8f05a7c188
- Update ASR, face, OCR, pose processors - Add release pre-flight check script - Add synonym generation, chunk processing scripts - Add face recognition, stamp search utilities
358 lines
12 KiB
Python
358 lines
12 KiB
Python
#!/usr/bin/env python3
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"""
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提取女性最多的畫面並標記人臉
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"""
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import cv2
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import numpy as np
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import json
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import os
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from datetime import datetime
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def draw_female_faces(image_path, frame_number, output_dir="/tmp/female_faces"):
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"""在圖像上標記女性人臉"""
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# 創建輸出目錄
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os.makedirs(output_dir, exist_ok=True)
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# 讀取圖像
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image = cv2.imread(image_path)
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if image is None:
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print(f"❌ 無法讀取圖像: {image_path}")
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return None
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# 從數據庫獲取女性人臉信息
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import psycopg2
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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,
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(attributes->>'age')::numeric as age
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FROM face_detections
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WHERE frame_number = %s
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AND attributes->>'gender' = 'female'
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ORDER BY confidence DESC
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""",
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(frame_number,),
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)
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female_faces = cursor.fetchall()
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cursor.close()
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conn.close()
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if not female_faces:
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print(f"❌ 在幀 {frame_number} 中未找到女性人臉")
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return None
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print(f"✅ 在幀 {frame_number} 中找到 {len(female_faces)} 個女性人臉")
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# 複製圖像用於標記
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marked_image = image.copy()
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# 標記每個人臉
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for i, (x, y, w, h, confidence, age) in enumerate(female_faces):
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# 繪製邊界框（粉色表示女性）
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color = (255, 105, 180) # 粉色
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thickness = 3
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# 繪製矩形邊界框
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cv2.rectangle(marked_image, (x, y), (x + w, y + h), color, thickness)
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# 添加標籤
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label = f"女 {i + 1}"
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if age:
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label += f" ({int(age)}歲)"
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label += f" {confidence:.1%}"
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# 計算標籤位置
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label_size, baseline = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.7, 2)
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label_y = max(y - 10, label_size[1] + 10)
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# 繪製標籤背景
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cv2.rectangle(
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marked_image,
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(x, label_y - label_size[1] - 10),
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(x + label_size[0] + 10, label_y + 5),
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color,
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-1, # 填充
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)
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# 繪製標籤文字
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cv2.putText(
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marked_image,
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label,
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(x + 5, label_y - 5),
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cv2.FONT_HERSHEY_SIMPLEX,
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0.7,
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(255, 255, 255), # 白色文字
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2,
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)
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print(
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f" 人臉 {i + 1}: 位置 [{x},{y},{w},{h}], 置信度 {confidence:.1%}, 年齡 {int(age) if age else '未知'}"
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)
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# 添加標題
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title = f"女性最多的畫面 - 幀 {frame_number} - {len(female_faces)} 個女性"
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title_size, _ = cv2.getTextSize(title, cv2.FONT_HERSHEY_SIMPLEX, 1.2, 3)
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# 繪製標題背景
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cv2.rectangle(
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marked_image,
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(10, 10),
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(10 + title_size[0] + 20, 10 + title_size[1] + 20),
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(0, 0, 0), # 黑色背景
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-1,
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)
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# 繪製標題
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cv2.putText(
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marked_image,
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title,
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(20, 20 + title_size[1]),
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cv2.FONT_HERSHEY_SIMPLEX,
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1.2,
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(255, 255, 255), # 白色文字
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3,
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)
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# 添加時間戳信息
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timestamp = frame_number / 59.94 # 假設 59.94 FPS
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minutes = int(timestamp // 60)
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seconds = int(timestamp % 60)
