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
This commit is contained in:
Accusys
790
v1.1/scripts/demo_dashboard_v1.11.py
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790
v1.1/scripts/demo_dashboard_v1.11.py
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#!/opt/homebrew/bin/python3.11
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"""
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Momentry Core Visual Demo Dashboard
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職責：提供處理器模組的視覺化預覽，支持時間軸檢查與多模組疊加顯示。
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"""
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import os
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import json
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import cv2
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import numpy as np
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import streamlit as st
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import pandas as pd
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import altair as alt
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from PIL import Image, ImageDraw, ImageFont
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import time
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# ==========================================
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# 設定與輔助函數
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# ==========================================
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OUTPUT_DIR = os.getenv("MOMENTRY_OUTPUT_DIR", "./output")
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VIDEO_BASE_DIR = os.path.join(OUTPUT_DIR, "quick_preview") # 指向預覽目錄
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# 色彩定義 (OpenCV BGR 格式)
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COLORS = {
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"YOLO": (0, 255, 0), # 綠
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"FACE": (255, 0, 0), # 藍
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"POSE": (0, 0, 255), # 紅
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"OCR": (0, 255, 255), # 黃
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"SCENE": (255, 255, 255), # 白 (文字)
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}
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# 骨架連接對 (MediaPipe Pose)
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POSE_CONNECTIONS = [
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(11, 12),
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(11, 13),
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(13, 15),
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(12, 14),
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(14, 16), # 上半身
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(11, 23),
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(12, 23),
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(23, 24),
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(23, 25),
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(25, 27), # 下半身左
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(24, 26),
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(26, 28), # 下半身右
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]
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def load_json_safe(uuid, module):
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path = os.path.join(OUTPUT_DIR, "quick_preview", f"preview.{module}.json")
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if not os.path.exists(path):
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return None
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with open(path, "r") as f:
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return json.load(f)
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def get_video_path(uuid):
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# 直接返回預覽影片
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return os.path.join(OUTPUT_DIR, "quick_preview", "preview.mp4")
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# ==========================================
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# 渲染邏輯 (Renderers)
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# ==========================================
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def draw_yolo_overlay(frame, yolo_data, timestamp):
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"""繪製 YOLO 檢測框"""
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if not yolo_data:
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return frame
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h, w = frame.shape[:2]
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# 尋找最接近的幀
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best_frame = None
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min_diff = float("inf")
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frames_data = yolo_data.get("frames", {})
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if isinstance(frames_data, dict):
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frames_list = list(frames_data.values())
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else:
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frames_list = frames_data
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for f in frames_list:
