From a833332c4a1138d32fb3ff9132673836b6bbd532 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Gr=C3=A9goire=20MUREAU?= Date: Tue, 2 Sep 2025 08:12:32 +0200 Subject: [PATCH] Project 9 added. --- .dockerignore | 106 + .env.example | 41 + .gitignore | 1 + Dockerfile | 43 + README.MD | 3 + app/main.py | 427 + app/requirements.txt | 141 + app/streamlit_app.py | 686 + cityscapes_config.json | 63 + docker-compose.yml.example | 47 + ...The_Cityscapes_Dataset_CVPR_2016_paper.pdf | 21162 ++++++++++++++++ ...c-segmentation-with-transformers-Paper.pdf | Bin 0 -> 3108703 bytes docs/fpn_resnet34_confusion_matrix.png | Bin 0 -> 84789 bytes docs/fpn_resnet34_training_curves.png | Bin 0 -> 135025 bytes docs/iou_delta_iou_per_class.png | Bin 0 -> 80790 bytes docs/mIoU_accuracy.png | Bin 0 -> 110186 bytes docs/note_methodologique.md | 323 + docs/note_methodologique.pdf | Bin 0 -> 547971 bytes docs/plan_previsionnel.md | 58 + docs/plan_previsionnel.pdf | Bin 0 -> 79766 bytes docs/segformer_b1_confusion_matrix.png | Bin 0 -> 86632 bytes docs/segformer_b1_training_curves.png | Bin 0 -> 132178 bytes nginx.conf | 57 + .../new_images_to_predict/paris_street.jpg | Bin 0 -> 1115237 bytes ...rankfurt_000000_000294_gtFine_labelIds.png | Bin 0 -> 14174 bytes ...rankfurt_000000_000576_gtFine_labelIds.png | Bin 0 -> 14775 bytes ...rankfurt_000000_001016_gtFine_labelIds.png | Bin 0 -> 13982 bytes ...rankfurt_000000_001236_gtFine_labelIds.png | Bin 0 -> 18756 bytes ...rankfurt_000000_001751_gtFine_labelIds.png | Bin 0 -> 16990 bytes .../frankfurt_000000_000294_leftImg8bit.png | Bin 0 -> 2275595 bytes .../frankfurt_000000_000576_leftImg8bit.png | Bin 0 -> 2324344 bytes .../frankfurt_000000_001016_leftImg8bit.png | Bin 0 -> 2076407 bytes .../frankfurt_000000_001236_leftImg8bit.png | Bin 0 -> 2398885 bytes .../frankfurt_000000_001751_leftImg8bit.png | Bin 0 -> 2296553 bytes notebooks/p9_notebook.ipynb | 1 + notebooks/requirements.txt | 671 + start.sh | 52 + utils/__init__.py | 0 utils/utils.py | 741 + 39 files changed, 24623 insertions(+) create mode 100644 .dockerignore create mode 100644 .env.example create mode 100644 .gitignore create mode 100644 Dockerfile create mode 100644 README.MD create mode 100644 app/main.py create mode 100644 app/requirements.txt create mode 100644 app/streamlit_app.py create mode 100644 cityscapes_config.json create mode 100644 docker-compose.yml.example create mode 100644 docs/Cordts_The_Cityscapes_Dataset_CVPR_2016_paper.pdf create mode 100644 docs/NeurIPS-2021-segformer-simple-and-efficient-design-for-semantic-segmentation-with-transformers-Paper.pdf create mode 100644 docs/fpn_resnet34_confusion_matrix.png create mode 100644 docs/fpn_resnet34_training_curves.png create mode 100644 docs/iou_delta_iou_per_class.png create mode 100644 docs/mIoU_accuracy.png create mode 100644 docs/note_methodologique.md create mode 100644 docs/note_methodologique.pdf create mode 100644 docs/plan_previsionnel.md create mode 100644 docs/plan_previsionnel.pdf create mode 100644 docs/segformer_b1_confusion_matrix.png create mode 100644 docs/segformer_b1_training_curves.png create mode 100644 nginx.conf create mode 100644 notebooks/content/data/new_images_to_predict/paris_street.jpg create mode 100644 notebooks/content/data/test_images_sample/mask/frankfurt_000000_000294_gtFine_labelIds.png create mode 100644 notebooks/content/data/test_images_sample/mask/frankfurt_000000_000576_gtFine_labelIds.png create mode 100644 notebooks/content/data/test_images_sample/mask/frankfurt_000000_001016_gtFine_labelIds.png create mode 100644 notebooks/content/data/test_images_sample/mask/frankfurt_000000_001236_gtFine_labelIds.png create mode 100644 notebooks/content/data/test_images_sample/mask/frankfurt_000000_001751_gtFine_labelIds.png create mode 100644 notebooks/content/data/test_images_sample/original/frankfurt_000000_000294_leftImg8bit.png create mode 100644 notebooks/content/data/test_images_sample/original/frankfurt_000000_000576_leftImg8bit.png create mode 100644 notebooks/content/data/test_images_sample/original/frankfurt_000000_001016_leftImg8bit.png create mode 100644 notebooks/content/data/test_images_sample/original/frankfurt_000000_001236_leftImg8bit.png create mode 100644 notebooks/content/data/test_images_sample/original/frankfurt_000000_001751_leftImg8bit.png create mode 100644 notebooks/p9_notebook.ipynb create mode 100644 notebooks/requirements.txt create mode 100644 start.sh create mode 100644 utils/__init__.py create mode 100644 utils/utils.py diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000..4628bdd --- /dev/null +++ b/.dockerignore @@ -0,0 +1,106 @@ +# Notebooks de développement mais GARDER les samples +notebooks/*.ipynb +notebooks/p8_notebook.ipynb +notebooks/requirements.txt +.ipynb_checkpoints/ +# GARDER: notebooks/content/data/test_images_sample/ (nécessaire pour l'app) + +# Environment files (variables passées via docker run ou compose) +.env +.env.* +!.env.example + +# Python cache et build files +__pycache__/ +*.py[cod] +*$py.class +*.so +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# Virtual environments +venv/ +env/ +ENV/ +env.bak/ +venv.bak/ +.venv/ + +# IDE et editor files +.vscode/ +.idea/ +*.swp +*.swo +*~ +.DS_Store +Thumbs.db + +# Git files +.git/ +.gitignore +.gitattributes + +# Documentation +*.md +!README.MD +docs/ +documentation/ + +# Test files +tests/ +test_*/ +*_test.py +debug_*.py +pytest.ini +tox.ini +.coverage +.pytest_cache/ + +# Jupyter notebook outputs +*.nbconvert + +# Fichiers temporaires +.cache/ +.tmp/ +tmp/ +temp/ +*.tmp +*.temp +*.log +logs/ + +# OS generated files +.DS_Store? +ehthumbs.db +Icon? + +# MLflow artifacts locaux (seront téléchargés depuis le registry) +mlruns/ +mlruns.backup/ +mlartifacts/ + +# Backup files +*.bak +*.backup + +# Large datasets (samples nécessaires sont gardés) +*.zip +*.tar.gz +*.rar diff --git a/.env.example b/.env.example new file mode 100644 index 0000000..7c6bb04 --- /dev/null +++ b/.env.example @@ -0,0 +1,41 @@ +# ============================================================================= +# Configuration du serveur +# ============================================================================= +# Port sur lequel nginx écoute (défaut: 8080 pour compatibilité Cloud Run) +NGINX_PORT=8080 + +# ============================================================================= +# Configuration MLflow (pour le versioning et le téléchargement des modèles) +# ============================================================================= +# URI du serveur MLflow de tracking +MLFLOW_TRACKING_URI=http://localhost:5000 + +# URL du stockage S3/MinIO pour les artefacts MLflow +MLFLOW_S3_ENDPOINT_URL=http://localhost:9000 + +# Clés d'accès S3/MinIO +AWS_ACCESS_KEY_ID=minio_access_key +AWS_SECRET_ACCESS_KEY=minio_secret_key + +# ============================================================================= +# Configuration email (optionnel - pour les notifications) +# ============================================================================= +# Serveur SMTP +SMTP_SERVER=smtp.gmail.com +SMTP_PORT=587 + +# Identifiants email +SMTP_EMAIL=noreply@yourcompany.com +SMTP_PASSWORD=your_app_password +SMTP_FROM_ALIAS=Multi-Segmentation API + +# Email de l'administrateur (destinataire des notifications) +ADMIN_EMAIL=admin@yourcompany.com + +# ============================================================================= +# Configuration de l'API +# ============================================================================= +# URL de base de l'API (utilisée par Streamlit pour faire les appels) +# En développement local: http://localhost:8080/api +# En production: https://your-domain.com/api +MULTISEG_API_BASE_URL=http://localhost:8080/api diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..242c724 --- /dev/null +++ b/.gitignore @@ -0,0 +1 @@ +./mlruns* \ No newline at end of file diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..1d7898c --- /dev/null +++ b/Dockerfile @@ -0,0 +1,43 @@ +FROM python:3.12-slim + +WORKDIR /app + +# Install system dependencies (ajout de git pour segmentation_models) +RUN apt-get update && \ + apt-get install -y nginx curl gettext-base git && \ + # Pour OpenCV si nécessaire + apt-get install -y libglib2.0-0 libsm6 libxext6 libxrender-dev libgomp1 && \ + adduser --system --no-create-home --group www-data || true && \ + apt-get clean && \ + rm -rf /var/lib/apt/lists/* + +# Copy app/ d'abord pour avoir requirements.txt +COPY app/ ./app/ + +# Install requirements depuis le bon chemin +RUN pip install --no-cache-dir -r app/requirements.txt + +# ✅ CORRECTION: Copy start.sh AVANT les permissions +COPY start.sh ./start.sh + +# Copy du reste après installation (optimise les layers Docker) +COPY utils/ ./utils/ +COPY cityscapes_config.json . +COPY notebooks/content/data/test_images_sample/ ./notebooks/content/data/test_images_sample/ + +# Copy nginx config +COPY nginx.conf /etc/nginx/nginx.conf.template + +# ✅ CORRECTION: Setup permissions APRÈS avoir copié start.sh + correction fins de ligne +RUN mkdir -p /var/log/nginx /var/lib/nginx /run && \ + sed -i 's/\r$//' ./start.sh && \ + chmod +x ./start.sh + +# Environment variables +ENV NGINX_PORT=8080 +ENV MULTISEG_API_BASE_URL=http://localhost:8080/api + +EXPOSE $NGINX_PORT + +# ✅ CORRECTION: Chemin cohérent (./start.sh au lieu de /app/start.sh) +CMD ["./start.sh"] diff --git a/README.MD b/README.MD new file mode 100644 index 0000000..adf1992 --- /dev/null +++ b/README.MD @@ -0,0 +1,3 @@ +# Projet 9 + +Documentation à venir \ No newline at end of file diff --git a/app/main.py b/app/main.py new file mode 100644 index 0000000..3487456 --- /dev/null +++ b/app/main.py @@ -0,0 +1,427 @@ +import os +import sys +import tempfile +import shutil +from datetime import datetime +from typing import Optional, List +from pathlib import Path +import mlflow +from fastapi import FastAPI, HTTPException, File, UploadFile, Form +from fastapi.responses import FileResponse +from pydantic import BaseModel +import numpy as np +import pickle +import base64 + +# Configuration MLflow avec variables d'environnement +def configure_mlflow(): + """Configure MLflow avec les variables d'environnement""" + print("=== DEBUG VARIABLES D'ENVIRONNEMENT ===") + + # ✅ NETTOYAGE automatique des variables d'environnement + mlflow_tracking_uri = os.getenv("MLFLOW_TRACKING_URI", "").strip() + mlflow_s3_endpoint_url = os.getenv("MLFLOW_S3_ENDPOINT_URL", "").strip() + aws_access_key_id = os.getenv("AWS_ACCESS_KEY_ID", "").strip() + aws_secret_access_key = os.getenv("AWS_SECRET_ACCESS_KEY", "").strip() + + print(f"MLFLOW_TRACKING_URI: {mlflow_tracking_uri}") + print(f"MLFLOW_S3_ENDPOINT_URL: {mlflow_s3_endpoint_url}") + print(f"AWS_ACCESS_KEY_ID: {aws_access_key_id}") + print(f"AWS_SECRET_ACCESS_KEY: {'***' if aws_secret_access_key else None} ({'set' if aws_secret_access_key else 'not set'})") + print("============================================") + + if not mlflow_tracking_uri: + raise