feat: update Python processors and add utility scripts
- Update ASR, face, OCR, pose processors - Add release pre-flight check script - Add synonym generation, chunk processing scripts - Add face recognition, stamp search utilities
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Warren
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scripts/test_face_learning.py
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332
scripts/test_face_learning.py
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#!/usr/bin/env python3
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"""
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Test face learning (registration) and recognition
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This demonstrates the system's ability to learn new faces
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"""
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import requests
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import json
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import os
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import base64
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from PIL import Image
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import io
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BASE_URL = "http://localhost:3002"
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API_KEY = "muser_243c6725b09f43e29f319a648645b992_1774874668_f224a6d2"
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VIDEO_UUID = "384b0ff44aaaa1f1"
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def extract_face_from_frame(frame_path, face_bbox):
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"""Extract a face from frame image"""
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if not os.path.exists(frame_path):
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print(f"⚠️ Frame not found: {frame_path}")
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return None
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try:
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img = Image.open(frame_path)
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# Extract face region (x, y, width, height)
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x, y, w, h = face_bbox
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face_img = img.crop((x, y, x + w, y + h))
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# Convert to bytes
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img_byte_arr = io.BytesIO()
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face_img.save(img_byte_arr, format="JPEG")
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img_byte_arr.seek(0)
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return img_byte_arr
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except Exception as e:
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print(f"❌ Error extracting face: {e}")
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return None
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def register_face_from_detection(frame_number, face_index, person_name):
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"""Register a face from detected face in video"""
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print(
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f"\n📝 Registering {person_name} from frame {frame_number}, face {face_index}..."
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)
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# First, get face detection details
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import psycopg2
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try:
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conn = psycopg2.connect(
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host="localhost",
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port=5432,
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database="momentry",
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user="accusys",
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password="accusys",
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)
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cursor = conn.cursor()
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cursor.execute(
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"""
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SELECT x, y, width, height, confidence, attributes::text
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FROM face_detections
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WHERE video_uuid = %s AND frame_number = %s
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ORDER BY confidence DESC
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LIMIT 1 OFFSET %s
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""",
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(VIDEO_UUID, frame_number, face_index),
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)
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result = cursor.fetchone()
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if not result:
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print(f"❌ No face found at frame {frame_number}, index {face_index}")
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return False
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x, y, width, height, confidence, attributes_json = result
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# Parse attributes
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attributes = json.loads(attributes_json) if attributes_json else {}
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print(f" Face bbox: ({x}, {y}, {width}, {height})")
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print(f" Confidence: {confidence:.3f}")
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print(f" Attributes: {attributes}")
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# Register via API
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headers = {"X-API-Key": API_KEY, "Content-Type": "application/json"}
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payload = {
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"video_uuid": VIDEO_UUID,
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"frame_number": frame_number,
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"face_index": face_index,
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"person_name": person_name,
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"metadata": {
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"gender": attributes.get("gender", "unknown"),
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"age": attributes.get("age", 0),
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"confidence": float(confidence),
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"source": "Charade (1963)",
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"bbox": [x, y, width, height],
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},
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}
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response = requests.post(
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f"{BASE_URL}/api/v1/face/register",
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headers=headers,
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json=payload,
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timeout=30,
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)
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print(f" API Status: {response.status_code}")
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if response.status_code == 200:
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data = response.json()
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print(f" ✅ Registered! Face ID: {data.get('face_id')}")
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print(f" Embedding: {len(data.get('embedding', []))} dimensions")
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return True
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else:
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print(f" ❌ Registration failed: {response.text}")
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return False
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except Exception as e:
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print(f"❌ Error: {e}")
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import traceback
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traceback.print_exc()
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return False
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finally:
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if "cursor" in locals():
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cursor.close()
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if "conn" in locals():
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conn.close()
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def test_face_recognition(test_image_path):
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"""Test face recognition with a test image"""
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print(f"\n🔍 Testing recognition with: {test_image_path}")
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if not os.path.exists(test_image_path):
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print(f"❌ Test image not found: {test_image_path}")
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return False
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headers = {"X-API-Key": API_KEY}
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files = {"image": open(test_image_path, "rb")}
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try:
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response = requests.post(
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f"{BASE_URL}/api/v1/face/recognize",
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headers=headers,
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files=files,
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timeout=30,
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)
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print(f"Status: {response.status_code}")
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if response.status_code == 200:
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data = response.json()
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print(f"✅ Success!")
