Anpassungen und Reduzierung auf OCR
This commit is contained in:
parent
f3cd175ae6
commit
a569b9a1ab
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@ -0,0 +1,6 @@
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from flask import Flask
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# Erstelle die Flask-App
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app = Flask(__name__)
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# Konfigurationen und Erweiterungen hier hinzufügen
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@ -1,10 +1,13 @@
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# deck_endpoints.py
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from app_factory import app
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from flask import Blueprint, request, jsonify
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import sqlite3
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import os
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import shutil
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import logging
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from sqlalchemy import create_engine
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from sqlalchemy.orm import scoped_session, sessionmaker
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from sqlalchemy.sql import text
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deck_bp = Blueprint('deck_bp', __name__)
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@ -195,6 +198,43 @@ def get_decks():
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deck_list = list(decks.values())
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return jsonify(deck_list)
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@deck_bp.route('/api/decks/<deckname>', methods=['GET'])
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def get_deck(deckname):
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conn = get_db_connection()
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cursor = conn.cursor()
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# Einträge für das spezifische Deck abrufen
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entries = cursor.execute('SELECT * FROM Deck WHERE deckname = ?', (deckname,)).fetchall()
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conn.close()
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if not entries:
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return jsonify({'error': 'Deck not found'}), 404
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deck = {
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'name': deckname,
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'images': []
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}
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for entry in entries:
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if entry['bildname'] and entry['bildid']:
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image = {
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'name': entry['bildname'],
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'id': entry['bildid'],
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'iconindex': entry['iconindex'],
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'boxid': entry['id'],
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'x1': entry['x1'],
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'x2': entry['x2'],
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'y1': entry['y1'],
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'y2': entry['y2'],
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'due': entry['due'],
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'ivl': entry['ivl'],
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'factor': entry['factor'],
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'reps': entry['reps'],
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'lapses': entry['lapses'],
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'isGraduated': bool(entry['isGraduated'])
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}
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deck['images'].append(image)
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return jsonify(deck)
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@deck_bp.route('/api/decks/<deckname>', methods=['DELETE'])
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def delete_deck(deckname):
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conn = get_db_connection()
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273
ocr_server.py
273
ocr_server.py
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@ -1,17 +1,98 @@
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from flask import Flask, request, jsonify
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from flask import Flask, request, jsonify, send_file
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from paddleocr import PaddleOCR
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import base64
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from PIL import Image
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from io import BytesIO
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import traceback
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import numpy as np # Importieren von numpy
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import numpy as np
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import cv2
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import logging
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import os
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import uuid
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import datetime
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from app_factory import app
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# from deck_endpoints import deck_bp # Importieren des Blueprints
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app = Flask(__name__)
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ocr = PaddleOCR(use_angle_cls=True, lang='en') # Passen Sie die Sprache nach Bedarf an
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logging.basicConfig(
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level=logging.DEBUG,
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format='%(asctime)s - %(levelname)s - %(message)s'
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)
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logger = logging.getLogger(__name__)
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@app.route('/ocr', methods=['POST'])
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# app = Flask(__name__)
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# app.register_blueprint(deck_bp) # Registrieren des Blueprints
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def get_dir_name():
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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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return f"{timestamp}_{unique_id}"
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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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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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try:
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# Umwandlung in Graustufen
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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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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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# 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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# 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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# 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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try:
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# Erstelle eindeutiges Debug-Verzeichnis für diesen Request
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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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@ -20,41 +101,167 @@ def ocr_endpoint():
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return jsonify({'error': 'No image provided'}), 400
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image_b64 = data['image']
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if not image_b64:
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return jsonify({'error': 'Empty image data'}), 400
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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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return jsonify({'error': 'Base64 decode error', 'details': str(decode_err)}), 400
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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) # Konvertieren zu numpy.ndarray
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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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return jsonify({'error': 'Invalid image data', 'details': str(img_err)}), 400
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# Optional: Bildgröße anpassen, falls erforderlich
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# PaddleOCR kann große Bilder verarbeiten, aber zur Effizienz können Sie eine maximale Größe setzen
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max_width = 1920
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max_height = 1080
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height, width, _ = image.shape
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if width > max_width or height > max_height:
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aspect_ratio = width / height
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if aspect_ratio > 1:
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new_width = max_width
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new_height = int(max_width / aspect_ratio)
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else:
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new_height = max_height
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new_width = int(max_height * aspect_ratio)
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image = np.array(Image.fromarray(image).resize((new_width, new_height)))
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result = ocr.ocr(image, rec=True, cls=True)
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return jsonify(result)
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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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# Bildvorverarbeitung
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processed_image = preprocess_image(image, debug_dir)
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logger.info("Preprocessing completed")
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# PaddleOCR 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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use_dilation=True,
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det_db_score_mode='fast',
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show_log=True
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)
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# OCR durchführen
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try:
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result = ocr.ocr(processed_image, rec=True, cls=True)
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# Debug-Informationen in Datei speichern
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with open(os.path.join(debug_dir, 'ocr_results.txt'), 'w') as f:
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f.write(f"Raw OCR result:\n{result}\n\n")
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if not result:
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logger.warning("No results returned from OCR")
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return jsonify({
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'warning': 'No text detected',
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'debug_dir': debug_dir
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}), 200
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if not result[0]:
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logger.warning("Empty results list from OCR")
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return jsonify({
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'warning': 'Empty results list',
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'debug_dir': debug_dir
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}), 200
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# Ergebnisse verarbeiten
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extracted_results = []
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for idx, item in enumerate(result[0]):
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try:
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box = item[0]
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text = item[1][0] if item[1] else ''
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confidence = float(item[1][1]) if item[1] and len(item[1]) > 1 else 0.0
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extracted_results.append({
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'box': box,
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'text': text,
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'confidence': confidence,
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'name': dir_name
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})
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except Exception as proc_err:
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logger.error(f"Error processing result {idx}: {str(proc_err)}")
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# Statistiken in Debug-Datei speichern
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with open(os.path.join(debug_dir, 'statistics.txt'), 'w') as f:
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f.write(f"Total results: {len(extracted_results)}\n")
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if extracted_results:
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avg_confidence = np.mean([r['confidence'] for r in extracted_results])
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f.write(f"Average confidence: {avg_confidence}\n")
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f.write("\nDetailed results:\n")
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for idx, result in enumerate(extracted_results):
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f.write(f"Result {idx+1}:\n")
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f.write(f"Text: {result['text']}\n")
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f.write(f"Confidence: {result['confidence']}\n")
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f.write(f"Name: {dir_name}\n")
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f.write(f"Box coordinates: {result['box']}\n\n")
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return jsonify({
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'status': 'success',
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'results': extracted_results,
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})