ValueError("MLFLOW_TRACKING_URI not found in environment variables") + + # Configuration MLflow avec URLs nettoyées + mlflow.set_tracking_uri(mlflow_tracking_uri) + print(f"MLflow Tracking URI: {mlflow_tracking_uri}") + + # ✅ CORRECTION: Configuration EXPLICITE des identifiants avec nettoyage + if not mlflow_s3_endpoint_url or not aws_access_key_id or not aws_secret_access_key: + raise ValueError("Variables d'environnement MLflow manquantes (S3_ENDPOINT, ACCESS_KEY, SECRET_KEY)") + + os.environ["MLFLOW_S3_ENDPOINT_URL"] = mlflow_s3_endpoint_url + os.environ["AWS_ACCESS_KEY_ID"] = aws_access_key_id + os.environ["AWS_SECRET_ACCESS_KEY"] = aws_secret_access_key + print(f"MLflow S3 Endpoint: {mlflow_s3_endpoint_url}") + print("✅ Identifiants AWS configurés") + + # ✅ SKIP set_experiment pour éviter le problème 404 + # mlflow.set_experiment("OC Projet 8") # Commenté temporairement + print("✅ Configuration MLflow terminée (sans set_experiment)") + +# Import des fonctions utils +sys.path.append('.') +from utils.utils import ( + load_cityscapes_config, + load_best_model_from_registry, + run_inference_and_visualize, + colorize_mask +) + +# ✅ AJOUT: Import segmentation_models pour le chargement des modèles +try: + import segmentation_models as sm + print("✅ segmentation_models importé - modèles compatibles") +except ImportError as e: + print(f"⚠️ segmentation_models non disponible: {e}") + print("💡 Installez avec: pip install git+https://github.com/qubvel/segmentation_models.git") + +# Configuration +MODEL_NAME = "StreetsSegmentation" +SAMPLE_IMAGES_DIR = "./notebooks/content/data/test_images_sample" +CITYSCAPES_CONFIG_PATH = "./cityscapes_config.json" +TEMP_UPLOAD_DIR = "/tmp/uploads" + +# Modèles de données +class ImageInfo(BaseModel): + filename: str + display_name: str + has_ground_truth: bool + +class PredictionResult(BaseModel): + success: bool + message: str + image_path: Optional[str] = None + mask_path: Optional[str] = None + prediction_stats: Optional[dict] = None + ground_truth_available: bool = False + figure_data: Optional[str] = None + +class AvailableImagesResponse(BaseModel): + images: List[ImageInfo] + total_count: int + +# Variables globales pour le modèle +model = None +encoder_name = None +script_img_size = None +mapping_config = None +run_id = None + +def initialize_model(): + """Initialise le modèle et la configuration au démarrage""" + global model, encoder_name, script_img_size, mapping_config, run_id + + try: + # Configuration MLflow + print("🔄 Configuration de MLflow...") + configure_mlflow() + + print("🔄 Chargement de la configuration Cityscapes...") + mapping_config = load_cityscapes_config(CITYSCAPES_CONFIG_PATH, verbose=False) + print("✅ Configuration Cityscapes chargée") + + print("🔄 Chargement du modèle depuis MLflow...") + print(f"🔍 Recherche du modèle: {MODEL_NAME}") + model, run_id, encoder_name, script_img_size = load_best_model_from_registry(MODEL_NAME) + print("✅ Modèle téléchargé depuis MLflow") + + print(f"✅ Modèle chargé du run {run_id}") + print(f"📐 Encoder: {encoder_name}") + print(f"📐 Input shape: {script_img_size}") + print(f"🎨 Nombre de classes: {mapping_config['num_classes']}") + + # Créer le dossier temporaire + os.makedirs(TEMP_UPLOAD_DIR, exist_ok=True) + + except Exception as e: + print(f"❌ Erreur lors de l'initialisation: {e}") + import traceback + traceback.print_exc() + raise e + +# Initialisation de l'API +app = FastAPI( + title="Multi-Class Segmentation API", + description="API de segmentation sémantique pour véhicules autonomes", + version="1.0.0" +) + +# ✅ AJOUT: Initialiser le modèle de manière plus robuste +@app.on_event("startup") +async def startup_event(): + """Initialise le modèle au démarrage de l'API""" + try: + initialize_model() + print("🚀 API démarrée avec succès - Modèle chargé!") + except Exception as e: + print(f"⚠️ ATTENTION: Impossible de charger le modèle au démarrage: {e}") + print("L'API démarre quand même mais les prédictions ne fonctionneront pas.") + +@app.get("/") +def root(): + return { + "message": "Multi-Class Segmentation API - Online", + "model_loaded": model is not None, + "model_run_id": run_id, + "encoder": encoder_name, + "num_classes": mapping_config['num_classes'] if mapping_config else None + } + +@app.get("/health") +def health_check(): + """Health check endpoint pour nginx""" + if model is None or mapping_config is None: + raise HTTPException(status_code=503, detail="Model not loaded") + + return { + "status": "healthy", + "model_loaded": True, + "timestamp": datetime.utcnow().isoformat() + } + +@app.get("/models", response_model=dict) +def get_model_info(): + """Retourne les informations du modèle chargé""" + if model is None: + raise HTTPException(status_code=503, detail="Model not loaded") + + return { + "model_name": MODEL_NAME, + "run_id": run_id, + "encoder": encoder_name, + "input_size": script_img_size, + "num_classes": mapping_config['num_classes'], + "class_names": mapping_config['group_names'], + "class_colors": mapping_config['group_colors'].tolist() + } + +@app.get("/sample-images", response_model=AvailableImagesResponse) +def get_sample_images(): + """Retourne la liste des images d'exemple disponibles""" + try: + original_dir = Path(SAMPLE_IMAGES_DIR) / "original" + mask_dir = Path(SAMPLE_IMAGES_DIR) / "mask" + + if not original_dir.exists(): + raise HTTPException(status_code=404, detail="Sample images directory not found") + + images = [] + for img_path in original_dir.glob("*leftImg8bit.png"): + # Construire le nom du masque correspondant + base_name = img_path.stem.replace("_leftImg8bit", "") + mask_name = f"{base_name}_gtFine_labelIds.png" + mask_path = mask_dir / mask_name + + # Créer un nom d'affichage plus convivial + display_name = base_name.replace("_", " ").title() + + images.append(ImageInfo( + filename=img_path.name, + display_name=display_name, + has_ground_truth=mask_path.exists() + )) + + # Trier par nom pour un affichage cohérent + images.sort(key=lambda x: x.filename) + + return AvailableImagesResponse( + images=images, + total_count=len(images) + ) + + except Exception as e: + raise HTTPException(status_code=500, detail=f"Error listing sample images: {str(e)}") + +@app.get("/sample-image/{filename}") +def get_sample_image(filename: str): + """Sert une image d'exemple pour prévisualisation""" + try: + print(f"🔍 DEBUG: Demande d'image: {filename}") + + original_dir = Path(SAMPLE_IMAGES_DIR) / "original" + image_path = original_dir / filename + + print(f"🔍 DEBUG: Chemin construit: {image_path}") + print(f"🔍 DEBUG: Fichier existe: {image_path.exists()}") + + if not image_path.exists(): + print(f"❌ DEBUG: Image non trouvée: {image_path}") + raise HTTPException(status_code=404, detail=f"Sample image {filename} not found") + + # Vérifier que c'est bien un fichier image attendu (sécurité) + if not filename.endswith('_leftImg8bit.png'): + print(f"❌ DEBUG: Format invalide: {filename}") + raise HTTPException(status_code=400, detail="Invalid image filename format") + + print(f"✅ DEBUG: Envoi de l'image: {filename}") + return FileResponse( + image_path, + media_type="image/png", + headers={"Content-Disposition": f"inline; filename={filename}"} + ) + + except HTTPException: + # Re-raise les HTTPException pour qu'elles soient gérées correctement + raise + except Exception as e: + print(f"❌ Erreur lors du service de l'image {filename}: {e}") + import traceback + traceback.print_exc() + raise HTTPException(status_code=500, detail=f"Error serving image: {str(e)}") + +class SampleImageRequest(BaseModel): + filename: str + +@app.post("/predict-sample", response_model=PredictionResult) +def predict_sample_image(request: SampleImageRequest): + """Lance une prédiction sur une image d'exemple""" + if model is None or mapping_config is None: + raise HTTPException(status_code=503, detail="Model not loaded") + + try: + # Vérifier que l'image existe + original_dir = Path(SAMPLE_IMAGES_DIR) / "original" + image_path = original_dir / request.filename + + if not image_path.exists(): + raise HTTPException(status_code=404, detail=f"Sample image {request.filename} not found") + + # Construire le chemin du masque de vérité + base_name = image_path.stem.replace("_leftImg8bit", "") + mask_name = f"{base_name}_gtFine_labelIds.png" + mask_path = Path(SAMPLE_IMAGES_DIR) / "mask" / mask_name + + mask_path_str = str(mask_path) if mask_path.exists() else None + + print(f"🔮 Lancement de l'inférence sur {request.filename}") + + # Lancer l'inférence (sans affichage pour l'API) + predicted_mask, fig = run_inference_and_visualize( + image_path=str(image_path), + mask_path=mask_path_str, + model=model, + encoder_name=encoder_name, + img_size=script_img_size, + mapping_config=mapping_config, + save_dir=None, + show_stats=False, + show_plot=False # ← AJOUTÉ pour récupérer la figure + ) + + # Sérialiser la figure + fig_bytes = pickle.dumps(fig) + fig_b64 = base64.b64encode(fig_bytes).decode() + + # Calculer les statistiques + unique_classes, counts = np.unique(predicted_mask, return_counts=True) + total_pixels = predicted_mask.size + + stats = {} + for class_id, count in zip(unique_classes, counts): + if class_id < len(mapping_config['group_names']): + percentage = (count / total_pixels) * 100 + stats[mapping_config['group_names'][class_id]] = { + "pixels": int(count), + "percentage": round(percentage, 1) + } + + return PredictionResult( + success=True, + message="Prediction completed successfully", + image_path=str(image_path), + mask_path=mask_path_str, + prediction_stats=stats, + ground_truth_available=mask_path.exists(), + figure_data=fig_b64 + ) + + except Exception as e: + print(f"❌ Erreur lors de la prédiction: {e}") + import traceback + traceback.print_exc() + raise HTTPException(status_code=500, detail=f"Prediction error: {str(e)}") + +@app.post("/predict-upload", response_model=PredictionResult) +def predict_uploaded_image(file: UploadFile = File(...)): + """Lance une prédiction sur une image uploadée""" + if model is None or mapping_config is None: + raise HTTPException(status_code=503, detail="Model not loaded") + + # Vérifier le type de fichier + allowed_types = ["image/jpeg", "image/jpg", "image/png"] + if file.content_type not in allowed_types: + raise HTTPException( + status_code=400, + detail=f"Invalid file type. Allowed: {', '.join(allowed_types)}" + ) + + temp_file_path = None + try: + # Créer un fichier temporaire + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + temp_filename = f"upload_{timestamp}_{file.filename}" + temp_file_path = os.path.join(TEMP_UPLOAD_DIR, temp_filename) + + # Sauvegarder le fichier uploadé + with open(temp_file_path, "wb") as buffer: + shutil.copyfileobj(file.file, buffer) + + print(f"🔮 Lancement de l'inférence sur {file.filename}") + + # Lancer l'inférence (sans masque de vérité) + predicted_mask, fig = run_inference_and_visualize( + image_path=temp_file_path, + mask_path=None, # Pas de masque de vérité pour les uploads + model=model, + encoder_name=encoder_name, + img_size=script_img_size, + mapping_config=mapping_config, + save_dir=None, + show_stats=False, + show_plot=False # ← AJOUTÉ pour récupérer la figure + ) + + # Sérialiser la figure + fig_bytes = pickle.dumps(fig) + fig_b64 = base64.b64encode(fig_bytes).decode() + + # Calculer les statistiques + unique_classes, counts = np.unique(predicted_mask, return_counts=True) + total_pixels = predicted_mask.size + + stats = {} + for class_id, count in zip(unique_classes, counts): + if class_id < len(mapping_config['group_names']): + percentage = (count / total_pixels) * 100 + stats[mapping_config['group_names'][class_id]] = { + "pixels": int(count), + "percentage": round(percentage, 1) + } + + return PredictionResult( + success=True, + message="Prediction completed successfully", + image_path=temp_file_path, + prediction_stats=stats, + ground_truth_available=False, + figure_data=fig_b64 + ) + + except Exception as e: + print(f"❌ Erreur lors de la prédiction: {e}") + import traceback + traceback.print_exc() + raise HTTPException(status_code=500, detail=f"Prediction error: {str(e)}") + + finally: + # Nettoyer le fichier temporaire + if temp_file_path and os.path.exists(temp_file_path): + try: + os.remove(temp_file_path) + print(f"🗑️ Fichier temporaire supprimé: {temp_filename}") + except Exception as e: + print(f"⚠️ Impossible de supprimer le fichier temporaire: {e}") + +@app.get("/metrics") +def get_metrics(): + """Endpoint de métriques pour monitoring""" + return { + "model_loaded": model is not None, + "model_name": MODEL_NAME, + "run_id": run_id, + "num_classes": mapping_config['num_classes'] if mapping_config else None, + "timestamp": datetime.utcnow().isoformat() + } \ No newline at end of file diff --git a/app/requirements.txt b/app/requirements.txt new file mode 100644 index 0000000..c78ee04 --- /dev/null +++ b/app/requirements.txt @@ -0,0 +1,141 @@ +# ============================================================================= +# Multi-Class Segmentation API - Requirements +# ============================================================================= + +# ===== CORE WEB FRAMEWORK ===== +fastapi==0.115.14 +uvicorn==0.35.0 +starlette==0.46.2 +pydantic==2.11.7 +pydantic_core==2.33.2 + +# ===== STREAMLIT ===== +streamlit==1.46.1 +altair==5.5.0 # Utilisé par Streamlit pour les graphiques +blinker==1.9.0 # Streamlit dependency + +# ===== HTTP REQUESTS ===== +requests==2.32.4 +httpx==0.28.1 # Modern HTTP client (utilisé par FastAPI) +httpcore==1.0.9 +h11==0.16.0 + +# ===== MACHINE LEARNING CORE ===== +tensorflow==2.19.0 +keras==3.10.0 +numpy>=1.24.0,<2.0.0 + +# Note: tensorflow-addons n'est plus nécessaire avec TensorFlow 2.19+ +# Les fonctionnalités sont maintenant intégrées dans TensorFlow core + +# ===== COMPUTER VISION ===== +pillow==11.3.0 +opencv-python-headless==4.9.0.80 # Pas dans votre freeze, mais nécessaire pour Docker +scikit-image==0.25.2 +imageio==2.37.0 + +# ===== DATA SCIENCE ===== +pandas==2.3.0 +matplotlib==3.10.3 +scipy==1.16.0 +scikit-learn==1.7.0 + +# ===== MLFLOW & MODEL REGISTRY ===== +mlflow==3.2.0 +mlflow-skinny==3.2.0 +mlflow-tracing==3.2.0 + +# ===== AWS/S3 SUPPORT ===== +boto3==1.39.2 +botocore==1.39.2 +s3transfer==0.13.0 + +# ===== SEGMENTATION MODELS ===== +# Option 1: Version figée par commit (RECOMMANDÉE pour production) +segmentation_models @ git+https://github.com/qubvel/segmentation_models.git@5d24bbfb28af6134e25e2c0b79e7727f6c0491d0 + +# Option 2: Si vous préférez une version PyPI (décommentez et commentez la ligne du dessus) +# segmentation-models==1.0.1 + +# ===== SUPPORTING LIBRARIES ===== +# TensorFlow ecosystem +tensorboard==2.19.0 +tensorboard-data-server==0.7.2 +protobuf==3.20.3 +flatbuffers==25.2.10 +h5py==3.14.0 +astunparse==1.6.3 +gast==0.6.0 +google-pasta==0.2.0 +keras-applications==1.0.8 +opt_einsum==3.4.0 +termcolor==2.5.0 +wrapt==1.17.2 + +# Data processing +networkx==3.5 +tifffile==2025.6.11 +lazy_loader==0.4 + +# Utilities +click==8.2.1 +tqdm==4.67.1 +joblib==1.5.1 +python-dateutil +packaging +typing_extensions==4.14.0 + +# File handling +python-multipart==0.0.20 # Pour FastAPI file uploads + +# ===== AJOUTS SPÉCIFIQUES POUR UTILS/UTILS.PY ===== +# Pour la gestion des couleurs et légendes matplotlib +cycler==0.12.1 # Pour matplotlib customization + +# Font handling pour matplotlib (nécessaire dans Docker) +fonttools==4.58.5 + +# ===== REMOVED FROM ORIGINAL ===== +# Les packages suivants ont été supprimés car non utilisés dans votre app: +# - awsebcli (AWS CLI - pas nécessaire dans le container) +# - blessed, fabric, invoke (outils dev) +# - jupyter, ipython, spyder (dev environment) +# - nltk (pas d'NLP dans votre app) +# - pytest (tests - à garder seulement pour dev) +# - flask, gunicorn (vous utilisez FastAPI+uvicorn) +# - docker (pas besoin dans le container) +# - git* packages (dev tools) + +# ============================================================================= +# VERSION ALTERNATIVE - PLUS MINIMALISTE +# ============================================================================= + +# Si vous voulez une version encore plus légère, voici les STRICTEMENT nécessaires: +# fastapi==0.115.14 +# uvicorn==0.35.0 +# streamlit==1.46.1 +# tensorflow==2.19.0 +# numpy==2.1.3 +# pillow==11.3.0 +# matplotlib==3.10.3 +# pandas==2.3.0 +# requests==2.32.4 +# mlflow==3.2.0 +# boto3==1.39.2 +# segmentation_models @ git+https://github.com/qubvel/segmentation_models.git@5d24bbfb28af6134e25e2c0b79e7727f6c0491d0 + +# ============================================================================= +# NOTES SUR SEGMENTATION_MODELS +# ============================================================================= + +# OPTION A: Git avec commit hash (ACTUEL - RECOMMANDÉ) +# Avantages: Version exacte, pas de surprises +# Inconvénients: Nécessite git dans le container de build + +# OPTION B: Version PyPI officielle +# segmentation-models==1.0.1 +# Avantages: Plus simple, pas besoin de git +# Inconvénients: Peut être différent de votre version dev + +# OPTION C: Version locale (si vous avez des modifications) +# Copier le package dans le container et faire pip install -e . diff --git a/app/streamlit_app.py b/app/streamlit_app.py new file mode 100644 index 0000000..6f9a090 --- /dev/null +++ b/app/streamlit_app.py @@ -0,0 +1,686 @@ +import os +import sys +import requests +import streamlit as st +import numpy as np +import matplotlib.pyplot as plt +from PIL import Image +import io +import base64 +import pickle + +# Configuration de l'API +# prod docker +API_BASE_URL = os.getenv("MULTISEG_API_BASE_URL", "http://127.0.0.1:8080/api") +# test local +# API_BASE_URL = os.getenv("MULTISEG_API_BASE_URL", "http://127.0.0.1:8000") # ← Port 8000 au lieu de 8080 + +# Import des fonctions utils pour l'affichage local +sys.path.append('/app') +# APRÈS (pour test local) +sys.path.append('.') +from utils.utils import colorize_mask + +# Configuration de la page +st.set_page_config( + page_title="Multi-Class Segmentation", + page_icon="🚗", + layout="wide" +) + +# CSS personnalisé +st.markdown(""" + +""", unsafe_allow_html=True) + +# En-tête principal +st.markdown(""" +
+

🚗 Segmentation Multi-Classes

+

Système de segmentation sémantique pour véhicules autonomes

+
+""", unsafe_allow_html=True) + +# Initialisation des états +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_state(): + """Reset des états de session""" + st.session_state.prediction_result = None + st.session_state.selected_image = None + st.session_state.uploaded_file = None + +def get_model_info(): + """Récupère les informations du modèle""" + try: + response = requests.get(f"{API_BASE_URL}/models") + if response.status_code == 200: + return response.json() + else: + st.error(f"Erreur lors de la récupération des infos modèle: {response.status_code}") + return None + except Exception as e: + st.error(f"Erreur de connexion à l'API: {e}") + return None + +def get_sample_images(): + """Récupère la liste des images d'exemple""" + try: + response = requests.get(f"{API_BASE_URL}/sample-images") + if response.status_code == 200: + return response.json() + else: + st.error(f"Erreur lors de la récupération des images: {response.status_code}") + return None + except Exception as e: + st.error(f"Erreur de connexion à l'API: {e}") + return None + +def predict_sample_image(filename): + """Lance une prédiction sur une image d'exemple""" + try: + # CORRECTION: Envoyer en JSON au lieu de form data + data = {"filename": filename} + response = requests.post( + f"{API_BASE_URL}/predict-sample", + json=data, # ← Changé de 'data=' à 'json=' + headers={"Content-Type": "application/json"} # ← Ajout du header explicite + ) + + if response.status_code == 200: + return response.json() + else: + st.error(f"Erreur API: {response.status_code} - {response.text}") + return None + except Exception as e: + st.error(f"Erreur lors de la prédiction: {e}") + return None + +def predict_uploaded_image(uploaded_file): + """Lance une prédiction sur une image uploadée""" + try: + files = {"file": (uploaded_file.name, uploaded_file.getvalue(), uploaded_file.type)} + response = requests.post(f"{API_BASE_URL}/predict-upload", files=files) + + if response.status_code == 200: + return response.json() + else: + st.error(f"Erreur API: {response.status_code} - {response.text}") + return None + except Exception as e: + st.error(f"Erreur lors de la prédiction: {e}") + return None + +def display_segmentation_plots(fig): + """Affiche chaque subplot séparément dans des colonnes""" + if fig is None: + st.warning("Aucune figure de segmentation disponible") + return + + axes = fig.get_axes() + num_plots = len(axes) + + st.markdown("### 🎨 Résultat de la Segmentation") + + # Créer les colonnes selon le nombre de subplots + if num_plots <= 4: + cols = st.columns(num_plots) + else: + cols = st.columns(4) # Maximum 4 colonnes + + for i, ax in enumerate(axes): + col_idx = i % len(cols) + + with cols[col_idx]: + # Récupérer le titre existant + title = ax.get_title() + # st.markdown(f"**{title}:**") + + # Créer une figure individuelle avec ce subplot + individual_fig, individual_ax = plt.subplots(figsize=(6, 6)) + + # Copier toutes les images de l'axe original + for image in ax.get_images(): + individual_ax.imshow( + image.get_array(), + cmap=image.get_cmap() if image.get_cmap() else None, + alpha=image.get_alpha() if image.get_alpha() else None, + vmin=image.get_clim()[0] if image.get_clim() else None, + vmax=image.get_clim()[1] if image.get_clim() else None + ) + + # Copier les propriétés de l'axe + individual_ax.set_title(title) + individual_ax.axis('off') + + # Ajuster la mise en page + individual_fig.tight_layout() + + # Afficher dans Streamlit + st.pyplot(individual_fig) + + # Libérer la mémoire + plt.close(individual_fig) + + # Fermer la figure originale pour libérer la mémoire + plt.close(fig) + + +def display_prediction_stats_with_chart(stats, model_info): + """Affiche les statistiques avec un graphique en barres""" + st.markdown("### 📊 Statistiques de Segmentation") + + if not stats: + st.warning("Aucune statistique disponible") + return + + # Ratio 1:2 pour donner plus de place au graphique + col1, col2 = st.columns([1, 2]) + + # Colonne 1: Cartes de statistiques compactes avec carrés colorés + with col1: + st.markdown("**Détails par classe:**") + + # Récupérer les couleurs des classes pour les carrés + class_colors_dict = {} + if model_info and 'class_colors' in model_info and 'class_names' in model_info: + model_class_names = model_info['class_names'] + model_class_colors = model_info['class_colors'] + + for class_name in stats.keys(): + try: + idx = model_class_names.index(class_name) + color = model_class_colors[idx] + # Convertir en hex pour CSS + color_hex = f"#{color[0]:02x}{color[1]:02x}{color[2]:02x}" + class_colors_dict[class_name] = color_hex + except (ValueError, IndexError): + # Couleur par défaut si pas trouvée + class_colors_dict[class_name] = "#808080" + + for class_name, data in stats.items(): + # Récupérer la couleur pour cette classe + color_hex = class_colors_dict.get(class_name, "#808080") + + # ✨ NOUVEAU: Carré coloré + nom de classe + st.markdown(f""" +