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time_info = f"時間: {minutes:02d}:{seconds:02d}"
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cv2.putText(
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marked_image,
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time_info,
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(20, 60 + title_size[1]),
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cv2.FONT_HERSHEY_SIMPLEX,
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0.8,
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(200, 200, 200), # 淺灰色
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2,
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)
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# 保存標記後的圖像
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output_path = os.path.join(output_dir, f"female_faces_frame_{frame_number}.jpg")
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cv2.imwrite(output_path, marked_image)
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print(f"✅ 已保存標記圖像: {output_path}")
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# 創建縮略圖（便於查看）
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height, width = marked_image.shape[:2]
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scale = 800 / width
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thumbnail = cv2.resize(marked_image, (800, int(height * scale)))
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thumbnail_path = os.path.join(
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output_dir, f"female_faces_frame_{frame_number}_thumbnail.jpg"
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)
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cv2.imwrite(thumbnail_path, thumbnail)
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print(f"✅ 已保存縮略圖: {thumbnail_path}")
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return {
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"original_image": image_path,
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"marked_image": output_path,
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"thumbnail": thumbnail_path,
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"frame_number": frame_number,
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"timestamp_seconds": timestamp,
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"timestamp_formatted": f"{minutes:02d}:{seconds:02d}",
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"female_count": len(female_faces),
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"female_faces": [
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{
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"index": i + 1,
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"x": int(x),
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"y": int(y),
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"width": int(w),
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"height": int(h),
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"confidence": float(confidence),
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"age": int(age) if age else None,
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}
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for i, (x, y, w, h, confidence, age) in enumerate(female_faces)
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],
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}
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def create_female_faces_report(female_frames_info, output_dir="/tmp/female_faces"):
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"""創建女性人臉報告"""
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report_path = os.path.join(output_dir, "female_faces_report.md")
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with open(report_path, "w", encoding="utf-8") as f:
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f.write("# 女性人臉分析報告\n\n")
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f.write(f"生成時間: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n")
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f.write("## 📊 統計摘要\n\n")
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total_females = sum(info["female_count"] for info in female_frames_info)
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f.write(f"- **總女性人臉數**: {total_females}\n")
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f.write(f"- **分析畫面數**: {len(female_frames_info)}\n")
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f.write(
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f"- **女性最多畫面**: {max(female_frames_info, key=lambda x: x['female_count'])['female_count']} 個女性\n\n"
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)
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f.write("## 🖼️ 女性最多的畫面\n\n")
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for info in female_frames_info:
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if info["female_count"] >= 2: # 只顯示有2個或以上女性的畫面
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f.write(
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f"### 幀 {info['frame_number']} - {info['timestamp_formatted']}\n\n"
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)
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f.write(f"- **女性數量**: {info['female_count']} 人\n")
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f.write(
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f"- **時間位置**: {info['timestamp_formatted']} ({info['timestamp_seconds']:.1f}秒)\n"
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)
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f.write(f"- **標記圖像**: `{os.path.basename(info['marked_image'])}`\n")
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f.write(f"- **縮略圖**: `{os.path.basename(info['thumbnail'])}`\n\n")
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f.write("#### 人臉詳細信息\n\n")
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f.write("| 編號 | 位置 (x,y,w,h) | 置信度 | 年齡 |\n")
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f.write("|------|----------------|--------|------|\n")
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for face in info["female_faces"]:
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position = (
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f"{face['x']},{face['y']},{face['width']},{face['height']}"
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)
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confidence = f"{face['confidence']:.1%}"
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age = str(face["age"]) if face["age"] else "未知"
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f.write(
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f"| {face['index']} | {position} | {confidence} | {age} |\n"
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)
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f.write("\n")
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# 添加圖像引用
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f.write(f"})\n\n")
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f.write(
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f"*圖像大小: 原始 {os.path.getsize(info['original_image']):,} bytes, 標記 {os.path.getsize(info['marked_image']):,} bytes*\n\n"
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)
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f.write("## 📁 生成文件\n\n")
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f.write("以下文件已生成:\n\n")
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for info in female_frames_info:
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if info["female_count"] >= 2:
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f.write(
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f"- `{os.path.basename(info['marked_image'])}` - 標記女性人臉的完整圖像\n"
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)
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f.write(
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f"- `{os.path.basename(info['thumbnail'])}` - 縮略圖（800px寬）\n"
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)
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f.write(f"- `female_faces_report.md` - 本報告文件\n\n")
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f.write("## 🔍 分析說明\n\n")
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f.write("1. **邊界框顏色**: 粉色 (RGB: 255,105,180) 表示女性人臉\n")
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f.write("2. **標籤格式**: `女 [編號] ([年齡]歲) [置信度]`\n")
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f.write("3. **置信度**: 人臉檢測的準確度，越高越好\n")
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f.write("4. **年齡**: 基於深度學習模型的估計，可能有±5歲誤差\n")
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f.write("5. **時間位置**: 從視頻開始計算的時間\n\n")
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f.write("## 🎬 視頻內容分析\n\n")
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# 根據女性分布推測視頻內容
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multi_female_frames = [
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info for info in female_frames_info if info["female_count"] >= 2
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]
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if multi_female_frames:
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f.write("根據女性人臉分布，視頻可能包含:\n\n")
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f.write("1. **社交場合**: 多個女性同時出現，可能是聚會或社交活動\n")
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f.write("2. **對話場景**: 女性之間的對話或互動\n")
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f.write("3. **群體鏡頭**: 包含多個女性的群體畫面\n")
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f.write(
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f"4. **女性主導場景**: 在 {len(multi_female_frames)} 個畫面中有2個或以上女性\n"
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)
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else:
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f.write("視頻中女性主要單獨出現，可能包含:\n\n")
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f.write("1. **單人鏡頭**: 女性單獨出現的特寫\n")
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f.write("2. **分散場景**: 女性分散在不同的畫面中\n")
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f.write("3. **配角角色**: 女性可能不是主要角色\n")
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print(f"✅ 報告已生成: {report_path}")
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return report_path
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def main():
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print("=" * 70)
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print("提取女性最多的畫面")
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print("=" * 70)
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# 輸出目錄
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output_dir = "/tmp/female_faces"
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# 找到女性最多的幾個畫面
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female_frames = [
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19778, # 3個女性（最多）
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17980, # 2個女性
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62930, # 2個女性
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66526, # 2個女性
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70122, # 2個女性
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71920, # 2個女性
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]
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print(f"分析以下幀的女性人臉: {female_frames}")
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print()
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female_frames_info = []
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for frame_number in female_frames:
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image_path = (
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f"/tmp/face_analysis_results/384b0ff44aaaa1f1_frame_{frame_number:06d}.jpg"
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)
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if os.path.exists(image_path):
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print(f"處理幀 {frame_number}...")
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info = draw_female_faces(image_path, frame_number, output_dir)
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if info:
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female_frames_info.append(info)
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print()
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else:
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print(f"❌ 圖像文件不存在: {image_path}")
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if female_frames_info:
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# 創建報告
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report_path = create_female_faces_report(female_frames_info, output_dir)
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print("=" * 70)
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print("✅ 提取完成！")
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print("=" * 70)
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# 顯示摘要
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max_females = max(info["female_count"] for info in female_frames_info)
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max_frame_info = [
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info for info in female_frames_info if info["female_count"] == max_females
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][0]
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print(f"📊 統計摘要:")
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print(f" - 總分析畫面: {len(female_frames_info)}")
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print(f" - 女性最多畫面: 幀 {max_frame_info['frame_number']}")
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print(f" - 女性數量: {max_females} 人")
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print(f" - 時間位置: {max_frame_info['timestamp_formatted']}")
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print()
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print(f"📁 生成文件:")
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print(f" - 標記圖像: {output_dir}/female_faces_frame_*.jpg")
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print(f" - 縮略圖: {output_dir}/female_faces_frame_*_thumbnail.jpg")
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print(f" - 分析報告: {report_path}")
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print()
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print(f"🔍 查看結果:")
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print(f" ls -la {output_dir}/")
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print(f" open {output_dir}/female_faces_report.md")
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else:
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print("❌ 未找到任何女性人臉畫面")
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if __name__ == "__main__":
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main()
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