#13 use webp instead of jpeg/png
This commit is contained in:
parent
e80ff12242
commit
297e2b9489
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@ -2,5 +2,7 @@
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__pycache__
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database.db
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debug_images
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images
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en_PP-OCRv3*
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*.db
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*.lock
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@ -0,0 +1,62 @@
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import os
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from PIL import Image
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from pathlib import Path
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def convert_debug_images():
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debug_base = Path('debug_images')
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images_base = Path('images')
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if not debug_base.exists():
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print(f"Fehler: {debug_base} existiert nicht")
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return
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# Alle Unterordner in debug_images durchlaufen
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for root, dirs, files in os.walk(debug_base):
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current_dir = Path(root)
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relative_path = current_dir.relative_to(debug_base)
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dest_dir = images_base / relative_path
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# Prüfen ob original.png existiert
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if 'original.png' not in files:
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continue
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# Zielverzeichnis erstellen
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dest_dir.mkdir(parents=True, exist_ok=True)
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# Pfade definieren
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png_path = current_dir / 'original.png'
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webp_path = dest_dir / 'original.webp'
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thumb_path = dest_dir / 'thumbnail.webp'
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try:
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# Originalbild öffnen und konvertieren
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with Image.open(png_path) as img:
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# Konvertierung zu RGB falls notwendig
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if img.mode in ('RGBA', 'LA'):
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img = img.convert('RGB')
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# Original als WebP speichern
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img.save(
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webp_path,
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'WEBP',
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quality=50,
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method=6 # Qualitätsoptimierung
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)
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print(f"Konvertiert: {webp_path}")
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# Thumbnail erstellen
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img.thumbnail((256, 256), resample=Image.LANCZOS)
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img.save(
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thumb_path,
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'WEBP',
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quality=50,
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method=6
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)
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print(f"Thumbnail erstellt: {thumb_path}")
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except Exception as e:
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print(f"Fehler bei {png_path}: {str(e)}")
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if __name__ == '__main__':
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convert_debug_images()
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print("Konvertierung abgeschlossen")
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@ -0,0 +1,181 @@
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import os
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import sys
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import pandas as pd
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from paddleocr import PaddleOCR
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from PIL import Image
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from tqdm import tqdm
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import logging
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import argparse
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# Konfiguriere das Logging
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(levelname)s - %(message)s',
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handlers=[
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logging.StreamHandler(sys.stdout),
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logging.FileHandler('ocr_comparison.log')
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]
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)
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logger = logging.getLogger(__name__)
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# Pfad zum debug_images-Verzeichnis
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DEBUG_IMAGES_DIR = 'debug_images'
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# Bilddateinamen, die verglichen werden sollen
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IMAGE_FILES = {
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'original': 'original.png',
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'original_compressed': 'original_compressed.jpg',
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'denoised': 'denoised.png'
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}
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# Initialisiere PaddleOCR
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logger.info("Initialisiere PaddleOCR...")
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ocr = PaddleOCR(
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use_angle_cls=True,
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lang='en',
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det_db_thresh=0.3,
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det_db_box_thresh=0.3,
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det_db_unclip_ratio=2.0,
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rec_char_type='en',
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det_limit_side_len=960,
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det_limit_type='max',
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use_dilation=True,
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det_db_score_mode='fast',
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show_log=False # Setze auf False, um die PaddleOCR-Logs zu unterdrücken
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)
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def perform_ocr(image_path):
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"""Führt OCR auf dem gegebenen Bildpfad durch und gibt die Ergebnisse zurück."""
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try:
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result = ocr.ocr(image_path, rec=True, cls=True)
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if not result:
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return {'num_texts': 0, 'avg_confidence': 0.0}
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num_texts = 0
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total_confidence = 0.0
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for line in result:
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for word in line:
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text, confidence = word[1]
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num_texts += 1
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total_confidence += float(confidence)
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avg_confidence = total_confidence / num_texts if num_texts > 0 else 0.0
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return {'num_texts': num_texts, 'avg_confidence': avg_confidence}
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except Exception as e:
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logger.error(f"Fehler bei OCR für Bild {image_path}: {e}")
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return {'num_texts': 0, 'avg_confidence': 0.0}
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def compare_ocr_results(results):
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"""
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Vergleicht die OCR-Ergebnisse zwischen den verschiedenen Bildversionen.
