commit 8d90d7bf25d40785469a78706c3c7b2db111512b Author: Johan Date: Tue Dec 16 09:56:21 2025 +0100 first comit diff --git a/README.md b/README.md new file mode 100644 index 0000000..4eee972 --- /dev/null +++ b/README.md @@ -0,0 +1,331 @@ +# TP : Traitement d'images par lots (multiprocessing) + +## Informations générales + +**Cours** : Python Avancé > Programmation concurrente > Parallélisation de tâches CPU-bound avec multiprocessing \ +**Objectifs pédagogiques** : +- Python avancé : programmation fonctionnelle +- Python avancé : utilisation de bibliothèques spécialisées (pathlib, pandas, pillow, etc.) +- Programmation concurrente : parallélisation de tâches CPU-bound avec multiprocessing +- Cryptographie et sécurité : hachage et vérification de l'intégrité des données +- Gestion des erreurs et logging avancé : capture d'exceptions ciblées, logging +- Tests : découverte de pytest +- Outils modernes (poetry, PyCharm) +- Bonnes pratiques de l'entreprise + +--- + +## Prérequis + +### Installation et configuration de l’environnement + +Installer les dépendances via `poetry install` depuis un terminal PyCharm. + +Pour lancer les tests unitaires : + +- `poetry run pytest -p no:warnings` +- `poetry run pytest tests/test_calculate_new_height.py -p no:warnings` +- `poetry run pytest tests/test_calculate_new_height.py::test_calculate_new_height_various_scenarios` + +### Connaissances préalables + +- Connaissances de base en programmation + +--- + +## Énoncé + +L'objectif de ce TP est de concevoir et de réaliser un script Python robuste et performant pour le traitement d'images (redimensionnement) en lots. +Vous partirez d'un **squelette de script `process.py`** pour progressivement implémenter la logique métier, paralléliser l'exécution pour des performances maximales, et enfin ajouter un système de cache pour optimiser les traitements répétitifs. + +Plus tard, nous pourrons utiliser les images générées pour alimenter un site web responsive, afin de charger les images les plus adaptées à la taille de l'écran de l'utilisateur, et ainsi améliorer les performances SEO (référencement naturel), et réduire le coût du SEA (référencement payant type Google Ads). + +### Ce que l'on cherche à accomplir + +Redimensionner des images en plusieurs tailles et formats, en utilisant la bibliothèque Pillow. + +#### Exemples de redimensionnement + +Exemple : à partir d'une image source `PIA04921.jpg` de 5184x3456 pixels, le script va générer les images suivantes : +- `PIA04921_4000x2667.png` (4000 pixels de large, format PNG) +- `PIA04921_4000x2667.webp` (4000 pixels de large, format WebP) +- `PIA04921_2500x1667.png` (2500 pixels de large, format PNG) +- `PIA04921_2500x1667.webp` (2500 pixels de large, format WebP) +- `PIA04921_1928x1286.png` (1928 pixels de large, format PNG) +- `PIA04921_1928x1286.webp` (1928 pixels de large, format WebP) +- etc. + +#### Version sans hachage (plus simple, mais moins optimisé) + +Pour vous donner une idée du résultat final du script côté console, voici des exemples de console d'exécution du script. + +Lancement du script, avec le mode d'exécution séquentiel (sans multiprocessing) : + +``` +INFO - --- Mode d'exécution sélectionné : SEQUENTIAL --- +INFO - Trouvé 6 fichier(s) dans 'input_images'. +INFO - -> 6 fichier(s) à traiter. +INFO - Démarrage de 'Redimensionnement des images' (132 tâches) en mode séquentiel... +INFO - Tâche 'Redimensionnement des images' terminée en 53.42 secondes. +INFO - --- Résumé des temps --- +INFO - Temps de redimensionnement : 53.42 secondes +INFO - -------------------------- +``` + +Avec le mode d'exécution parallèle (avec multiprocessing). C'est beaucoup plus rapide : + +``` +INFO - --- Mode