206 lines
7.5 KiB
Python
206 lines
7.5 KiB
Python
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
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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 deck_endpoints import deck_bp # Importieren des Blueprints
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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 = 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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"""
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try:
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gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
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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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denoised = cv2.fastNlMeansDenoising(enhanced)
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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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logger.info(f"Debug images saved in: {debug_dir}")
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return binary
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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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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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# 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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# 'debug_info': {
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# 'total_boxes_detected': len(result[0]) if result and result[0] else 0,
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# 'processed_results': len(extracted_results),
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# 'debug_dir': debug_dir
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# }
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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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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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if __name__ == '__main__':
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app.run(host='0.0.0.0', port=5000, debug=True) |