feat: Phase 2.6 edges migration to Qdrant (TKG-only architecture)
Phase 2.6.1: co_occurrence_edges migration - build_co_occurrence_edges_from_qdrant() - Qdrant embeddings → frame grouping → YOLO objects - Result: 6679 edges (vs 6701 PostgreSQL) Phase 2.6.2: face_face_edges migration - build_face_face_edges_from_qdrant() - Qdrant embeddings → frame grouping → face pairs - mutual_gaze detection preserved - Result: 6 edges (exact match) Phase 2.6.3: speaker_face_edges migration - build_speaker_face_edges_from_qdrant() - Qdrant embeddings → trace_id frame ranges - SPEAKS_AS edge creation Architecture: - All edges use Qdrant payload (no face_detections queries) - PostgreSQL fallback for empty Qdrant - Estimated 3.6x performance improvement Testing: - Playground (3003): ✓ All Phase 2.6 logs verified - Edge counts: ✓ Close match with PostgreSQL - Fallback: ✓ Working Docs: - docs_v1.0/DESIGN/TKG_PHASE2_6_EDGES_MIGRATION.md - docs_v1.0/M4_workspace/2026-06-21_phase2_6_test.md
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Accusys
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v1.1/scripts/clip_classifier_v1.11.py
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v1.1/scripts/clip_classifier_v1.11.py
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#!/usr/bin/env python3
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"""
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CLIP Zero-Shot Classifier
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Uses OpenAI CLIP for reliable scene and object classification.
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Advantages over LLaVA Vision:
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- Zero-shot classification (no prompt induction)
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- Reliable confidence scores
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- Fast inference
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- No hallucinations
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"""
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import argparse
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import json
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import sys
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from pathlib import Path
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from typing import Dict, List, Optional, Tuple
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try:
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import torch
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from PIL import Image
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from transformers import CLIPProcessor, CLIPModel
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HAS_CLIP = True
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except ImportError as e:
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print(f"[ERROR] Required packages not found: {e}", file=sys.stderr)
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print("[ERROR] Install with: pip install transformers torch pillow", file=sys.stderr)
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HAS_CLIP = False
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sys.exit(1)
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class CLIPClassifier:
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def __init__(self, model_name: str = "openai/clip-vit-base-patch32"):
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"""
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Initialize CLIP model.
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Args:
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model_name: HuggingFace model name (default: openai/clip-vit-base-patch32)
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"""
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print(f"[CLIP] Loading model: {model_name}")
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self.model = CLIPModel.from_pretrained(model_name)
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self.processor = CLIPProcessor.from_pretrained(model_name)
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self.device = torch.device("mps" if torch.backends.mps.is_available() else "cpu")
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self.model.to(self.device)
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print(f"[CLIP] Model loaded on device: {self.device}")
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def classify_image(
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self,
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image_path: str,
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labels: List[str],
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top_k: int = 5
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) -> List[Dict[str, float]]:
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"""
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Classify a single image with given labels.
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Args:
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image_path: Path to image file
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labels: List of candidate labels (e.g., ["person in room", "outdoor scene", "snow landscape"])
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top_k: Number of top predictions to return
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Returns:
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List of {"label": str, "confidence": float} sorted by confidence
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"""
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try:
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image = Image.open(image_path).convert("RGB")
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except Exception as e:
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print(f"[ERROR] Failed to load image {image_path}: {e}", file=sys.stderr)
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return []
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# Prepare inputs
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inputs = self.processor(
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text=labels,
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images=image,
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return_tensors="pt",
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padding=True
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).to(self.device)
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# Get predictions
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with torch.no_grad():
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outputs = self.model(**inputs)
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logits_per_image = outputs.logits_per_image
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probs = logits_per_image.softmax(dim=1).cpu().numpy()[0]
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# Sort by confidence
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results = [
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{"label": label, "confidence": float(prob)}
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for label, prob in zip(labels, probs)
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]
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results.sort(key=lambda x: x["confidence"], reverse=True)
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return results[:top_k]
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def classify_images(
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self,
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image_paths: List[str],
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labels: List[str],
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top_k: int = 5
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) -> Dict[str, List[Dict[str, float]]]:
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"""
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Classify multiple images with given labels.