d'exécution sélectionné : PARALLEL --- +INFO - Trouvé 6 fichier(s) dans 'input_images'. +INFO - -> 6 fichier(s) à traiter. +INFO - Démarrage de 'Redimensionnement des images' (132 tâches) avec 19 processus... +INFO - Tâche 'Redimensionnement des images' terminée en 6.88 secondes. +INFO - --- Résumé des temps --- +INFO - Temps de redimensionnement : 6.88 secondes +INFO - -------------------------- +``` + +#### Version avec hachage (plus optimisé) + +Lancement du script, avec le mode d'exécution séquentiel (sans multiprocessing) : + +``` +INFO - --- Mode d'exécution sélectionné : SEQUENTIAL --- +INFO - Trouvé 6 fichier(s) dans 'input_images'. +INFO - Démarrage de 'Calcul des hashes' (6 tâches) en mode séquentiel... +INFO - Tâche 'Calcul des hashes' terminée en 0.08 secondes. +INFO - Filtrage des fichiers modifiés... +INFO - Fichier de hash 'hashes.csv' mis à jour. +INFO - -> 6 fichier(s) à traiter. +INFO - Démarrage de 'Redimensionnement des images' (132 tâches) en mode séquentiel... +INFO - Tâche 'Redimensionnement des images' terminée en 53.42 secondes. +INFO - --- Résumé des temps --- +INFO - Temps de hachage : 0.08 secondes +INFO - Temps de redimensionnement : 53.42 secondes +INFO - -------------------------- +INFO - Traitement complet terminé en 53.51 secondes. +``` + +Avec le mode d'exécution parallèle (avec multiprocessing). C'est toujours beaucoup plus rapide : + +``` +INFO - --- Mode d'exécution sélectionné : PARALLEL --- +INFO - Trouvé 6 fichier(s) dans 'input_images'. +INFO - Démarrage de 'Calcul des hashes' (6 tâches) avec 19 processus... +INFO - Tâche 'Calcul des hashes' terminée en 0.52 secondes. +INFO - Filtrage des fichiers modifiés... +INFO - Fichier de hash 'hashes.csv' mis à jour. +INFO - -> 6 fichier(s) à traiter. +INFO - Démarrage de 'Redimensionnement des images' (132 tâches) avec 19 processus... +INFO - Tâche 'Redimensionnement des images' terminée en 6.88 secondes. +INFO - --- Résumé des temps --- +INFO - Temps de hachage : 0.52 secondes +INFO - Temps de redimensionnement : 6.88 secondes +INFO - -------------------------- +INFO - Traitement complet terminé en 7.40 secondes. +``` + +Dans le cas où le hachage des images n'a pas changé, le script ira très vite (on ne redimensionne rien, on ne fait que calculer les hashes) : + +``` +INFO - --- Mode d'exécution sélectionné : PARALLEL --- +INFO - Trouvé 6 fichier(s) dans 'input_images'. +INFO - Démarrage de 'Calcul des hashes' (6 tâches) avec 19 processus... +INFO - Tâche 'Calcul des hashes' terminée en 0.52 secondes. +INFO - Filtrage des fichiers modifiés... +INFO - Fichier de hash 'hashes.csv' mis à jour. +INFO - -> 0 fichier(s) à traiter. +INFO - Aucun fichier à redimensionner. +INFO - --- Résumé des temps --- +INFO - Temps de hachage : 0.52 secondes +INFO - Temps de redimensionnement : 0.00 secondes +INFO - -------------------------- +INFO - Traitement complet terminé en 0.52 secondes. +``` + +#### Vision d'ensemble du script (sans la partie hachage) + +```mermaid +graph TD + A[Démarrage du script] --> B["Lister les fichiers source .jpg"] + B --> G[Générer les tâches de redimensionnement pour tous les fichiers] + G --> H{Aucune tâche de redimensionnement?