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import cv2 |
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import numpy as np |
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from PIL import Image |
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import pytesseract |
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import logging |
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from datetime import datetime |
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class EngineeringDrawingAnalyzer: |
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"""محلل الرسومات الهندسية المتقدم""" |
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def __init__(self, claude_client=None): |
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self.claude_client = claude_client |
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self.supported_formats = ['.dwg', '.dxf', '.pdf', '.jpg', '.jpeg', '.png'] |
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def analyze_drawing(self, file_path): |
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"""تحليل الرسم الهندسي""" |
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try: |
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image = cv2.imread(file_path) |
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lines, shapes = self._analyze_shapes(image) |
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text, dimensions = self._extract_text_and_dimensions(image) |
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if self.claude_client: |
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drawing_info = { |
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'lines': lines, |
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'shapes': shapes, |
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'text': text, |
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'dimensions': dimensions |
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} |
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analysis_prompt = f""" |
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قم بتحليل معلومات الرسم الهندسي التالية وتقديم: |
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1. تحليل شامل للرسم |
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2. تحديد التخصص (إنشائي، معماري، ميكانيكي، كهربائي) |
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3. تحليل التفاصيل الفنية |
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4. توصيات للتحسين |
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5. تقدير التكاليف |
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معلومات الرسم: |
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{json.dumps(drawing_info, indent=2, ensure_ascii=False)} |
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""" |
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response = self.claude_client.messages.create( |
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model="claude-3-sonnet-20240229", |
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max_tokens=4000, |
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temperature=0.7, |
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messages=[{"role": "user", "content": analysis_prompt}] |
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) |
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ai_analysis = response.content[0].text |
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else: |
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ai_analysis = "تحليل الذكاء الاصطناعي غير متاح" |
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return { |
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'file_info': { |
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'path': file_path, |
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'type': os.path.splitext(file_path)[1], |
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'size': os.path.getsize(file_path) |
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}, |
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'analysis': { |
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'lines': lines, |
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'shapes': shapes, |
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'text': text, |
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'dimensions': dimensions, |
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'ai_analysis': ai_analysis |
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} |
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} |
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except Exception as e: |
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logging.error(f"خطأ في تحليل الرسم: {str(e)}") |
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raise |
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def _analyze_shapes(self, image): |
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"""تحليل الخطوط والأشكال""" |
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) |
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edges = cv2.Canny(gray, 50, 150) |
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lines = cv2.HoughLinesP(edges, 1, np.pi/180, 100) |
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contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) |
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shapes = [] |
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for contour in contours: |
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approx = cv2.approxPolyDP(contour, 0.04 * cv2.arcLength(contour, True), True) |
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if len(approx) == 3: |
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shapes.append('triangle') |
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elif len(approx) == 4: |
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shapes.append('rectangle') |
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elif len(approx) == 5: |
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shapes.append('pentagon') |
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elif len(approx) > 5: |
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shapes.append('circle') |
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return { |
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'line_count': len(lines) if lines is not None else 0, |
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'line_angles': self._analyze_line_angles(lines) if lines is not None else [], |
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'line_lengths': self._analyze_line_lengths(lines) if lines is not None else [] |
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}, { |
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'shape_count': len(shapes), |
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'shape_types': dict(Counter(shapes)) |
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} |
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def _extract_text_and_dimensions(self, image): |
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"""استخراج النص والأبعاد""" |
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text = pytesseract.image_to_string(image, lang='ara+eng') |
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dimensions = self._analyze_dimensions_from_text(text) |
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return text, dimensions |
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def _analyze_dimensions_from_text(self, text): |
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"""تحليل الأبعاد من النص""" |
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import re |
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dimension_pattern = r'(\d+(?:\.\d+)?)\s*(م|متر|سم|مم|m|cm|mm)' |
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dimensions = re.findall(dimension_pattern, text) |
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processed_dimensions = [] |
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for value, unit in dimensions: |
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processed_dimensions.append({ |
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'value': float(value), |
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'unit': unit |
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}) |
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return processed_dimensions |
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