backend

ocr_server1.py

@@ -0,0 +1,90 @@
+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
+
+app = Flask(__name__)
+def preprocess_image(image):
+    # Konvertierung zu Graustufen
+    gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
+    
+    # Kontrastverstärkung
+    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
+    enhanced = clahe.apply(gray)
+    
+    # Rauschreduzierung
+    denoised = cv2.fastNlMeansDenoising(enhanced)
+    
+    # Binarisierung
+    _, binary = cv2.threshold(denoised, 127, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+    
+    return binary
+
+@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 = preprocess_image(image)
+            image = np.array(image)  # Konvertieren zu numpy.ndarray
+        except Exception as img_err:
+            return jsonify({'error': 'Invalid image data', 'details': str(img_err)}), 400
+
+        # Optional: Bildgröße anpassen, falls erforderlich
+        max_width = 1920
+        max_height = 1080
+        height, width, _ = image.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)
+            image = np.array(Image.fromarray(image).resize((new_width, new_height)))
+
+        # Initialisieren Sie PaddleOCR innerhalb des Handlers
+        ocr = PaddleOCR(use_angle_cls=True, lang='en')  # Initialisierung innerhalb des Handlers
+
+        result = ocr.ocr(image, rec=True, cls=True)
+
+        # 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)