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ts = f.get("time_seconds") or f.get("timestamp", 0)
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diff = abs(ts - timestamp)
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if diff < min_diff:
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min_diff = diff
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best_frame = f
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if best_frame and min_diff < 0.1:
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for obj in best_frame.get("detections", []):
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# YOLO output has x1, y1, x2, y2 directly
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x1 = int(obj.get("x1", 0))
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y1 = int(obj.get("y1", 0))
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x2 = int(obj.get("x2", 0))
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y2 = int(obj.get("y2", 0))
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label = f"{obj.get('class_name', '?')} {obj.get('confidence', 0):.2f}"
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# Draw Rectangle
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cv2.rectangle(frame, (x1, y1), (x2, y2), COLORS["YOLO"], 2)
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# Draw Label Background
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(tw, th), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
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cv2.rectangle(frame, (x1, y1 - 15), (x1 + tw, y1), COLORS["YOLO"], -1)
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# Draw Text
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cv2.putText(
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frame, label, (x1, y1 - 3), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1
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)
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return frame
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def draw_pose_overlay(frame, pose_data, timestamp):
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"""繪製 Pose 骨架"""
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if not pose_data:
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return frame
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h, w = frame.shape[:2]
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best_frame = None
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min_diff = float("inf")
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for f in pose_data.get("frames", []):
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diff = abs(f.get("timestamp", 0) - timestamp)
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if diff < min_diff:
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min_diff = diff
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best_frame = f
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if best_frame and min_diff < 0.5:
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for person in best_frame.get("persons", []):
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kps = person.get("keypoints", [])
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if not kps:
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continue
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# 繪製節點與連線
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for conn in POSE_CONNECTIONS:
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p1 = kps[conn[0]] if conn[0] < len(kps) else None
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p2 = kps[conn[1]] if conn[1] < len(kps) else None
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if (
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p1
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and p2
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and p1.get("confidence", 0) > 0.5
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and p2.get("confidence", 0) > 0.5
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):
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pt1 = (int(p1["x"] * w), int(p1["y"] * h))
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pt2 = (int(p2["x"] * w), int(p2["y"] * h))
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cv2.line(frame, pt1, pt2, COLORS["POSE"], 2)
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return frame
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def draw_ocr_overlay(frame, ocr_data, timestamp):
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"""繪製 OCR 文字區域"""
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if not ocr_data:
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return frame
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h, w = frame.shape[:2]
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frames_data = ocr_data.get("frames", [])
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if isinstance(frames_data, dict):
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frames_list = list(frames_data.values())
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else:
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frames_list = frames_data
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best_frame = None
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min_diff = float("inf")
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for f in frames_list:
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diff = abs(f.get("timestamp", 0) - timestamp)
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if diff < min_diff:
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min_diff = diff
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best_frame = f
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if best_frame and min_diff < 0.5:
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for text in best_frame.get("texts", []):
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# Check if bbox is a list of 4 points OR x,y,w,h
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box = text.get("bbox", [])
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if isinstance(box, list) and len(box) == 4:
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# Format: [[x1,y1], [x2,y2], [x3,y3], [x4,y4]]
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pts = np.array([[int(p[0]), int(p[1])] for p in box], np.int32)
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pts = pts.reshape((-1, 1, 2))
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cv2.polylines(frame, [pts], True, COLORS["OCR"], 2)
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cv2.putText(
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frame,
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text.get("text", ""),
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(pts[0][0][0], pts[0][0][1] - 5),
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cv2.FONT_HERSHEY_SIMPLEX,
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0.4,
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COLORS["OCR"],
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1,
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)
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else:
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# Format: x, y, width, height (EasyOCR style)
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x = text.get("x", 0)
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y = text.get("y", 0)
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width = text.get("width", 0)
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height = text.get("height", 0)
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# Normalize to pixels if < 1
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if x <= 1:
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x *= w
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if y <= 1:
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y *= h
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if width <= 1:
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width *= w
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if height <= 1:
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height *= h
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x, y, width, height = int(x), int(y), int(width), int(height)
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cv2.rectangle(frame, (x, y), (x + width, y + height), COLORS["OCR"], 2)
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cv2.putText(
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frame,
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text.get("text", ""),
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(x, y - 5),
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cv2.FONT_HERSHEY_SIMPLEX,
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0.4,
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COLORS["OCR"],
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1,
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)
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return frame
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def draw_scene_label(frame, scene_data, timestamp):
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"""繪製場景標籤"""
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if not scene_data:
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return frame
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for scene in scene_data.get("scenes", []):
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if scene.get("start_time", 0) <= timestamp <= scene.get("end_time", 0):
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label = f"📍 {scene.get('scene_type_zh') or scene.get('scene_type')}"
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cv2.putText(
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frame, label, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 0), 4
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) # 陰影
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cv2.putText(
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frame,
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label,
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(10, 30),
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cv2.FONT_HERSHEY_SIMPLEX,
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0.8,
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COLORS["SCENE"],
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2,
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)
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break
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return frame
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def draw_face_overlay(frame, face_data, timestamp):
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"""繪製 Face 檢測框"""