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Gibt an, welche Version tendenziell bessere Ergebnisse liefert.
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"""
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comparison = {}
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versions = list(IMAGE_FILES.keys())
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for version in versions:
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comparison[version] = {
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'num_texts': results[version]['num_texts'],
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'avg_confidence': results[version]['avg_confidence']
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}
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# Entscheidung basierend auf den Metriken
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# Kriterien können angepasst werden
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# Hier priorisieren wir höhere avg_confidence und mehr num_texts
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best_version = None
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best_score = -1
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for version in versions:
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score = comparison[version]['avg_confidence'] + (comparison[version]['num_texts'] / 100) # Gewichtung anpassen
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if score > best_score:
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best_score = score
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best_version = version
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return best_version, comparison
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def parse_arguments():
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"""Parst Kommandozeilenargumente."""
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parser = argparse.ArgumentParser(description='Vergleicht OCR-Ergebnisse verschiedener Bildversionen in debug_images-Ordnern.')
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parser.add_argument(
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'folders',
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nargs='?',
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default=None,
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help='Durch Kommata getrennte Liste von Ordner-IDs (max. 10), z.B. 20250112_121938_2172d7b3,20250112_122055_ea9e2a72,20250130_182431_2498fcba'
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)
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return parser.parse_args()
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def main():
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args = parse_arguments()
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if args.folders:
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# Verarbeite die durch Kommata getrennte Liste von Ordner-IDs
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folder_ids = [folder.strip() for folder in args.folders.split(',')]
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if len(folder_ids) > 10:
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logger.warning("Mehr als 10 Ordner-IDs angegeben. Es werden nur die ersten 10 verarbeitet.")
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folder_ids = folder_ids[:10]
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else:
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# Automatisch die ersten 10 Ordner im debug_images-Verzeichnis auswählen
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if not os.path.exists(DEBUG_IMAGES_DIR):
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logger.error(f"Verzeichnis '{DEBUG_IMAGES_DIR}' existiert nicht.")
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sys.exit(1)
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# Sammle alle Unterverzeichnisse und wähle die ersten 10 aus
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subdirs = [d for d in os.listdir(DEBUG_IMAGES_DIR) if os.path.isdir(os.path.join(DEBUG_IMAGES_DIR, d))]
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folder_ids = subdirs[:10]
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logger.info(f"Keine Ordner-IDs angegeben. Es werden die ersten {len(folder_ids)} Ordner verarbeitet.")
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logger.info(f"Starte die OCR-Vergleichsanalyse für {len(folder_ids)} Ordner: {', '.join(folder_ids)}")
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# Liste zum Speichern der Ergebnisse
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results_list = []
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for subdir in tqdm(folder_ids, desc="Verarbeitung der Ordner"):
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subdir_path = os.path.join(DEBUG_IMAGES_DIR, subdir)
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if not os.path.isdir(subdir_path):
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logger.warning(f"Ordner '{subdir}' existiert nicht im '{DEBUG_IMAGES_DIR}' Verzeichnis.")
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continue
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ocr_results = {}
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for version, filename in IMAGE_FILES.items():
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image_path = os.path.join(subdir_path, filename)
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if not os.path.isfile(image_path):
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logger.warning(f"Bild '{filename}' fehlt im Ordner '{subdir}'.")
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ocr_results[version] = {'num_texts': 0, 'avg_confidence': 0.0}
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continue
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ocr_result = perform_ocr(image_path)
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ocr_results[version] = ocr_result
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best_version, comparison = compare_ocr_results(ocr_results)
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results_list.append({
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'folder_id': subdir,
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'best_version': best_version,
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'original_num_texts': ocr_results['original']['num_texts'],
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'original_avg_confidence': ocr_results['original']['avg_confidence'],
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'original_compressed_num_texts': ocr_results['original_compressed']['num_texts'],
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'original_compressed_avg_confidence': ocr_results['original_compressed']['avg_confidence'],
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'denoised_num_texts': ocr_results['denoised']['num_texts'],
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'denoised_avg_confidence': ocr_results['denoised']['avg_confidence']
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})
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if not results_list:
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logger.warning("Keine Ergebnisse zum Speichern vorhanden.")
