Delete image_processing_cpu.py

image_processing_cpu.py

@@ -1,117 +0,0 @@
-# ==========================================
-# image_processing_cpu.py - Version CPU avec EasyOCR (SANS BASE64)
-# ==========================================
-
-"""
-Module de traitement d'images CPU-optimisé pour calculs mathématiques
-Utilise EasyOCR pour des performances rapides sur CPU
-VERSION MODIFIÉE: Format Image natif au lieu de Base64
-"""
-
-import time
-from utils import (
-    optimize_image_for_ocr,
-    prepare_image_for_dataset, 
-    create_thumbnail_fast,
-    create_white_canvas,
-    log_memory_usage,
-    cleanup_memory,
-    validate_ocr_result
-)
-
-# Variables globales pour OCR EasyOCR
-easyocr_reader = None
-OCR_MODEL_NAME = "EasyOCR"
-
-def init_ocr_model() -> bool:
-    """Initialise EasyOCR (optimisé CPU)"""
-    global easyocr_reader
-    
-    try:
-        print("🔄 Chargement EasyOCR (CPU optimisé)...")
-        import easyocr
-        easyocr_reader = easyocr.Reader(['en'], gpu=False, verbose=False)
-        print("✅ EasyOCR prêt (CPU) !")
-        return True
-        
-    except Exception as e:
-        print(f"❌ Erreur lors du chargement EasyOCR: {e}")
-        return False
-
-def get_ocr_model_info() -> dict:
-    """Retourne les informations du modèle OCR utilisé"""
-    return {
-        "model_name": OCR_MODEL_NAME,
-        "device": "CPU",
-        "framework": "EasyOCR",
-        "optimized_for": "speed",
-        "version": "1.7.x"
-    }
-
-def recognize_number_fast_with_image(image_dict, debug: bool = False) -> tuple[str, any, dict | None]:
-    """
-    OCR avec EasyOCR (CPU optimisé) - VERSION SANS BASE64
-    
-    Args:
-        image_dict: Image d'entrée (format Gradio)
-        debug: Afficher les logs de debug
-        
-    Returns:
-        (résultat_ocr, image_optimisée, données_dataset_avec_image_native)
-    """
-    if image_dict is None or easyocr_reader is None:
-        if debug:
-            print("  ❌ Image manquante ou EasyOCR non initialisé")
-        return "0", None, None
-
-    try:
-        start_time = time.time()
-        if debug:
-            print("  🔄 Début OCR EasyOCR...")
-        
-        # Optimiser image (fonction commune)
-        optimized_image = optimize_image_for_ocr(image_dict, max_size=300)
-        if optimized_image is None:
-            if debug:
-                print("  ❌ Échec optimisation image")
-            return "0", None, None
-
-        # EasyOCR - traitement spécialisé CPU
-        if debug:
-            print("  ⚡ Lancement EasyOCR...")
-        
-        import numpy as np
-        img_array = np.array(optimized_image)
-        results = easyocr_reader.readtext(img_array, detail=0, paragraph=False)
-        
-        # Traitement des résultats EasyOCR
-        if results:
-            all_text = ' '.join(str(r) for r in results)
-            final_result = validate_ocr_result(all_text, max_length=4)
-        else:
-            final_result = "0"
-        
-        # Préparer pour dataset (NOUVELLE VERSION SANS BASE64)
-        dataset_image_data = prepare_image_for_dataset(optimized_image)
-        
-        if debug:
-            total_time = time.time() - start_time
-            print(f"  ✅ EasyOCR terminé en {total_time:.1f}s → '{final_result}'")
-            if dataset_image_data:
-                print(f"  🖼️ Image dataset: {type(dataset_image_data.get('handwriting_image', 'None'))}")
-        
-        return final_result, optimized_image, dataset_image_data
-
-    except Exception as e:
-        print(f"❌ Erreur OCR EasyOCR: {e}")
-        return "0", None, None
-
-def recognize_number_fast(image_dict) -> tuple[str, any]:
-    """Version rapide standard"""
-    result, optimized_image, _ = recognize_number_fast_with_image(image_dict)
-    return result, optimized_image
-
-def recognize_number(image_dict) -> str:
-    """Interface standard"""
-    result, _ = recognize_number_fast(image_dict)
-    return result