
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class_names = model_info['class_names']
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class_colors = model_info['class_colors']
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for i, (class_name, color) in enumerate(zip(class_names, class_colors)):
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# Convertir RGB en hex
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color_hex = f"#{color[0]:02x}{color[1]:02x}{color[2]:02x}"
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# Affichage avec carré coloré
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st.markdown(f"""
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<div style="
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display: flex;
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align-items: center;
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gap: 0.5rem;
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margin: 0.3rem 0;
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font-size: 0.9rem;
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">
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<div style="
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width: 14px;
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height: 14px;
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background-color: {color_hex};
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border-radius: 2px;
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border: 1px solid rgba(0,0,0,0.2);
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flex-shrink: 0;
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"></div>
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<span style="font-weight: 500;">{i}. {class_name}</span>
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</div>
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""", unsafe_allow_html=True)
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else:
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# Fallback si pas d'info couleur
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if model_info and 'class_names' in model_info:
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for i, class_name in enumerate(model_info['class_names']):
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st.write(f"{i}. {class_name}")
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else:
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st.write("Informations des classes non disponibles")
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# Initialisation des états - MODIFIÉE pour séparer les résultats par onglet
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if "sample_prediction_result" not in st.session_state:
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st.session_state.sample_prediction_result = None
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if "sample_selected_image" not in st.session_state:
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st.session_state.sample_selected_image = None
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if "upload_prediction_result" not in st.session_state:
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st.session_state.upload_prediction_result = None
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if "upload_uploaded_file" not in st.session_state:
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st.session_state.upload_uploaded_file = None
|
||
|
||
# ANCIEN système (gardé pour compatibilité si besoin)
|
||
if "prediction_result" not in st.session_state:
|
||
st.session_state.prediction_result = None
|
||
if "selected_image" not in st.session_state:
|
||
st.session_state.selected_image = None
|
||
if "uploaded_file" not in st.session_state:
|
||
st.session_state.uploaded_file = None
|
||
|
||
def reset_sample_state():
|
||
"""Reset des états de session pour l'onglet Sample"""
|
||
st.session_state.sample_prediction_result = None
|
||
st.session_state.sample_selected_image = None
|
||
|
||
def reset_upload_state():
|
||
"""Reset des états de session pour l'onglet Upload"""
|
||
st.session_state.upload_prediction_result = None
|
||
st.session_state.upload_uploaded_file = None
|
||
|
||
def reset_state():
|
||
"""Reset complet (garde l'ancienne fonction pour compatibilité)"""
|
||
reset_sample_state()
|
||
reset_upload_state()
|
||
|
||
# Interface principale - MODIFIÉE pour résultats indépendants
|
||
tab1, tab2 = st.tabs(["📸 Images d'Exemple", "📤 Upload Personnel"])
|
||
|
||
# Tab 1: Images d'exemple
|
||
with tab1:
|
||
st.markdown("## 📸 Sélectionner une Image d'Exemple")
|
||
st.markdown("Ces images proviennent du dataset Cityscapes avec masques de vérité disponibles.")
|
||
|
||
# Récupérer les images d'exemple
|
||
sample_data = get_sample_images()
|
||
if sample_data and sample_data['images']:
|
||
# Sélecteur d'image
|
||
image_options = {img['display_name']: img['filename'] for img in sample_data['images']}
|
||
selected_display_name = st.selectbox(
|
||
"Choisissez une image:",
|
||
options=list(image_options.keys()),
|
||
help=f"{sample_data['total_count']} images disponibles"
|
||
)
|
||
|
||
selected_filename = image_options[selected_display_name]
|
||
|
||
# Afficher les infos de l'image sélectionnée
|
||
selected_img_info = next(img for img in sample_data['images'] if img['filename'] == selected_filename)
|
||
|
||
col1, col2 = st.columns([2, 1])
|
||
with col1:
|
||
st.info(f"📁 **Fichier:** {selected_filename}")
|
||
with col2:
|
||
ground_truth_status = "✅ Disponible" if selected_img_info['has_ground_truth'] else "❌ Non disponible"
|
||
st.info(f"🎯 **Vérité terrain:** {ground_truth_status}")
|
||
|
||
# Prévisualisation de l'image sélectionnée
|
||
if selected_filename:
|
||
try:
|
||
# Récupérer l'image via l'API
|
||
image_url = f"{API_BASE_URL}/sample-image/{selected_filename}"
|
||
response = requests.get(image_url, timeout=5)
|
||
|
||
if response.status_code == 200:
|
||
# Charger l'image depuis les bytes
|
||
sample_image = Image.open(io.BytesIO(response.content))
|
||
st.image(
|
||
sample_image,
|
||
caption=f"Aperçu: {selected_display_name}",
|
||
use_container_width=True
|
||
)
|
||
elif response.status_code == 404:
|
||
st.warning("📷 Image non trouvée sur le serveur")
|
||
else:
|
||
st.info("📷 Prévisualisation temporairement indisponible")
|
||
|
||
except requests.exceptions.Timeout:
|
||
st.warning("⏱️ Timeout lors du chargement de l'aperçu")
|
||
except Exception as e:
|
||
st.info("📷 Aperçu non disponible - L'image sera chargée lors de la prédiction")
|
||
|
||
# Bouton de prédiction
|
||
if st.button("🔮 Lancer la Segmentation", key="predict_sample", type="primary"):
|
||
if selected_filename:
|
||
with st.spinner("Segmentation en cours..."):
|
||
result = predict_sample_image(selected_filename)
|
||
if result and result['success']:
|
||
# ✨ NOUVEAU: Stocker dans les variables Sample
|
||
st.session_state.sample_prediction_result = result
|
||
st.session_state.sample_selected_image = selected_filename
|
||
st.success("✅ Segmentation terminée!")