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if not face_data:
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return frame
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h, w = frame.shape[:2]
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frames_data = face_data.get("frames", [])
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if isinstance(frames_data, dict):
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frames_list = list(frames_data.values())
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else:
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frames_list = frames_data
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best_frame = None
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min_diff = float("inf")
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for f in frames_list:
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diff = abs(f.get("timestamp", 0) - timestamp)
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if diff < min_diff:
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min_diff = diff
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best_frame = f
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if best_frame and min_diff < 1.5: # 放寬容忍度到 1.5 秒，以匹配稀疏的關鍵幀
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for face in best_frame.get("faces", []):
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# Format: x, y, width, height (pixels)
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x = face.get("x", 0)
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y = face.get("y", 0)
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width = face.get("width", 0)
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height = face.get("height", 0)
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cv2.rectangle(frame, (x, y), (x + width, y + height), COLORS["FACE"], 2)
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# 優先顯示聚類後的 Person ID (使用 PIL 支援中文)
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person_id = face.get("person_id")
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if person_id:
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label = f"ID: {person_id}"
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color_rgb = (255, 255, 0) # Yellow
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else:
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label = f"Face {face.get('confidence', 0):.2f}"
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color_rgb = tuple(COLORS["FACE"][::-1]) # RGB
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# 1. 轉換為 PIL 格式以繪製中文
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from PIL import Image, ImageDraw, ImageFont
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img_pil = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
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draw = ImageDraw.Draw(img_pil)
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# 2. 載入中文字型 (直接使用 STHeiti，因為 PingFang.ttc 是集合檔有時無法讀取)
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try:
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font = ImageFont.truetype(
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"/System/Library/Fonts/STHeiti Medium.ttc", 24
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)
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except:
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# 備案：如果 STHeiti 也失敗，嘗試 Arial Unicode 或預設
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try:
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font = ImageFont.truetype("/Library/Fonts/Arial Unicode.ttf", 24)
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except:
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font = ImageFont.load_default()
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# 3. 計算文字大小
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bbox = draw.textbbox((0, 0), label, font=font)
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tw = bbox[2] - bbox[0]
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th = bbox[3] - bbox[1]
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# 4. 繪製位置 (臉部框上方)
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px = x
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py = max(th + 5, y) # 確保文字不會超出畫面頂部
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# 5. 繪製黑色背景
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draw.rectangle([px, py - th - 4, px + tw + 4, py], fill=(0, 0, 0))
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# 6. 繪製文字
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draw.text((px + 2, py - th - 2), label, font=font, fill=color_rgb)
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# 7. 轉回 OpenCV 格式 (BGR)
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frame = cv2.cvtColor(np.array(img_pil), cv2.COLOR_RGB2BGR)
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return frame
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def draw_speaker_overlay(frame, asrx_data, timestamp):
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"""繪製 Speaker 標籤 (右上角)"""
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if not asrx_data:
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return frame
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# 尋找當前時間段的說話人
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segments = asrx_data.get("segments", [])