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sys.exit(0)
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# Erstelle einen DataFrame und speichere ihn als CSV
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output_csv = 'ocr_comparison_results.csv'
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df = pd.DataFrame(results_list)
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df.to_csv(output_csv, index=False)
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logger.info(f"OCR-Vergleichsanalyse abgeschlossen. Ergebnisse gespeichert in '{output_csv}'.")
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# Optional: Statistiken anzeigen
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total = len(df)
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best_counts = df['best_version'].value_counts()
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logger.info("Zusammenfassung der besten Versionen:")
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for version, count in best_counts.items():
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percentage = (count / total) * 100 if total > 0 else 0
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logger.info(f"{version}: {count} von {total} ({percentage:.2f}%)")
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if __name__ == "__main__":
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main()
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177
ocr_server.py
177
ocr_server.py
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@ -26,116 +26,84 @@ def get_dir_name():
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def create_debug_directory(dir_name):
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"""Erstellt ein eindeutiges Verzeichnis für Debug-Bilder"""
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base_dir = 'debug_images'
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timestamp = datetime.datetime.now().strftime('%Y%m%d_%H%M%S')
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unique_id = str(uuid.uuid4())[:8]
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base_dir = 'images'
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full_path = os.path.join(base_dir, dir_name)
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# Erstelle Hauptverzeichnis falls nicht vorhanden
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if not os.path.exists(base_dir):
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os.makedirs(base_dir)
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# Erstelle spezifisches Verzeichnis für diesen Durchlauf
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os.makedirs(full_path)
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return full_path
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def preprocess_image(image, debug_dir):
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"""
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Verarbeitet das Bild und speichert Zwischenergebnisse im angegebenen Verzeichnis,
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einschließlich einer komprimierten JPG-Version und eines Thumbnails.
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"""
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"""Bildverarbeitung mit optionalen Optimierungen"""
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try:
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# Umwandlung in Graustufen
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# Graustufenkonvertierung
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gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
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# Anwendung von CLAHE zur Kontrastverbesserung
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clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
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# Kontrastverbesserung mit CLAHE
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clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8,8)) # Erhöhter Clip-Limit
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enhanced = clahe.apply(gray)
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# Rauschunterdrückung
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denoised = cv2.fastNlMeansDenoising(enhanced)
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# Optional: Binärschwellenwert (auskommentiert)
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# _, binary = cv2.threshold(denoised, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
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# Rauschunterdrückung mit optimierten Parametern
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denoised = cv2.fastNlMeansDenoising(
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enhanced,
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h=15, # Stärkere Rauschreduzierung
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templateWindowSize=7,
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searchWindowSize=21
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)
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# Speichern der Zwischenergebnisse im spezifischen Verzeichnis
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cv2.imwrite(os.path.join(debug_dir, 'gray.png'), gray)
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cv2.imwrite(os.path.join(debug_dir, 'enhanced.png'), enhanced)
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cv2.imwrite(os.path.join(debug_dir, 'denoised.png'), denoised)
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# cv2.imwrite(os.path.join(debug_dir, 'binary.png'), binary)
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# Debug-Bilder speichern
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# cv2.imwrite(os.path.join(debug_dir, 'gray.png'), gray)
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# cv2.imwrite(os.path.join(debug_dir, 'enhanced.png'), enhanced)
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# cv2.imwrite(os.path.join(debug_dir, 'denoised.png'), denoised)
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# Speichern der komprimierten JPG-Version des Originalbildes
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compressed_jpg_path = os.path.join(debug_dir, 'original_compressed.jpg')
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original_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
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cv2.imwrite(compressed_jpg_path, original_bgr, [int(cv2.IMWRITE_JPEG_QUALITY), 80]) # Qualität auf 80 setzen
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logger.info(f"Komprimiertes Original JPG gespeichert: {compressed_jpg_path}")
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# Thumbnail als WebP
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denoised_rgb = cv2.cvtColor(denoised, cv2.COLOR_GRAY2RGB)
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thumbnail = Image.fromarray(denoised_rgb)
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thumbnail.thumbnail((256, 256))
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thumbnail_path = os.path.join(debug_dir, 'thumbnail.webp')
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thumbnail.save(thumbnail_path, 'WEBP', quality=85)
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# Erstellen und Speichern des Thumbnails
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thumbnail_path = os.path.join(debug_dir, 'thumbnail.jpg')
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image_pil = Image.fromarray(denoised)