|
||
st.rerun()
|
||
else:
|
||
st.error("❌ Erreur lors de la segmentation")
|
||
else:
|
||
st.warning("Aucune image d'exemple disponible")
|
||
|
||
# ✨ NOUVEAU: Affichage des résultats Sample (indépendants)
|
||
if st.session_state.sample_prediction_result and st.session_state.sample_selected_image:
|
||
st.markdown("---")
|
||
result = st.session_state.sample_prediction_result
|
||
|
||
st.markdown(f"## 🎯 Résultats pour: {st.session_state.sample_selected_image}")
|
||
|
||
# Afficher la segmentation
|
||
if result.get('figure_data'):
|
||
try:
|
||
# Désérialiser la figure
|
||
fig_bytes = base64.b64decode(result['figure_data'])
|
||
fig = pickle.loads(fig_bytes)
|
||
display_segmentation_plots(fig)
|
||
except Exception as e:
|
||
st.error(f"Erreur lors de l'affichage de la segmentation: {e}")
|
||
# Fallback sur la légende des classes
|
||
display_class_legend(model_info)
|
||
else:
|
||
# Fallback si pas de figure_data
|
||
display_class_legend(model_info)
|
||
|
||
# Afficher les statistiques
|
||
if result.get('prediction_stats'):
|
||
display_prediction_stats_with_chart(result['prediction_stats'], model_info)
|
||
|
||
# Informations additionnelles
|
||
if result.get('ground_truth_available'):
|
||
st.success("🎯 Masque de vérité terrain disponible pour comparaison")
|
||
else:
|
||
st.info("ℹ️ Pas de vérité terrain disponible")
|
||
|
||
# Bouton de reset
|
||
if st.button("🔄 Nouvelle Analyse", key="reset_sample", type="secondary"):
|
||
reset_sample_state()
|
||
st.rerun()
|
||
|
||
# Tab 2: Upload personnel
|
||
with tab2:
|
||
st.markdown("## 📤 Uploader Votre Image")
|
||
st.markdown("Uploadez une image de rue pour analyse. Formats acceptés: JPG, PNG")
|
||
|
||
uploaded_file = st.file_uploader(
|
||
"Sélectionnez une image:",
|
||
type=['jpg', 'jpeg', 'png'],
|
||
help="Taille maximale recommandée: 5MB"
|
||
)
|
||
|
||
if uploaded_file is not None:
|
||
# Afficher l'image uploadée
|
||
image = Image.open(uploaded_file)
|
||
st.image(image, caption=f"Image uploadée: {uploaded_file.name}", use_container_width=True)
|
||
|
||
# Informations sur le fichier
|
||
file_size_mb = len(uploaded_file.getvalue()) / 1024 / 1024
|
||
st.info(f"📁 **Fichier:** {uploaded_file.name} | 📊 **Taille:** {file_size_mb:.2f} MB")
|
||
|
||
# Bouton de prédiction
|
||
if st.button("🔮 Lancer la Segmentation", key="predict_upload", type="primary"):
|
||
with st.spinner("Segmentation en cours..."):
|
||
result = predict_uploaded_image(uploaded_file)
|
||
if result and result['success']:
|
||
# ✨ NOUVEAU: Stocker dans les variables Upload
|
||
st.session_state.upload_prediction_result = result
|
||
st.session_state.upload_uploaded_file = uploaded_file.name
|
||
st.success("✅ Segmentation terminée!")
|
||
st.rerun()
|
||
else:
|
||
st.error("❌ Erreur lors de la segmentation")
|
||
|
||
# ✨ NOUVEAU: Affichage des résultats Upload (indépendants)
|
||
if st.session_state.upload_prediction_result and st.session_state.upload_uploaded_file:
|
||
st.markdown("---")
|
||
result = st.session_state.upload_prediction_result
|
||
|
||
st.markdown(f"## 🎯 Résultats pour: {st.session_state.upload_uploaded_file}")
|
||
|
||
# Afficher la segmentation
|
||
if result.get('figure_data'):
|
||
try:
|
||
# Désérialiser la figure
|
||
fig_bytes = base64.b64decode(result['figure_data'])
|
||
fig = pickle.loads(fig_bytes)
|
||
display_segmentation_plots(fig)
|
||
except Exception as e:
|
||
st.error(f"Erreur lors de l'affichage de la segmentation: {e}")
|
||
# Fallback sur la légende des classes
|
||
display_class_legend(model_info)
|
||
else:
|
||
# Fallback si pas de figure_data
|
||
display_class_legend(model_info)
|
||
|
||
# Afficher les statistiques
|
||
if result.get('prediction_stats'):
|
||
display_prediction_stats_with_chart(result['prediction_stats'], model_info)
|
||
|
||
# Informations additionnelles
|
||
if result.get('ground_truth_available'):
|
||
st.success("🎯 Masque de vérité terrain disponible pour comparaison")
|
||
else:
|
||
st.info("ℹ️ Pas de vérité terrain disponible (image personnelle)")
|
||
|
||
# Bouton de reset
|
||
if st.button("🔄 Nouvelle Analyse", key="reset_upload", type="secondary"):
|
||
reset_upload_state()
|
||
st.rerun()
|
||
|
||
# Footer
|
||
st.markdown("---")
|
||
st.markdown("""
|
||
<div style="text-align: center; color: #666; padding: 1rem;">
|
||
🚗 <strong>Multi-Class Segmentation API</strong> -
|
||
Système de vision par ordinateur pour véhicules autonomes<br>
|
||
<small>Développé avec FastAPI, Streamlit et TensorFlow</small>
|
||
</div>
|
||
""", unsafe_allow_html=True) |