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except Exception as ocr_err:
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logger.error(f"OCR processing error: {str(ocr_err)}")
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logger.error(traceback.format_exc())
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return jsonify({
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'error': 'OCR processing failed',
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'details': str(ocr_err),
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'debug_dir': debug_dir
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}), 500
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except Exception as e:
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traceback.print_exc()
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return jsonify({'error': str(e)}), 500
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logger.error(f"Unexpected error: {str(e)}")
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logger.error(traceback.format_exc())
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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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}), 500
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@app.route('/api/debug_image/<name>/<filename>', methods=['GET'])
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def get_debug_image(name, filename):
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"""
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Gibt das angeforderte Bild unter 'debug_images/[name]/[filename]' direkt zurück.
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"""
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try:
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# Sicherheitsmaßnahme: Nur erlaubte Zeichen im Verzeichnisnamen
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if not all(c.isalnum() or c in ('_', '-') for c in name):
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logger.warning(f"Ungültiger Verzeichnisname angefordert: {name}")
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return jsonify({'error': 'Invalid directory name'}), 400
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# Sicherheitsmaßnahme: Nur erlaubte Zeichen im Dateinamen
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if not all(c.isalnum() or c in ('_', '-', '.',) for c in filename):
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logger.warning(f"Ungültiger Dateiname angefordert: {filename}")
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return jsonify({'error': 'Invalid file name'}), 400
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# Vollständigen Pfad zum Bild erstellen
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image_path = os.path.join('debug_images', name, filename)
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# Überprüfen, ob die Datei existiert
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if not os.path.isfile(image_path):
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logger.warning(f"Bild nicht gefunden: {image_path}")
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return jsonify({'error': 'Image not found'}), 404
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# Bestimmen des MIME-Typs basierend auf der Dateiendung
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mime_type = 'image/png' # Standard-MIME-Typ
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if filename.lower().endswith('.jpg') or filename.lower().endswith('.jpeg'):
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mime_type = 'image/jpeg'
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elif filename.lower().endswith('.gif'):
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mime_type = 'image/gif'
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elif filename.lower().endswith('.bmp'):
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mime_type = 'image/bmp'
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elif filename.lower().endswith('.tiff') or filename.lower().endswith('.tif'):
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mime_type = 'image/tiff'
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return send_file(
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image_path,
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mimetype=mime_type,
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as_attachment=False
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)
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except Exception as e:
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logger.error(f"Fehler beim Abrufen des Bildes '{name}/{filename}': {str(e)}")
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logger.error(traceback.format_exc())
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return jsonify({'error': 'Failed to retrieve image'}), 500
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if __name__ == '__main__':
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app.run(host='0.0.0.0', port=5000, debug=True)
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app.run(host='0.0.0.0', port=5000, debug=True)
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from flask import Flask, request, jsonify
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from paddleocr import PaddleOCR
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import base64
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from PIL import Image
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from io import BytesIO
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import traceback
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import numpy as np
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import cv2 # Import von OpenCV
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app = Flask(__name__)
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def preprocess_image(image):
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# Konvertierung zu Graustufen
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gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
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# Kontrastverstärkung
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clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
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enhanced = clahe.apply(gray)
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# Rauschreduzierung
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denoised = cv2.fastNlMeansDenoising(enhanced)
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# Binarisierung
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_, binary = cv2.threshold(denoised, 127, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
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return binary
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@app.route('/ocr', methods=['POST'])
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def ocr_endpoint():
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try:
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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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if not image_b64:
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return jsonify({'error': 'Empty image data'}), 400
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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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return jsonify({'error': 'Base64 decode error', 'details': str(decode_err)}), 400
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try:
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image = Image.open(BytesIO(image_data)).convert('RGB')