} + H -- Non --> I["Exécuter le redimensionnement (parallèle ou séquentiel)"] + I --> J[Sauvegarder les nouvelles images] + J --> L[Fin] + H -- Oui --> L[Fin] +``` + +----- + +### Partie 1 : le redimensionnement d'images séquentiel + +Le squelette du script `process.py` vous est fourni. +Votre première tâche est de compléter les sections marquées par des `TODO` pour implémenter la logique de base du traitement séquentiel. + +1. **Prise en main de la structure :** + * Ouvrez le fichier `process.py` et prenez connaissance de sa structure : les imports, les constantes globales (`SOURCE_DIRECTORY`, `TARGET_DIRECTORY`, etc.) et les fonctions déjà définies. + * Remarquez que la fonction `calculate_new_height` est déjà implémentée. + +2. **Création de la logique de redimensionnement :** + * Votre premier objectif est de compléter la fonction `resize_single_image`. Suivez les instructions laissées dans le commentaire `TODO` pour ouvrir, redimensionner et sauvegarder une image. + * Ensuite, dans la fonction `main`, complétez la section `TODO` qui liste les fichiers sources (`all_source_files`). + +### Partie 2 : flexibilité et traitement par lots + +Améliorez le script pour qu'il ne soit plus limité à une seule taille et un seul format. + +1. **Configuration avancée :** + * Repérez en haut du script les deux listes de configuration déjà définies : + * `RESIZE_WIDTHS`: une liste d'entiers contenant toutes les largeurs de sortie souhaitées (ex: `[4000, 2500, 1928, 992, 768, 576, 480, 260, 150, 100, 50]` inspirée des breakpoints Bootstrap). + * `FORMATS`: une liste de chaînes de caractères pour les formats de sortie (ex: `["png", "webp"]`). + +2. **Mise à jour de la logique :** + * Dans la fonction `main`, modifiez la section `TODO` pour construire la liste `resize_tasks`. Vous devez maintenant construire cette liste pour que **chaque image source** soit traitée pour **chaque largeur** de `RESIZE_WIDTHS` et sauvegardée dans **chaque format** de `FORMATS`. + * Adaptez le nommage des fichiers de sortie pour qu'il inclue la taille, par exemple : `{nom_original}_{largeur}x{hauteur}.{format}`. + +Le schéma ci-dessous illustre comment une seule image source génère une multitude de tâches de redimensionnement : + +```mermaid +graph TD + subgraph "Entrées" + A["Fichier img.jpg"] + subgraph "Configuration" + B1[Largeur 4000] + B2[Largeur 2500] + B3[...] + C1["Format png"] + C2["Format webp"] + end + end + + subgraph "Tâches générées" + D1["Tâche: img, 4000, png"] + D2["Tâche: img, 4000, webp"] + D3["Tâche: img, 2500, png"] + D4["Tâche: img, 2500, webp"] + D5[...] + end + + A --> D1 & D2 & D3 & D4 & D5 + B1 & C1 --> D1 + B1 & C2 --> D2 + B2 & C1 --> D3 + B2 & C2 --> D4 +``` + +> **ASTUCE POUR TESTER** : À ce stade, la logique de hachage n'est pas encore faite. Pour que votre script traite les images, vous pouvez temporairement forcer le traitement de tous les fichiers en ajoutant cette ligne dans `main()`, juste après avoir défini `all_source_files` : +> `files_to_process = all_source_files` +> Vous pourrez retirer cette ligne lorsque vous implémenterez la Partie 4. + +À ce stade, votre script est fonctionnel, mais probablement lent si vous avez beaucoup d'images et de tailles cibles. + +### Partie 3 : Parallélisation des tâches (Multiprocessing) + +C'est le cœur du TP. Vous allez drastiquement accélérer le script en utilisant la programmation parallèle pour exploiter tous les cœurs de votre CPU. + +La différence entre l'exécution séquentielle et parallèle est la suivante : + +**Mode Séquentiel :** Les tâches sont exécutées les unes après les autres. + +```mermaid +graph TD + A[Démarrage] --> B[Tâche 1] + B --> C[Tâche 2] + C --> D[Tâche 3] + D --> E[...] + E --> F[Fin] +``` + +**Mode Parallèle :** Les tâches sont distribuées à un pool de processus (workers) et s'exécutent en même temps. + +```mermaid +graph TD + A[Démarrage] --> B[Pool de processus] + B --> T1[Tâche 1] + B --> T2[Tâche 2] + B --> T3[Tâche 3] + B --> T4[Tâche 4] + B --> T...