+
+
+ {class_name} +
+
+ 🔢 {data['pixels']:,} pixels | 📊 {data['percentage']}% +
+
+ """, unsafe_allow_html=True) + + # Colonne 2: Graphique en barres (reste identique) + with col2: + st.markdown("**Répartition visuelle:**") + + # Préparer les données pour le graphique + class_names = list(stats.keys()) + percentages = [data['percentage'] for data in stats.values()] + + # Récupérer les couleurs des classes depuis model_info + colors = [] + if model_info and 'class_colors' in model_info and 'class_names' in model_info: + model_class_names = model_info['class_names'] + model_class_colors = model_info['class_colors'] + + for class_name in class_names: + # Trouver l'index de cette classe dans le modèle + try: + idx = model_class_names.index(class_name) + color = model_class_colors[idx] + # Convertir RGB en format matplotlib + colors.append([c/255.0 for c in color]) + except (ValueError, IndexError): + # Couleur par défaut si pas trouvée + colors.append([0.5, 0.5, 0.5]) + else: + # Couleurs par défaut si pas d'info du modèle + colors = plt.cm.Set3(np.linspace(0, 1, len(class_names))) + + # Créer un graphique plus grand et mieux adapté + fig, ax = plt.subplots(figsize=(10, 6)) # Plus large + + bars = ax.bar(class_names, percentages, color=colors, alpha=0.8, edgecolor='white', linewidth=1) + + # Personnalisation du graphique améliorée + ax.set_xlabel('Classes de Segmentation', fontsize=11, fontweight='bold') + ax.set_ylabel('Pourcentage (%)', fontsize=11, fontweight='bold') + ax.set_title('Répartition des Classes Détectées', fontsize=13, fontweight='bold', pad=20) + + # Meilleure gestion des labels selon le nombre de classes + if len(class_names) <= 4: + plt.xticks(rotation=0, ha='center', fontsize=10) + elif len(class_names) <= 6: + plt.xticks(rotation=30, ha='right', fontsize=9) + else: + plt.xticks(rotation=45, ha='right', fontsize=8) + + plt.yticks(fontsize=10) + + # Valeurs sur les barres avec meilleur positionnement + for bar, percentage in zip(bars, percentages): + height = bar.get_height() + # Adapter la position du texte selon la hauteur de la barre + if height < max(percentages) * 0.1: + # Si la barre est très petite, mettre le texte au-dessus + va = 'bottom' + y_pos = height + max(percentages) * 0.02 + else: + # Sinon, centrer dans la barre + va = 'center' + y_pos = height / 2 + + ax.text(bar.get_x() + bar.get_width()/2., y_pos, + f'{percentage:.1f}%', + ha='center', va=va, fontsize=9, fontweight='bold', + color='white' if va == 'center' else 'black', + bbox=dict(boxstyle='round,pad=0.2', facecolor='black', alpha=0.7) if va == 'center' else None) + + # Améliorer la mise en page et le style + plt.tight_layout() + + # Grille plus subtile + ax.grid(axis='y', alpha=0.2, linestyle='--') + ax.set_axisbelow(True) + + # Limites et style des axes + ax.set_ylim(0, max(percentages) * 1.15) # Plus d'espace au-dessus + ax.spines['top'].set_visible(False) + ax.spines['right'].set_visible(False) + ax.spines['left'].set_color('#666') + ax.spines['bottom'].set_color('#666') + + # Couleur de fond + fig.patch.set_facecolor('white') + ax.set_facecolor('#fafafa') + + # Afficher dans Streamlit + st.pyplot(fig) + plt.close(fig) + + +def display_class_legend(model_info): + """Affiche la légende des classes de segmentation""" + if model_info and 'class_colors' in model_info: + st.markdown("**Légende des classes:**") + + class_names = model_info.get('class_names', []) + class_colors = model_info.get('class_colors', []) + + # Créer une légende colorée + legend_cols = st.columns(len(class_names)) + for i, (name, color) in enumerate(zip(class_names, class_colors)): + with legend_cols[i]: + color_hex = f"#{color[0]:02x}{color[1]:02x}{color[2]:02x}" + st.markdown(f""" +
+ {name} +
+ """, unsafe_allow_html=True) + +# # Sidebar avec informations du modèle +# with st.sidebar: +# st.markdown("## 🔧 Informations du Modèle") + +# model_info = get_model_info() +# if model_info: +# st.success("✅ Modèle chargé") +# st.write(f"**Nom:** {model_info.get('model_name', 'N/A')}") +# st.write(f"**Encoder:** {model_info.get('encoder', 'N/A')}") +# st.write(f"**Classes:** {model_info.get('num_classes', 'N/A')}") +# st.write(f"**Taille d'entrée:** {model_info.get('input_size', 'N/A')}") +# st.write(f"**Run ID:** {model_info.get('run_id', 'N/A')[:8]}...") +# else: +# st.error("❌ Modèle non disponible") + +# st.markdown("---") +# st.markdown("## 🎯 Classes Segmentées") +# if model_info and 'class_names' in model_info: +# for i, class_name in enumerate(model_info['class_names']): +# st.write(f"{i}. {class_name}") + +# Sidebar avec informations du modèle +with st.sidebar: + st.markdown("## 🔧 Informations du Modèle") + + model_info = get_model_info() + if model_info: + st.success("✅ Modèle chargé") + st.write(f"**Nom:** {model_info.get('model_name', 'N/A')}") + st.write(f"**Encoder:** {model_info.get('encoder', 'N/A')}") + st.write(f"**Classes:** {model_info.get('num_classes', 'N/A')}") + st.write(f"**Taille d'entrée:** {model_info.get('input_size', 'N/A')}") + st.write(f"**Run ID:** {model_info.get('run_id', 'N/A')[:8]}...") + else: + st.error("❌ Modèle non disponible") + + st.markdown("---") + st.markdown("## 🎯 Classes Segmentées") + + # ✨ NOUVEAU: Affichage avec carrés colorés + if model_info and 'class_names' in model_info and 'class_colors' in model_info: + class_names = model_info['class_names'] + class_colors = model_info['class_colors'] + + for i, (class_name, color) in enumerate(zip(class_names, class_colors)): + # Convertir RGB en hex + color_hex = f"#{color[0]:02x}{color[1]:02x}{color[2]:02x}" + + # Affichage avec carré coloré + st.markdown(f""" +
+
+ {i}. {class_name} +
+ """, unsafe_allow_html=True) + else: + # Fallback si pas d'info couleur + if model_info and 'class_names' in model_info: + for i, class_name in enumerate(model_info['class_names']): + st.write(f"{i}. {class_name}") + else: + st.write("Informations des classes non disponibles") + + +# Initialisation des états - MODIFIÉE pour séparer les résultats par onglet +if "sample_prediction_result" not in st.session_state: + st.session_state.sample_prediction_result = None +if "sample_selected_image" not in st.session_state: + st.session_state.sample_selected_image = None + +if "upload_prediction_result" not in st.session_state: + st.session_state.upload_prediction_result = None +if "upload_uploaded_file" not in st.session_state: + 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(""" +
+ 🚗 Multi-Class Segmentation API - + Système de vision par ordinateur pour véhicules autonomes
+ Développé avec FastAPI, Streamlit et TensorFlow +
+""", unsafe_allow_html=True) \ No newline at end of file diff --git a/cityscapes_config.json b/cityscapes_config.json new file mode 100644 index 0000000..62b5034 --- /dev/null +++ b/cityscapes_config.json @@ -0,0 +1,63 @@ +{ + "class_groups": { + "flat": ["road", "sidewalk", "parking", "rail track"], + "human": ["person", "rider"], + "vehicle": ["car", "truck", "bus", "train", "motorcycle", "bicycle", "caravan", "trailer"], + "construction": ["building", "wall", "fence", "guard rail", "bridge", "tunnel"], + "object": ["pole", "pole group", "traffic sign", "traffic light"], + "nature": ["vegetation", "terrain"], + "sky": ["sky"], + "void": ["unlabeled", "ego vehicle", "rectification border", "out of roi", "static", "dynamic", "ground"] + }, + "cityscapes_mapping": { + "0": 7, "1": 7, "2": 7, "3": 7, "4": 7, "5": 7, "6": 7, + "7": 0, "8": 0, "9": 0, "10": 0, + "11": 3, "12": 3, "13": 3, "14": 3, "15": 3, "16": 3, + "17": 4, "18": 4, "19": 4, "20": 4, + "21": 5, "22": 5, + "23": 6, + "24": 1, "25": 1, + "26": 2, "27": 2, "28": 2, "29": 2, "30": 2, "31": 2, "32": 2, "33": 2 + }, + "id_to_label": { + "0": "unlabeled", + "1": "ego vehicle", + "2": "rectification border", + "3": "out of roi", + "4": "static", + "5": "dynamic", + "6": "ground", + "7": "road", + "8": "sidewalk", + "9": "parking", + "10": "rail track", + "11": "building", + "12": "wall", + "13": "fence", + "14": "guard rail", + "15": "bridge", + "16": "tunnel", + "17": "pole", + "18": "pole group", + "19": "traffic light", + "20": "traffic sign", + "21": "vegetation", + "22": "terrain", + "23": "sky", + "24": "person", + "25": "rider", + "26": "car", + "27": "truck", + "28": "bus", + "29": "caravan", + "30": "trailer", + "31": "train", + "32": "motorcycle", + "33": "bicycle" + }, + "group_colors": [ + [128, 64, 128], [220, 20, 60], [0, 0, 142], [70, 70, 70], + [220, 220, 0], [107, 142, 35], [70, 130, 180], [0, 0, 0] + ], + "group_names": ["flat", "human", "vehicle", "construction", "object", "nature", "sky", "void"] +} diff --git a/docker-compose.yml.example b/docker-compose.yml.example new file mode 100644 index 0000000..b5f8514 --- /dev/null +++ b/docker-compose.yml.example @@ -0,0 +1,47 @@ +version: '3.8' + +services: + street-vision-api: + # ✅ PRODUCTION: Image depuis Docker Hub + image: grgmdmn/street-vision_api:latest + + container_name: street_vision_api + + ports: + - "${NGINX_PORT:-8080}:${NGINX_PORT:-8080}" # Port nginx configurable + + environment: + # Configuration du port nginx + NGINX_PORT: "${NGINX_PORT:-8080}" + + # Configuration MLflow pour le suivi des modèles + MLFLOW_TRACKING_URI: "${MLFLOW_TRACKING_URI}" # ex: https://mlflow.domain.com + MLFLOW_S3_ENDPOINT_URL: "${MLFLOW_S3_ENDPOINT_URL}" # ex: url MinIO ou S3 + AWS_ACCESS_KEY_ID: "${AWS_ACCESS_KEY_ID}" # clé ID S3 + AWS_SECRET_ACCESS_KEY: "${AWS_SECRET_ACCESS_KEY}" # clé secrète S3 + + # Configuration email pour notifications (optionnel) + SMTP_SERVER: "${SMTP_SERVER:-}" # serveur SMTP + SMTP_PORT: "${SMTP_PORT:-587}" # port SMTP (ex: 587) + SMTP_EMAIL: "${SMTP_EMAIL:-}" # email expéditeur + SMTP_PASSWORD: "${SMTP_PASSWORD:-}" # mot de passe SMTP + SMTP_FROM_ALIAS: "${SMTP_FROM_ALIAS:-Street Vision API}" # alias (optionnel) + ADMIN_EMAIL: "${ADMIN_EMAIL:-}" # email admin destinataire + + # URL de base de l'API (utilisée par Streamlit pour rediriger vers FastAPI) + MULTISEG_API_BASE_URL: "${MULTISEG_API_BASE_URL}" # ex: https://vision.domain.com/api + + # Pour une mise à jour automatique par watchtower + labels: + com.centurylinklabs.watchtower.enable: "true" + + # Seulement si vous avez un reverse proxy server + # networks: + # - swag + + restart: unless-stopped + +# Seulement si vous avez un reverse proxy server +# networks: +# swag: +# external: true diff --git a/docs/Cordts_The_Cityscapes_Dataset_CVPR_2016_paper.pdf b/docs/Cordts_The_Cityscapes_Dataset_CVPR_2016_paper.pdf new file mode 100644 index 0000000..5145822 --- /dev/null +++ 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+62.02700 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55977 0 Td +[ (7) -1336.92000 (8) -0.90126 (2) -0.90126 ] TJ +/R131 8.96640 Tf +25.80430 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55977 0 Td +[ (8) -1336.92000 (9) -0.90126 (2) -0.90126 ] TJ +/R131 8.96640 Tf +25.80430 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55977 0 Td +[ (1) -1336.92000 (8) -0.90126 (3) -0.90126 ] TJ +/R131 8.96640 Tf +25.80510 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55898 0 Td +[ (9) -0.90126 ] TJ +/R40 8.96640 Tf +-219.66100 -10.95900 Td +[ (GT) -249.99300 (subsampled) -249.99100 (by) ] TJ +/R70 8.96640 Tf +69.98200 0 Td +[ (1) -0.89990 (6) -4862.88000 (8) -0.89990 (4) -0.89854 ] TJ +/R131 8.96640 Tf +62.02700 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55977 0 Td +[ (6) -1336.92000 (7) -0.90126 (0) -0.90126 ] TJ +/R131 8.96640 Tf +25.80430 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55977 0 Td +[ (8) -1336.92000 (8) -0.90126 (7) -0.90126 ] TJ +/R131 8.96640 Tf +25.80430 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55977 0 Td +[ (4) -1336.92000 (7) -0.90126 (2) -0.90126 ] TJ +/R131 8.96640 Tf +25.80510 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55898 0 Td +[ (9) -0.90126 ] TJ +/R40 8.96640 Tf +-219.66100 -10.95780 Td +[ (GT) -249.99300 (subsampled) -249.99100 (by) ] TJ +/R70 8.96640 Tf +69.98200 0 Td +[ (3) -0.89990 (2) -4862.88000 (7) -0.89990 (5) -0.89854 ] TJ +/R131 8.96640 Tf +62.02700 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55977 0 Td +[ (4) -1336.92000 (5) -0.90126 (3) -0.90126 ] TJ +/R131 8.96640 Tf +25.80430 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55977 0 Td +[ (7) -1336.92000 (8) -0.90126 (0) -0.90126 ] TJ +/R131 8.96640 Tf +25.80430 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55977 0 Td +[ (2) -1336.92000 (5) -0.90126 (8) -0.90126 ] TJ +/R131 8.96640 Tf +25.80510 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55898 0 Td +[ (1) -0.90126 ] TJ +/R40 8.96640 Tf +-219.66100 -10.95900 Td +[ (GT) -249.99300 (subsampled) -249.99100 (by) ] TJ +/R70 8.96640 Tf +69.98200 0 Td +[ (6) -0.89990 (4) -4862.88000 (6) -0.89990 (3) -0.89854 ] TJ +/R131 8.96640 Tf +62.02700 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55977 0 Td +[ (8) -1336.92000 (3) -0.90126 (5) -0.90126 ] TJ +/R131 8.96640 Tf +25.80430 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55977 0 Td +[ (1) -1336.92000 (7) -0.90126 (1) -0.90126 ] TJ +/R131 8.96640 Tf +25.80430 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55977 0 Td +[ (0) -1336.92000 (3) -0.90126 (9) -0.90126 ] TJ +/R131 8.96640 Tf +25.80390 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.56016 0 Td +[ (6) -0.90126 ] TJ +/R40 8.96640 Tf +-219.66100 -10.95900 Td +[ (GT) -249.99300 (subsampled) -249.99100 (by) ] TJ +/R70 8.96640 Tf +69.98200 0 Td +[ (1) -0.89990 (2) -0.89854 (8) -4348.90000 (5) -0.89990 (0) -0.89854 ] TJ +/R131 8.96640 Tf +62.02700 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55977 0 Td +[ (6) -1336.92000 (2) -0.90126 (1) -0.90126 ] TJ +/R131 8.96640 Tf +25.80430 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55977 0 Td +[ (1) -1336.92000 (6) -0.90126 (0) -0.90126 ] TJ +/R131 8.96640 Tf +25.80430 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55977 0 Td +[ (6) -1336.92000 (2) -0.90126 (9) -0.90126 ] TJ +/R131 8.96640 Tf +25.80390 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.56016 0 Td +[ (9) -0.90126 ] TJ +/R40 8.96640 Tf +-219.66100 -13.94800 Td +[ (nearest) -249.99200 (training) -249.98500 (neighbor) ] TJ +/R70 8.96640 Tf +122.79300 0 Td +[ (2) -0.89990 (1) -0.89854 ] TJ +/R131 8.96640 Tf +9.21602 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55977 0 Td +[ (3) -1593.91000 (5) -0.90126 ] TJ +/R131 8.96640 Tf +23.50000 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.56016 0 Td +[ (9) -1593.91000 (3) -0.90126 (9) -0.90126 ] TJ +/R131 8.96640 Tf +28.10820 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55977 0 Td +[ (7) -1336.92000 (1) -0.90126 (8) -0.90126 ] TJ +/R131 8.96640 Tf +25.80390 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.56016 0 Td +[ (6) -0.90126 ] TJ +ET +Q +7.97000 w +501.12100 5311.50000 m +2863.62000 5311.50000 l +S +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 50.11210 518.94800 Tm +[ (T) 79.98130 (able) -327.98900 (3\056) -328.98500 (Quantitati) 25.00380 (v) 14.98650 (e) -327.98000 (results) -328.01100 (of) -329.02100 (control) -328.02000 (e) 15.01100 (xperiments) -327.98200 (for) -327.98800 (semantic) ] TJ +10.95900 TL +T* +[ (labeling) -249.99000 (using) -250.01000 (the) -249.98800 (metrics) -249.99900 (presented) -249.99300 (in) -250.01400 (Sec\056) ] TJ +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 208.00200 507.98900 Tm +(3\0561) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 219.21000 507.98900 Tm +(\056) Tj +ET +Q +1 1 1 rg +501.12100 2830.88000 2362.50000 1962.42000 re +f +501.12100 4789.32000 m +2863.62000 4789.32000 l +S +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +0 1 -1 0 121.86600 452.97200 Tm +(train) Tj +12.45310 TL +T* +[ (v) 25.00660 (al) ] TJ +11.39880 TL +(coarse) ' +12.85590 TL +(sub) ' +1 0 0 1 181.62200 468.07500 Tm +[ (Classes) -3157.01000 (Cate) 15.01370 (gories) ] TJ +ET +Q +2.99000 w +1696.22000 4629.23000 m +2205.22000 4629.23000 l +S +2304.82000 4629.23000 m +2863.62000 4629.23000 l +S +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 172.88500 452.31500 Tm +[ (IoU) -1699.99000 (iIoU) -1699.98000 (IoU) -1698.99000 (iIoU) ] TJ +ET +Q +4.98000 w +501.12100 4470.63000 m +2863.62000 4470.63000 l +S +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 56.08980 436.35500 Tm +(FCN\05532s) Tj +/R276 8.96640 Tf +59.04530 0 Td +[ (X) -556.30200 (X) -0.30087 ] TJ +/R70 8.96640 Tf +56.52970 0 Td +[ (6) -0.89990 (1) -0.89854 ] TJ +/R131 8.96640 Tf +9.21523 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55977 0 Td +[ (3) -1567.90000 (3) -0.90126 (8) -0.90126 ] TJ +/R131 8.96640 Tf +27.87110 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55898 0 Td +[ (2) -1567.90000 (8) -0.90126 (2) -0.90126 ] TJ +/R131 8.96640 Tf +27.87110 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.56016 0 Td +[ (2) -1567.90000 (6) -0.90126 (5) -0.90126 ] TJ +/R131 8.96640 Tf +27.86990 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55977 0 Td +[ (4) -0.90126 ] TJ +/R40 8.96640 Tf +-218.64100 -10.95900 Td +(FCN\05516s) Tj +/R276 8.96640 Tf +59.04530 0 Td +[ (X) -556.30200 (X) -0.30087 ] TJ +/R70 8.96640 Tf +56.52970 0 Td +[ (6) -0.89990 (4) -0.89854 ] TJ +/R131 8.96640 Tf +9.21523 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55977 0 Td +[ (3) -1567.90000 (4) -0.90126 (1) -0.90126 ] TJ +/R131 8.96640 Tf +27.87110 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55898 0 Td +[ (1) -1567.90000 (8) -0.90126 (4) -0.90126 ] TJ +/R131 8.96640 Tf +27.87110 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.56016 0 Td +[ (5) -1567.90000 (6) -0.90126 (9) -0.90126 ] TJ +/R131 8.96640 Tf +27.86990 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55977 0 Td +[ (2) -0.90126 ] TJ +/R40 8.96640 Tf +-218.64100 -10.95900 Td +(FCN\0558s) Tj +/R276 8.96640 Tf +59.04530 0 Td +[ (X) -556.30200 (X) -0.30087 ] TJ +/R70 8.96640 Tf +56.52970 0 Td +[ (6) -0.89990 (5) -0.89854 ] TJ +/R131 8.96640 Tf +9.21523 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55977 0 Td +[ (3) -1567.90000 (4) -0.90126 (1) -0.90126 ] TJ +/R131 8.96640 Tf +27.87110 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55898 0 Td +[ (7) -1567.90000 (8) -0.90126 (5) -0.90126 ] TJ +/R131 8.96640 Tf +27.87110 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.56016 0 Td +[ (7) -1567.90000 (7) -0.90126 (0) -0.90126 ] TJ +/R131 8.96640 Tf +27.86990 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55977 0 Td +[ (1) -0.90126 ] TJ +/R40 8.96640 Tf +-218.64100 -10.95900 Td +(FCN\0558s) Tj +/R276 8.96640 Tf +59.04530 0 Td +[ (X) -556.30200 (X) -0.30087 ] TJ +/R70 8.96640 Tf +38.14060 0 Td +[ (2) -1537.89000 (6) -0.89990 (1) -0.89854 ] TJ +/R131 8.96640 Tf +27.60430 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55977 0 Td +[ (9) -1567.90000 (3) -0.90126 (3) -0.90126 ] TJ +/R131 8.96640 Tf +27.87110 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55898 0 Td +[ (6) -1567.90000 (8) -0.90126 (1) -0.90126 ] TJ +/R131 8.96640 Tf +27.87110 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.56016 0 Td +[ (6) -1567.90000 (6) -0.90126 (0) -0.90126 ] TJ +/R131 8.96640 Tf +27.86990 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55977 0 Td +[ (9) -0.90126 ] TJ +/R40 8.96640 Tf +-218.64100 -10.95900 Td +(FCN\0558s) Tj +/R276 8.96640 Tf +71.49920 0 Td +[ (X) -0.30087 ] TJ +/R70 8.96640 Tf +44.07580 0 Td +[ (5) -0.89990 (8) -0.89854 ] TJ +/R131 8.96640 Tf +9.21523 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55977 0 Td +[ (3) -1567.90000 (3) -0.90126 (7) -0.90126 ] TJ +/R131 8.96640 Tf +27.87110 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55898 0 Td +[ (4) -1567.90000 (8) -0.90126 (3) -0.90126 ] TJ +/R131 8.96640 Tf +27.87110 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.56016 0 Td +[ (4) -1567.90000 (6) -0.90126 (7) -0.90126 ] TJ +/R131 8.96640 Tf +27.86990 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55977 0 Td +[ (2) -0.90126 ] TJ +/R40 8.96640 Tf +-218.64100 -10.95900 Td +(FCN\0558s) Tj +/R276 8.96640 Tf +83.95230 0 Td +[ (X) -0.30087 ] TJ +/R70 8.96640 Tf +31.62270 0 Td +[ (5) -0.89990 (8) -0.89854 ] TJ +/R131 8.96640 Tf +9.21523 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55977 0 Td +[ (0) -1567.90000 (3) -0.90126 (1) -0.90126 ] TJ +/R131 8.96640 Tf +27.87110 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55898 0 Td +[ (8) -1567.90000 (7) -0.90126 (8) -0.90126 ] TJ +/R131 8.96640 Tf +27.87110 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.56016 0 Td +[ (2) -1567.90000 (5) -0.90126 (8) -0.90126 ] TJ +/R131 8.96640 Tf +27.86990 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55977 0 Td +[ (4) -0.90126 ] TJ +ET +Q +501.12100 3763.08000 m +2863.62000 3763.08000 l +S +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 58.28980 365.60100 Tm +(\133) Tj +ET +Q +0 1 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 