328 lines
13 KiB
Python
328 lines
13 KiB
Python
import io
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import os
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import json
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import base64
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import requests
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import re
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from PIL import Image
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from datetime import datetime, timedelta
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from decord import VideoReader, cpu
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SILICONFLOW_URL = "https://api.siliconflow.cn/v1/chat/completions"
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API_KEY = "sk-ytxabphvgxrjbvnqiwercjyrabvlukwddqsmvnqnvwuazamd"
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class MediaAnalysisSystem:
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def __init__(self):
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self.MAX_NUM_FRAMES = 5 # 最大帧数设为10
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self.MIN_NUM_FRAMES = 3 # 最小帧数设为3
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def encode_video(self, video_data):
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def uniform_sample(l, n):
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gap = len(l) / n
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return [l[int(i * gap + gap / 2)] for i in range(n)]
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video_file = io.BytesIO(video_data)
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vr = VideoReader(video_file, ctx=cpu(0))
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sample_fps = round(vr.get_avg_fps() / 1)
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frame_idx = list(range(0, len(vr), sample_fps))
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# 确保帧数在3-10之间
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num_frames = min(max(3, len(frame_idx)), self.MAX_NUM_FRAMES)
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if len(frame_idx) > num_frames:
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frame_idx = uniform_sample(frame_idx, num_frames)
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frames = vr.get_batch(frame_idx).asnumpy()
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frames = [Image.fromarray(v.astype('uint8')) for v in frames]
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# 压缩图片尺寸和质量
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compressed_frames = []
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for frame in frames:
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# 保持宽高比的情况下调整大小
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frame.thumbnail((600, 600), Image.Resampling.LANCZOS)
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buffered = io.BytesIO()
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frame.save(buffered, format="JPEG", quality=85)
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compressed_frames.append(Image.open(buffered))
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print(f'处理后的帧数: {len(compressed_frames)}')
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return compressed_frames
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def process_video(self, video_data, object_name):
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if not video_data:
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raise ValueError(f"Empty video data for {object_name}")
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print(f"Processing video: {object_name}, data size: {len(video_data)} bytes")
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frames = self.encode_video(video_data)
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# 构建单个请求的消息内容
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messages = [{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": """请将这些图片作为一个时间序列进行详细分析，包括以下方面：
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1. 场景中人数的精确统计
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2. 每个人的个人行为分析
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3. 面部表情识别和情绪状态评估
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4. 整体场景和环境的详细描述
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5. 人与人之间的互动情况
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6. 详细的环境条件描述
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7. 环境中出现的物品和家具
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8. 任何可疑或异常活动
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9. 人员的具体特征（估计年龄范围、性别、着装）
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10. 人员的移动模式和方向
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11. 携带的物品或物体
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12. 群体动态和聚集情况
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13. 视频中的时间戳分析（如果有）"""
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}
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]
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}]
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# 一次性添加所有图片到消息内容
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for frame in frames:
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base64_image = self.image_to_base64(frame)
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messages[0]["content"].append({
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"type": "image_url",
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"image_url": {
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"url": f"data:image/jpeg;base64,{base64_image}",
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"detail": "auto"
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}
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})
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try:
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response = self._make_api_request(messages)
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answer = response["choices"][0]["message"]["content"]
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extracted_info = self.extract_info(answer)
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return {
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"original_answer": answer,
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"extracted_info": extracted_info,
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"num_frames": len(frames),
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}
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except Exception as e:
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print(f"API请求失败: {str(e)}")
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raise
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def _make_api_request(self, messages):
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payload = {
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"model": "deepseek-ai/deepseek-vl2",
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"messages": messages,
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"stream": False,
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"max_tokens": 1024,
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"temperature": 0.7,
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"top_p": 0.7,