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image_pil.thumbnail((128, 128)) # Thumbnail-Größe auf 128x128 Pixel setzen
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image_pil.save(thumbnail_path, 'JPEG')
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logger.info(f"Thumbnail gespeichert: {thumbnail_path}")
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logger.info(f"Debug images saved in: {debug_dir}")
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return denoised
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except Exception as e:
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logger.error(f"Preprocessing error: {str(e)}")
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raise
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@app.route('/api/ocr', methods=['POST'])
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def ocr_endpoint():
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debug_dir = None
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try:
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# Erstelle eindeutiges Debug-Verzeichnis für diesen Request
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# Verzeichnis erstellen
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dir_name = get_dir_name()
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debug_dir = create_debug_directory(dir_name)
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logger.info(f"Created debug directory: {debug_dir}")
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if not request.is_json:
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return jsonify({'error': 'Content-Type must be application/json'}), 400
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data = request.get_json()
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if not data or 'image' not in data:
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return jsonify({'error': 'No image provided'}), 400
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image_b64 = data['image']
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# Base64 Dekodierung
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try:
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image_data = base64.b64decode(image_b64)
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except Exception as decode_err:
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logger.error(f"Base64 decode error: {str(decode_err)}")
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return jsonify({'error': 'Base64 decode error'}), 400
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# Bildverarbeitung
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try:
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image = Image.open(BytesIO(image_data)).convert('RGB')
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image = np.array(image)
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logger.info(f"Image loaded successfully. Shape: {image.shape}")
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# Originalbild speichern
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cv2.imwrite(os.path.join(debug_dir, 'original.png'),
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cv2.cvtColor(image, cv2.COLOR_RGB2BGR))
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except Exception as img_err:
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logger.error(f"Image processing error: {str(img_err)}")
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return jsonify({'error': 'Invalid image data'}), 400
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data = request.get_json()
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image_data = base64.b64decode(data['image'])
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# Originalbild als WebP speichern
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original_image = Image.open(BytesIO(image_data)).convert('RGB')
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webp_path = os.path.join(debug_dir, 'original.webp')
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original_image.save(webp_path, 'WEBP', quality=50)
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# Bildvorverarbeitung
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processed_image = preprocess_image(image, debug_dir)
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logger.info("Preprocessing completed")
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# WebP-Bild für Verarbeitung laden
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with open(webp_path, 'rb') as f:
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webp_image = Image.open(BytesIO(f.read())).convert('RGB')
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# Vorverarbeitung
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processed_image = preprocess_image(np.array(webp_image), debug_dir)
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# PaddleOCR Konfiguration
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# OCR mit optimierter Konfiguration
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ocr = PaddleOCR(
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use_angle_cls=True,
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lang='en',
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det_db_thresh=0.3,
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det_db_box_thresh=0.3,
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det_db_unclip_ratio=2.0,
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rec_char_type='en',
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det_limit_side_len=960,
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det_limit_type='max',
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det_model_dir='en_PP-OCRv3_det',
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rec_model_dir='en_PP-OCRv3_rec',
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det_limit_side_len=processed_image.shape[0] * 2,
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use_dilation=True,
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det_db_score_mode='fast',
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show_log=True
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det_db_score_mode='fast'
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)
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# OCR durchführen
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@ -206,59 +174,12 @@ def ocr_endpoint():
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}), 500
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except Exception as e:
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logger.error(f"Unexpected error: {str(e)}")
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logger.error(traceback.format_exc())
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logger.error(f"Fehler: {str(e)}")
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return jsonify({
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'error': 'Internal server error',
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'debug_dir': debug_dir if 'debug_dir' in locals() else None
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'error': 'Verarbeitungsfehler',
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'details': str(e),
|
||||
'debug_dir': dir_name if debug_dir else None
|
||||
}), 500
|
||||
|
||||
@app.route('/api/debug_image/<name>/<filename>', methods=['GET'])
|
||||
def get_debug_image(name, filename):
|
||||
"""
|
||||
Gibt das angeforderte Bild unter 'debug_images/[name]/[filename]' direkt zurück.