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current_speaker = None
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for seg in segments:
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start = seg.get("start", 0)
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end = seg.get("end", 0)
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if start <= timestamp <= end:
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current_speaker = seg.get("speaker_id")
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break
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if current_speaker:
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# 檢查是否有綁定身份 (這裡暫時直接顯示 ID，未來可擴展查詢 DB)
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label = f"🎤 {current_speaker}"
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# 繪製標籤
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font = cv2.FONT_HERSHEY_SIMPLEX
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font_scale = 1.0
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thickness = 2
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color = (255, 165, 0) # 橙色
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(tw, th), _ = cv2.getTextSize(label, font, font_scale, thickness)
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margin = 10
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x, y = frame.shape[1] - tw - margin, th + margin
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# 背景
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cv2.rectangle(frame, (x - 5, y - th - 5), (x + tw + 5, y + 5), color, -1)
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# 文字
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cv2.putText(frame, label, (x, y), font, font_scale, (0, 0, 0), thickness)
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return frame
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|
||||
def draw_asr_subtitle(frame, asr_data, timestamp):
|
||||
"""繪製字幕 (Support Chinese)"""
|
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if not asr_data:
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return frame
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h, w = frame.shape[:2]
|
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|
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# 尋找當前句子
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text = ""
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for seg in asr_data.get("segments", []):
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if seg.get("start", 0) <= timestamp <= seg.get("end", 0):
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text = seg.get("text", "")
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break
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|
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if text:
|
||||
# Convert BGR (OpenCV) to RGB (PIL)
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img_pil = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
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draw = ImageDraw.Draw(img_pil)
|
||||
|
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# Measure text size to draw background
|
||||
try:
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font = ImageFont.truetype("/System/Library/Fonts/STHeiti Medium.ttc", 24)
|
||||
except:
|
||||
try:
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||||
font = ImageFont.truetype("/System/Library/Fonts/PingFang.ttc", 24)
|
||||
except:
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||||
font = ImageFont.load_default()
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||||
|
||||
bbox = draw.textbbox((0, 0), text, font=font)
|
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text_w = bbox[2] - bbox[0]
|
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text_h = bbox[3] - bbox[1]
|
||||
|
||||
# Background position
|
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bg_x = (w - text_w) // 2
|
||||
bg_y = h - text_h - 20
|
||||
|
||||
# Draw Background
|
||||
draw.rectangle(
|
||||
[bg_x - 10, bg_y - 10, bg_x + text_w + 10, bg_y + text_h + 10],
|
||||
fill=(0, 0, 0),
|
||||
)
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||||
|
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# Draw Text
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||||
draw.text((bg_x, bg_y), text, font=font, fill=(255, 255, 255))
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||||
|
||||
# Convert back to BGR
|
||||
frame = cv2.cvtColor(np.array(img_pil), cv2.COLOR_RGB2BGR)
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return frame
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||||
h, w = frame.shape[:2]
|
||||
|
||||
# 尋找當前句子
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||||
text = ""
|
||||
for seg in asr_data.get("segments", []):
|
||||
if seg.get("start", 0) <= timestamp <= seg.get("end", 0):
|
||||
text = seg.get("text", "")
|
||||
break
|
||||
|
||||
if text:
|
||||
# 黑底白字
|
||||
text_size = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 0.6, 2)[0]
|
||||
text_x = (w - text_size[0]) // 2
|
||||
text_y = h - 30
|
||||
cv2.rectangle(
|
||||
frame,
|
||||
(text_x - 5, text_y - 25),
|
||||
(text_x + text_size[0] + 5, text_y + 5),
|
||||
(0, 0, 0),
|
||||
-1,
|
||||
)
|
||||
cv2.putText(
|
||||
frame,
|
||||
text,
|
||||
(text_x, text_y),
|
||||
cv2.FONT_HERSHEY_SIMPLEX,
|
||||
0.6,
|
||||
(255, 255, 255),
|
||||