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image = preprocess_image(image)
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image = np.array(image) # Konvertieren zu numpy.ndarray
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except Exception as img_err:
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return jsonify({'error': 'Invalid image data', 'details': str(img_err)}), 400
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# Optional: Bildgröße anpassen, falls erforderlich
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max_width = 1920
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max_height = 1080
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height, width, _ = image.shape
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if width > max_width or height > max_height:
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aspect_ratio = width / height
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if aspect_ratio > 1:
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new_width = max_width
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new_height = int(max_width / aspect_ratio)
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else:
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new_height = max_height
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new_width = int(max_height * aspect_ratio)
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image = np.array(Image.fromarray(image).resize((new_width, new_height)))
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# Initialisieren Sie PaddleOCR innerhalb des Handlers
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ocr = PaddleOCR(use_angle_cls=True, lang='en') # Initialisierung innerhalb des Handlers
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result = ocr.ocr(image, rec=True, cls=True)
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|
||||
# Extrahieren der Texte und Konfidenzwerte
|
||||
extracted_results = []
|
||||
for item in result[0]:
|
||||
box = item[0] # Die Koordinaten der Textbox
|
||||
text = item[1][0] # Der erkannte Text
|
||||
confidence = item[1][1] # Der Konfidenzwert
|
||||
extracted_results.append({
|
||||
'box': box,
|
||||
'text': text,
|
||||
'confidence': confidence
|
||||
})
|
||||
|
||||
return jsonify(extracted_results)
|
||||
except Exception as e:
|
||||
traceback.print_exc()
|
||||
return jsonify({'error': str(e)}), 500
|
||||
|
||||
if __name__ == '__main__':
|
||||
app.run(host='0.0.0.0', port=5000, debug=True)
|
||||
114
ocr_server2.py
114
ocr_server2.py
|
|
@ -1,114 +0,0 @@
|
|||
from flask import Flask, request, jsonify
|
||||
from paddleocr import PaddleOCR
|
||||
import base64
|
||||
from PIL import Image
|
||||
from io import BytesIO
|
||||
import traceback
|
||||
import numpy as np
|
||||
import cv2 # Import von OpenCV
|
||||
import os # Import für das Speichern von Dateien
|
||||
import time # Import für Zeitstempel
|
||||
|
||||
app = Flask(__name__)
|
||||
|
||||
# Initialisiere PaddleOCR einmal außerhalb der Anfrage, um die Leistung zu verbessern
|
||||
ocr = PaddleOCR(use_angle_cls=True, lang='en') # Initialisierung außerhalb des Handlers
|
||||
|
||||
@app.route('/ocr', methods=['POST'])
|
||||
def ocr_endpoint():
|
||||
try:
|
||||
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']
|
||||
if not image_b64:
|
||||
return jsonify({'error': 'Empty image data'}), 400
|
||||
|
||||
try:
|
||||
image_data = base64.b64decode(image_b64)
|
||||
except Exception as decode_err:
|
||||
return jsonify({'error': 'Base64 decode error', 'details': str(decode_err)}), 400
|
||||
|
||||
try:
|
||||
image = Image.open(BytesIO(image_data)).convert('RGB')
|
||||
image_np = np.array(image) # Konvertieren zu numpy.ndarray
|
||||
except Exception as img_err:
|
||||
return jsonify({'error': 'Invalid image data'}), 400
|
||||
|
||||
# Vorverarbeitung: Behalte nur dunkle (schwarze) Bereiche des Bildes
|
||||
# Konvertiere das Bild zu Graustufen
|
||||
gray = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
|
||||
|
||||
# Wende einen Schwellenwert an, um nur die dunklen Bereiche zu behalten
|
||||
threshold_value = 150 # Passe diesen Wert nach Bedarf an
|
||||
_, mask = cv2.threshold(gray, threshold_value, 255, cv2.THRESH_BINARY_INV)
|
||||
|
||||
# Optional: Morphologische Operationen zur Verbesserung der Maske
|
||||
kernel = np.ones((3,3), np.uint8)
|
||||
mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel, iterations=1)
|
||||
mask = cv2.morphologyEx(mask, cv2.MORPH_DILATE, kernel, iterations=1)
|
||||
|
||||
# Wende die Maske auf das Originalbild an
|
||||
filtered_image_np = cv2.bitwise_and(image_np, image_np, mask=mask)
|
||||
|
||||
# Konvertiere das gefilterte Bild zurück zu PIL Image
|
||||
filtered_image = Image.fromarray(filtered_image_np)
|
||||
|
||||
# Optional: Bildgröße anpassen, falls erforderlich
|
||||
max_width = 1920
|
||||
max_height = 1080
|
||||
height, width, _ = filtered_image_np.shape
|
||||
if width > max_width or height > max_height:
|
||||
aspect_ratio = width / height
|
||||
if aspect_ratio > 1:
|
||||
new_width = max_width
|
||||
new_height = int(max_width / aspect_ratio)
|
||||
else:
|
||||
new_height = max_height
|
||||
new_width = int(max_height * aspect_ratio)
|
||||
filtered_image = filtered_image.resize((new_width, new_height))
|
||||
filtered_image_np = np.array(filtered_image)
|
||||
|
||||
# **Speichern des vorverarbeiteten Bildes zur Überprüfung**
|
||||
output_dir = 'processed_images'
|
||||
if not os.path.exists(output_dir):
|
||||
os.makedirs(output_dir)
|
||||
|
||||
# Generiere einen einzigartigen Dateinamen basierend auf dem aktuellen Zeitstempel
|
||||
timestamp = int(time.time() * 1000)
|
||||
processed_image_path = os.path.join(output_dir, f'processed_{timestamp}.png')
|
||||
filtered_image.save(processed_image_path)
|
||||
print(f'Processed image saved at: {processed_image_path}')
|
||||
|
||||
# **Speichern der Maske zur Überprüfung**
|
||||
mask_image = Image.fromarray(mask)
|
||||
mask_image_path = os.path.join(output_dir, f'mask_{timestamp}.png')
|
||||
mask_image.save(mask_image_path)
|
||||
print(f'Mask image saved at: {mask_image_path}')
|
||||
|
||||
# Führe OCR auf dem gefilterten Bild durch
|
||||
result = ocr.ocr(filtered_image_np, rec=True, cls=True)
|
||||
|
||||
# Extrahieren der Texte und Konfidenzwerte
|
||||
extracted_results = []
|
||||
for item in result:
|
||||
box = item[0] # Die Koordinaten der Textbox
|
||||
text = item[1][0] # Der erkannte Text
|
||||
confidence = item[1][1] # Der Konfidenzwert
|
||||
extracted_results.append({
|
||||
'box': box,
|
||||
'text': text,
|
||||
'confidence': confidence
|
||||
})
|
||||
|
||||
return jsonify(extracted_results)
|
||||
except Exception as e:
|
||||
traceback.print_exc()
|
||||
return jsonify({'error': str(e)}), 500
|
||||
|
||||
if __name__ == '__main__':
|
||||
app.run(host='0.0.0.0', port=5000, debug=True, threaded=False) # Single-Threaded
|
||||
272
ocr_server3.py
272
ocr_server3.py
|
|
@ -1,272 +0,0 @@
|
|||
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
|
||||
|
||||
from deck_endpoints import deck_bp # Importieren des Blueprints
|
||||
|
||||
logging.basicConfig(
|
||||
level=logging.DEBUG,
|
||||
format='%(asctime)s - %(levelname)s - %(message)s'
|
||||
)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
app = Flask(__name__)
|
||||
app.register_blueprint(deck_bp) # Registrieren des Blueprints
|
||||
|
||||
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,
|
||||
# 'debug_info': {
|
||||
# 'total_boxes_detected': len(result[0]) if result and result[0] else 0,
|
||||
# 'processed_results': len(extracted_results),
|
||||
# 'debug_dir': debug_dir
|
||||
# }
|
||||
})
|
||||
|
||||
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=True)
|
||||
|
|
@ -4,4 +4,5 @@ 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
|
||||
SQLAlchemy==2.0.20
|
||||
Loading…
Reference in New Issue