|
||||
"""
|
||||
try:
|
||||
# Sicherheitsmaßnahme: Nur erlaubte Zeichen im Verzeichnisnamen
|
||||
if not all(c.isalnum() or c in ('_', '-') for c in name):
|
||||
logger.warning(f"Ungültiger Verzeichnisname angefordert: {name}")
|
||||
return jsonify({'error': 'Invalid directory name'}), 400
|
||||
|
||||
# Sicherheitsmaßnahme: Nur erlaubte Zeichen im Dateinamen
|
||||
if not all(c.isalnum() or c in ('_', '-', '.',) for c in filename):
|
||||
logger.warning(f"Ungültiger Dateiname angefordert: {filename}")
|
||||
return jsonify({'error': 'Invalid file name'}), 400
|
||||
|
||||
# Vollständigen Pfad zum Bild erstellen
|
||||
image_path = os.path.join('debug_images', name, filename)
|
||||
|
||||
# Überprüfen, ob die Datei existiert
|
||||
if not os.path.isfile(image_path):
|
||||
logger.warning(f"Bild nicht gefunden: {image_path}")
|
||||
return jsonify({'error': 'Image not found'}), 404
|
||||
|
||||
# Bestimmen des MIME-Typs basierend auf der Dateiendung
|
||||
mime_type = 'image/png' # Standard-MIME-Typ
|
||||
if filename.lower().endswith('.jpg') or filename.lower().endswith('.jpeg'):
|
||||
mime_type = 'image/jpeg'
|
||||
elif filename.lower().endswith('.gif'):
|
||||
mime_type = 'image/gif'
|
||||
elif filename.lower().endswith('.bmp'):
|
||||
mime_type = 'image/bmp'
|
||||
elif filename.lower().endswith('.tiff') or filename.lower().endswith('.tif'):
|
||||
mime_type = 'image/tiff'
|
||||
|
||||
return send_file(
|
||||
image_path,
|
||||
mimetype=mime_type,
|
||||
as_attachment=False
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Fehler beim Abrufen des Bildes '{name}/{filename}': {str(e)}")
|
||||
logger.error(traceback.format_exc())
|
||||
return jsonify({'error': 'Failed to retrieve image'}), 500
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
app.run(host='0.0.0.0', port=5000, debug=False)
|
||||
|
|
@ -0,0 +1,264 @@
|
|||
from flask import Flask, request, jsonify, send_file
|
||||
from paddleocr import PaddleOCR
|
||||
import base64
|
||||
from PIL import Image
|
||||
from io import BytesIO
|
||||
import traceback
|
||||
import numpy as np
|
||||
import cv2
|
||||
import logging
|
||||
import os
|
||||
import uuid
|
||||
import datetime
|
||||
|
||||
logging.basicConfig(
|
||||
level=logging.DEBUG,
|
||||
format='%(asctime)s - %(levelname)s - %(message)s'
|
||||
)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
app = Flask(__name__)
|
||||
|
||||
def get_dir_name():
|
||||
timestamp = datetime.datetime.now().strftime('%Y%m%d_%H%M%S')
|
||||
unique_id = str(uuid.uuid4())[:8]
|
||||
return f"{timestamp}_{unique_id}"
|
||||
|
||||
def create_debug_directory(dir_name):
|
||||
"""Erstellt ein eindeutiges Verzeichnis für Debug-Bilder"""
|
||||
base_dir = 'debug_images'
|
||||
timestamp = datetime.datetime.now().strftime('%Y%m%d_%H%M%S')
|
||||
unique_id = str(uuid.uuid4())[:8]
|
||||
full_path = os.path.join(base_dir, dir_name)
|
||||
|
||||
# Erstelle Hauptverzeichnis falls nicht vorhanden
|
||||
if not os.path.exists(base_dir):
|
||||
os.makedirs(base_dir)
|
||||
|
||||
# Erstelle spezifisches Verzeichnis für diesen Durchlauf
|
||||
os.makedirs(full_path)
|
||||
|
||||
return full_path
|
||||
|
||||
def preprocess_image(image, debug_dir):
|
||||
"""
|
||||
Verarbeitet das Bild und speichert Zwischenergebnisse im angegebenen Verzeichnis,
|
||||
einschließlich einer komprimierten JPG-Version und eines Thumbnails.