61.27580 365.60100 Tm +(4) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 65.75900 365.60100 Tm +[ (\135) -245.98500 (e) 15.01100 (xte) 0.99179 (nded) ] TJ +/R276 8.96640 Tf +49.37620 0 Td +[ (X) -0.30087 ] TJ +/R70 8.96640 Tf +38.14100 0 Td +[ (4) -1537.89000 (5) -0.89990 (6) -0.89854 ] TJ +/R131 8.96640 Tf +27.60390 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55977 0 Td +[ (1) -1567.90000 (3) -0.90126 (4) -0.90126 ] TJ +/R131 8.96640 Tf +27.87110 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55898 0 Td +[ (2) -1567.90000 (7) -0.90126 (9) -0.90126 ] TJ +/R131 8.96640 Tf +27.87110 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55898 0 Td +[ (8) -1567.90000 (6) -0.90126 (6) -0.90126 ] TJ +/R131 8.96640 Tf +27.87110 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55977 0 Td +[ (4) -0.90126 ] TJ +/R40 8.96640 Tf +-216.40000 -10.95900 Td +(\133) Tj +ET +Q +0 1 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 61.31680 354.64200 Tm +(4) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 65.80000 354.64200 Tm +[ (\135) -249.99300 (basic) ] TJ +/R276 8.96640 Tf +49.33520 0 Td +[ (X) -0.30087 ] TJ +/R70 8.96640 Tf +38.14100 0 Td +[ (4) -1537.89000 (5) -0.89990 (7) -0.89854 ] TJ +/R131 8.96640 Tf +27.60390 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55977 0 Td +[ (0) -1567.90000 (3) -0.90126 (2) -0.90126 ] TJ +/R131 8.96640 Tf +27.87110 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55898 0 Td +[ (0) -1567.90000 (7) -0.90126 (9) -0.90126 ] TJ +/R131 8.96640 Tf +27.87110 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.55898 0 Td +[ (1) -1567.90000 (6) -0.90126 (1) -0.90126 ] TJ +/R131 8.96640 Tf +27.87110 0 Td +[ (\072) -0.50100 ] TJ +/R70 8.96640 Tf +2.56016 0 Td +[ (9) -0.90126 ] TJ +/R40 8.96640 Tf +-216.40000 -10.95900 Td +(\133) Tj +ET +Q +0 1 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 61.31720 343.68300 Tm +(40) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT 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8.96640 Tf +1 0 0 1 229.65900 544.99100 Tm +(6) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 54.59490 532.90300 Tm +[ (\1336\135) -556.01000 (R\056) -422.98600 (Benenson\054) -467.02100 (M\056) -422.99400 (Mathias\054) -467.01200 (R\056) -422.98500 (T) 35.02120 (imofte\054) -467.00500 (and) -423.99800 (L\056) -423.00400 (V) 110.99400 (an) -422.99600 (Gool\056) ] TJ +15.43590 -10.95900 Td +[ (Pedestrian) -383.01400 (detection) -383.01400 (at) -381.99100 (100) -382.98500 (frames) -382.99300 (per) -383.01600 (second\056) -785.01900 (In) ] TJ +/R53 8.96640 Tf +191.67300 0 Td +(CVPR) Tj +/R40 8.96640 Tf +22.41600 0 Td +(\054) Tj +-214.08900 -10.95900 Td +(2012\056) Tj +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 93.43320 510.98500 Tm +(1) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 54.59490 498.89700 Tm +[ (\1337\135) -556.01000 (G\056) -273.01300 (J\056) -272.99900 (Brosto) 25.01880 (w) 64.99010 (\054) -280.01700 (J\056) -272.99900 (F) 15.00420 (auqueur) 39.98350 (\054) -279.01500 (and) -274.00200 (R\056) -272.98900 (Cipolla\056) -435 (Semantic) -272.99200 (object) ] TJ +15.43590 -10.95900 Td +[ (classes) -397.00200 (in) -398.00600 (video\072) -604.99400 (A) -397.02100 <686967682d6465026e6974696f6e> -397.99500 (ground) -396.98400 (truth) -398.01100 (database\056) ] TJ +/R53 8.96640 Tf +10.95900 TL +T* +[ (P) 79.98130 (attern) -250.01900 (Reco) 10.02000 (gnition) -249.99600 (Letter) 9.97920 (s) ] TJ +/R40 8.96640 Tf +98.72810 0 Td +[ (\054) -250 (30\0502\051\07288\22697\054) -249.97800 (2009\056) ] TJ +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 245.45700 476.97900 Tm +(1) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 249.94100 476.97900 Tm +(\054) Tj +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 254.42400 476.97900 Tm +(3) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 258.90700 476.97900 Tm +(\054) Tj +ET +Q +1 0 0 rg 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0 0 1 93.43320 442.97400 Tm +(6) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 54.59490 430.88600 Tm +[ (\1339\135) -556.01000 (L\056\055C\056) -384.99400 (Chen\054) -420 (G\056) -386.02100 (P) 15.00420 (apandreou\054) -419.01900 (I\056) -385.98100 (K) 35.01710 (okkinos\054) -419.98200 (K\056) -386.02200 (Murph) 5 (y) 65 (\054) -418.99000 (and) ] TJ +15.43590 -10.95900 Td +[ (A\056) -311.00200 (L\056) -309.99500 (Y) 110.99500 (uille\056) -553.99200 (Semantic) -310.98200 (image) -309.98800 (se) 15.01510 (gmentation) -311.01600 (with) -310.00300 (deep) -311.00800 (con\055) ] TJ +10.95940 TL +T* +[ (v) 19.99650 (olutional) -236.02100 (nets) -236 (and) -237.01500 (fully) -235.99100 (connected) -236.01800 (CRFs\056) -329.98100 (In) ] TJ +/R53 8.96640 Tf +162.68100 0 Td +(ICLR) Tj +/R40 8.96640 Tf +19.43010 0 Td +[ (\054) -236.01600 (2015\056) ] TJ +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 279.63300 408.96800 Tm +(5) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 284.11600 408.96800 Tm +(\054) Tj +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 70.03090 398.00900 Tm +(6) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 74.51410 398.00900 Tm +(\054) Tj +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 78.99730 398.00900 Tm +(7) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 50.11210 385.92200 Tm +[ (\13310\135) -556.01000 (Y) 128.98800 (\056\055T) 74.02040 (\056) -305.98200 (Chen\054) -320.01700 (X\056) -305.99100 (Liu\054) -320.00600 (and) -305.97900 (M\056\055H\056) -306.98200 (Y) 100.01700 (ang\056) -538.98900 (Multi\055instance) -306.02300 (object) ] TJ +19.91870 -10.95900 Td +[ (se) 15.01580 (gmentation) -249.98100 (with) -250.01400 (occlusion) -249.99500 (handling\056) -360.00600 (In) ] TJ +/R53 8.96640 Tf +151.27900 0 Td +(CVPR) Tj +/R40 8.96640 Tf +22.41600 0 Td +[ (\054) -250 (2015\056) ] TJ +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 271.61200 374.96300 Tm +(7) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 50.11210 362.87500 Tm +[ (\13311\135) -556.01000 (J\056) -241.02200 (Dai\054) -244.00800 (K\056) -241.99400 (He\054) -243.01500 (and) -241.98100 (J\056) -241.98000 (Sun\056) -342.99500 (Con) 40.00530 (v) 19.99650 (olutional) -241.99000 (feature) -241.99500 (maski) 1.00473 (ng) -242.00600 (for) ] TJ +19.91870 -10.95900 Td +[ (joint) -249.99500 (object) -249.99900 (and) -249.99700 (stuf) 25.00520 (f) -249.99200 (se) 15.01510 (gmentation\056) -360.00600 (In) ] TJ +/R53 8.96640 Tf +139.34500 0 Td +(CVPR) Tj +/R40 8.96640 Tf +22.41600 0 Td +[ (\054) -250 (2015\056) ] TJ +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 259.67800 351.91600 Tm +(6) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 264.16100 351.91600 Tm +(\054) Tj +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 268.64400 351.91600 Tm +(7) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 50.11210 339.82800 Tm +[ (\13312\135) -556.01000 (P) 110.98000 (\056) -342.01000 (Doll\341r) 40.01210 (\054) -367.01700 (C\056) -342.99900 (W) 80.01060 (ojek\054) -365.98200 (B\056) -342.99900 (Schiele\054) -365.99200 (and) -343.01000 (P) 110.98000 (\056) -343.01200 (Perona\056) -656.98000 (Pedestrian) ] TJ +19.91870 -10.95900 Td +[ (detection\072) -304.02100 (An) -237.00500 (e) 24.98750 (v) 25.00660 (aluation) -237.00700 (of) -238.01300 (the) -237.00500 (state) -237.01100 (of) -238.01200 (the) -237.00700 (art\056) ] TJ +/R53 8.96640 Tf +168.81500 0 Td +[ (T) 54.99860 (r) 14.99200 (ans\056) -237.01500 (P) 90 (AMI) ] TJ +/R40 8.96640 Tf +45.27380 0 Td +(\054) Tj +-214.08900 -10.95900 Td +[ (34\0504\051\072743\226761\054) -249.98000 (2012\056) ] TJ +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 151.21300 317.91000 Tm +(1) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 155.69600 317.91000 Tm +(\054) Tj +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 160.17900 317.91000 Tm +(4) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 50.11210 305.82300 Tm +[ (\13313\135) -556.01000 (M\056) -276.00500 (Enz) 1.02175 (weiler) -275.98700 (and) -276.00600 (D\056) -276.01800 (M\056) -276.00500 (Ga) 20.01150 (vrila\056) -444.01200 (Monocula) 1.02106 (r) -276 (pedestrian) -275.99200 (de\055) ] TJ +19.91870 -10.95900 Td +[ (tection\072) -402.98200 (Surv) 14.98920 (e) 15.01100 (y) -295.98300 (and) -297.00500 (e) 15.01100 (xperiments\056) -509.01800 (31\05012\051\0722179\2262195\054) -308.01000 (2009\056) ] TJ +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 70.03090 283.90500 Tm +(1) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 50.11210 271.81700 Tm +[ (\13314\135) -556.01000 (M\056) -421.99200 (Ev) 14.98110 (eringham\054) -466.99100 (S\056) -422.98100 (M\056) -421.99200 (A\056) -423.00800 (Eslami\054) -466.99300 (L\056) -422.00200 (V) 110.99400 (an) -422.99600 (Gool\054) -467.00200 (C\056) -421.98300 (K\056) -423.00900 (I\056) ] TJ +19.91870 -10.95900 Td +[ (W) 40.01760 (illiams\054) -314.01100 (J\056) -302.01300 (W) 40.01760 (inn\054) -313.99000 (and) -302.01500 (A\056) -300.98100 (Zisserman\056) -525.01500 (The) -301.01800 (P) 91.98570 (ASCAL) -301.98700 (visual) ] TJ +10.95900 TL +T* +[ (object) -280.01500 (classes) -280.98700 (challenge\072) -371.00900 (A) -280.00400 (retrospecti) 24.99570 (v) 14.98650 (e\056) ] TJ +/R53 8.96640 Tf +153.30200 0 Td +(IJCV) Tj +/R40 8.96640 Tf +18.42620 0 Td +[ (\054) -280.01700 (111\0501\051\07298\226) ] TJ +-171.72800 -10.95900 Td +[ (136\054) -250.00200 (2015\056) ] TJ +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 111.36600 238.94000 Tm +(1) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 115.84900 238.94000 Tm +(\054) Tj +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 120.33200 238.94000 Tm +(4) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 124.81600 238.94000 Tm +(\054) Tj +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 129.29900 238.94000 Tm +(5) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 133.78200 238.94000 Tm +(\054) Tj +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 138.26500 238.94000 Tm +(6) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 142.74800 238.94000 Tm +(\054) Tj +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 147.23200 238.94000 Tm +(7) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 50.11210 226.85200 Tm +[ (\13315\135) -556.01000 (P) 110.98000 (\056) -191.01300 (F) 80.00450 (\056) -192.01500 (Felzenszw) 10.01050 (alb\054) -204.00600 (R\056) -192 (B\056) -192 (Girshick\054) -203.01400 (D\056) -191.98100 (McAllester) 39.99310 (\054) -203.99500 (and) -192.01200 (D\056) -190.98000 (Ra\055) ] TJ +19.91870 -10.95820 Td +[ (manan\056) -503.01500 (Object) -295.01100 (de) 0.98294 (tection) -294.99200 (with) -295.01700 (discriminati) 25.01200 (v) 14.98650 (ely) -293.99000 (trained) -295.01700 (part\055) ] TJ +10.95900 TL +T* +[ (based) -250.01200 (models\056) ] TJ +/R53 8.96640 Tf +54.03130 0 Td +[ (T) 54.99730 (r) 14.99060 (ans\056) -249.99600 (P) 90 (AMI) ] TJ +/R40 8.96640 Tf +45.38670 0 