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All services: admin-v2, studio-v2, website, ai-compliance-sdk, consent-service, klausur-service, voice-service, and infrastructure. Large PDFs and compiled binaries excluded via .gitignore.
65 lines
2.2 KiB
Python
65 lines
2.2 KiB
Python
"""
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Debug visualization to see what the AI detected
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"""
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import cv2
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import json
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from pathlib import Path
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def visualize_detection(image_path: Path, analysis_json_path: Path, output_path: Path):
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"""
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Zeichne Bounding Boxes auf das Bild um zu sehen was die AI erkannt hat
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"""
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# Load image
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img = cv2.imread(str(image_path))
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if img is None:
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print(f"❌ Cannot load image: {image_path}")
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return
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# Load analysis
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with open(analysis_json_path, 'r', encoding='utf-8') as f:
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analysis = json.load(f)
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# Draw handwriting regions in RED
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hw_regions = analysis.get('handwriting_regions', [])
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for i, region in enumerate(hw_regions, 1):
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bbox = region.get('bounding_box', {})
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x, y, w, h = bbox.get('x', 0), bbox.get('y', 0), bbox.get('width', 0), bbox.get('height', 0)
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if w > 0 and h > 0:
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# Draw rectangle
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cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 3)
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# Draw label
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label = f"HW{i}: {region.get('type')} ({region.get('color_hint')})"
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cv2.putText(img, label, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
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# Draw diagram regions in GREEN
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diagrams = analysis.get('layout', {}).get('diagram_elements', [])
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for i, diagram in enumerate(diagrams, 1):
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bbox = diagram.get('bounding_box', {})
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x, y, w, h = bbox.get('x', 0), bbox.get('y', 0), bbox.get('width', 0), bbox.get('height', 0)
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if w > 0 and h > 0:
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cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
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label = f"DIAGRAM{i}"
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cv2.putText(img, label, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
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# Save
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cv2.imwrite(str(output_path), img)
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print(f"✅ Debug visualization saved: {output_path}")
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print(f" 📍 {len(hw_regions)} handwriting regions (RED)")
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print(f" 📍 {len(diagrams)} diagram regions (GREEN)")
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if __name__ == "__main__":
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eingang = Path.home() / "Arbeitsblaetter" / "Eingang"
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bereinigt = Path.home() / "Arbeitsblaetter" / "Bereinigt"
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image_file = "2025-12-11_Auge1.JPG"
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visualize_detection(
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eingang / image_file,
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bereinigt / "2025-12-11_Auge1_analyse.json",
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bereinigt / "2025-12-11_Auge1_DEBUG.jpg"
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)
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