|
||||
"""
|
||||
try:
|
||||
# Umwandlung in Graustufen
|
||||
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
|
||||
# Anwendung von CLAHE zur Kontrastverbesserung
|
||||
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
|
||||
enhanced = clahe.apply(gray)
|
||||
# Rauschunterdrückung
|
||||
denoised = cv2.fastNlMeansDenoising(enhanced)
|
||||
# Optional: Binärschwellenwert (auskommentiert)
|
||||
# _, binary = cv2.threshold(denoised, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
|
||||
|
||||
# Speichern der Zwischenergebnisse im spezifischen Verzeichnis
|
||||
cv2.imwrite(os.path.join(debug_dir, 'gray.png'), gray)
|
||||
cv2.imwrite(os.path.join(debug_dir, 'enhanced.png'), enhanced)
|
||||
cv2.imwrite(os.path.join(debug_dir, 'denoised.png'), denoised)
|
||||
# cv2.imwrite(os.path.join(debug_dir, 'binary.png'), binary)
|
||||
|
||||
# Speichern der komprimierten JPG-Version des Originalbildes
|
||||
compressed_jpg_path = os.path.join(debug_dir, 'original_compressed.jpg')
|
||||
original_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
|
||||
cv2.imwrite(compressed_jpg_path, original_bgr, [int(cv2.IMWRITE_JPEG_QUALITY), 80]) # Qualität auf 80 setzen
|
||||
logger.info(f"Komprimiertes Original JPG gespeichert: {compressed_jpg_path}")
|
||||
|
||||
# Erstellen und Speichern des Thumbnails
|
||||
thumbnail_path = os.path.join(debug_dir, 'thumbnail.jpg')
|
||||
image_pil = Image.fromarray(denoised)
|
||||
image_pil.thumbnail((128, 128)) # Thumbnail-Größe auf 128x128 Pixel setzen
|
||||
image_pil.save(thumbnail_path, 'JPEG')
|
||||
logger.info(f"Thumbnail gespeichert: {thumbnail_path}")
|
||||
|
||||
logger.info(f"Debug images saved in: {debug_dir}")
|
||||
return denoised
|
||||
except Exception as e:
|
||||
logger.error(f"Preprocessing error: {str(e)}")
|
||||
raise
|
||||
|
||||
|
||||
@app.route('/api/ocr', methods=['POST'])
|
||||
def ocr_endpoint():
|
||||
try:
|
||||
# Erstelle eindeutiges Debug-Verzeichnis für diesen Request
|
||||
dir_name = get_dir_name()
|
||||
debug_dir = create_debug_directory(dir_name)
|
||||
logger.info(f"Created debug directory: {debug_dir}")
|
||||
|
||||
if not request.is_json:
|
||||
return jsonify({'error': 'Content-Type must be application/json'}), 400
|
||||
|
||||
data = request.get_json()
|
||||
if not data or 'image' not in data:
|
||||
return jsonify({'error': 'No image provided'}), 400
|
||||
|
||||
image_b64 = data['image']
|
||||
|
||||
# Base64 Dekodierung
|
||||
try:
|
||||
image_data = base64.b64decode(image_b64)
|
||||
except Exception as decode_err:
|
||||
logger.error(f"Base64 decode error: {str(decode_err)}")
|
||||
return jsonify({'error': 'Base64 decode error'}), 400
|
||||
|
||||
# Bildverarbeitung
|
||||
try:
|
||||
image = Image.open(BytesIO(image_data)).convert('RGB')
|
||||
image = np.array(image)
|
||||
logger.info(f"Image loaded successfully. Shape: {image.shape}")
|
||||
|
||||
# Originalbild speichern
|
||||
cv2.imwrite(os.path.join(debug_dir, 'original.png'),
|
||||
cv2.cvtColor(image, cv2.COLOR_RGB2BGR))
|
||||
except Exception as img_err:
|
||||
logger.error(f"Image processing error: {str(img_err)}")
|
||||
return jsonify({'error': 'Invalid image data'}), 400
|
||||
|
||||
# Bildvorverarbeitung
|
||||
processed_image = preprocess_image(image, debug_dir)
|
||||
logger.info("Preprocessing completed")
|
||||
|
||||
# PaddleOCR Konfiguration
|
||||
ocr = PaddleOCR(
|
||||
use_angle_cls=True,
|
||||
lang='en',
|
||||
det_db_thresh=0.3,
|
||||
det_db_box_thresh=0.3,
|
||||
det_db_unclip_ratio=2.0,
|
||||
rec_char_type='en',
|
||||
det_limit_side_len=960,
|
||||
det_limit_type='max',
|
||||
use_dilation=True,
|
||||
det_db_score_mode='fast',
|
||||
show_log=True
|
||||
)
|
||||
|
||||
# OCR durchführen
|
||||
try:
|
||||
result = ocr.ocr(processed_image, rec=True, cls=True)
|
||||
|
||||
# Debug-Informationen in Datei speichern
|
||||
with open(os.path.join(debug_dir, 'ocr_results.txt'), 'w') as f:
|
||||
f.write(f"Raw OCR result:\n{result}\n\n")
|
||||
|
||||
if not result:
|
||||
logger.warning("No results returned from OCR")
|
||||
return jsonify({
|
||||
'warning': 'No text detected',
|
||||
'debug_dir': debug_dir
|
||||
}), 200
|
||||
|
||||
if not result[0]:
|
||||
logger.warning("Empty results list from OCR")
|
||||
return jsonify({
|
||||
'warning': 'Empty results list',
|
||||
'debug_dir': debug_dir
|
||||
}), 200
|
||||
|
||||
# Ergebnisse verarbeiten
|
||||
extracted_results = []
|
||||
for idx, item in enumerate(result[0]):
|
||||
try:
|
||||
box = item[0]
|
||||
text = item[1][0] if item[1] else ''
|
||||
confidence = float(item[1][1]) if item[1] and len(item[1]) > 1 else 0.0
|
||||
|
||||
extracted_results.append({
|
||||
'box': box,
|
||||
'text': text,
|
||||
'confidence': confidence,
|
||||
'name': dir_name
|
||||
})
|
||||
except Exception as proc_err:
|
||||
logger.error(f"Error processing result {idx}: {str(proc_err)}")
|
||||
|
||||
# Statistiken in Debug-Datei speichern
|
||||
with open(os.path.join(debug_dir, 'statistics.txt'), 'w') as f:
|
||||
f.write(f"Total results: {len(extracted_results)}\n")
|
||||
if extracted_results:
|
||||
avg_confidence = np.mean([r['confidence'] for r in extracted_results])
|
||||
f.write(f"Average confidence: {avg_confidence}\n")
|
||||
f.write("\nDetailed results:\n")
|
||||
for idx, result in enumerate(extracted_results):
|
||||
f.write(f"Result {idx+1}:\n")
|
||||
f.write(f"Text: {result['text']}\n")
|
||||
f.write(f"Confidence: {result['confidence']}\n")
|
||||
f.write(f"Name: {dir_name}\n")
|
||||
f.write(f"Box coordinates: {result['box']}\n\n")
|
||||
|
||||
return jsonify({
|
||||
'status': 'success',
|
||||
'results': extracted_results,
|
||||
})
|
||||
|
||||