Td +[ (\054) -250 (32\0509\051\0721627\2261645\054) -249.98100 (2010\056) ] TJ +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 264.08000 204.93500 Tm +(1) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 50.11210 192.84700 Tm +[ (\13316\135) -556.01000 (U\056) -294.01100 (Frank) 10.00440 (e\054) -304.99900 (D\056) -294.01100 (Pfeif) 24.98610 (fer) 39.99030 (\054) -304.98000 (C\056) -293.98800 (Rabe\054) -305.00400 (C\056) -293.98800 (Kn\366ppel\054) -305.00200 (M\056) -293.99900 (Enzweiler) 40.01210 (\054) ] TJ +19.91870 -10.95900 Td +[ (F) 80.00450 (\056) -272 (Stein\054) -277.99600 (and) -271.99800 (R\056) -271.98700 (G\056) -272.01000 (Herrtwich\056) -429.99300 (Making) -271.98400 (Bertha) -272.00900 (see\056) -431.00400 (In) ] TJ +/R53 8.96640 Tf +195.90600 0 Td +(ICCV) Tj +-195.90600 -10.95900 Td +[ (W) 91.99590 (orkshops) ] TJ +/R40 8.96640 Tf +39.02110 0 Td +[ (\054) -250 (2013\056) ] TJ +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 136.93800 170.92900 Tm +(1) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 50.11210 158.84100 Tm +[ (\13317\135) -556.01000 (P) 110.98000 (\056) -663 (Fur) 17.99590 (g) 5.01002 (ale\054) -766.01800 (U\056) -663.01000 (Schwesinger) 39.98760 (\054) -766.99100 (M\056) -662.99600 <527503692c> -765.99200 (W) 91.99110 (\056) -663 (Derendarz\054) ] TJ +19.91870 -10.95900 Td +[ (H\056) -346.98700 (Grimmett\054) -371.98200 (P) 110.97900 (\056) -347.97900 (M) 1.00609 (\374hlfellner) 39.99710 (\054) -371.98300 (S\056) -348.00500 (W) 80.01060 (onneber) 17.98440 (ger) 40.00390 (\054) -370.98100 (B\056) -348.00900 (Li\054) -371.98100 (et) -347.00900 (al\056) ] TJ +10.95900 TL +T* +[ (T) 79.98130 (o) 25.00660 (w) 10.01050 (ard) -463.00300 (automated) -464.00200 (dri) 25.00110 (ving) -463.00500 (in) -463.00500 (cities) -464.02100 (using) -463 (close\055to\055mark) 9.99554 (et) ] TJ +10.95780 TL +T* +[ (sensors\072) -306.01500 (A) 0.99247 (n) -242.00600 (o) 14.98650 (v) 14.98650 (ervie) 25.00660 (w) -241.01300 (of) -241.01900 (the) -241.01300 (V) 100.01700 (\055Char) 18.00200 (ge) -240.97900 (project\056) -341 (In) ] TJ +/R53 8.96640 Tf +178.81500 0 Td +[ (IV) -240.97900 (Sympo\055) ] TJ +-178.81500 -10.95900 Td +(sium) Tj +/R40 8.96640 Tf +16.93710 0 Td +[ (\054) -250 (2013\056) ] TJ +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 114.85400 115.00600 Tm +(1) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 50.11210 102.91800 Tm +[ (\13318\135) -556.01000 (A\056) -192.98300 (Geiger) 39.98350 (\054) -204.99700 (M\056) -194.01500 (Lauer) 39.97940 (\054) -203.99500 (C\056) -194.00500 (W) 80.01060 (ojek\054) -205.00800 (C\056) -194.00500 (Stiller) 40.01480 (\054) -203.99500 (and) -194.01600 (R\056) -194.00500 (Urtasun\056) -237.00400 (3D) ] TJ +19.91870 -10.95940 Td +[ (traf) 25.01200 <0263> -289.01400 (scene) -289.02100 (understanding) -290.00300 (from) -288.99000 (mo) 14.99470 (v) 25.00660 (able) -289 (platforms\056) ] TJ +/R53 8.96640 Tf +193.78900 0 Td +[ (T) 54.99860 (r) 14.99200 (ans\056) ] TJ +-193.78900 -10.95900 Td +[ (P) 90.00140 (AMI) ] TJ +/R40 8.96640 Tf +20.60510 0 Td +[ (\054) -250 (36\0505\051\0721012\2261025\054) -249.98000 (2014\056) ] TJ +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 185.26700 81 Tm +(1) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 308.86200 710.03700 Tm +[ (\13319\135) -556.01200 (A\056) -278.98200 (Geiger) 39.98490 (\054) -286.98700 (P) 110.98000 (\056) -279.01500 (Lenz\054) -286.98700 (C\056) -279.00100 (Stiller) 40.01480 (\054) -286.98700 (and) -279.01500 (R\056) -280.00600 (Urtasun\056) -454.00600 (V) 59.97870 (ision) -279.01500 (meets) ] TJ +19.91870 -10.95900 Td +[ (robotics\072) -310.01400 (The) -250.00300 (KITTI) -249.98400 (dataset\056) ] TJ +/R53 8.96640 Tf +106.35800 0 Td +(IJRR) Tj +/R40 8.96640 Tf +17.92380 0 Td +[ (\054) -250 (32\05011\051\054) -249.98400 (2013\056) ] TJ +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 509.33600 699.07800 Tm +(1) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 513.81900 699.07800 Tm +(\054) Tj +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 518.30200 699.07800 Tm +(3) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 522.78600 699.07800 Tm +(\054) Tj +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 527.26900 699.07800 Tm +(4) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 308.86200 686.86700 Tm +[ (\13320\135) -556.01200 (R\056) -249.98700 (Gir) 0.98839 (shick\056) -360.00800 (F) 15.00560 (ast) -249.98400 (R\055CNN\056) -360.00800 (In) ] TJ +/R53 8.96640 Tf +126.86000 0 Td +(ICCV) Tj +/R40 8.96640 Tf +20.42500 0 Td +[ (\054) -250 (2015\056) ] TJ +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 484.03200 686.86700 Tm +(8) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 308.86200 674.65600 Tm +[ (\13321\135) -556.01200 (R\056) -278 (Girshick\054) -286.00700 (J\056) -279.00900 (Donahue\054) -285.99100 (T) 74.01220 (\056) -279.01500 (Darrell\054) -286.00200 (and) -279.01500 (J\056) -278.00700 (Malik\056) -453.01000 (Rich) -277.98600 (fea\055) ] TJ +19.91870 -10.95900 Td +[ (ture) -368.02200 (hierarchies) -368.99400 (for) -367.98100 (accurate) -369.01000 (object) -368.01900 (detection) -368.98800 (a) 0.98567 (nd) -369 (semantic) ] TJ +10.95900 TL +T* +[ (se) 15.01650 (gmentation\056) -360.00600 (In) ] TJ +/R53 8.96640 Tf +62.86330 0 Td +(CVPR) Tj +/R40 8.96640 Tf +22.41480 0 Td +[ (\054) -250 (2014\056) ] TJ +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 441.94500 652.73800 Tm +(7) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 308.86200 640.52700 Tm +[ (\13322\135) -556.01200 (F) 80.00520 (\056) -291.99700 (Guene) 14.98110 (y) -292.01900 (and) -291.99500 (A\056) -292.00800 (Geiger) 55.01220 (\056) -494.01000 (Displets\072) -393.98900 (Resolving) -292.00300 (stereo) -292.01400 (ambi\055) ] TJ +19.91870 -10.95900 Td +[ (guities) -250.01100 (using) -250.00800 (object) -249.99700 (kno) 25.00660 (wledge\056) -359.99700 (In) ] TJ +/R53 8.96640 Tf +126.28100 0 Td +(CVPR) Tj +/R40 8.96640 Tf +22.41600 0 Td +[ (\054) -250 (2015\056) ] TJ +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 505.36400 629.56800 Tm +(4) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 308.86200 617.35700 Tm +[ (\13323\135) -556.01200 (B\056) -279.00100 (Hariharan\054) -286.98700 (P) 110.98000 (\056) -279.01500 (Arbel\341ez\054) -286.99300 (R\056) -279.00400 (B\056) -280.00600 (Girshick\054) -286.00700 (and) -280.01700 (J\056) -279.00900 (Malik\056) -454.01200 (Si\055) ] TJ +19.91870 -10.95900 Td +[ (multaneous) -249.98700 (detection) -250.00800 (and) -250 (se) 15.01370 (gmentation\056) -360.00800 (In) ] TJ +/R53 8.96640 Tf +156.74900 0 Td +(ECCV) Tj +/R40 8.96640 Tf +22.91680 0 Td +[ (\054) -250 (2014\056) ] TJ +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 536.33200 606.39800 Tm +(7) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 308.86200 594.18700 Tm +[ (\13324\135) -556.01200 (B\056) -312.98200 (Hariharan\054) -329.98600 (P) 110.98000 (\056) -313.99800 (A\056) -314.00900 (Arbel\341ez\054) -328.99000 (R\056) -313.98700 (B\056) -313.98700 (Girshick\054) -330.00800 (and) -313.99800 (J\056) -312.99000 (Malik\056) ] TJ +19.91870 -10.95900 Td +[ (Hypercolumns) -320.98700 (for) -320.01500 (object) -321.01200 (se) 15.01370 (gmentation) -320.99500 (and) -320.01000 <026e652d677261696e6564> -321.00600 (lo\055) ] TJ +10.95900 TL +T* +[ (calization\056) -360 (In) ] TJ +/R53 8.96640 Tf +50.04100 0 Td +(CVPR) Tj +/R40 8.96640 Tf +22.41600 0 Td +[ (\054) -250 (2015\056) ] TJ +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 429.12300 572.26900 Tm +(7) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 308.86200 560.05800 Tm +[ (\13325\135) -556.01200 (H\056) -429.98000 (Hattori\054) -475.99000 (V) 128.98600 (\056) -431.01400 (N\056) -430.98200 (Boddeti\054) -476.00700 (K\056) -430.98200 (M\056) -431.00900 (Kitani\054) -476.01800 (and) -431.01400 (T) 74.01220 (\056) -430.01200 (Kanade\056) ] TJ +19.91870 -10.95900 Td +[ (Learning) -527.01600 <7363656e652d73706563690263> -526.01400 (pedestrian) -527.01600 (detectors) -526.01400 (without) -526.99500 (real) ] TJ +10.95900 TL +T* +[ (data\056) -359.98900 (In) ] TJ +/R53 8.96640 Tf +30.11840 0 Td +(CVPR) Tj +/R40 8.96640 Tf +22.41600 0 Td +[ (\054) -250 (2015\056) ] TJ +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 409.20100 538.14000 Tm +(7) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 308.86200 525.92900 Tm +[ (\13326\135) -556.01200 (X\056) -394.99700 (He) -394.99100 (and) -394.98300 (S\056) -395.01000 (Gould\056) -824.02100 (An) -395.01900 (e) 15.01370 (x) 14.98650 (emplar) 20.00740 (\055based) -395.01300 (CRF) -395.00800 (for) -394.99100 (multi\055) ] TJ +19.91910 -10.95900 Td +[ (instance) -250.01400 (object) -249.99700 (se) 15.01650 (gmentation\056) -360.00800 (In) ] TJ +/R53 8.96640 Tf +118.64200 0 Td +(CVPR) Tj +/R40 8.96640 Tf +22.41600 0 Td +[ (\054) -250 (2014\056) ] TJ +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 497.72500 514.97000 Tm +(7) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 308.86200 502.75900 Tm +[ (\13327\135) -556.01200 (D\056) -277.02200 (Hoiem\054) -283.99200 (J\056) -277.00500 (Hays\054) -284.00600 (J\056) -277.00500 (Xiao\054) -284.00300 (and) -278.01300 (A\056) -277.02200 (Khosla\056) -447.01900 (Guest) -277.00500 (editorial\072) ] TJ +19.91910 -10.95900 Td +[ (Scene) -250.01700 (understanding\056) ] TJ +/R53 8.96640 Tf +79.92580 0 Td +(IJCV) Tj +/R40 8.96640 Tf +18.42500 0 Td +[ (\054) -250 (2015\056) ] TJ +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 455.01800 491.80000 Tm +(1) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 308.86200 479.58900 Tm +[ (\13328\135) -556.01200 (J\056) -377.99000 (Hosang\054) -409.99700 (R\056) -377.98200 (Benenson\054) -409.99400 (P) 110.97700 (\056) -377.99500 (Doll\341r) 40.00940 (\054) -410.01600 (and) -377.99500 (B\056) -377.98400 (Schiele\056) -770.01900 (What) ] TJ +19.91910 -10.95900 Td +[ (mak) 10.00920 (es) -476.00900 (for) -476.98100 (ef) 25.01740 (fect) 1.00200 (i) 25.01470 (v) 14.98650 (e) -477.01700 (detection) -475.98500 (proposals\077) ] TJ +/R53 8.96640 Tf +166.67300 0 Td +[ (T) 54.99590 (r) 14.99200 (ans\056) -476.01200 (P) 90 (AMI) ] TJ +/R40 8.96640 Tf +47.41600 0 Td +(\054) Tj +-214.08900 -10.95900 Td +[ (38\0504\051\072814\226830\054) -249.97800 (2015\056) ] TJ +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 409.96300 457.67100 Tm +(7) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 308.86200 445.46100 Tm +[ (\13329\135) -556.01200 (H\056) -325.98900 (Hu) -327.01300 (and) -326.01900 (B\056) -327.01000 (Upcroft\056) -605.02000 (Nonparam) 0.99111 (etric) -326.99100 (semantic) -325.98400 (se) 15.01370 (gmenta\055) ] TJ +19.91910 -10.95900 Td +[ (tion) -250.00600 (for) -250.00600 (3D) -249.98900 (street) -250.00800 (scenes\056) -360.00800 (In) ] TJ +/R53 8.96640 Tf +102.33200 0 Td +[ (IR) 40.01480 (OS) ] TJ +/R40 8.96640 Tf +19.06290 0 Td +[ (\054) -250 (2013\056) ] TJ +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 478.06200 434.50200 Tm +(4) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 308.86200 422.29100 Tm +[ (\13330\135) -556.01200 (A\056) -376.00200 (Krizhe) 24.99840 (vsk) 14.98920 (y) 65 (\054) -408.01200 (I\056) -377.00700 (Sutsk) 10.00920 (e) 24.99020 (v) 14.98650 (er) 40.00390 (\054) -408.01200 (and) -376.99300 (G\056) -377.00400 (E\056) -375.99700 (Hinton\056) -764.98200 (ImageNet) ] TJ +19.91910 -10.95900 Td +[ <636c6173736902636174696f6e> -366.00700 (with) -365.98500 (deep) -365.98800 (con) 40.01480 (v) 19.99650 (olutional) -366.02000 (neural) -365.99300 (netw) 9.99826 (orks\056) -731.01700 (In) ] TJ +/R53 8.96640 Tf +10.95940 TL +(NIPS) ' +/R40 8.96640 Tf +18.92770 0 Td +[ (\054) -250 (2012\056) ] TJ +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 375.59500 400.37200 Tm +(1) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 308.86200 388.16100 Tm +[ (\13331\135) -556.01200 (L\056) -375.99700 (Kr\374ger) 39.97940 (\054) -408.01200 (C\056) -376.98000 (W\366hler) 39.99310 (\054) -408.01200 (A\056) -376.00200 (W\374rz\055W) 79.98610 (essel\054) -408.99200 (a) 0.98567 (nd) -377.01500 (F) 80.00240 (\056) -376.99300 (Stein\056) -764.01200 (In\055) ] TJ +19.91910 -10.95900 Td +[ (f) 10.00640 (actory) -308.00800 (calibration) -308 (of) -307.01900 (multiocular) -308.01300 (camera) -308.00200 (systems\056) -545.01400 (In) ] TJ +/R53 8.96640 Tf +197.90500 0 Td +(SPIE) Tj +-197.90500 -10.95900 Td +[ (Photonics) -387.99600 (Eur) 45.00310 (ope) -388.01300 (\050Optical) -388.01000 (Metr) 44.98130 (olo) 9.98737 (gy) -389.01700 (in) -387.98300 (Pr) 45.00310 (oduction) -387.99400 (Engi\055) ] TJ +10.95900 TL +(neering\051) ' +/R40 8.96640 Tf +30.37810 0 Td +[ (\054) -250 (2004\056) ] TJ +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 387.04500 355.28400 Tm +(2) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 308.86200 343.07300 Tm +[ (\13332\135) -556.01200 (A\056) -346.98800 (K) 15.01920 (undu\054) -372.98800 (Y) 128.98600 (\056) -347.97900 (Li\054) -371.98100 (F) 80.00520 (\056) -347.97900 (Dellaert\054) -372.99600 (F) 80.00240 (\056) -347.97900 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-467.99500 (From) -283.98900 (image\055le) 25.00660 (v) 14.98650 (el) -284.01400 (to) -282.99300 (pix) 14.99470 (el\055) ] TJ +19.91870 -10.95900 Td +[ (le) 24.99700 (v) 14.98650 (el) -269.98400 (labeling) -269.98600 (with) -270.01000 (con) 40.01760 (v) 19.99650 (olutional) -270.00200 (netw) 10 (orks\056) -424.01100 (In) ] TJ +/R53 8.96640 Tf +169.07800 0 Td +(CVPR) Tj +/R40 8.96640 Tf +22.41600 0 Td +[ (\054) -269.99700 (2015\056) ] TJ +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 70.03090 287.02600 Tm +(6) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 50.11210 274.60600 Tm +[ (\13354\135) -556.01000 (J\056) -324.01400 (Pont\055T) 45.00990 (uset) -324.00100 (and) -324.01500 (L\056) -323.98000 (V) 110.99500 (an) -325.01700 (Gool\056) -597 (Boosting) -324.00400 (object) -324.01500 (proposals\072) ] TJ +19.91870 -10.95900 Td +[ (From) -250.01000 (P) 15.00490 (ascal) -250.02200 (to) -250.01400 (COCO\056) -359.99600 (In) ] TJ +/R53 8.96640 Tf 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0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 153.84800 229.31000 Tm +(7) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 50.11210 216.89000 Tm +[ (\13356\135) -556.01000 (H\056) -485.00200 (Riemenschneider) 40.01480 (\054) -544.98200 (A\056) -485.00200 (B\363dis\055Szomor\372\054) -545.02000 (J\056) -484.98700 (W) 80.01060 (eissenber) 17.98160 (g\054) ] TJ +19.91870 -10.95900 Td +[ (and) -305.98000 (L\056) -305.98700 (V) 110.99500 (an) -306.98100 (Gool) 1 (\056) -540.01500 (Learning) -306.00900 (where) -306.98000 (to) -305.99500 (classify) -305.98400 (in) -305.99500 (multi\055vie) 25.01740 (w) ] TJ +10.95900 TL +T* +[ (semantic) -250.00400 (se) 15.01510 (gmentation\056) -360.00600 (In) ] TJ +/R53 8.96640 Tf +96.97890 0 Td +(ECCV) Tj +/R40 8.96640 Tf +22.91840 0 Td +[ (\056) -250 (2014\056) ] TJ +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 217.81400 194.97200 Tm +(4) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 50.11210 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(Granados\054) -435.99600 (D\056) -398.00300 (V) 110.99400 (azquez\054) -435.01200 (and) -397.98900 (A\056) -398.00300 (M\056) ] TJ +19.91870 -10.95780 Td +[ (Lopez\056) -456.97900 (V) 59.98010 (ision\055based) -280.02100 (of) 24.99160 <03696e652d6f6e6c696e65> -280.00300 (perception) -281.00800 (paradigm) -279.98700 (for) ] TJ +10.95900 TL +T* +[ (autonomous) -250.02100 (dri) 25.00110 (ving\056) -359.99500 (In) ] TJ +/R53 8.96640 Tf +86.93830 0 Td +[ (W) 60.01820 (A) 30.01110 (CV) ] TJ +/R40 8.96640 Tf +23.59880 0 Td +[ (\054) -250 (2015\056) ] TJ +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 208.45400 126.29700 Tm +(1) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 212.93700 126.29700 Tm +(\054) Tj +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 217.42000 126.29700 Tm +(4) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 221.90300 126.29700 Tm +(\054) Tj +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 226.38600 126.29700 Tm +(7) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 50.11210 113.87700 Tm +[ (\13359\135) -556.01000 (O\056) -430.98100 (Russak) 9.97920 (o) 14.98650 (vsk) 14.99060 (y) 65 (\054) -477.02000 (J\056) -432.01200 (Deng\054) -477.00600 (H\056) -431.98200 (Su\054) -477.00200 (J\056) -432.01100 (Krause\054) -476.99000 (S\056) -430.99500 (Satheesh\054) ] TJ +19.91870 -10.95900 Td +[ (S\056) -405.99000 (Ma\054) -445.01400 (Z\056) -406.01300 (Huang\054) -444.98500 (A\056) -407.01900 (Karpath) 5 (y) 65 (\054) -444.99900 (A\056) -406.01700 (Khosla\054) -445.01500 (M\056) -406.00500 (Bernstein\054) ] TJ +10.95900 TL +T* +[ (A\056) -191.98100 (C\056) -192.00200 (Ber) 18.01700 (g\054) -203.99500 (and) -192.01200 (L\056) -192.02000 (Fei\055Fei\056) -234.00900 (Imagenet) -192.00200 (lar) 18.01160 (ge) -192.01200 (scale) -191.99300 (visual) -191.99000 (recog\055) ] TJ +T* +[ (nition) -249.99600 (challenge\056) ] TJ +/R53 8.96640 Tf +62.99730 0 Td +(IJCV) Tj +/R40 8.96640 Tf +18.42580 0 Td +[ (\054) -250 (115\0503\051\072211\226252\054) -249.97800 (2015\056) ] TJ +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 241.60200 81 Tm +(1) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 308.86200 710.03700 Tm +[ (\13360\135) -556.01200 (B\056) -355.98100 (C\056) -355.98100 (Russell\054) -383.98600 (A\056) -356.00600 (T) 79.98070 (orralba\054) -383.02200 (K\056) -357.00800 (P) 111.97900 (\056) -356.99700 (Murph) 5 (y) 65 (\054) -383.00500 (and) -356.99700 (W) 91.98840 (\056) -355.99500 (T) 74.01220 (\056) -355.99500 (Free\055) ] TJ +19.91870 -10.95780 Td +[ (man\056) -659.00300 (LabelMe\072) -496.99200 (A) -343.00100 (database) -343.00700 (and) -344.01400 (web\055based) -342.98000 (tool) -344.02000 (for) -343.01800 (image) ] TJ +10.95900 TL +(annotation\056) ' +/R53 8.96640 Tf +43.32500 0 Td +(IJCV) Tj +/R40 8.96640 Tf +18.42620 0 Td +[ (\054) -250 (77\0501\0553\051\072157\226173\054) -249.99500 (2008\056) ] TJ +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 483.66600 688.12000 Tm +(2) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 308.86200 675.70000 Tm +[ (\13361\135) -556.01200 (T) 74.01220 (\056) -266.99100 (Scharw\344chter) 40.01760 (\054) -273.00300 (M\056) -267.99000 (Enzweiler) 40.01480 (\054) -272 (U\056) -268.00400 (Frank) 10.00370 (e\054) -272.01700 (and) -267.99300 (S\056) -268.02000 (Roth\056) -417.00800 (Ef\055) ] TJ +19.91870 -10.95900 Td +[ <026369656e74> -368 (multi\055cue) -367.01100 (scene) -368.00800 (se) 15.01370 (gmentation\056) -736.02100 (In) ] TJ +/R53 8.96640 Tf +153.87900 0 Td +(GCPR) Tj +/R40 8.96640 Tf +23.41170 0 Td +[ (\054) -368.01900 (2013\056) ] TJ +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 538.38700 664.74100 Tm +(1) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 542.87000 664.74100 Tm +(\054) Tj +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 328.78100 653.78200 Tm +(3) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 308.86200 641.36200 Tm +[ (\13362\135) -556.01200 (T) 74.01220 (\056) -201.99100 (Scharw\344chter) 40.01760 (\054) -212.01100 (M\056) -202.99000 (Enzweiler) 40.01480 (\054) -212.01100 (U\056) -202.00200 (Frank) 10.00370 (e\054) -211.98400 (and) -202.99300 (S\056) -202.01800 (Roth\056) -256.99200 (Stix\055) ] TJ +19.91870 -10.95900 Td +[ (mantics\072) -281.99600 (A) -195.01000 (medium\055le) 24.99020 (v) 14.98650 (el) -194.00800 (model) -194.99900 (for) -193.98000 (real\055time) -195.00400 (s) 1.00745 (emantic) -194.98200 (scene) ] TJ +10.95900 TL +T* +[ (understanding\056) -359.99200 (In) ] TJ +/R53 8.96640 Tf +65.98320 0 Td +(ECCV) Tj +/R40 8.96640 Tf +22.91800 0 Td +[ (\054) -250 (2014\056) ] TJ +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 445.56800 619.44400 Tm +(1) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 450.05100 619.44400 Tm +(\054) Tj +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 454.53400 619.44400 Tm +(4) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 308.86200 607.02400 Tm +[ (\13363\135) -556.01200 (A\056) -217.99000 (Schwing) -219.00600 (and) -218.97900 (R\056) -219.01200 (Urtasun\056) -292.98800 (Fully) -219.00900 (connected) -218.98200 (deep) -217.99600 (structured) ] TJ +19.91870 -10.95900 Td +[ (netw) 10 (orks\056) ] TJ +/R53 8.96640 Tf +38.25000 0 Td +[ (arXiv\0721503\05602351v1) -250.00600 (\133cs\056CV\135) ] TJ +/R40 8.96640 Tf +104.35000 0 Td +[ (\054) -250 (2015\056) ] TJ +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 499.26600 596.06500 Tm +(5) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 503.75000 596.06500 Tm +(\054) Tj +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 508.23300 596.06500 Tm +(6) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 308.86200 583.64600 Tm +[ (\13364\135) -556.01200 (S\056) -323.99900 (Sengupta\054) -343.98200 (E\056) -324.98200 (Gre) 25.00660 (v) 14.98650 (eson\054) -342.98500 (A\056) -324.98700 (Shahrokni\054) -343.01800 (and) -325.02000 (P) 110.97700 (\056) -325.02000 (H\056) -324.98700 (S\056) -324.00100 (T) 79.98070 (orr) 54.99590 (\056) ] TJ +19.91870 -10.95900 Td +[ (Semantic) -249.98900 (modelling) -250.00600 (of) -249.99500 (urban) -250.01100 (scenes\056) -360.00800 (In) ] TJ +/R53 8.96640 Tf +145.17300 0 Td +(ICRA) Tj +/R40 8.96640 Tf +19.92300 0 Td +[ (\054) -250 (2013\056) ] TJ +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 521.76300 572.68700 Tm +(4) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 526.24600 572.68700 Tm +(\054) Tj +ET +Q +1 0 0 rg +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 530.72900 572.68700 Tm +(7) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R40 8.96640 Tf +1 0 0 1 308.86200 560.26700 Tm +[ (\13365\135) -556.01200 (S\056) -285.00800 (Sengupta\054) -295.01400 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