[...] + + subgraph "Exécution concurrente" + T1 + T2 + T3 + T4 + T... + end + + T1 & T2 & T3 & T4 & T... --> E[Attente de la fin de toutes les tâches] + E --> F[Fin] +``` + +1. **Refactorisation du code :** + * L'architecture du code est déjà prête pour la parallélisation. Remarquez que la fonction "worker" `resize_single_image` est déjà conçue pour accepter un seul argument de type `Tuple`, ce qui est une condition requise pour `ProcessPoolExecutor.map`. + +2. **Implémentation des exécuteurs :** + * Votre mission est de compléter les deux fonctions "runner" : `run_sequential` et `run_parallel`. + * Suivez les `TODO` dans `run_sequential` pour implémenter une simple boucle `for` qui exécute les tâches les unes après les autres. + * Suivez les `TODO` dans `run_parallel` pour utiliser `concurrent.futures.ProcessPoolExecutor` afin de distribuer les tâches sur plusieurs processus. + * Les deux fonctions doivent mesurer et retourner le temps d'exécution. + +3. **Intégration et comparaison :** + * Dans `main`, la logique pour sélectionner le bon "runner" en fonction de la constante `EXECUTION_MODE` est déjà en place. Une fois vos runners implémentés, vous pourrez basculer entre `"parallel"` et `"sequential"` pour comparer les performances. + * Assurez-vous que l'appel au runner pour le redimensionnement est correctement effectué. Vous devriez constater un gain de performance spectaculaire en mode parallèle ! + +### Partie 4 (bonus) : optimisation avec un cache de hachage + +Votre script est rapide, mais il retraite toutes les images à chaque lancement. Vous allez maintenant implémenter un système de cache pour ne traiter que les fichiers nouveaux ou modifiés. + +Voici un schéma représentant la logique complète du script que vous allez construire : + +```mermaid +graph TD + A[Démarrage du script] --> B["Lister les fichiers source .jpg"] + B --> C[Calculer les nouveaux hashes SHA3 des fichiers] + C --> D["Lire les anciens hashes depuis hashes.csv"] + D --> E{Fichiers modifiés ou nouveaux?} + E -- Oui --> F[Filtrer la liste des fichiers à traiter] + F --> G[Générer les tâches de redimensionnement] + G --> H{Aucune tâche de redimensionnement?} + H -- Non --> I["Exécuter le redimensionnement (parallèle ou séquentiel)"] + I --> J[Sauvegarder les nouvelles images] + J --> K["Mettre à jour hashes.csv avec les nouveaux hashes"] + K --> L[Fin] + E -- Non --> H + H -- Oui --> K +``` + +1. **Hachage de fichiers :** + * La fonction `compute_sha3_512` qui calcule l'empreinte numérique d'un fichier vous est déjà fournie. Notez qu'elle est optimisée pour lire les fichiers par blocs et qu'elle est, comme `resize_single_image`, prête pour la parallélisation. + +2. **Mise en place du cache :** + * Complétez la fonction `get_hashes_from_csv` en suivant le `TODO`. Elle doit lire un fichier `hashes.csv` et retourner un dictionnaire des hashes existants. L'utilisation de la bibliothèque `pandas` est recommandée à cet effet. + * Dans la fonction `main`, suivez les `TODO` pour : + 1. Préparer les `hash_tasks` pour tous les fichiers sources. + 2. Appeler le "runner" pour exécuter ces tâches. Vous pourrez immédiatement appliquer le `run_parallel` que vous venez de créer pour accélérer également cette étape de hachage ! + 3. Implémenter la logique de filtrage pour ne garder que les fichiers modifiés, c'est à dire en comparant les nouveaux hashes aux anciens et ne gardant que les fichiers modifiés dans la liste `files_to_process`. + 4. Sauvegarder les nouveaux hashes dans le fichier CSV. + +Exemple de fichier `hashes.csv` généré par votre programme : +``` +filename,hash +GSFC_20171208.jpg,c84a997754ef10a2b8a66d793372983180bdb961753d16ffa9037c32ef09db483ed67ff774863c6591b9120f05e31ddd9893d7e5ac59c1049a993c396af6baa4 +ISS070E034016.jpg,82ffdf4f9c0b13bd42626f6026f268b4db078b3e26324038b925c5e563149b0d5e40124251378d0c6f085a4d877fb38a91465934d0f9c51aa34e82740168f7ee +ISS070E052303.jpg,4f8d0614830652a19e53571b26fa44d6f08cee386e19c69ab1347d2afc591e67cdbb48640e050fdf03ce44b1ce8ad8d43f26a2e407cd854205fb23de70ca52fc +PIA04921.jpg,775e80e7782f98cded5e4be2ee5933e7df73ef02470dbd017e1782bba7e01ad30b6c01e78ba825e218db316c8839233b5e9a0a7e8642a5c69e9949aa1f23a00b +PIA18033.jpg,2b6e0212d9185f58da5d7c2d37a732edb055a4032c1679857f00f2cd26bba16d91b5b54944333686bd72d79f0a33421c6edffad2f3c75ea30317a83d5b71b44f 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+authors = [ + {name = "Your Name",email = "you@example.com"} +] +readme = "README.md" +classifiers = [ + "Programming Language :: Python :: 3.13", +] +keywords = ["multiprocessing", "concurrency"] + +exclude = [ + { path = "tests", format = "wheel" } +] + +requires-python = ">=3.13" + +[tool.poetry.dependencies] +python = "^3.13" +pandas = "^2.3.1" +pillow = "^11.3.0" + +[tool.poetry.group.dev.dependencies] +pytest = "^8.4.1" +pytest-cov = "^6.2.1" +coverage = { version="*", extras=["toml"]} + +[tool.black] +line-length = 120 + +[tool.pycln] +all = true + +[tool.isort] +line_length = 120 +multi_line_output = 3 +include_trailing_comma = true +force_grid_wrap = 0 +use_parentheses = true +ensure_newline_before_comments = true + +[build-system] +requires = ["poetry-core>=2.0.0,<3.0.0"] +build-backend = "poetry.core.masonry.api" diff --git a/resources/input_images.zip b/resources/input_images.zip new file mode 100644 index 0000000..f343226 Binary files /dev/null and b/resources/input_images.zip differ diff --git a/src/tp_multiprocessing/__init__.py b/src/tp_multiprocessing/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/tp_multiprocessing/process.py b/src/tp_multiprocessing/process.py new file mode 100644 index 0000000..2d15485 --- /dev/null +++ b/src/tp_multiprocessing/process.py @@ -0,0 +1,263 @@ +""" +Script pour redimensionner des images en parallèle ou séquentiellement. + +Ce script inclut : + +- Calcul du hash SHA3-512 des fichiers pour détecter les modifications (la fonction de hachage est volontairement consommatrice en temps pour simuler une charge de travail). +- Redimensionnement des images à plusieurs largeurs cibles tout en préservant le ratio hauteur/largeur. +- Sauvegarde des images redimensionnées dans différents formats (PNG, WebP). +- Gestion des erreurs de lecture de fichiers et de redimensionnement. +- Utilisation de multiprocessing pour paralléliser les tâches de redimensionnement, et de hashing. +""" + +import hashlib +import time +import logging +import multiprocessing +import sys +import pandas as pd +from pathlib import Path +from concurrent.futures import ProcessPoolExecutor +from PIL import Image +from typing import List, Tuple, Dict, Callable, Any, Optional + +# --- CONFIGURATION --- +EXECUTION_MODE: str = "sequential" # Options : "parallel" ou "sequential" +SOURCE_DIRECTORY: Path = Path("../../resources/input_images/") +TARGET_DIRECTORY: Path = Path("../../resources/output_images/") +HASHES_CSV_PATH: Path = Path("../../resources/hash/hashes.csv") +RESIZE_WIDTHS: List[int] = [4000, 2500, 1928, 992, 768, 576, 480, 260, 150, 100, 50] # inspiré des breakpoints de Bootstrap +FORMATS: List[str] = ["png", "webp"] +FILENAME_COL: str = 'filename' +HASH_COL: str = 'hash' + + +def setup_logging() -> None: + """ + Configure le système de logging pour le script. + """ + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(levelname)s - %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + handlers=[ + logging.StreamHandler(sys.stdout), + ] + ) + +def calculate_new_height(orig_width: int, orig_height: int, new_width: int) -> int: + """ + Calcule la hauteur d'une image pour une nouvelle largeur donnée, + tout en préservant le