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print(f"Total faces detected: {data.get('total_faces', 0)}")
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matches = data.get("matches", [])
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if matches:
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print(f"\n🎯 Recognition results:")
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for i, match in enumerate(matches):
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print(f" {i + 1}. {match.get('person_name', 'Unknown')}")
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print(f" Confidence: {match.get('confidence', 0):.3f}")
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print(f" Distance: {match.get('distance', 0):.3f}")
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metadata = match.get("metadata", {})
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if metadata:
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print(f" Gender: {metadata.get('gender', 'unknown')}")
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print(f" Age: {metadata.get('age', 'unknown')}")
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print()
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else:
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print("No matches found")
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return True
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else:
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print(f"❌ Recognition failed: {response.text}")
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return False
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except Exception as e:
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print(f"❌ Error: {e}")
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return False
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finally:
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if "files" in locals():
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files["image"].close()
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def list_registered_faces():
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"""List all registered faces"""
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print("\n📋 Listing registered faces...")
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headers = {"X-API-Key": API_KEY}
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try:
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response = requests.get(
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f"{BASE_URL}/api/v1/face/list", headers=headers, timeout=10
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)
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print(f"Status: {response.status_code}")
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if response.status_code == 200:
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data = response.json()
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faces = data.get("faces", [])
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print(f"Total registered faces: {len(faces)}")
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for face in faces:
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print(f"\n 👤 {face.get('name')}")
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print(f" ID: {face.get('face_id')}")
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print(f" Created: {face.get('created_at')}")
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print(f" Active: {face.get('is_active', False)}")
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metadata = face.get("metadata", {})
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if metadata:
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print(f" Gender: {metadata.get('gender', 'unknown')}")
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print(f" Age: {metadata.get('age', 'unknown')}")
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return True
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else:
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print(f"❌ Failed: {response.text}")
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return False
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except Exception as e:
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print(f"❌ Error: {e}")
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return False
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def main():
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print("=" * 70)
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print("🧠 Face Learning and Recognition Test")
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print("=" * 70)
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print(f"\n🎬 Source video: {VIDEO_UUID}")
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print(" Charade (1963) - Audrey Hepburn and Cary Grant")
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# Step 1: List current registered faces
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print("\n" + "-" * 50)
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print("STEP 1: Check current registered faces")
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print("-" * 50)
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list_registered_faces()
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# Step 2: Register sample faces
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print("\n" + "-" * 50)
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print("STEP 2: Register faces from video analysis")
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print("-" * 50)
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# Register faces from our analysis
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# Frame 19778 has 3 faces (based on our analysis)
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faces_to_learn = [
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{
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"frame": 19778,
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"index": 0,
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"name": "Audrey_Hepburn",
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"description": "Main actress in Charade",
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},
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{
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"frame": 19778,
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"index": 1,
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"name": "Cary_Grant",
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"description": "Main actor in Charade",
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},
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{
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"frame": 17980,
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"index": 0,
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"name": "Walter_Mathau",
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"description": "Supporting actor in Charade",
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},
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]
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learned_count = 0
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for face in faces_to_learn:
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if register_face_from_detection(face["frame"], face["index"], face["name"]):
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learned_count += 1
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print(f"\n📊 Learned {learned_count}/{len(faces_to_learn)} faces")
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# Step 3: List registered faces again
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print("\n" + "-" * 50)
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print("STEP 3: Verify registered faces")
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print("-" * 50)
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list_registered_faces()
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# Step 4: Test recognition
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print("\n" + "-" * 50)
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print("STEP 4: Test face recognition")
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print("-" * 50)
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# Test with the female faces frame we extracted earlier
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test_images = [
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"/tmp/female_faces/female_faces_frame_19778.jpg",
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"/tmp/face_analysis_results/384b0ff44aaaa1f1_frame_19778.jpg",
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]
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for test_image in test_images:
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if os.path.exists(test_image):
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test_face_recognition(test_image)
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break
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# Step 5: Test with a different frame
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print("\n" + "-" * 50)
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print("STEP 5: Test with another frame")
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print("-" * 50)
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# Try frame 17980 (has 2 females)
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test_image_2 = "/tmp/face_analysis_results/384b0ff44aaaa1f1_frame_17980.jpg"
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if os.path.exists(test_image_2):
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test_face_recognition(test_image_2)
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print("\n" + "=" * 70)
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print("✅ Face Learning Test Completed!")
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print("=" * 70)
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# Summary
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print("\n📋 SUMMARY:")
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print(f"• API Server: {BASE_URL}")
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print(f"• Video analyzed: {VIDEO_UUID}")
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print(f"• Faces detected: 78 (from analysis)")
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print(f"• Faces registered: {learned_count}")
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print(f"• System ready for learning new faces!")
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print("\n💡 NEXT STEPS:")
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print("1. Upload new photos to recognize registered faces")
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print("2. Register more faces from other videos")
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print("3. Build a face database for your organization")
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print("4. Integrate with your applications via API")
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if __name__ == "__main__":
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main()
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