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# 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
PIA25163.jpg,c3f5d3268b2bc4ec810e084d2156492aee02f6d10d2950d52a5d7471a76de96cb9f5505e565b2b064b4d81cd14814416579e8ebb430d3e9a72346a3a175bf619
```
### Partie 5 : Tests unitaires
Pour garantir la fiabilité de votre code, nous allons examiner et utiliser des tests unitaires.
1. **Examen d'un test existant :**
* Le test `test_calculate_new_height_various_scenarios` vous est intégralement fourni.
* Analysez sa structure, notamment l'utilisation de `@pytest.mark.parametrize` et `pytest.raises`.
* Exécutez-le pour vous assurer que tout fonctionne.
2. **Ajout d'un test unitaire :**
* En s'inspirant du test existant, ajoutez un autre test `test_calculate_new_height_with_invalid_width_raises_error`, qui vérifie qu'une exception `ValueError` est bien levée pour des largeurs (`orig_width`) incorrectes : `0` et `-100`.

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docs = ["furo", "olefile", "sphinx (>=8.2)", "sphinx-autobuild", "sphinx-copybutton", "sphinx-inline-tabs", "sphinxext-opengraph"]
fpx = ["olefile"]
mic = ["olefile"]
test-arrow = ["pyarrow"]
tests = ["check-manifest", "coverage (>=7.4.2)", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "trove-classifiers (>=2024.10.12)"]
typing = ["typing-extensions ; python_version < \"3.10\""]
xmp = ["defusedxml"]
[[package]]
name = "pluggy"
version = "1.6.0"
description = "plugin and hook calling mechanisms for python"
optional = false
python-versions = ">=3.9"
groups = ["dev"]
files = [
{file = "pluggy-1.6.0-py3-none-any.whl", hash = "sha256:e920276dd6813095e9377c0bc5566d94c932c33b27a3e3945d8389c374dd4746"},
{file = "pluggy-1.6.0.tar.gz", hash = "sha256:7dcc130b76258d33b90f61b658791dede3486c3e6bfb003ee5c9bfb396dd22f3"},
]
[package.extras]
dev = ["pre-commit", "tox"]
testing = ["coverage", "pytest", "pytest-benchmark"]
[[package]]
name = "pygments"
version = "2.19.2"
description = "Pygments is a syntax highlighting package written in Python."
optional = false
python-versions = ">=3.8"
groups = ["dev"]
files = [
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]
[package.extras]
windows-terminal = ["colorama (>=0.4.6)"]
[[package]]
name = "pytest"
version = "8.4.1"
description = "pytest: simple powerful testing with Python"
optional = false
python-versions = ">=3.9"
groups = ["dev"]
files = [
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colorama = {version = ">=0.4", markers = "sys_platform == \"win32\""}
iniconfig = ">=1"
packaging = ">=20"
pluggy = ">=1.5,<2"
pygments = ">=2.7.2"
[package.extras]
dev = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "requests", "setuptools", "xmlschema"]
[[package]]
name = "pytest-cov"
version = "6.2.1"
description = "Pytest plugin for measuring coverage."
optional = false
python-versions = ">=3.9"
groups = ["dev"]
files = [
{file = "pytest_cov-6.2.1-py3-none-any.whl", hash = "sha256:f5bc4c23f42f1cdd23c70b1dab1bbaef4fc505ba950d53e0081d0730dd7e86d5"},
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[package.dependencies]
coverage = {version = ">=7.5", extras = ["toml"]}
pluggy = ">=1.2"
pytest = ">=6.2.5"
[package.extras]
testing = ["fields", "hunter", "process-tests", "pytest-xdist", "virtualenv"]
[[package]]
name = "python-dateutil"
version = "2.9.0.post0"
description = "Extensions to the standard Python datetime module"
optional = false
python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
groups = ["main"]
files = [
{file = "python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3"},
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[package.dependencies]
six = ">=1.5"
[[package]]
name = "pytz"
version = "2025.2"
description = "World timezone definitions, modern and historical"
optional = false
python-versions = "*"
groups = ["main"]
files = [
{file = "pytz-2025.2-py2.py3-none-any.whl", hash = "sha256:5ddf76296dd8c44c26eb8f4b6f35488f3ccbf6fbbd7adee0b7262d43f0ec2f00"},
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name = "six"
version = "1.17.0"
description = "Python 2 and 3 compatibility utilities"
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python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
groups = ["main"]
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{file = "six-1.17.0.tar.gz", hash = "sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81"},
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[[package]]
name = "tzdata"
version = "2025.2"
description = "Provider of IANA time zone data"
optional = false
python-versions = ">=2"
groups = ["main"]
files = [
{file = "tzdata-2025.2-py2.py3-none-any.whl", hash = "sha256:1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8"},
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[metadata]
lock-version = "2.1"
python-versions = "^3.13"
content-hash = "7d05498c899519f4f05c0a6105d4ef33c146dd410b14138f08cfb020e7406590"

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pyproject.toml Normal file
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[project]
name = "tp-multiprocessing"
version = "0.1.0"
description = "TP Multiprocessing"
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"

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0
src/tp_multiprocessing/__init__.py Normal file
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src/tp_multiprocessing/process.py Normal file
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"""
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()

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tests/__init__.py Normal file
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tests/test_calculate_new_height.py Normal file
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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)