ratio hauteur/largeur original. + + Paramètres: + orig_width (int): la largeur originale de l'image. + orig_height (int): la hauteur originale de l'image. + new_width (int): la nouvelle largeur souhaitée pour l'image. + + Retourne: + int: la nouvelle hauteur calculée qui maintient le ratio de l'image. + """ + if orig_width <= 0: + raise ValueError("La largeur d'origine doit être positive.") + scale_ratio = new_width / orig_width + return int(orig_height * scale_ratio) + + +def compute_sha3_512(args: Tuple[Path]) -> Tuple[Optional[str], Path]: + """ + Calcule l'empreinte numérique (hash) SHA3-512 d'un fichier. + La fonction lit le fichier par blocs pour gérer efficacement les fichiers volumineux. + L'argument est un tuple pour assurer la compatibilité avec `ProcessPoolExecutor.map`. + + Paramètres: + args (Tuple[Path]): un tuple contenant le chemin du fichier à traiter. + Ex: (filepath,) + + Retourne: + tuple (Tuple[Optional[str], Path]): un tuple contenant le hash hexadécimal (str) et le chemin du fichier (Path). + En cas d'erreur de lecture, retourne (None, filepath). + """ + filepath, = args + hasher = hashlib.sha3_512() + try: + with open(filepath, 'rb') as f: + for block in iter(lambda: f.read(65536), b''): + hasher.update(block) + return hasher.hexdigest(), filepath + except IOError as e: + logging.error("Erreur de lecture du fichier %s: %s", filepath.name, e) + return None, filepath + + +def resize_single_image(args: Tuple[Path, int, str]) -> str: + """ + Redimensionne une image à une largeur cible et la sauvegarde dans un format spécifié. + Le redimensionnement n'est effectué que si la largeur cible est inférieure à la largeur originale. + L'algorithme de rééchantillonnage LANCZOS est utilisé pour une haute qualité de réduction. + + Paramètres: + args (Tuple[Path, int, str]): un tuple contenant les informations nécessaires : + - source_file (Path): Chemin de l'image d'origine. + - output_width (int): Largeur de l'image de sortie. + - fmt (str): Format de sortie souhaité (ex: "png", "webp"). + + Retourne: + str: un message de statut indiquant le résultat de l'opération (succès, échec, ignoré). + """ + source_file, output_width, fmt = args + # TODO : Partie 1 - implémenter la logique de redimensionnement. + # 1. Ouvrir l'image source avec Pillow (`Image.open`). Utiliser un bloc `with`. + # 2. Récupérer la largeur et la hauteur originales de l'image. + # 3. Vérifier si `output_width` est >= à la largeur originale. Si c'est le cas, retourner un message d'information. + # 4. Calculer la nouvelle hauteur en utilisant la fonction `calculate_new_height`. + # 5. Redimensionner l'image avec `img.resize`, en utilisant `Image.Resampling.LANCZOS`. + # 6. Construire le chemin de sortie du fichier (ex: f"{source_file.stem}_{output_width}x{new_height}.{fmt}"). + # 7. Sauvegarder l'image redimensionnée. + # 8. Retourner un message de succès. + # 9. Encadrer la logique dans un bloc `try...except Exception` pour capturer les erreurs et retourner un message d'erreur. + pass + + +def run_sequential(worker_function: Callable[[Any], Any], tasks: List[Any], description: str = "") -> Tuple[List[Any], float]: + """ + Exécute une série de tâches de manière séquentielle, l'une après l'autre. + Cette fonction est utile pour le débogage ou sur des systèmes mono-cœur. + + Paramètres: + worker_function (Callable[[Any], Any]): la fonction à appliquer à chaque élément de la liste de tâches. + tasks (List[Any]): une liste d'arguments, où chaque argument est destiné à un appel de `worker_function`. + description (str, optional): une description de la tâche globale pour l'affichage. + + Retourne: + float: durée totale d'exécution en secondes. + """ + # TODO : Partie 3 - implémenter l'exécuteur séquentiel. + # 1. Logguer le message de démarrage. + # 2. Enregistrer le temps de début (`time.time()`). + # 3. Exécuter les tâches avec une list comprehension (ou boucle for): `[worker_function(task) for task in tasks]`. + # 4. Enregistrer le temps de fin. + # 5. Calculer la durée et logguer le message de fin. + # 6. Retourner la liste des résultats et la durée. + pass + + +def run_parallel(worker_function: Callable[[Any], Any], tasks: List[Any], description: str = "") -> Tuple[List[Any], float]: + """ + Exécute une série de tâches en parallèle en utilisant un pool de processus. + Le nombre de processus est basé sur le nombre de cœurs CPU disponibles pour optimiser les performances. + + Paramètres: + worker_function (Callable[[Any], Any]): la fonction à exécuter pour chaque tâche. + tasks (List[Any]): une liste d'arguments à passer à `worker_function`. + description (str, optional): une description de la tâche globale pour l'affichage. + + Retourne: + float: durée totale d'exécution en secondes. + """ + # TODO : Partie 3 - implémenter l'exécuteur parallèle. + # 1. Déterminer le nombre de workers (`multiprocessing.cpu_count()`). + # 2. Logguer le message de démarrage. + # 3. Enregistrer le temps de début. + # 4. Utiliser un `ProcessPoolExecutor` dans un bloc `with`, en passant `initializer=setup_logging`. + # 5. Appeler `executor.map(worker_function, tasks)` et convertir le résultat en liste. + # 6. Enregistrer le temps de fin. + # 7. Calculer la durée et logguer le message de fin. + # 8. Retourner la liste des résultats et la durée. + pass + + +def get_hashes_from_csv() -> Dict[str, str]: + """ + Charge les hashes de fichiers précédemment calculés depuis un fichier CSV. + Permet de comparer les hashes actuels aux anciens pour détecter les fichiers modifiés. + + Paramètres: + Aucun. + + Retourne: + dict (Dict[str, str]) : un dictionnaire où les clés sont les noms de fichiers (str) et les valeurs + sont leurs hashes SHA3-512 (str). Retourne un dictionnaire vide si le + fichier CSV n'existe pas. + """ + # TODO : Partie 4 - implémenter la lecture du CSV de hashes. + # 1. Vérifier si `HASHES_CSV_PATH` existe. Si non, retourner un dictionnaire vide. + # 2. Utiliser un bloc `try...except` pour lire le CSV avec `pd.read_csv`. + # 3. Convertir le DataFrame en dictionnaire (ex: `pd.Series(df[HASH_COL].values, index=df[FILENAME_COL]).to_dict()`). + # 4. Retourner le dictionnaire. En cas d'erreur (fichier vide...), retourner un dictionnaire vide. + pass + + +def main() -> None: + """ + Point d'entrée principal du script. + Orchestre le processus de traitement d'images. + """ + setup_logging() + + # --- Initialisation --- + TARGET_DIRECTORY.mkdir(parents=True, exist_ok=True) + HASHES_CSV_PATH.parent.mkdir(parents=True, exist_ok=True) + total_start_time = time.time() + + # --- Sélection du mode d'exécution --- + if EXECUTION_MODE == "parallel": + runner = run_parallel + elif EXECUTION_MODE == "sequential": + runner = run_sequential + else: + logging.critical("Mode d'exécution inconnu : '%s'.", EXECUTION_MODE) + raise ValueError(f"Mode d'exécution inconnu : '{EXECUTION_MODE}'.") + logging.info("--- Mode d'exécution sélectionné : %s ---", EXECUTION_MODE.upper()) + + # TODO : Partie 1 - lister les fichiers sources dans SOURCE_DIRECTORY. + # Utiliser `SOURCE_DIRECTORY.iterdir()` avec une list comprehension (ou boucle for) pour ne garder que les fichiers (exclusion des dossiers) dans `all_source_files`. + all_source_files = [] + logging.info("Trouvé %d fichier(s) dans '%s'.", len(all_source_files), SOURCE_DIRECTORY.name) + + # La logique de Hachage et Filtrage sera implémentée en Partie 4. + # --- Hachage --- + # hash_tasks = [(f,) for f in all_source_files] + # hash_results, hash_duration = runner(compute_sha3_512, hash_tasks, "Calcul des hashes") + + # --- Filtrage --- + logging.info("Filtrage des fichiers modifiés...") + # Tant qu'on n'a pas implémenté le hachage, on traite tous les fichiers. + files_to_process = all_source_files + # TODO : Partie 4 - implémenter la logique de filtrage. + # 1. Charger les anciens hashes depuis le fichier CSV en appelant `get_hashes_from_csv`. + # old_hashes = get_hashes_from_csv() + # files_to_process: List[Path] = [] + # new_hash_records: List[Dict[str, str]] = [] + # + # 2. Itérer sur `hash_results` pour comparer les hashes et remplir `files_to_process`. + # for result in hash_results: + # file_hash: Optional[str] = result[0] + # filepath: Path = result[1] + # ... + # 3. Préparer `new_hash_records` pour la sauvegarde. + #if new_hash_records: + # 4. Sauvegarder les nouveaux hashes dans le CSV avec pandas. + # logging.info("-> %d fichier(s) à traiter.", len(files_to_process)) + + # --- Redimensionnement --- + resize_duration = 0.0 + if files_to_process: + # TODO : Partie 2 - préparer les tâches de redimensionnement (triple boucle : chaque fichier d'entrée, chaque largeur, chaque format). + resize_tasks = [] + resize_duration = runner(resize_single_image, resize_tasks, "Redimensionnement des images") + else: + logging.info("Aucun fichier à redimensionner.") + + # --- Résumé Final --- + total_duration = time.time() - total_start_time + logging.info("--- Résumé des temps ---") + #logging.info(f"Temps de hachage : {hash_duration:.2f} secondes") + logging.info(f"Temps de redimensionnement : {resize_duration:.2f} secondes") + logging.info("--------------------------") + logging.info(f"Traitement complet terminé en {total_duration:.2f} secondes.") + + +if __name__ == "__main__": + multiprocessing.freeze_support() + main() \ No newline at end of file diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/test_calculate_new_height.py b/tests/test_calculate_new_height.py new file mode 100644 index 0000000..6317800 --- /dev/null +++ b/tests/test_calculate_new_height.py @@ -0,0 +1,41 @@ +from tp_multiprocessing.process import calculate_new_height +import pytest + +# ## Tests des cas de succès et des cas limites ## + +@pytest.mark.parametrize("orig_width, orig_height, new_width, expected_height", [ + # Cas 1: Réduction standard (ratio 16:9) + (1920, 1080, 960, 540), + + # Cas 2: Format portrait + (800, 1200, 400, 600), + + # Cas 3: Pas de redimensionnement (la nouvelle largeur est identique) + (1024, 768, 1024, 768), + + # Cas 4: Le calcul du ratio produit un flottant (test de la conversion en int) + (300, 200, 100, 66), # 200 * (100/300) = 66.66... -> 66 + + # Cas 5: Agrandissement (upscaling) + (100, 100, 250, 250), + + # Cas 6: Hauteur d'origine nulle + (1000, 0, 500, 0) +]) +def test_calculate_new_height_various_scenarios(orig_width, orig_height, new_width, expected_height): + """ + Vérifie le calcul de la nouvelle hauteur pour plusieurs scénarios valides. + """ + assert calculate_new_height(orig_width, orig_height, new_width) == expected_height + +# ## Tests des cas d'erreur ## + +@pytest.mark.parametrize("invalid_width", [0, -1, -100]) +def test_calculate_new_height_with_invalid_width_raises_error(invalid_width): + """ + Vérifie qu'une ValueError est levée si la largeur d'origine est nulle ou négative. + """ + # Le contexte `pytest.raises` vérifie qu'une exception est bien levée. + # L'argument `match` vérifie que le message d'erreur contient le texte attendu. + with pytest.raises(ValueError, match="La largeur d'origine doit être positive"): + calculate_new_height(invalid_width, 1080, 500)