2,
|
||||
)
|
||||
return frame
|
||||
|
||||
|
||||
# ==========================================
|
||||
# 主應用邏輯
|
||||
# ==========================================
|
||||
|
||||
|
||||
def main():
|
||||
st.set_page_config(layout="wide", page_title="Momentry Visual Demo")
|
||||
st.title("🎬 Momentry Processor Visual Demo")
|
||||
|
||||
uuid = "quick_preview"
|
||||
video_path = get_video_path(uuid)
|
||||
if not video_path or not os.path.exists(video_path):
|
||||
st.error(f"Video file not found at {video_path}")
|
||||
return
|
||||
|
||||
# 1. 原始音視頻播放器 (讓用戶聽到聲音)
|
||||
st.subheader("🔊 原始聲音播放器 (可聽 Speaker 聲音)")
|
||||
st.video(video_path, start_time=0)
|
||||
st.markdown("---")
|
||||
|
||||
# 2. 使用說明 (How to Use)
|
||||
with st.expander("📖 如何使用本工具？(點擊展開說明)"):
|
||||
st.markdown(
|
||||
"""
|
||||
1. **時間軸控制**: 拖動下方的滑動條 (Slider) 來移動影片時間點。
|
||||
2. **開啟/關閉功能**: 在右側的 **Layers** 面板中，勾選您想看到的效果。
|
||||
- **✅ YOLO**: 綠色框標記物體 (如人、桌子)。
|
||||
- **✅ ASR**: 底部顯示白色字幕。
|
||||
- **✅ Scene**: 左上角顯示場景名稱。
|
||||
3. **查看統計**: 底部圖表顯示各模組在哪些時間段有數據。
|
||||
"""
|
||||
)
|
||||
|
||||
# 3. 載入 JSON 數據
|
||||
col1, col2 = st.columns([3, 1])
|
||||
with col1:
|
||||
st.header("Frame Inspector (幀檢查器)")
|
||||
with col2:
|
||||
st.subheader("顯示層控制 (Layers)")
|
||||
show_yolo = st.checkbox("YOLO (Object)", value=True)
|
||||
show_face = st.checkbox("Face (Person)", value=True)
|
||||
show_pose = st.checkbox("Pose (Skeleton)", value=False)
|
||||
show_ocr = st.checkbox("OCR (Text)", value=False)
|
||||
show_scene = st.checkbox("Scene (Label)", value=True)
|
||||
show_asr = st.checkbox("ASR (Subtitle)", value=True)
|
||||
|
||||
# 3. 數據載入
|
||||
yolo_data = load_json_safe(uuid, "yolo") if show_yolo else None
|
||||
# 強制嘗試載入聚類數據
|
||||
face_data = load_json_safe(uuid, "face_clustered")
|
||||
if face_data:
|
||||
st.success("✅ 已載入聚類數據 (Face Clustered)")
|
||||
else:
|
||||
face_data = load_json_safe(uuid, "face")
|
||||
st.warning("⚠️ 未找到聚類數據，使用原始數據")
|
||||
|
||||
pose_data = load_json_safe(uuid, "pose") if show_pose else None
|
||||
ocr_data = load_json_safe(uuid, "ocr") if show_ocr else None
|
||||
scene_data = load_json_safe(uuid, "scene") if show_scene else None
|
||||
asr_data = load_json_safe(uuid, "asr") if show_asr else None
|
||||
# 載入 ASRX (Speaker) 數據
|
||||
asrx_data = load_json_safe(uuid, "asrx")
|
||||
|
||||
# 4. 視頻與幀控制與播放邏輯
|
||||
cap = cv2.VideoCapture(video_path)
|
||||
fps = cap.get(cv2.CAP_PROP_FPS)
|
||||
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
|
||||
duration = total_frames / fps if fps else 0
|
||||
|
||||
# 初始化 Session State
|
||||
if "playing" not in st.session_state:
|
||||
st.session_state.playing = False
|
||||
if "current_time" not in st.session_state:
|
||||
st.session_state.current_time = 0.0
|
||||
|
||||
# 播放控制區
|
||||
col_play, col_reset, col_info = st.columns([1, 1, 4])
|
||||
|
||||
with col_play:
|
||||
if st.button("▶ 播放"):
|
||||
st.session_state.playing = True
|
||||
with col_reset:
|
||||
if st.button("⏹ 重置"):
|
||||
st.session_state.playing = False
|
||||
st.session_state.current_time = 0.0
|
||||
with col_info:
|
||||
st.write(f"時間: {st.session_state.current_time:.2f} / {duration:.1f} s")
|
||||
|
||||
# 自動播放邏輯
|
||||
placeholder = st.empty()
|
||||
progress_bar = st.progress(0.0)
|
||||
|
||||
while st.session_state.playing:
|
||||
if st.session_state.current_time >= duration:
|
||||
st.session_state.playing = False
|
||||
st.session_state.current_time = 0.0
|
||||
break
|
||||
|
||||
current_time = st.session_state.current_time
|
||||
frame_idx = int(current_time * fps)
|
||||
|
||||
cap.set(cv2.CAP_PROP_POS_FRAMES, frame_idx)
|
||||
ret, frame = cap.read()
|
||||
|
||||
if ret:
|
||||
# 渲染
|
||||
if show_asr:
|
||||
frame = draw_asr_subtitle(frame, asr_data, current_time)
|
||||
frame = draw_speaker_overlay(frame, asrx_data, current_time)
|
||||
if show_scene:
|
||||
frame = draw_scene_label(frame, scene_data, current_time)
|
||||
if show_yolo:
|
||||
frame = draw_yolo_overlay(frame, yolo_data, current_time)
|
||||
if show_face:
|
||||
frame = draw_face_overlay(frame, face_data, current_time)
|
||||
if show_pose:
|
||||
frame = draw_pose_overlay(frame, pose_data, current_time)
|
||||
if show_ocr:
|
||||
frame = draw_ocr_overlay(frame, ocr_data, current_time)
|
||||
|
||||
# 顯示
|
||||
with placeholder.container():
|
||||
st.image(frame, channels="BGR", use_container_width=True)
|
||||
progress_bar.progress(
|
||||
current_time / duration, text=f"播放中: {current_time:.1f}s"
|
||||
)
|
||||
|
||||
# 更新時間 (每幀間隔)
|
||||
time.sleep(1.0 / fps if fps > 0 else 0.04)
|
||||
st.session_state.current_time += 1.0 / fps if fps > 0 else 0.04
|
||||
else:
|
||||
st.session_state.playing = False
|
||||
break
|
||||
|
||||
# 手動拖動條 (僅在暫停時顯示/可用)
|
||||
if not st.session_state.playing:
|
||||
st.session_state.current_time = st.slider(
|
||||
"⏯ 手動調整時間",
|
||||
0.0,
|
||||
duration,
|
||||
st.session_state.current_time,
|
||||
step=0.1,
|
||||
key="manual_slider",
|
||||
)
|
||||
progress_bar.progress(
|
||||
st.session_state.current_time / duration,
|
||||
text=f"已暫停: {st.session_state.current_time:.1f}s",
|
||||
)
|
||||
|
||||
# 最後一幀顯示 (如果是暫停狀態)
|
||||
if not st.session_state.playing:
|
||||
current_time = st.session_state.current_time
|
||||
frame_idx = int(current_time * fps)
|
||||
cap.set(cv2.CAP_PROP_POS_FRAMES, frame_idx)
|
||||
ret, frame = cap.read()
|
||||
if ret:
|
||||
if show_asr:
|
||||
frame = draw_asr_subtitle(frame, asr_data, current_time)
|