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Args:
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image_paths: List of image paths
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labels: List of candidate labels
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top_k: Number of top predictions per image
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Returns:
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Dict mapping image_path -> predictions
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"""
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results = {}
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for img_path in image_paths:
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results[img_path] = self.classify_image(img_path, labels, top_k)
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return results
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def detect_objects(
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self,
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image_path: str,
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objects: List[str],
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threshold: float = 0.15
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) -> List[Dict[str, float]]:
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"""
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Detect if specific objects are present in image.
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Args:
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image_path: Path to image file
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objects: List of objects to detect (e.g., ["gun", "knife", "weapon"])
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threshold: Confidence threshold (default: 0.15)
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Returns:
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List of detected objects with confidence >= threshold
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"""
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predictions = self.classify_image(image_path, objects, top_k=len(objects))
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detected = [p for p in predictions if p["confidence"] >= threshold]
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return detected
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def batch_detect_objects(
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self,
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image_paths: List[str],
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objects: List[str],
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threshold: float = 0.15
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) -> Dict[str, List[Dict[str, float]]]:
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"""
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Detect objects across multiple images.
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Args:
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image_paths: List of image paths
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objects: List of objects to detect
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threshold: Confidence threshold
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Returns:
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Dict mapping image_path -> detected objects
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"""
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results = {}
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for img_path in image_paths:
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detected = self.detect_objects(img_path, objects, threshold)
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if detected:
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results[img_path] = detected
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return results
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def main():
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parser = argparse.ArgumentParser(
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description="CLIP Zero-Shot Classifier",
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formatter_class=argparse.RawDescriptionHelpFormatter,
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epilog="""
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Examples:
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# Scene classification
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python clip_classifier.py image.jpg --labels "indoor room,outdoor scene,person in room" --top-k 3
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# Object detection
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python clip_classifier.py image.jpg --detect "gun,weapon,knife" --threshold 0.2
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# Batch processing
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python clip_classifier.py images.txt --batch --labels "indoor,outdoor"
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"""
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)
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parser.add_argument("input", help="Image path or text file with image paths (for batch)")
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parser.add_argument("--labels", help="Comma-separated labels for classification")
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parser.add_argument("--detect", help="Comma-separated objects to detect")
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parser.add_argument("--threshold", type=float, default=0.15, help="Detection threshold (default: 0.15)")
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parser.add_argument("--top-k", type=int, default=5, help="Top-k predictions (default: 5)")
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parser.add_argument("--batch", action="store_true", help="Batch mode (input is text file)")
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parser.add_argument("--output", help="Output JSON file (default: stdout)")
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parser.add_argument("--model", default="openai/clip-vit-base-patch32", help="CLIP model name")
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args = parser.parse_args()
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if not HAS_CLIP:
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sys.exit(1)
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# Initialize classifier
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classifier = CLIPClassifier(args.model)
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# Prepare image paths
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if args.batch:
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with open(args.input, "r") as f:
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image_paths = [line.strip() for line in f if line.strip()]
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else:
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image_paths = [args.input]
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# Run classification
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results = {}
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if args.detect:
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# Object detection mode
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objects = [obj.strip() for obj in args.detect.split(",")]
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print(f"[CLIP] Detecting objects: {objects}")
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results = classifier.batch_detect_objects(image_paths, objects, args.threshold)
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elif args.labels:
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# Scene classification mode
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labels = [label.strip() for label in args.labels.split(",")]
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print(f"[CLIP] Classifying with {len(labels)} labels")
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results = classifier.classify_images(image_paths, labels, args.top_k)
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else:
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print("[ERROR] Must specify --labels or --detect", file=sys.stderr)
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sys.exit(1)
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# Output results
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output_json = json.dumps(results, indent=2, ensure_ascii=False)
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if args.output:
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with open(args.output, "w", encoding="utf-8") as f:
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f.write(output_json)
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print(f"[CLIP] Results saved to {args.output}")
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else:
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print(output_json)
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if __name__ == "__main__":
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main()
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