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"top_k": 50,
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"frequency_penalty": 0.5,
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"n": 1,
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"response_format": {"type": "text"}
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}
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headers = {
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"Authorization": f"Bearer {API_KEY}",
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"Content-Type": "application/json"
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}
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response = requests.post(
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SILICONFLOW_URL,
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json=payload,
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headers=headers,
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timeout=60 # 增加超时时间到60秒
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)
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if response.status_code != 200:
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raise Exception(f"Siliconflow API 错误: {response.status_code}")
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return response.json()
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@staticmethod
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def image_to_base64(image):
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buffered = io.BytesIO()
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image.save(buffered, format="PNG")
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return base64.b64encode(buffered.getvalue()).decode()
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@staticmethod
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def extract_time_from_filename(object_name):
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filename = os.path.basename(object_name)
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time_str = filename.split('_')[0] + '_' + filename.split('_')[1].split('.')[0]
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try:
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start_time = datetime.strptime(time_str, "%Y%m%d_%H%M%S")
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end_time = start_time + timedelta(seconds=10)
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return start_time, end_time
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except ValueError:
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print(f"无法从文件名 '{filename}' 解析时间。使用默认时间。")
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return datetime.now(), datetime.now() + timedelta(seconds=10)
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@staticmethod
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def extract_info(answer):
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info = {
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"environment": None,
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"num_people": None,
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"actions": [],
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"objects": [],
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"furniture": [],
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"emotions": [],
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"features": []
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}
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environments = ["办公室", "室内", "室外", "会议室"]
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for env in environments:
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if env in answer.lower():
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info["environment"] = env
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break
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people_patterns = [
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r'(\d+)\s*(人|个人|位|名|员工|用户|小朋友|成年人|女性|男性)',
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r'(一|二|三|四|五|六|七|八|九|十)\s*(人|个人|位|名|员工|用户|小朋友|成年人|女性|男性)',
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r'(一个|几个)\s*(人|个人|员工|用户|小朋友|成年人|女性|男性)',
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r'几\s*(名|位)\s*(人|员工|用户|小朋友|成年人|女性|男性)?',
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r'(男|女)(性|生|士)',
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r'(成年|未成年|青少年|老年)\s*(人|群体)',
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r'(员工|职工|工人|学生|顾客|观众|游客|乘客)',
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r'(群众|民众|大众|公众)',
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r'(男女|老少|老幼|大人|小孩)'
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]
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for pattern in people_patterns:
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match = re.search(pattern, answer)
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if match:
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if match.group(1).isdigit():
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info["num_people"] = int(match.group(1))
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elif match.group(1) in ['一个', '一']:
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info["num_people"] = 1
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else:
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num_word_to_digit = {
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'二': 2, '三': 3, '四': 4, '五': 5,
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'六': 6, '七': 7, '八': 8, '九': 9, '十': 10
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}
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info["num_people"] = num_word_to_digit.get(match.group(1), 0)
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break
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actions = ["睡眠","坐", "吃","站", "摔倒", "跳舞", "蹲","蹲下","转身", "摔", "倒", "倒下", "躺下", "转身", "转","跳跃", "跳", "躺", "睡", "说话","睡觉","起床","看书","写字","学习","玩手机","吃饭","搬东西","看风景","走路","散步","走","阅读","写作","使用手机","使用电脑","学习","工作","使用笔记本电脑","吃饭","喝水","整理"]
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for action in actions:
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if action in answer:
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info["actions"].append(action)
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emotions = ["高兴", "愤怒", "悲伤", "惊讶", "恐惧", "厌恶", "平静","放松","中性","专注","思考"]
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objects = ["水瓶", "办公用品", "文件", "电脑","风扇","鼠标","键盘","纸巾","书","笔","袋子","盒子","水杯","杯子","马克杯","玻璃杯","文件夹","书包","书架","文件柜","手机"]
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furniture = ["椅子", "桌子", "咖啡桌", "文件柜", "床", "沙发","柜子","架子","摄像头","靠垫","办公椅","电视","白板","显示器","置物架","文件架"]
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features = ["戴眼镜","不戴眼镜","长发","短发","长头发","短头发","戴帽子","不戴帽子","戴口罩","不戴口罩","男性","女性","胖","瘦","高","矮","男","女","成年人"]
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for obj in objects:
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if obj in answer:
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info["objects"].append(obj)
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for item in furniture:
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if item in answer:
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info["furniture"].append(item)
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for feature in features:
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if feature in answer:
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info["features"].append(feature)
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for emotion in emotions:
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if emotion in answer:
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info["emotions"].append(emotion)
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return info
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# 初始化 MediaAnalysisSystem
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media_analysis_system = MediaAnalysisSystem()
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class MediaAnalysisError(Exception):
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"""自定义媒体分析异常类"""
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pass
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def process_video_folder(system, folder_path, output_path=None):
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"""处理文件夹中的所有视频文件并保存结果"""
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# 支持的视频格式
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valid_extensions = {'.mp4', '.avi', '.mov', '.mkv'}
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results = {}
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# 确保文件夹存在
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if not os.path.exists(folder_path):
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raise MediaAnalysisError(f"错误：文件夹 '{folder_path}' 不存在")
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# 设置输出路径
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if output_path is None:
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output_path = os.getcwd() # 如果未指定，使用当前目录
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elif not os.path.exists(output_path):
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os.makedirs(output_path) # 如果输出目录不存在，创建它
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# 获取所有视频文件
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video_files = [
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f for f in os.listdir(folder_path)
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if os.path.splitext(f)[1].lower() in valid_extensions
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]
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if not video_files:
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raise MediaAnalysisError(f"错误：在文件夹 '{folder_path}' 中未找到支持的视频文件")
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print(f"\n找到 {len(video_files)} 个视频文件，开始处理...\n")
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# 生成输出文件名
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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folder_name = os.path.basename(os.path.normpath(folder_path))
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output_file = os.path.join(output_path, f"analysis_results_{folder_name}_{timestamp}.json")
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# 处理每个视频文件并实时保存结果
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for i, video_file in enumerate(video_files, 1):
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video_path = os.path.join(folder_path, video_file)
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print(f"正在处理 ({i}/{len(video_files)}): {video_file}")
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try:
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with open(video_path, "rb") as f:
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video_data = f.read()
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result = system.process_video(video_data, video_file)
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# 修改结果存储格式
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results[video_file] = {
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"video_analysis": {
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"deepseek-vl2": result
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}
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}
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# 实时保存当前结果到JSON文件
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with open(output_file, 'w', encoding='utf-8') as f:
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json.dump(results, f, ensure_ascii=False, indent=2)
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print(f"✓ 成功处理并保存: {video_file}")
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except Exception as e:
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print(f"✗ 处理失败 {video_file}: {str(e)}")
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results[video_file] = {
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"video_analysis": {
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"deepseek-vl2": {"error": str(e)}
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}
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}
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# 即使处理失败也保存当前结果
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with open(output_file, 'w', encoding='utf-8') as f:
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json.dump(results, f, ensure_ascii=False, indent=2)
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print(f"\n所有分析结果已保存到: {output_file}")
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return results
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class MediaAnalysisError(Exception):
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"""自定义媒体分析异常类"""
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pass
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def main():
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try:
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system = MediaAnalysisSystem()
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# 添加文件夹路径输入处理
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folder_path = input("请输入视频文件夹路径: ").strip()
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output_path = input("请输入结果保存路径 (直接回车使用当前目录): ").strip()
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# 如果用户没有输入输出路径，则使用None（将使用当前目录）
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output_path = output_path if output_path else None
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# 处理文件夹中的视频
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results = process_video_folder(system, folder_path, output_path)
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# 显示处理统计
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success_count = sum(1 for r in results.values() if "error" not in r)
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print(f"\n处理完成！成功: {success_count}/{len(results)}")
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except MediaAnalysisError as e:
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print(f"\n错误: {str(e)}")
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except Exception as e:
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print(f"\n未预期的错误: {str(e)}")
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if __name__ == "__main__":
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main() |