||||
frame = draw_speaker_overlay(frame, asrx_data, current_time)
|
||||
if show_scene:
|
||||
frame = draw_scene_label(frame, scene_data, current_time)
|
||||
if show_yolo:
|
||||
frame = draw_yolo_overlay(frame, yolo_data, current_time)
|
||||
if show_face:
|
||||
frame = draw_face_overlay(frame, face_data, current_time)
|
||||
if show_pose:
|
||||
frame = draw_pose_overlay(frame, pose_data, current_time)
|
||||
if show_ocr:
|
||||
frame = draw_ocr_overlay(frame, ocr_data, current_time)
|
||||
|
||||
with placeholder.container():
|
||||
st.image(frame, channels="BGR", use_container_width=True)
|
||||
|
||||
# 5. 人工互動聚類介面 (Identity Manager)
|
||||
st.header("👥 身份管理與合併 (Identity Manager)")
|
||||
|
||||
# 找出所有 Person 截圖
|
||||
thumbnail_dir = os.path.join(OUTPUT_DIR, "quick_preview")
|
||||
person_thumbnails = [
|
||||
f
|
||||
for f in os.listdir(thumbnail_dir)
|
||||
if f.startswith("Person_") and f.endswith(".jpg")
|
||||
]
|
||||
|
||||
if person_thumbnails:
|
||||
# 顯示所有面孔
|
||||
cols = st.columns(min(len(person_thumbnails), 4))
|
||||
selected_ids = []
|
||||
|
||||
for i, fname in enumerate(sorted(person_thumbnails)):
|
||||
person_id = fname.replace(".jpg", "")
|
||||
img_path = os.path.join(thumbnail_dir, fname)
|
||||
|
||||
with cols[i % 4]:
|
||||
st.image(img_path, caption=person_id, use_container_width=True)
|
||||
if st.checkbox(f"選擇 {person_id}", key=f"chk_{person_id}"):
|
||||
selected_ids.append(person_id)
|
||||
|
||||
# 合併操作區
|
||||
if selected_ids:
|
||||
st.markdown("---")
|
||||
st.write(f"已選擇: **{', '.join(selected_ids)}**")
|
||||
|
||||
with st.form(key="merge_form"):
|
||||
new_name = st.text_input(
|
||||
"合併後的身份名稱 (e.g., 主角, 張三)", value="Speaker_A"
|
||||
)
|
||||
submitted = st.form_submit_button("✅ 確認合併與綁定")
|
||||
|
||||
if submitted:
|
||||
# 1. 更新 JSON
|
||||
face_json_path = os.path.join(
|
||||
OUTPUT_DIR, "quick_preview", "preview.face_clustered.json"
|
||||
)
|
||||
if os.path.exists(face_json_path):
|
||||
with open(face_json_path, "r") as f:
|
||||
face_data = json.load(f)
|
||||
|
||||
count = 0
|
||||
for frame in face_data.get("frames", []):
|
||||
for face in frame.get("faces", []):
|
||||
if face.get("person_id") in selected_ids:
|
||||
face["person_id"] = new_name
|
||||
count += 1
|
||||
|
||||
with open(face_json_path, "w", encoding="utf-8") as f:
|
||||
json.dump(face_data, f, indent=2, ensure_ascii=False)
|
||||
st.success(f"✅ 已更新 {count} 個臉部標籤為 '{new_name}'")
|
||||
|
||||
# 2. 更新資料庫 (綁定 Talent)
|
||||
import psycopg2
|
||||
|
||||
try:
|
||||
conn = psycopg2.connect(
|
||||
"postgresql://accusys@localhost:5432/momentry"
|
||||
)
|
||||
cur = conn.cursor()
|
||||
|
||||
# 創建或更新 Talent
|
||||
cur.execute(
|
||||
"SELECT id FROM talents WHERE real_name = %s", (new_name,)
|
||||
)
|
||||
row = cur.fetchone()
|
||||
|
||||
if row:
|
||||
talent_id = row[0]
|
||||
else:
|
||||
cur.execute(
|
||||
"INSERT INTO talents (real_name) VALUES (%s) RETURNING id",
|
||||
(new_name,),
|
||||
)
|
||||
talent_id = cur.fetchone()[0]
|
||||
|
||||
# 綁定 Faces
|
||||
# (注意：這裡簡化為將對應的 Person ID 在 DB 中視為 Talent，實際應更新 JSON ID)
|
||||
# 這裡我們主要更新 Speaker 綁定邏輯，確保這個 Talent 有綁定到的 Speaker
|
||||
|
||||
# 找出這些 Person ID 曾經綁定的 Speaker
|
||||
# 為了簡單，我們直接提示用戶去綁定 Speaker，或者我們掃描 ASRX 對應關係
|
||||
|
||||
conn.commit()
|
||||
cur.close()
|
||||
conn.close()
|
||||
st.success(
|
||||
f"✅ 資料庫已建立 Talent '{new_name}' (ID: {talent_id})"
|
||||
)
|
||||
|
||||
# 重新載入頁面以反映變更
|
||||
st.rerun()
|
||||
except Exception as e:
|
||||
st.error(f"資料庫錯誤: {e}")
|
||||
|
||||
else:
|
||||
st.info("未發現聚類截圖。請先執行 `face_clustering_processor.py`。")
|
||||
|
||||
# 6. 時間軸視覺化 (Timeline)
|
||||
st.header("📅 Processor Timeline (處理器活動軸)")
|
||||
plot_timeline(uuid, duration)
|
||||
|
||||
cap.release()
|
||||
|
||||
|
||||
def plot_timeline(uuid, duration):
|
||||
"""使用 Altair 繪製各模組的活動時間軸"""
|
||||
data = []
|
||||
|
||||
# 解析 ASR 活動
|
||||
asr = load_json_safe(uuid, "asr")
|
||||
if asr:
|
||||
for seg in asr.get("segments", []):
|
||||
data.append(
|
||||
{
|
||||
"Module": "ASR Speech",
|
||||
"Start": seg["start"],
|
||||
"End": seg["end"],
|
||||
"Task": "Speech",
|
||||
}
|
||||
)
|
||||
|
||||
# 解析 YOLO 活動 (隨機取樣)
|
||||
yolo = load_json_safe(uuid, "yolo")
|
||||
if yolo:
|
||||
# frames 可能是 dict (keyed by frame_index) 或 list
|
||||
frames_data = yolo.get("frames", {})
|
||||
if isinstance(frames_data, dict):
|
||||
frames_list = list(frames_data.values())
|
||||
else:
|
||||
frames_list = frames_data
|
||||
|
||||
# 取樣以避免圖表過慢 (取前 50 幀)
|
||||
sample_count = 0
|
||||
for f in frames_list:
|
||||
if sample_count > 50:
|
||||
break
|
||||
detections = f.get("detections", []) or f.get("objects", [])
|
||||
if detections:
|
||||
ts = f.get("time_seconds") or f.get("timestamp", 0)
|
||||
data.append(
|
||||
{
|
||||
"Module": "YOLO Detect",
|
||||
"Start": ts,
|
||||
"End": ts + 0.5,
|
||||
"Task": "Obj",
|
||||
}
|
||||
)
|
||||
sample_count += 1
|
||||
|
||||
if not data:
|
||||
st.info("No timeline data available.")
|
||||
return
|
||||
|
||||
df = pd.DataFrame(data)
|
||||
|
||||
chart = (
|
||||
alt.Chart(df)
|
||||
.mark_bar()
|
||||
.encode(
|
||||
x=alt.X("Start:Q", title="Time (sec)"),
|
||||
x2="End:Q",
|
||||
y=alt.Y("Module:N", title=""),
|
||||
color=alt.Color("Module:N", scale=alt.Scale(scheme="category10")),
|
||||
)
|
||||
.properties(height=200)
|
||||
)
|
||||
|
||||
st.altair_chart(chart, use_container_width=True)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user