except Exception as ocr_err:
|
||||
logger.error(f"OCR processing error: {str(ocr_err)}")
|
||||
logger.error(traceback.format_exc())
|
||||
return jsonify({
|
||||
'error': 'OCR processing failed',
|
||||
'details': str(ocr_err),
|
||||
'debug_dir': debug_dir
|
||||
}), 500
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Unexpected error: {str(e)}")
|
||||
logger.error(traceback.format_exc())
|
||||
return jsonify({
|
||||
'error': 'Internal server error',
|
||||
'debug_dir': debug_dir if 'debug_dir' in locals() else None
|
||||
}), 500
|
||||
|
||||
@app.route('/api/debug_image/<name>/<filename>', methods=['GET'])
|
||||
def get_debug_image(name, filename):
|
||||
"""
|
||||
Gibt das angeforderte Bild unter 'debug_images/[name]/[filename]' direkt zurück.
|
||||
"""
|
||||
try:
|
||||
# Sicherheitsmaßnahme: Nur erlaubte Zeichen im Verzeichnisnamen
|
||||
if not all(c.isalnum() or c in ('_', '-') for c in name):
|
||||
logger.warning(f"Ungültiger Verzeichnisname angefordert: {name}")
|
||||
return jsonify({'error': 'Invalid directory name'}), 400
|
||||
|
||||
# Sicherheitsmaßnahme: Nur erlaubte Zeichen im Dateinamen
|
||||
if not all(c.isalnum() or c in ('_', '-', '.',) for c in filename):
|
||||
logger.warning(f"Ungültiger Dateiname angefordert: {filename}")
|
||||
return jsonify({'error': 'Invalid file name'}), 400
|
||||
|
||||
# Vollständigen Pfad zum Bild erstellen
|
||||
image_path = os.path.join('debug_images', name, filename)
|
||||
|
||||
# Überprüfen, ob die Datei existiert
|
||||
if not os.path.isfile(image_path):
|
||||
logger.warning(f"Bild nicht gefunden: {image_path}")
|
||||
return jsonify({'error': 'Image not found'}), 404
|
||||
|
||||
# Bestimmen des MIME-Typs basierend auf der Dateiendung
|
||||
mime_type = 'image/png' # Standard-MIME-Typ
|
||||
if filename.lower().endswith('.jpg') or filename.lower().endswith('.jpeg'):
|
||||
mime_type = 'image/jpeg'
|
||||
elif filename.lower().endswith('.gif'):
|
||||
mime_type = 'image/gif'
|
||||
elif filename.lower().endswith('.bmp'):
|
||||
mime_type = 'image/bmp'
|
||||
elif filename.lower().endswith('.tiff') or filename.lower().endswith('.tif'):
|
||||
mime_type = 'image/tiff'
|
||||
|
||||
return send_file(
|
||||
image_path,
|
||||
mimetype=mime_type,
|
||||
as_attachment=False
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Fehler beim Abrufen des Bildes '{name}/{filename}': {str(e)}")
|
||||
logger.error(traceback.format_exc())
|
||||
return jsonify({'error': 'Failed to retrieve image'}), 500
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
app.run(host='0.0.0.0', port=5000, debug=False)
|
||||
|
|
@ -4,4 +4,6 @@ pillow>=10.0.0
|
|||
numpy>=1.24.4,<2.0.0
|
||||
opencv-python==4.6.0.66
|
||||
paddlepaddle==2.6.2
|
||||
werkzeug<2.3
|
||||
werkzeug<2.3
|
||||
pandas>=1.3.0
|
||||
tqdm>=4.64.0
|
||||
Loading…
Reference in New Issue